Next Article in Journal
Mathematical Modeling of Non-Small-Cell Lung Cancer Biology through the Experimental Data on Cell Composition and Growth of Patient-Derived Organoids
Previous Article in Journal
Diabetes Mellitus in Acute Coronary Syndrome
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Life
Volume 13
Issue 11
10.3390/life13112227
life-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Genome-Wide Comparative Profiles of Triterpenoid Biosynthesis Genes in Ginseng and Pseudo Ginseng Medicinal Plants

by
Jing Lu
Division of General Education, Seokyeong University, Seoul 02173, Republic of Korea
Life 2023, 13(11), 2227; https://doi.org/10.3390/life13112227
Submission received: 7 October 2023 / Revised: 5 November 2023 / Accepted: 10 November 2023 / Published: 19 November 2023
(This article belongs to the Section Plant Science)
Browse Figures
Versions Notes

Abstract

:
Saponin-rich medicinal plants, particularly ginseng and Pseudo ginseng, are valuable in traditional medical practice due to the presence of different saponins. These plants benefit from natural saponins/triterpenoids drugs, such as Ginsenosides, Gypenosides, Platycodins, and Lancemasides. Ginsenosides are highly required for research and functional materials preparation in industrial practices, and some compounds, like Compound-K, have been taken to human trials for various therapeutic applications. To elucidate the genes/transcripts profiles responsible for secondary metabolites and ginsenoside biosynthesis in Ginseng and Pseudo ginseng plant genomes, a comparative analysis was conducted in this study. Nine plant genomes with a 99% BUSCO completeness score were used, resulting in 49 KEGG secondary metabolite pathways, 571 cytochromes genes with 42 families, and 3529 carbohydrate genes with 103 superfamilies. The comparative analysis revealed 24 genes/transcripts belonging to the CYP716 family, which is involved in the ginsenoside biosynthesis pathway. Additionally, it found that various ginsenosides demonstrated strong binding affinity with twelve targets, with ginsenoside Rg3, Rg2, Rh1, Rh5, F3, Rh9, Panaxadione, Protopanaxatriol, Floral ginsenoside C, and Floral ginsenoside E exhibiting the highest binding affinities with the tested enzymes. Since these groups of enzymes are not yet fully characterized for Pseudo ginseng plants in the interconversion of triterpenoids, this comparative bioinformatics analysis could aid experimentalists in selecting and conducting characterization with practical knowledge.

Graphical Abstract

1. Introduction

Ginseng, a non-model plant, is recognized as an adaptogen within the Panax genus in the Araliaceae family, encompassing 15 species and 7 subspecies. The term “Panax” originates from the Greek word “panacea”, indicating a universal remedy. Notably, the anthropomorphic root of Panax ginseng stands out for its medicinal attributes and shares a homologous genome size correlation with the human genome. The usage of ginseng is very prevalent in traditional Chinese medicine (TCM)- and oriental medicine (OM)-based health supplement industries. The first generation of drug discovery was based on the alkaloid drugs that were isolated from medicinal plants. After that, the medicinal plant’s effectiveness was determined by its phytochemical ingredients [1]. However, converting traditional medicine formulations to modern medicine has always been a challenge. This is because identifying the active ingredients of traditional medicine has been a long-standing problem. Nevertheless, modern evidence-based “high-throughput” technologies, especially “genome-wide” omics technologies, have recently paved the way for exploring the hidden nature of medicinal plants [2]. The ginseng medicinal plant has been the subject of over 10,000 research articles and thousands of patents for its various formulations and therapeutic phytochemicals. These articles can be classified into four broad perspectives: (1) Identifying and enhancing the ethnopharmacological properties; (2) Improving the plant cultivation process and biomass production in a short time; (3) Phytochemical conversion/synthesis; and (4) Identifying adulteration in ginseng products [3]. Among these, ginsenoside’s phytochemical conversion and synthesis are significant parts of ginseng research. The research mainly focuses on the cytochrome and carbohydrate enzymes from the plant and microbes from food and soil sources.
Ginseng has been used for centuries in traditional Chinese medicine and oriental medicine due to its yin–yang properties. In 1854, a German scientist named Garrigues isolated the Panaquilon chemical component from Panax plants, leading to the discovery of around 330 ginsenosides [4,5,6,7]. These triterpenoid chemical components are mainly found in the Panax family and have a dammarane backbone moiety with an array of glycans in their functional groups [8]. Some of these components are naturally occurring and called significant ginsenosides, while others are converted forms, known as minor ginsenosides [9,10]. However, major ginsenosides are not absorbed into our bloodstream because the intestinal bacterial population converts them into minor ginsenosides. Therefore, the pharmaceutical industry uses various natural nonpathogenic microbes to convert ginsenosides, primarily for removing glycans present in the backbone moiety [11]. One such ginsenoside is Compound-K, which has undergone clinical studies up to the human trial stage [12]. Ginsenosides are similar to cardiac-glycosides drugs, such as Deslanoside and Acetyldigitoxin, which have been used in pharmaceuticals. Additionally, ginsenosides are used as supplements to treat various hormone imbalances in humans [13]. The conversion of ginsenosides in microbiomes is mainly observed by cytochrome enzymes, and various cytochromes in Panax family plants characterize the ginsenoside biosynthesis pathways [14,15].
It is not just ginseng plants that are widely valued in the market. Pseudo ginseng, which is more affordable due to its wider accessibility, has also gained popularity. One such example is Gynostemma pantaphyllum, which is often referred to as the “poor man’s ginseng”. This plant contains triterpenoids called gypenosides, which are similar to ginsenosides (Rg3, Rc, Rd, MRb1, MRd, F2, Rb3, and Rb1) and are widely used in green tea around the world [16,17]. Due to the high demand for ginsenosides in the pharmaceutical industry, bacterial enzymes convert gypenosides and other gypenosides to ginsenosides. This has led to an increase in attention towards G. pantaphylum in medicinal plant research [17].
Codonopsis [18] and platycodons [19] roots are also considered poor man’s ginseng due to their similar root morphologies to ginseng. These roots contain triterpenoids such as Platycodins and Lancemasides, which are similar to ginsenoside Ro, making them beneficial in traditional medicines [20,21]. To understand the diversity of cytochrome and carbohydrate enzymes present in these plants and their triterpenoid biosynthesis pathways, a comparative genome/transcriptome analysis was conducted. This analysis aimed to harness the benefits of these enzymes in an in vitro yeast model to enhance the production of various ginsenosides or triterpenoid glycosides for therapeutic applications, similar to opioids biosynthesis [22]. The ginsenoside biosynthesis pathway is believed to be downstream of the isoprenoid biosynthesis pathway rooted in squalene synthase. Various cytochromes and glycosyltransferase enzymes are then characterized for major ginsenoside isoform biosynthesis, while minor ginsenosides are artificially synthesized from microbial cytochrome enzymes. In this study, genome mining was used to profile cytochrome and glucosyl transferase and hydrolase enzymes from ginseng, Pseudo ginseng, and gut microbiota to aid experimentalists in choosing the enzymes for saponin/ginsenoside biosynthesis applications.

2. Materials and Methods

2.1. Genome and Completeness Assessment

Nine assembled genomes (five species from Panax (i.e., Panax ginseng (PAGI0), Panax notoginseng (PANO0), Panax japonicus (PAJA0), Panax stipuleanatus (PAST0), and Panax quinquefolius (PAQU0) [14])) and Pseudo ginseng (Codonopsis lanceolata (COLA0) [23], Platycodon grandiflorus (PLGR0) [24], Gynostemma pentaphyllum (GYPE0) [25], and outlier Daucus carota (DACA0)) were selected and downloaded from the Refseq assembly database. The respective annotations were obtained from the corresponding authors via email request [14,23,24,25]. The assemblies conformed with the genome completeness assessment with BUSCO v5.0 with the embryophyta10.0 dataset [26]. The details are given in Supplementary Table S1.

2.2. Clustering of Proteome

We included the manually curated ginsenoside conversion microbial enzymes [27] and enzymes involved in the ginsenoside biosynthesis pathway from MetaCyc database [28], along with nine genome proteomes. Moreover, we obtained the KEGG secondary metabolite pathways from KEGG pathway database (https://www.genome.jp/kegg/pathway.html, accessed on 1 February 2023) and extracted respective protein sequences and other information, such as pathway name and KEGG orthologs ids, using Python scripts. Additionally, the coverage values for the pathway were calculated following the method described by Kim et al. [29], as follows:
Normalized   value = ∑ a b × 100
The normalized value was calculated as (number of KEGG orthologs (KO) that have similar transcripts to the reference transcriptome/total number of KO in each pathway) × 100. Furthermore, we downloaded the terpenoid biosynthesis pathway proteome from UniProt database for additional confirmations.

2.3. Cytochrome and Glycosyl Transferase/Hydrolyse Family Analysis

The complete transcripts were compared with the CYPED (https://cyped.biocatnet.de/, accessed on 1 February 2023) [30] database using the CD-HIT method to obtain the CYP family. The parameters C:70 and S:70 were used for this purpose. The selected family enzymes were aligned using MAFFT v7.2 with default parameters [31]. The multiple alignments with MAFFT with –auto parameter and the aligned file in PHYLIP format subjected to Gblock (-t = p-e = -gb1-b4 = 5-d = y) to reduce the noise in the multiple alignments to secure the highly conserved regions of the given protein sequences. Finally, the concatenated conserved blocks were subject to IQTree with option -m MFP (model finder plus) to generate a phylogenetic tree. The multiple alignments were initially corrected with [32] and were used for constructing a phylogenetic tree by IQ-TREE v2.0 [33]. Finally, the tree was imported to FigTree v1.4.3 (http://tree.bio.ed.ac.uk/software/figtree/, accessed on 1 February 2023) to obtain an image, which is shown in Figures 3–5 and S1.

2.4. Docking Assessment

As per the genome assessment carried out in this study, seven enzymes from the CYP716 family, namely, CYP716A52, CYP716A12, CYP716AL1, CYP716A15, CYP716A17, CYP716A47, CYP716A53, were found to be involved in the secondary metabolites and ginsenoside biosynthesis pathways, with reference to the MetaCyc pathway database. Additionally, the study also profiled five UGT1 family carbohydrate enzymes, UGT71A27, UGTPg101, UGTPg100, UGT74AE2, and UGT94Q2, and more details about them are provided in Table 1. To assess the binding potential of these twelve predominant enzymes, a molecular docking analysis was conducted for saponin/ginsenoside biosynthesis applications. Around sixty-six selected ginsenosides were virtually screened against these selected enzymes. For the ginsenosides whose 3D structure was not available in PubChem, their structures were manually sketched in ACD ChemSketch [34] and converted to 3D structures for further analysis. All the compounds and reference compounds were converted to PDB format using Open Babel [35]. The ligand molecules were then processed and converted to the required pdbqt format using Autodock tools [36].
The 3D structures of all the selected proteins were available in their native forms and were downloaded from the RCSB PDB database [37] in the PDB format. The structure preparation process involved several steps, including deleting all water molecules and inhibitors (ligands), checking and repairing the missing atoms, and adding hydrogens and required charges using Autodock tools. The final file was saved in the required format (pdbqt) for docking analysis. All docking experiments were performed using AutoDock Vina [38]. The selected twelve targets were used for molecular docking of ginsenosides. The compounds were ranked based on their docking scores, which represent their binding energies. The ligand interactions with the active sites of the receptors were visualized using the academic version of PyMOL [39] (DeLano, 2) and BIOVIA Discovery Studio Visualizer (BIOVIA, Dassault Systèmes, https://discover.3ds.com/discovery-studio-visualizer-download, accessed on 1 February 2023). Two-dimensional figures were drafted using the same software, and 2D diagrams were generated to depict hydrogen bonds and hydrophobically interacting residues. Each ligand cluster was inspected for amino acids interacting with the ligand, hydrogen bonds (H bonds), and the specific atoms involved.

3. Results and Discussion

3.1. Comparative Genomes

The comparative analysis in this study included a total of five Panax genomes, three Pseudo ginseng plants (i.e., C. lanceolata, P. grandiflorus, and G.pentaphyllum), and an outgroup D. carota (Figure 1). The genome size of the selected plants varied, with P. ginseng having the largest genome size and D. carota having the lowest (Supplementary Table S1). The genome assembly assessment showed that almost 99% of genomes were assembled completely (Figure 1), ensuring that the genes/transcripts in this profile were completely covered and assessed for comparative profiles. Among the nine species, P. quinquefolius had the largest genome, while D. carota had the shortest. When looking at the BUSCO completeness assessments, P. ginseng, P. japonicus, and P. quinquefolius had more duplicated core genes than the others. This is due to the ploidy nature of the genus and the assembled genomes [23,24,40,41]. This may influence the high transcript isoforms in the gene/transcript numbers in genome annotation compared to others. It is worth noting that these Pseudo ginseng plants are widely used as an alternative for ginseng due to the saponin content present in their roots, making them popular in the traditional medicinal market [23,24,40].

3.2. Secondary Metabolite Biosynthesis

Medicinal plants are consumed for their medicinal properties and effective use in the therapeutic functional supplement food industries. However, traditional characterization methods that involve characterizing individual secondary metabolite components are limited in summarizing the whole array of secondary metabolites available in medicinal plants. In the genomic era, with the availability of individual plant genomes, it helps predict the available secondary metabolites through bioinformatics analysis (as shown in the graphical abstract). This study conducted a comparative genome analysis among Panax families and Pseudo ginseng plants using genome data from public repositories and published genome articles. Secondary metabolites were clustered with KEGG secondary metabolite pathways, resulting in 6933 sequences from nine genomes. Among them, 1178 and 1074 genes were present in P. japonicus and P. ginseng, respectively. The lowest 445 transcripts were present in C. lanceolata. The sequences were mapped to 49 secondary metabolite biosynthesis KEGG pathways (as shown in Figure 2). Using bioinformatics principles, this study provides a comprehensive analysis of the secondary metabolite biosynthesis pathways in these plants. In this study, it was observed that among the secondary metabolite biosynthesis pathways, the Acridone alkaloid biosynthesis pathway is rich in Pseudoginsengs, such as C. lanceolata and P. grandiflorus, compared to other ginsengs in the Panax family. The brassinosteroid biosynthesis pathway showed a difference in gene presence, with more than 70% of pathway genes present in carrot, P. ginseng, P. japonicus, and P. grandiflorus when compared to others. Similarly, the phenylpropanoid, terpenoid backbone, carotenoid, and flavonoid biosynthesis pathways covered more than 50% of KEGG pathways (as shown in Figure 2). This in silico pathway assessment could be a cost-effective approach for those plants with decoded genomes and aid the experimentalist in performing experiments for specific targets [42,43,44,45]. This approach could be a substitute for total secondary metabolite profiles experiments such as total phenolic content, total flavonoid content, and other subclasses of secondary metabolite quantifications.

3.3. Cytochrome Profiles

The advantage of “genome-wide” comparison is that it provides an overview of the selected targets/biosynthesis pathways from a desired plant. Earlier, a similar profiling process was conducted for cytochrome multifamily genes with expressed sequence tags (EST) in P. ginseng [46], which was later improved with next-generation sequencing through de novo transcriptome assemblies [47], and, finally, performed with chromosome-scale assembled genomes [14]. Limited CYPs were observed when EST and de novo transcriptome assemblies were used, as compared to whole-genome assemblies [14]. Only three clans of partial cytochrome, i.e., CYP71, CYP90, and CYP72, were identified when compared to whole-genome-based CYP profiles, which is a partial result [14,46,47]. This study is the first comparative CYP profiling study for Pseudo ginseng along with the Panax families. Through systematic bioinformatics analysis, as illustrated in Figure 1, forty-two cytochrome families were identified from Panax and other Pseudo ginseng plants. Among those, 16 cytochrome families were found to be involved in terpenoid biosynthesis, and 19 others were found to be involved in other secondary metabolite biosynthesis pathways. According to the MetaCyc pathway database, the CYP716 family enzymes are primarily characterized for ginsenoside biosynthesis. In total, twenty-four sequences from all selected genomes were plotted as a phylogenetic tree in Figure 3. As previously mentioned in Zang et al. [48], Panax plants were summarized into three groups based on their available ginsenoside profiles. However, due to the lack of enough datasets, a similar process has not yet been carried out for Pseudo ginseng plants. Therefore, in this study, Panax plants were considered as a model/reference for Pseudo ginseng to gain knowledge on triterpenoid biosynthesis pathway possibilities. These findings could be used to develop a similar yeast model for the industrial production of opioids cascade biosynthesis in one step [22]. For example, due to the long-life cycle of ginseng, raw material shortage is common in various industries. As a result, researchers are exploring the conversion of saponins from Pseudo-ginseng, such as ginsenosides from gypenosides, under laboratory conditions [49,50]. Our bioinformatics analysis identified CYP716A (CYP716A47 [50], CYP716A53v2 (protopanaxadiol to protopanaxatriol) [51], CYP716A52v2 (oleanane-type ginsenoside biosynthesis) [52], and CYP716A52v2 (oleanane-type ginsenoside biosynthesis) [53]) group enzymes from Pseudo ginseng, which are similar to the well-characterized CYP716A enzymes involved in the ginsenoside biosynthesis pathway (as shown in Figure 3). For instance, the sequence GINO0PEP0000017 is representative of the CYP716A47 family enzyme involved in the catalytic process of dammarenediol-II to protopanaxadiol in the ginsenoside biosynthesis pathway [51], and similar sequences are present in PLGR0 and PAQU0. Similarly, other CYP716 families in ginseng plants are not characterized in detail. Other sequences with high similarity may have similar characteristics, which need to be checked through experiments [54].

3.4. Carbohydrate Enzymes Profiles

The biosynthesis of glycosides such as Ginsenosides, Gypenosides, Platycodins, and Lancemasides in plants is diverse, and subsequent glycosylation enzymes contribute to the synthesis of various glucoside secondary metabolites. Various carbohydrates such as UDP-alpha-D-glucose, UDP-GluA, UDP-Xyl, UDP-Gal, UDP-Arap, UDP-Araf, and UDP-Rha are present in these terpenoid backbone moieties due to the presence of various carbohydrate enzymes. Since around 300 ginsenoside [27] and 200 gypenoside isoforms [17] have been identified, most isoforms vary based on the different types and numbers of carbohydrates in the backbone moiety. In ginsenosides, most of the UDP-sugars are attached to the functional group in C-6 and C-20 in PPT-Type, C-3 and C-20 hydroxyl groups in PPD-type, and C-3 hydroxyl and C-28 carboxyl groups in OA-type ginsenosides. Profiling the carbohydrate enzymes, as explained in the Materials and Methods, resulted in 3529 sequences belonging to carbohydrate enzymes, which belong to 103 superfamilies of carbohydrate enzymes in the CAZY database.
It is worth noting that there are several subcategories of enzymes that play important roles in plant physiology. For example, the Auxiliary activity family enzymes (AA0, 1, 5, 6) are widely present in all genomes and are involved in handling antioxidants. Carbohydrate-binding modules (CBM13, 43, 45, 48, 50, and 57) are also present in all genomes, as are members of the carbohydrate esterase family (CE11, CE8, 13). Glycoside Hydrolase (GH1 and GH3) families are involved in ginsenoside biosynthesis. The glycosyl-transferase family (which includes 41 families) is also important, with GT1 being characterized for ginsenoside biosynthesis. Three superfamilies within this group are involved in terpenoid biosynthesis (GT1, GT2, GT4). It is interesting to note that there are two families of pectin lyase present (PL1, PL4), and among the Panax family plants, UGT71-100 is the most prevalent. Within this family, the UGT1 group of enzymes is the largest. Enzymes from the AA0, GH3, and GH1 families are involved in secondary metabolic pathways, while GT1,2,4 and GH1 are involved in terpenoid biosynthesis. Specifically, in the ginsenoside biosynthesis pathway, the GH1, GH3, and GT1 family enzymes are involved, as per the MetaCyc pathway database. Interestingly, the enzyme beta-glucosidase is also involved in the process of converting saponins from Pseudo ginseng to ginsenoside, similar to cytochromes [27]. However, many of these sequences have not been characterized in detail for triterpenoid biosynthesis. The phylogenetic tree in Figure 4, Figure 5 and Figure S1 plots these sequences.

3.5. Ginsenoside Interactions with Genes in Ginsenosides Biosynthesis Pathway

Based on molecular docking studies, it was found that most of the ginsenosides tested in this study showed highly competitive binding affinity with all twelve targets. The binding energies ranged from −11.5 kcal/mol−1 to −5.9 kcal/mol−1, which is significant. Further analysis of the ligand with the highest binding affinity for the twelve targets was conducted to assess the molecular interaction. You can find more details about the binding energies of the targets and ligands in the Supplementary Table S2. According to the results, ginsenoside Rg3, Rg2, Rh1, Rh5, F3, Rh9, Panaxadione, Protopanaxatriol, Floral ginsenoside C, and Floral ginsenoside E showed the highest binding affinity with the enzymes tested. Protopanaxatriol had the highest binding energy with the enzyme CYP716A15, which was −11.5 kcal/mol−1. It interacted with the TRP110 amino acid residue of the target. Ginsenoside Rg3, on the other hand, had a higher affinity of −11.1 kcal/mol−1 with the UGT71A27 enzyme, and interacted with the ASN366, GLU386, TYR279, and SER278 amino acid residues. The 2D structures, binding scores, and interactions of each enzyme’s top compounds are provided in Table 2 and Table 3 and Figure 6 and Figure 7.

4. Conclusions

The biosynthesis of ginsenosides comes from two significant steps, i.e., formation and modification of ginsenoside backbone moiety and carbohydrates ligation and hydrolysis processes by cytochrome and carbohydrate enzymes. In the ginseng plants, the major ginsenosides are highly dominating, and the minor ginsenosides are absent or low in quantity. As explained earlier, to enhance the production of minor ginsenosides, the enzymes are taken from the microbes of the food products and ginseng plant rhizosphere. When it comes to carbohydrates, the difference in sidechains majorly takes place by the difference in cytochrome and carbohydrate enzymes present in plants. As observed in the cytochromes profile, CYP716, already well characterized for ginsenoside biosynthesis, is present in all nine plants in our study. Furthermore, two superfamilies of UDP-dependent glycosyltransferases (UGTs) are widely present in all nine plants, similar to enzymes in the ginsenoside biosynthesis pathway. The superfamily GT1 contains the plant subfamily 71–100, which contributes to inverting the catalytic function of carbohydrates. The molecular docking studies also reveal that the ginsenosides interact with the target enzymes with higher binding affinity, which require further in-depth experimental validation such as experimental assays like enzyme activity tests and targeted metabolomics, which can confirm predicted functions. CRISPR/Cas9 gene editing can be employed to validate specific gene roles. Further, integrating these experiments with computational predictions enhances understanding and aids in developing new production strategies for bioactive compounds.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/life13112227/s1, Figure S1: The phylogenetic tree for the carbohydrate enzyme superfamily GT1 proteins along with the reference of metacyc ginsenoside biosynthesis pathway; Table S1; Table S2.

Funding

This Research was supported by Seokyeong University in 2022.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The proposed sequence files are deposited in the figshare repository under the https://doi.org/10.6084/m9.figshare.24590688.v1. The genome and proteome sequences used in this study were obtained from the respective authors, as outlined in the method section. You can find the GenBank identifier for those sequences in Supplementary Table S1.

Conflicts of Interest

The author declares no conflict of interest.

References

  1. Raviña, E. The Evolution of Drug Discovery: From Traditional Medicines to Modern Drugs. ChemMedChem 2011, 6, 1746–1747. [Google Scholar]
  2. Alami, M.M.; Ouyang, Z.; Zhang, Y.; Shu, S.; Yang, G.; Mei, Z.; Wang, X. The Current Developments in Medicinal Plant Genomics Enabled the Diversification of Secondary Metabolites & rsquo; Biosynthesis. Int. J. Mol. Sci. 2022, 23, 15932. [Google Scholar] [CrossRef]
  3. Salmerón-Manzano, E.; Garrido-Cardenas, J.A.; Manzano-Agugliaro, F. Worldwide Research Trends on Medicinal Plants. Int. J. Environ. Res. Public Health 2020, 17, 3376. [Google Scholar] [CrossRef]
  4. Garrigues, S. Ueber das Panaquilon, einen neuen Pflanzenstoff. Justus Liebigs Ann. Der Chem. 1854, 90, 231–234. [Google Scholar] [CrossRef]
  5. Shibata, S.; Fujita, M.; Itokawa, H.; Tanaka, O.; Ishii, T. The structure of panaxadiol a sapogenin of ginseng. Tetrahedron Lett. 1962, 3, 419–422. [Google Scholar] [CrossRef]
  6. Shibata, S.; Tanaka, O.; Sôma, K.; Iida, Y.; Ando, T.; Nakamura, H. Studies on saponins and sapogenins of ginseng the structure of panaxatriol. Tetrahedron Lett. 1965, 6, 207–213. [Google Scholar] [CrossRef]
  7. Proctor, J.T.A.; Bailey, W.G. Ginseng: Industry, Botany, and Culture. In Horticultural Reviews; John Wiley & Sons, Inc.: Hoboken, NJ, USA, 2021; pp. 187–236. [Google Scholar]
  8. Mohanan, P.; Subramaniyam, S.; Mathiyalagan, R.; Yang, D.-C. Molecular signaling of ginsenosides Rb1, Rg1, and Rg3 and their mode of actions. J. Ginseng Res. 2018, 42, 123–132. [Google Scholar] [CrossRef]
  9. Piao, X.M.; Huo, Y.; Kang, J.P.; Mathiyalagan, R.; Zhang, H.; Yang, D.U.; Kim, M.; Yang, D.C.; Kang, S.C.; Wang, Y.P. Diversity of Ginsenoside Profiles Produced by Various Processing Technologies. Molecules 2020, 25, 4390. [Google Scholar] [CrossRef] [PubMed]
  10. Mathiyalagan, R.; Subramaniyam, S.; Kim, Y.J.; Kim, Y.-C.; Yang, D.C. Ginsenoside compound K-bearing glycol chitosan conjugates: Synthesis, physicochemical characterization, and in vitro biological studies. Carbohydr. Polym. 2014, 112, 359–366. [Google Scholar] [CrossRef] [PubMed]
  11. Wang, D.-D.; Kim, Y.-J.; Baek, N.I.; Mathiyalagan, R.; Wang, C.; Jin, Y.; Xu, X.Y.; Yang, D.-C. Glycosyltransformation of ginsenoside Rh2 into two novel ginsenosides using recombinant glycosyltransferase from Lactobacillus rhamnosus and its in vitro applications. J. Ginseng Res. 2021, 45, 48–57. [Google Scholar] [CrossRef]
  12. Ahn, J.C.; Mathiyalagan, R.; Nahar, J.; Ramadhania, Z.M.; Kong, B.M.; Lee, D.W.; Choi, S.K.; Lee, C.S.; Boopathi, V.; Yang, D.U.; et al. Transcriptome expression profile of compound-K-enriched red ginseng extract (DDK-401) in Korean volunteers and its apoptotic properties. Front. Pharmacol. 2022, 13, 999192. [Google Scholar] [CrossRef]
  13. Botelho, A.F.M.; Pierezan, F.; Soto-Blanco, B.; Melo, M.M. A review of cardiac glycosides: Structure, toxicokinetics, clinical signs, diagnosis and antineoplastic potential. Toxicon 2019, 158, 63–68. [Google Scholar] [CrossRef]
  14. Wang, Z.-H.; Wang, X.-F.; Lu, T.; Li, M.-R.; Jiang, P.; Zhao, J.; Liu, S.-T.; Fu, X.-Q.; Wendel, J.F.; Van de Peer, Y.; et al. Reshuffling of the ancestral core-eudicot genome shaped chromatin topology and epigenetic modification in Panax. Nat. Commun. 2022, 13, 1902. [Google Scholar] [CrossRef] [PubMed]
  15. Quan, L.-H.; Min, J.-W.; Sathiyamoorthy, S.; Yang, D.-U.; Kim, Y.-J.; Yang, D.-C. Biotransformation of ginsenosides Re and Rg1 into ginsenosides Rg2 and Rh1 by recombinant β-glucosidase. Biotechnol. Lett. 2012, 34, 913–917. [Google Scholar] [CrossRef] [PubMed]
  16. Subramaniyam, S.; Mathiyalagan, R.; Jun Gyo, I.; Bum-Soo, L.; Sungyoung, L.; Deok Chun, Y. Transcriptome profiling and insilico analysis of Gynostemma pentaphyllum using a next generation sequencer. Plant Cell Rep. 2011, 30, 2075–2083. [Google Scholar] [CrossRef]
  17. Su, C.; Li, N.; Ren, R.; Wang, Y.; Su, X.; Lu, F.; Zong, R.; Yang, L.; Ma, X. Progress in the Medicinal Value, Bioactive Compounds, and Pharmacological Activities of Gynostemma pentaphyllum. Molecules 2021, 26, 6249. [Google Scholar] [CrossRef]
  18. Sathiyamoorthy, S.; In, J.-G.; Lee, O.R.; Lee, B.-S.; Devi, S.R.; Yang, D.-C. In silico gene expression analysis in Codonopsis lanceolata root. Mol. Biol. Rep. 2011, 38, 3541–3549. [Google Scholar] [CrossRef] [PubMed]
  19. Yu, G.-E.; Shin, Y.; Subramaniyam, S.; Kang, S.-H.; Lee, S.-M.; Cho, C.; Lee, S.-S.; Kim, C.-K. Machine learning, transcriptome, and genotyping chip analyses provide insights into SNP markers identifying flower color in Platycodon grandiflorus. Sci. Rep. 2021, 11, 8019. [Google Scholar] [CrossRef]
  20. Zhang, S.; Chai, X.; Hou, G.; Zhao, F.; Meng, Q. Platycodon grandiflorum (Jacq.) A. DC.: A review of phytochemistry, pharmacology, toxicology and traditional use. Phytomedicine 2022, 106, 154422. [Google Scholar] [CrossRef]
  21. Rowan-Nash Aislinn, D.; Korry Benjamin, J.; Mylonakis, E.; Belenky, P. Cross-Domain and Viral Interactions in the Microbiome. Microbiol. Mol. Biol. Rev. 2019, 83, e00044-18. [Google Scholar] [CrossRef] [PubMed]
  22. Galanie, S.; Thodey, K.; Trenchard, I.J.; Filsinger Interrante, M.; Smolke, C.D. Complete biosynthesis of opioids in yeast. Science 2015, 349, 1095–1100. [Google Scholar] [CrossRef] [PubMed]
  23. Jang, W.; Kang, J.-N.; Jo, I.-H.; Lee, S.-M.; Park, G.-H.; Kim, C.-K. The chromosome-level genome assembly of lance asiabell (Codonopsis lanceolata), a medicinal and vegetable plant of the Campanulaceae family. Front. Genet. 2023, 14, 1100819. [Google Scholar] [CrossRef] [PubMed]
  24. Jia, Y.; Chen, S.; Chen, W.; Zhang, P.; Su, Z.; Zhang, L.; Xu, M.; Guo, L. A Chromosome-Level Reference Genome of Chinese Balloon Flower (Platycodon grandiflorus). Front. Genet. 2022, 13, 869784. [Google Scholar] [CrossRef] [PubMed]
  25. Zhang, X.; Zhao, Y.; Kou, Y.; Chen, X.; Yang, J.; Zhang, H.; Zhao, Z.; Zhao, Y.; Zhao, G.; Li, Z. Diploid chromosome-level reference genome and population genomic analyses provide insights into Gypenoside biosynthesis and demographic evolution of Gynostemma pentaphyllum (Cucurbitaceae). Hortic. Res. 2022, 10, uhac231. [Google Scholar] [CrossRef]
  26. Manni, M.; Berkeley, M.R.; Seppey, M.; Zdobnov, E.M. BUSCO: Assessing Genomic Data Quality and Beyond. Curr. Protoc. 2021, 1, e323. [Google Scholar] [CrossRef] [PubMed]
  27. Tran, T.N.; Son, J.-S.; Awais, M.; Ko, J.-H.; Yang, D.C.; Jung, S.-K. β-Glucosidase and Its Application in Bioconversion of Ginsenosides in Panax ginseng. Bioengineering 2023, 10, 484. [Google Scholar] [CrossRef] [PubMed]
  28. Caspi, R.; Billington, R.; Keseler, I.M.; Kothari, A.; Krummenacker, M.; Midford, P.E.; Ong, W.K.; Paley, S.; Subhraveti, P.; Karp, P.D. The MetaCyc database of metabolic pathways and enzymes—A 2019 update. Nucleic Acids Res 2020, 48, D445–D453. [Google Scholar] [CrossRef]
  29. Kim, D.; Jung, M.; Ha, I.J.; Lee, M.Y.; Lee, S.-G.; Shin, Y.; Subramaniyam, S.; Oh, J. Transcriptional Profiles of Secondary Metabolite Biosynthesis Genes and Cytochromes in the Leaves of Four Papaver Species. Data 2018, 3, 55. [Google Scholar] [CrossRef]
  30. Gricman, Ł.; Vogel, C.; Pleiss, J. Identification of universal selectivity-determining positions in cytochrome P450 monooxygenases by systematic sequence-based literature mining. Proteins 2015, 83, 1593–1603. [Google Scholar] [CrossRef] [PubMed]
  31. Katoh, K.; Misawa, K.; Kuma, K.i.; Miyata, T. MAFFT: A novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 2002, 30, 3059–3066. [Google Scholar] [CrossRef]
  32. Talavera, G.; Castresana, J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst. Biol. 2007, 56, 564–577. [Google Scholar] [CrossRef]
  33. Minh, B.Q.; Schmidt, H.A.; Chernomor, O.; Schrempf, D.; Woodhams, M.D.; von Haeseler, A.; Lanfear, R. IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era. Mol. Biol. Evol. 2020, 37, 1530–1534. [Google Scholar] [CrossRef]
  34. Hunter, A.D. ACD/ChemSketch 1.0 (Freeware); ACD/ChemSketch 2.0 and Its Tautomers, Dictionary, and 3D Plug-Ins; ACD/HNMR 2.0; ACD/CNMR 2.0; ACS Publications: Washington, DC, USA, 1997. [Google Scholar]
  35. O’Boyle, N.M.; Banck, M.; James, C.A.; Morley, C.; Vandermeersch, T.; Hutchison, G.R. Open Babel: An open chemical toolbox. J. Cheminform. 2011, 3, 1–14. [Google Scholar] [CrossRef]
  36. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem. 2009, 30, 2785–2791. [Google Scholar] [CrossRef]
  37. Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res. 2000, 28, 235–242. [Google Scholar] [CrossRef] [PubMed]
  38. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem. 2010, 31, 455–461. [Google Scholar] [CrossRef] [PubMed]
  39. Huang, D.; Ming, R.; Xu, S.; Wang, J.; Yao, S.; Li, L.; Huang, R.; Tan, Y. Chromosome-level genome assembly of Gynostemma pentaphyllum provides insights into gypenoside biosynthesis. DNA Res. 2021, 28, dsab018. [Google Scholar] [CrossRef] [PubMed]
  40. Li, K.; Xu, P.; Wang, J.; Yi, X.; Jiao, Y. Identification of errors in draft genome assemblies at single-nucleotide resolution for quality assessment and improvement. Nat. Commun. 2023, 14, 6556. [Google Scholar] [CrossRef] [PubMed]
  41. Gaudêncio, S.P.; Bayram, E.; Lukić Bilela, L.; Cueto, M.; Díaz-Marrero, A.R.; Haznedaroglu, B.Z.; Jimenez, C.; Mandalakis, M.; Pereira, F.; Reyes, F.; et al. Advanced Methods for Natural Products Discovery: Bioactivity Screening, Dereplication, Metabolomics Profiling, Genomic Sequencing, Databases and Informatic Tools, and Structure Elucidation. Mar. Drugs 2023, 21, 308. [Google Scholar] [CrossRef]
  42. Hua, X.; Song, W.; Wang, K.; Yin, X.; Hao, C.; Duan, B.; Xu, Z.; Su, T.; Xue, Z. Effective prediction of biosynthetic pathway genes involved in bioactive polyphyllins in Paris polyphylla. Commun. Biol. 2022, 5, 50. [Google Scholar] [CrossRef]
  43. Ma, A.; Qi, X. Mining plant metabolomes: Methods, applications, and perspectives. Plant Commun. 2021, 2, 100238. [Google Scholar] [CrossRef] [PubMed]
  44. Naika, M.B.N.; Sathyanarayanan, N.; Sajeevan, R.S.; Bhattacharyya, T.; Ghosh, P.; Iyer, M.S.; Jarjapu, M.; Joshi, A.G.; Harini, K.; Shafi, K.M.; et al. Exploring the medicinally important secondary metabolites landscape through the lens of transcriptome data in fenugreek (Trigonella foenum graecum L.). Sci. Rep. 2022, 12, 13534. [Google Scholar] [CrossRef]
  45. Devi, B.S.R.; Kim, Y.-J.; Sathiyamoorthy, S.; Khorolragchaa, A.; Gayathri, S.; Parvin, S.; Yang, D.-U.; Selvi, S.K.; Lee, O.R.; Lee, S.; et al. Classification and characterization of putative cytochrome P450 genes from Panax ginseng C. A. Meyer. Biochemistry 2011, 76, 1347–1359. [Google Scholar] [CrossRef]
  46. Subramaniyam, S.; Mathiyalagan, R.; Natarajan, S.; Kim, Y.-J.; Jang, M.-G.; Park, J.-H.; Yang, D.C. Transcript expression profiling for adventitious roots of Panax ginseng Meyer. Gene 2014, 546, 89–96. [Google Scholar] [CrossRef]
  47. Hou, M.; Wang, R.; Zhao, S.; Wang, Z. Ginsenosides in Panax genus and their biosynthesis. Acta Pharm. Sin. B 2021, 11, 1813–1834. [Google Scholar] [CrossRef] [PubMed]
  48. Zhang, X.; Xie, Y.; Dai, Z.; Liang, Y.; Zhu, C.; Su, C.; Song, L.; Wang, K.; Li, J.; Wei, X. Gypenoside biotransformation into ginsenoside F2 by endophytic Aspergillus niger from Gynostemma pentaphyllum. Nat. Prod. Res. 2023, 1–7. [Google Scholar] [CrossRef] [PubMed]
  49. Zhou, K.; Zhang, Y.; Zhou, Y.; Xu, M.; Yu, S. Production of Gypenoside XVII from Ginsenoside Rb1 by Enzymatic Transformation and Their Anti-Inflammatory Activity In Vitro and In Vivo. Molecules 2023, 28, 7001. [Google Scholar] [CrossRef]
  50. Han, J.Y.; Kim, H.J.; Kwon, Y.S.; Choi, Y.E. The Cyt P450 enzyme CYP716A47 catalyzes the formation of protopanaxadiol from dammarenediol-II during ginsenoside biosynthesis in Panax ginseng. Plant Cell Physiol. 2011, 52, 2062–2073. [Google Scholar] [CrossRef]
  51. Han, J.Y.; Hwang, H.S.; Choi, S.W.; Kim, H.J.; Choi, Y.E. Cytochrome P450 CYP716A53v2 catalyzes the formation of protopanaxatriol from protopanaxadiol during ginsenoside biosynthesis in Panax ginseng. Plant Cell Physiol. 2012, 53, 1535–1545. [Google Scholar] [CrossRef]
  52. Han, J.Y.; Kim, M.J.; Ban, Y.W.; Hwang, H.S.; Choi, Y.E. The involvement of β-amyrin 28-oxidase (CYP716A52v2) in oleanane-type ginsenoside biosynthesis in Panax ginseng. Plant Cell Physiol. 2013, 54, 2034–2046. [Google Scholar] [CrossRef]
  53. Chu, L.L.; Montecillo, J.A.V.; Bae, H. Recent Advances in the Metabolic Engineering of Yeasts for Ginsenoside Biosynthesis. Front. Bioeng. Biotechnol. 2020, 8, 139. [Google Scholar] [CrossRef] [PubMed]
  54. Christensen, L.P. Chapter 1 Ginsenosides: Chemistry, Biosynthesis, Analysis, and Potential Health Effects. In Advances in Food and Nutrition Research; Academic Press: Cambridge, MA, USA, 2008; pp. 1–99. [Google Scholar]
Life 13 02227 g001
Figure 1. Summary of ginseng and Pseudo ginseng genome completeness BUSCO assessment.
Figure 1. Summary of ginseng and Pseudo ginseng genome completeness BUSCO assessment.
Life 13 02227 g001
Life 13 02227 g002
Figure 2. Complete secondary metabolite biosynthesis pathway coverage profiles with the reference of KEGG metabolic pathway database.
Figure 2. Complete secondary metabolite biosynthesis pathway coverage profiles with the reference of KEGG metabolic pathway database.
Life 13 02227 g002
Life 13 02227 g003
Figure 3. The phylogenetic tree for the Cytochrome Superfamily 716 proteins was constructed with reference to the MetaCyc ginsenoside biosynthesis pathway. In this tree, the key GINO0 represents the MetaCyc ginsenoside biosynthesis pathway enzyme sequence.
Figure 3. The phylogenetic tree for the Cytochrome Superfamily 716 proteins was constructed with reference to the MetaCyc ginsenoside biosynthesis pathway. In this tree, the key GINO0 represents the MetaCyc ginsenoside biosynthesis pathway enzyme sequence.
Life 13 02227 g003
Life 13 02227 g004
Figure 4. The phylogenetic tree for the carbohydrate enzyme superfamily GH1 proteins is presented here, along with the reference of the MetaCyc ginsenoside biosynthesis pathway. The GINO0 key represents the enzyme sequence for the MetaCyc ginsenoside biosynthesis pathway.
Figure 4. The phylogenetic tree for the carbohydrate enzyme superfamily GH1 proteins is presented here, along with the reference of the MetaCyc ginsenoside biosynthesis pathway. The GINO0 key represents the enzyme sequence for the MetaCyc ginsenoside biosynthesis pathway.
Life 13 02227 g004
Life 13 02227 g005
Figure 5. The phylogenetic tree for the carbohydrate enzyme superfamily GH3 proteins is presented here, along with the reference of the MetaCyc ginsenoside biosynthesis pathway. The GINO0 key represents the enzyme sequence for the MetaCyc ginsenoside biosynthesis pathway.
Figure 5. The phylogenetic tree for the carbohydrate enzyme superfamily GH3 proteins is presented here, along with the reference of the MetaCyc ginsenoside biosynthesis pathway. The GINO0 key represents the enzyme sequence for the MetaCyc ginsenoside biosynthesis pathway.
Life 13 02227 g005
Life 13 02227 g006
Figure 6. 2D interaction diagram of seven CYP716 family enzymes with the highest ranking ginsenosides. (A) CYP716A12, (B) CYP716A52, (C) CYP716A53, (D) CYP716A15, (E) CYP716AL1, (F) CYP716A47, and (G) CYP716A17.
Figure 6. 2D interaction diagram of seven CYP716 family enzymes with the highest ranking ginsenosides. (A) CYP716A12, (B) CYP716A52, (C) CYP716A53, (D) CYP716A15, (E) CYP716AL1, (F) CYP716A47, and (G) CYP716A17.
Life 13 02227 g006
Life 13 02227 g007
Figure 7. 2D interaction diagram of five UGT1 family enzymes with the highest ranking ginsenosides. (A) UGTPg100, (B) UGTPg101, (C) UGT74AE2, (D) UGT71A27, and (E) UGT94Q2.
Figure 7. 2D interaction diagram of five UGT1 family enzymes with the highest ranking ginsenosides. (A) UGTPg100, (B) UGTPg101, (C) UGT74AE2, (D) UGT71A27, and (E) UGT94Q2.
Life 13 02227 g007
Table 1. Enzymes involved predominantly in secondary metabolite biosynthesis.
Table 1. Enzymes involved predominantly in secondary metabolite biosynthesis.
S.NoUniprot IDGeneProtein_Name
1I7C6E8CYP716A52v2Beta-amyrin 28-monooxygenase
2Q2MJ20CYP716A12Beta-amyrin 28-monooxygenase
3I1TEM3CYP716AL1Cytochrome P450
4F1T282CYP716A15Beta-amyrin 28-monooxygenase
5F1T283CYP716A17Beta-amyrin 28-monooxygenase
6H2DH16CYP716A47Dammarenediol 12-hydroxylase
7I7CT85CYP716A53v2Protopanaxadiol 6-hydroxylase
8A0A0A7HB61UGT71A27UDP-glycosyltransferase 71A27
9A0A0K0PVM5 UGTPg101UDP-glycosyltransferase 101
10A0A0K0PVW1UGTPg100UDP-glycosyltransferase 100
11A0A0A6ZFR4UGT74AE2UDP-glucosyltransferase 74AE2
12A0A0A6ZFY4UGT94Q2UDP-glucosyltransferase 29
Table 2. Interaction of compounds with amino acid residues of CYP716 family enzymes.
Table 2. Interaction of compounds with amino acid residues of CYP716 family enzymes.
Target EnzymeCompoundBinding Energy
(kcal/mol)		H-Bond InteractionsOther InteractionsNo. of
H-Bond
CYP716A12Ginsenoside F3−10.3CYS426; ARG424; GLU128; ASP281LYS282; ALA208; ILE211; PHE463; ILE2884
CYP716A52Panaxadione−10.9GLY291; SER122TRP1112
CYP716A53Floral ginsenoside C−10.7GLY349; SER347; SER123; PHE351; THR283; ALA279; CYS416; PRO408TRP1058
CYP716A15Protopanaxatriol−11.5TRP110SER121; GLY286; ARG4251
CYP716AL1Panaxadione−10.7ASP111ASN114; TRP1091
CYP716A47Ginsenoside F3−9.6ARG126GLY365; PRO426; VAL366; LEU294; CYS434; PHE1241
CYP716A17Ginsenoside Rh9−10.7ARG425GLY421; ILE289; MET464; TRP1101
Table 3. Interaction of compounds with amino acid residues of UGT1 family enzymes.
Table 3. Interaction of compounds with amino acid residues of UGT1 family enzymes.
Target EnzymeCompoundBinding Energy
(kcal/mol)		H-Bond InteractionsOther InteractionsNo. of
H-Bond
UGTPg100Ginsenoside Rg5−10.4LYS435; LYS447; ALA351; GLY374; LEU350; HIS352-
UGTPg101Floral ginsenoside E−10.7GLU411; SER187; GLN387; TYR384; HIS362; SER278GLY277; GLU3866
UGT74AE2Ginsenoside Rg2−9.2GLN42; ASP62TYR139; LEU186; PHE3682
UGT71A27Ginsenoside Rg3−11.1ASN366; GLU386; TYR279; SER278 4
UGT94Q2Ginsenoside Rg−10THR170; LEU358; ASN178PHE185; PHE1173
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Lu, J. Genome-Wide Comparative Profiles of Triterpenoid Biosynthesis Genes in Ginseng and Pseudo Ginseng Medicinal Plants. Life 2023, 13, 2227. https://doi.org/10.3390/life13112227

AMA Style

Lu J. Genome-Wide Comparative Profiles of Triterpenoid Biosynthesis Genes in Ginseng and Pseudo Ginseng Medicinal Plants. Life. 2023; 13(11):2227. https://doi.org/10.3390/life13112227

Chicago/Turabian Style

Lu, Jing. 2023. "Genome-Wide Comparative Profiles of Triterpenoid Biosynthesis Genes in Ginseng and Pseudo Ginseng Medicinal Plants" Life 13, no. 11: 2227. https://doi.org/10.3390/life13112227

APA Style

Lu, J. (2023). Genome-Wide Comparative Profiles of Triterpenoid Biosynthesis Genes in Ginseng and Pseudo Ginseng Medicinal Plants. Life, 13(11), 2227. https://doi.org/10.3390/life13112227

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop