Integrative metabolome and transcriptome analyses reveal the regulatory network of ginsenoside synthesis in ginseng

doi:10.21203/rs.3.rs-2229310/v1

Download PDF

Research Article

Integrative metabolome and transcriptome analyses reveal the regulatory network of ginsenoside synthesis in ginseng

https://doi.org/10.21203/rs.3.rs-2229310/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Ginsenoside is a vital component of ginseng, which has value in many important medical areas. However, the metabolic kinetics and underlying regulatory networks that regulate ginsenoside synthesis remain largely unknown.

Results: In this study, ginsenoside-associated metabolism and transcriptome data from the roots, stems, and leaves of ginseng with different growth years (1-year-old, 3-year-old, 5-year-old, and 6-year-old) were used to explore the regulatory mechanism of ginsenoside synthesis. We found thatthe content of ginsenosides increased with growth years in root tissues and decreased in stems and leaves, and the content in leaves was always much higher than the contentin roots and stems. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that differentially expressed genes (DEGs) in all groups were enriched primarily in the mitogen-activated protein kinase (MAPK) signaling pathway-plant, adenosine triphosphate (ATP)-binding cassette (ABC) transporters, and sesquiterpenoid and triterpenoid biosynthesis pathways. Moreover, some genes involved in ginsenoside synthesis were identified as differential alternative splicing genes (DASGs) in differentginseng growth years. In addition, weighted gene coexpression network analysis (WGCNA) was used to generate a coexpression network map that included a number of transcription factors (TFs) (such as WRKY, MYB, and ERF) and enzymes (such as UDP-glycosyltransferases (UGTs), hydroxymethylglutaryl-coenzyme A (CoA) reductase (HMGCR), and squalene epoxidase (SQE)) in the ginsenoside metabolism pathway.

Conclusions: These findings articulate the regulatory network of ginsenoside biosynthesis from the perspectives of the metabolome and transcriptome andlay a foundation for the improved control of gene expression related to ginsenoside synthesis and the content of ginsenosides in ginseng.

Panax ginseng C.A. Meyer

Gene expression regulation

Coexpression network analysis

Alternative splicing

Transcription factors

Panax ginseng C.A. Meyer (Panax L.) is a member of the Araliaceae family and is distributed mainly in northeast China, Korea, Russia, and Japan; it is a famous traditional medicinal herb that has been used in China for thousands of years [1]. Ginseng contains many active substances of medical value, including ginsenosides, polysaccharides, flavonoids, and carotenoids, of which ginsenosides are the more important components [2]. Ginsenosides are triterpenoid saponins that have significant clinical effects in the treatment of some diseases, such as cancerous tumors, cardiovascular diseases, Alzheimer's disease, and inflammation. In addition, ginsenosides can also enhance human immunity and vitality, with high development significance and research value, and ginsenosides, as triterpenoids, perform a certain defensive function in plants as antistress substances against pests and microorganisms [3-8].

Previous studies have focused on exploring the function of the genes and enzymes of the ginsenoside synthetic pathway, and the pathway can be divided roughly into 3 stages. The first stage is the synthesis of terpene precursors, namely, derivescisopentenyl pyrophosphate (IPP) and dimethylallyl pyrophosphate (DMAPP), which require the mevalonic acid (MVA) pathway in the cytoplasm and the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway in the plastid, respectively [9]. The biosynthesis of IPP starts with two acetyl-coenzyme A (CoA) molecules, which are catalyzed by acetyl-CoA C-acetyltransferase (ACAT) to form acetoacetyl-CoA and then by hydroxymethylglutaryl-CoA synthase (HMGCS) and hydroxymethylglutaryl-CoA reductase (HMGCR) to generate MVA, which is catalyzed by mevalonate kinase (MVK) and phosphomevalonate kinase (PMK) to yield mevalonate phosphate (MVP) and mevalonate pyrophosphate (MVPP) and finally by diphosphomevalonate decarboxylase (MVD) to produce IPP [10]. The synthesis of DMAPP starts with pyruvic acid (PA) and 3-phosphoglyceraldehyde (PGAL) as substrates, both of which together form 1-deoxy-D-xylulose-5-phosphate (DXP) in the presence of 1-deoxy-D-xylulose-5-phosphate synthase (DXS), which is the first rate-limiting enzyme in the MEP pathway. Then, DXP is reduced by 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) to produce MEP, which is modified by the enzymes 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase (ispD), 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase (ispE), 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase (ispF), 4-hydroxy-3-methylbut-2-enyl-diphosphate synthase (ispG), and 4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase (ispH) to finally form DMAPP. In addition, IPP and DMAPP can be interconverted via isopentenyl-diphosphate delta-isomerase (IDI) [11]. The second stage is the triterpene skeleton synthesis of ginsenoside, referred to as 2,3-oxidosqualene. Its synthesis begins with IPP and DMAPP, which undergo a condensation reaction under the catalytic action of geranyl diphosphate synthase (GPS) to form geranyl pyrophosphate (GPP). Then, an IPP is added to farnesyl pyrophosphate (FPP) with the help of farnesyl diphosphate synthase (FPS), followed by a cyclization reaction of two molecules of FPP under the action of farnesyl-diphosphate farnesyltransferase (SS) to form squalene. Then, the final triterpene skeleton 2,3-oxidosqualene is formed under the catalytic action of squalene epoxidase (SQE) [12]. The third stage is the downstream synthesis pathway of different types of ginsenosides. Oleanane (OA)-type ginsenosides (including Ro) are generated by chemical modification of β-amyrin synthase (β-AS) and UDP-glycosyltransferases (UGTs). While the synthesis of dammarane-type ginsenoside starts from 2,3-oxidosqualene under the catalysis of dammarenediol synthase (DDS), dammarenediol-II produced by this step is catalyzed by protopanaxadiol synthase (PPDS) to generate protopanaxadiol (PPD), which is modified by different UGTs to generate various types of PPD-type ginsenosides (including Rb₁, Rb₂, Rb₃, Rc, and Rd). The production of another dammarane, protopanaxatriol (PPT)-type ginsenoside, is derived from the further oxidation of PPD by protopanaxatriol synthase (PPTS) to form PPT, which is modified by UGTs to finally yield various PPT-type ginsenosides (including Re, Rf, Rg₁, Rg₂, and Rh₁) [13]. Commonly, the dammarane-type ginsenosides have been the dominant ginsenosides, and most of the ginsenosides belong to this type rather than the OA-type [14]. The PPD-type and PPT-type dammarane-type ginsenosides are mainly distinguished by their hydroxylation pattern and their different biological activities, and the ratio of PPT-type to PPD-type ginsenoside is considered a key parameter of the pharmacological activity of ginseng [15, 16]. Ginsenosides are distributed in different ginseng tissues, and the ginsenoside content is highest in fibrous roots of five-year-old ginseng, followed by leaves, rhizomes, main roots, and stems [17, 18]. Most studies have shown that the ginsenoside content in ginseng roots increases with years of cultivation [19, 20]. In conclusion, there are various factors affecting the content of ginsenosides, and more profound and comprehensive studies need to be conducted to explore the relationship between ginsenoside content and related gene expression regulation.

With the development of next-generation sequencing (NGS) technology, transcriptome sequencing has been used to identify functional genes related to plant active ingredient biosynthesis. Exploring synthesis pathways and analyzing gene expression regulation provide the molecular basis for the synthetic regulatory networks of the corresponding secondary metabolites [21]. As the main medicinal part, ginseng roots have attracted much attention regarding ginsenoside synthesis pathway genes compared to other tissues. Lin et al. found that the genes of the triterpene skeleton and downstream ginsenoside biosynthetic pathway of 2-year-old ginseng were more highly expressed than the genes of the triterpene skeleton and downstream ginsenoside biosynthetic pathway of 3-year-old and 4-year-old ginseng, whereas another study showed that the expression of genes involved in the triterpene skeleton and downstream ginsenoside synthesis pathway in 6-year-old ginseng was higher than the expression of genes involved in the triterpene skeleton and downstream ginsenoside synthesis in 1-year-old ginseng, and genes in 1-year-old ginseng were more highly expressed in the MVA and MEP synthesis pathways [22, 23]. At present, many researchers have conducted in-depth studies on ginsenoside synthesis genes with different growth years through transcriptomic technology, but the regulatory mechanism at the transcriptome level, such as alternative splicing (AS) and transcription factors (TFs) of ginsenoside biosynthesis genes, is not entirely clear. Moreover, AS occurring in Gingko biloba has been shown to lead to the accumulation of terpenoid precursors [24]. Another study found that several TFs cooperatively regulate the synthesis of terpenoid genes in Arabidopsis thaliana and enhance the output of terpenoids [25]. In general, it is necessary to explore the regulatory network of ginsenoside biosynthesis from the perspective of the transcriptome to achieve a more comprehensive approach toward increasing the yield of terpenoids in ginseng.

In our study, ginsenoside and RNA samples were extracted from roots, stems, and leaves of ginseng cultivated for different growth years, and metabolomics and transcriptome analyses were used to explore 1) the distribution characteristics of ginsenosides in ginseng with different growth years; 2) the differences in gene expression levels in ginseng with different growth years; and 3) the expression regulatory networks of ginsenoside biosynthesis genes. This study can provide a basis for identifying the regulatory mechanisms of ginsenoside synthesis in ginseng and an approach for agricultural breeding strategies to increase the yield of natural ginsenosides.

Quantitative analysis of ginsenosides in ginseng

Ginsenosides are among the most important terpenoids in ginseng, and to better investigate the effect of growth age on the composition and content of ginsenosides, the roots, stems, and leaves of ginseng with different growth years (1-year-old, 3-year-old, 5-year-old, and 6-year-old (marked as ONE, THR, FIV, and SIX)) were harvested. Total ginsenoside extraction solutions from each sample were determined by HPLC for quantitative determination. We found that ginsenoside content increased with increasing cultivation years in the roots and peaked in SIX (6.64 mg/g). In contrast, the ginsenoside content in the stem was highest in ONE (6.67 mg/g), decreased in THR (2.39 mg/g) and then tended to be stable, while the ginsenoside content in the leaf was also highest in ONE (45.63 mg/g) and tended to be stable from THR (25.33 mg/g) to SIX (27.15 mg/g) (Fig. 1). Among the three tissues, leaves contained the greatest amount of ginsenosides, which was approximately 9 times higher than the ginsenoside content in the roots and 7 times higher than the ginsenoside content in the stems in ONE. The variation trends of the PPT-type (Re, Rf, and Rg₁) and PPD-type (Rb₁, Rb₂, Rb₃, Rc, and Rd) ginsenoside contents were roughly similar to that of total ginsenosides. The ratio of PPT-type to PPD-type ginsenoside in stems gradually decreased across growth years and was between 2.79-4.02. The ratio of PPT-type to PPD-type ginsenoside across growth years between roots and leaves was found to be stable at approximately 0.92-1.30 (Table 1).

Table 1

Ginsenoside content profiles of ginseng with different growth years and the ratio of PPT-type to PPD-type ginsenosides.

Tissues	Growth years	Rg₁	Re	Rf	Rb₁	Rc	Rb₂	Rb₃	Rd	Sum	PPT	PPD	PPT/PPD
Roots	ONE	0.35	1.57	0.46	0.74	0.67	0.37	0.09	0.55	4.81	2.38	2.43	0.98
	THR	0.88	1.36	0.79	1.32	0.85	0.22	0.15	0.00	5.57	3.03	2.54	1.19
	FIV	0.97	1.03	0.92	1.08	0.67	0.52	0.10	0.34	5.63	2.92	2.72	1.07
	SIX	1.22	1.46	0.95	1.36	0.93	0.32	0.09	0.31	6.64	3.63	3.01	1.21

Stems	ONE	1.68	3.04	0.48	0.72	0.26	0.19	0.06	0.24	6.67	5.92	1.47	4.02
	THR	0.60	1.17	0.43	0.19	0.16	0.09	0.16	0.02	2.82	2.39	0.62	3.88
	FIV	0.79	0.81	0.47	0.25	0.14	0.27	0.04	0.13	2.89	2.31	0.82	2.81
	SIX	0.63	1.13	0.38	0.22	0.16	0.23	0.09	0.14	2.97	2.35	0.84	2.79

Leaves	ONE	5.78	13.61	1.85	1.22	5.16	4.58	0.81	12.62	45.63	22.45	24.39	0.92
	THR	3.97	8.38	0.40	2.12	2.45	2.27	0.42	5.32	25.33	14.86	12.58	1.18
	FIV	6.17	6.41	1.90	3.09	2.34	2.16	0.33	6.13	28.51	17.56	14.04	1.25
	SIX	3.34	10.06	0.96	2.26	2.54	2.24	0.47	5.28	27.15	16.62	12.79	1.30

Global gene expression profiling of ginseng with different growth years

To explore the differences in gene expression underlying variations in ginsenoside content in ginseng with different growth years, the transcripts of roots, stems, and leaves from 1-year-old ginseng were used as the standard and compared with 3-year-old, 5-year-old, and 6-year-old ginseng to identify the DEGs (log₂FoldChange > 1.5 and p < 0.05) by calculating FPKM values. The total RNA from roots, stems, and leaves of Panax ginseng for the ONE, THR, FIV, and SIX groups was extracted. After library construction, sequencing, read processing, and quantitative splicing of transcripts, the expression of 65,914 genes was obtained across 35 samples, and the average amount of clean bases per individual was 11.1 Gb.

The comparable group ONE_vs_THR (10,074) had the largest number of DEGs, including 5,591 DEGs in the roots, 4,509 DEGs in the stems and 2,783 DEGs in the leaves. This group was followed by the group ONE_vs_FIV (8,200), which included 3,330 DEGs in the roots, 3,270 DEGs in the stems, and 4,439 DEGs in the leaves. The ONE_vs_SIX group (5,007) had the lowest number of DEGs, with 2,783 DEGs in the roots, 2,348 DEGs in the stems, and 2,256 DEGs in the leaves (Fig. S1). Then, KEGG enrichment analyses of the DEGs were performed for all groups. The DEGs of each group for the roots, stems, and leaves were all enriched in the mitogen-activated protein kinase (MAPK) signaling pathway-plant and ATP-binding cassette (ABC) transporters by KEGG enrichment analysis. In addition, KEGG enrichment analysis revealed that these DEGs in the roots and stems of ONE_vs_SIX were significantly enriched in sesquiterpenoid and triterpenoid biosynthesis, which is the ginsenoside synthesis pathway, with a p value < 0.05 (Fig. 2 a-c).

DEGs related to the ginsenoside biosynthesis pathway

To reveal the differences in the gene expression levels of the ginsenoside synthesis pathway between ginseng with different growth years, the 105 genes corresponding to the enzymes involved in ginsenoside biosynthesis were identified by the Basic Local Alignment Search Tool (BLAST) and used for subsequent analysis (Table S1). Forty of the DEGs were differentially expressed in the 3 tissues (roots, stems, and leaves) (Fig. 2 d). The stems had the largest number of DEGs and ginsenoside synthesis enzymes, including 29 DEGs belonging to 12 enzymes, ACAT (EVM0054311), HMGCS, (EVM0023055, EVM0029495, EVM0026707, EVM0054724), HMGCR (EVM0024037, EVM0001999), MVD (EVM0027670), DXS (EVM0030205, EVM0037279, EVM0006073, EVM0045070), ispD (EVM0055836), ispH (EVM0051456), DDS (EVM0004551, EVM0060694), SQE (EVM0053925, EVM0023000, EVM0029946, EVM0064645), PPTS (EVM0038990), b-AS(EVM0025724, EVM0020790, EVM0062132), and UGTs (EVM0051342, EVM0054275, EVM0006186, EVM0056346, EVM0052072), followed by the roots with 22 DEGs belonging to 8 enzymes, HMGCR (EVM0039117, EVM0044950, EVM0009265, EVM0024037, EVM0001999), DXS (EVM0045070), ispF (EVM0045264), ispH (EVM0051456), SQE (EVM0029946), b-AS (EVM0025724), DDS (EVM0004551, EVM0060694, EVM0017889), and UGTs (EVM0023717, EVM0056346, EVM0054275, EVM0031115, EVM0002465, EVM0006186, EVM0062035, EVM0052072, EVM0051342). In the leaves, there were 9 DEGs belonging to 4 enzymes of the ginsenoside synthesis pathway, HMGCR (EVM0024037), DXS (EVM0037279), SQE (EVM0023000), and UGTs (EVM0019416, EVM0023717, EVM0006186, EVM0063186, EVM0054275, EVM0051342). By combing the DEGs in the roots, stems and leaves, we discovered that the genes were distributed in the MVA, MEP, triterpene skeleton, and downstream pathways of ginsenoside synthesis (Fig. 2 d).

Comparison of differential alternative splicing genes (DASGs) and DEGs

To investigate the divergence of AS events during the growth of ginseng, the differential AS events in the comparison groups ONE_vs_THR, ONE_vs_FIV, and ONE_vs_SIX were compared. Here, we present an overview of differential AS events during different developmental stages of ginseng in different tissues (roots, stems, and leaves) (Fig. 3 a). The distribution trends of the 5 types of differential AS events (A3SS, A5SS, MXE, RI, and SE) in ONE_vs_THR, ONE_vs_FIV, and ONE_vs_SIX were roughly similar among the different groups.

The DASGs and DEGs of each comparable group were compared by a Venn diagram to determine how many genes with differential AS events across different growth years also showed changed gene expression, and we found that there were few overlapping genes in each group (Fig. 3 b). The group with the largest number of overlapping genes was ONE_vs_THR in the roots, which accounted for 6.7% of DASGs, while the group with the smallest number of DASGs was ONE_vs_SIX in the leaves, which accounted for only 1.7%. The ratio of genes common to DASGs in the other groups and tissues ranged between 2.0% and 6.0%. The small proportion of common genes showed that the AS events were an independent regulatory mode in ginseng with different growth years that needed to be explored further.

Differential AS events involved in the regulation of ginsenoside synthesis.

To explore whether AS events affect ginsenoside synthesis genes, 105 ginsenoside synthesis genes with differential AS events were screened in each group (Table S2). The results of the comparison of differential AS events in 3 tissues showed that DASGs were distributed across 3 stages of ginsenoside synthesis (Fig. 3c). The terpene precursor synthesis genes that underwent differential AS events in different ginseng growth years were HMGCS (EVM0026707, EVM0029495), PMK (EVM0019632), ispD (EVM0055836, EVM0036709), ispG (EVM0016415), and DXS (EVM0006073). DASGs of the triterpene skeleton synthesis in ginseng with different growth years included GPS (EVM0048477), SS (EVM0016967, EVM0039608), and SQE (EVM0053925, EVM0064932), and the DASGs of the downstream synthesis of ginsenoside included β-AS (EVM0001127, EVM0008995) and DDS (EVM0017889). Where the genes encoding ispD extensively underwent differential AS events in all compared groups across the 3 tissues, genes encoding SS, β-AS, and DDS also underwent differential AS events in a large portion of compared groups across the 3 tissues. Overall, the genes involved in the terpene precursor, triterpene skeleton, and downstream synthesis of ginsenoside pathways underwent differential AS events in ginseng with different growth years.

Identification of TFs potentially regulating ginsenoside synthases via WGCNA

TFs are an important regulator of gene expression, and to identify the TFs that may regulate the expression of genes in the ginsenoside synthesis pathway, weighted gene coexpression network analysis (WGCNA) was performed using all genes expressed in ginseng for ONE, THR, FIV, and SIX from roots, stems, and leaves and their corresponding ginsenoside contents. In general, 30, 16, and 23 modules were identified in roots, stems, and leaves, and the soft-thresholding power values of 17, 24, and 22 were chosen for the 3 tissues, respectively (Fig. S2). Finally, a total of 6, 7, and 5 coexpression modules that were strongly correlated with the ginsenoside content were identified in the roots, stems, and leaves (Fig. 4 a-c). Among these modules, the module light cyan in roots, modules red, brown, pink, and cyan in stems and modules turquoise, pink, gray 60, and purple in leaves contained numerous ginsenoside synthesis genes (Table S3) and TFs (Table S4). Then, the expression of genes with Pearson correlation r-values (Table S5) greater than 0.75 and a p value (Table S6) less than 0.05 in the same coexpression modules were analyzed to explore their assumed regulatory relationship.

The results showed that in the light cyan module of the roots, the expression of B3 (EVM0011675) and SET (EVM0055308) was highly correlated with ginsenoside synthesis genes (Pearson correlation coefficient, r> 0.77, p < 0.01), which included HMGCR (EVM0016341), SQE (EVM0054399), and UGTs (EVM0026420) (Fig. 4 d). There were 4 modules containing target genes in the stems (Fig. 4 e), and the gene expression of AP2/ERF-ERF (EVM0058212) and MYB (EVM0037734) was highly correlated with UGTs (EVM0006186) in the red module (Pearson correlation coefficient, r> 0.97, p < 0.01). The TFs AP2/ERF-AP2 (EVM0031198) and MADS-MIKC (EVM0028800) in module brown had a fairly high correlation (Pearson correlation coefficient, r> 0.76, p < 0.01) with MVD (EVM0040479) and UGTs (EVM0064218). In addition, the expression of WRKYs (EVM0011571) and UGTs (EVM0062035) in the pink module (Pearson correlation coefficient, r> 0.92, p < 0.01) had a high degree of correlation, and the gene expression of UGTs (EVM0056346) in the cyan module was also highly correlated (Pearson correlation coefficient, r> 0.92, p < 0.01) with the expression of Tify (EVM0049056). In the stems, there were 4 coexpression modules in which the gene expression of TFs and ginsenoside synthesis genes was highly correlated (Fig. 4 f). B3 (EVM0046539) and DXR (EVM0049591) were highly correlated in the gray 60 module (Pearson correlation coefficient, r > 0.8, p < 0.01), and in the purple module, the gene expression of GARP-ARR-B (EVM0013378) had a highly positive correlation with PPTS (EVM0038990). The pink module in leaves showed a negative correlation (Pearson correlation coefficient, r < - 0.81, p < 0.01) between the gene expression of 2 TFs (HSF, EVM0028974, EVM0022431) and DXS (EVM0006073), and there was also a correlation in the turquoise module (Pearson correlation coefficient, r > 0.78, p < 0.01) between the gene expression of WRKY (EVM0065546) and 3 ginsenoside synthesis genes, ispH (EVM0014131), FPS (EVM0016553), and UGTs (EVM0054275). In general, by integrating the results of WGCNA in roots, stems, and leaves, we found that the expression of structural genes encoding UGTs (EVM0026420, EVM0006186, EVM0064218, EVM0062035, EVM0056346, EVM0054275) at the end of the triterpenoid synthesis pathway was largely correlated with TFs.

Characteristics of ginsenoside content variations in ginseng

Ginseng is a traditional medicinal Chinese herb that has been used to treat diseases for thousands of years, and the roots of ginseng have been regarded as a dietary supplement for health care and medical applications. In this study, we found that the total content of ginsenosides in leaves was always much higher than the total content of ginsenosides in roots and stems and as much as several times the total content of ginsenosides in roots and stems (Fig. 1). The ratio of PPT-type to PPD-type ginsenosides between leaves and roots was relatively close to 1 (Table 1), which means that the ginsenoside composition was fairly consistent. Furthermore, the leaves have a fast growth cycle and can be harvested once every year, whereas the roots take 4-6 years or more to grow, and the extracts of ginseng leaves and roots have many similar pharmacological activities [26, 27]. Overall, this study strengthens the idea that ginseng leaves are superior to roots in terms of ginsenoside content and sustainability, and it is recommended that ginseng leaves be exploited as a promising source of ginsenosides.

Traditionally, the root of ginseng was thought to contain more ginsenosides, particularly as it grows over a longer period of time [28]. The levels of ginsenosides also increased yearly in the roots and decreased in the stems and leaves in our study (Fig. 1). Some studies have shown that terpenoid transport could occur between different tissues of ginseng, so that the compounds synthesized in the leaves are transported to the roots for storage [29]. In addition, terpenoid transport between different organs occurs in other plants, such as Lavendula angustifolia, Arabidopsis thaliana, and Nicotiana plumbaginifolia [30-32]. Consequently, the increase in the ginsenoside content in the roots with increasing age might be due to transport from the stems and leaves. The evidence from this study suggests that the transportation mechanisms of ginsenosides and other secondary metabolites between different tissues of ginseng need to be further investigated.

DEGs in ginseng with different growth years

The growing age of ginseng affects the content of ginsenosides and modifies the differential expression of genes. A variety of DEGs were enriched in the MAPK signaling pathway, which functions in response to biotic and abiotic stresses during plant growth and development and is also associated with biosynthesis of secondary metabolites involved in resistance regulation [33, 34]. As the ginseng plants experienced stress across multiple years of growth, the genes in the MAPK signaling pathway were upregulated and acted in response to stress across different growth years. Furthermore, the DEGs were also enriched in the sesquiterpene and triterpene synthesis pathways in our compared groups of ginseng with different growth years (Fig. 2 a and b). Ginsenoside, as an important secondary metabolite involved in pathogen infection and stress in ginseng, also plays an invaluable role in resisting stress related to the MAPK signaling pathway [35, 36]. Moreover, the ginsenoside content of roots and stems also differed significantly between 1-year-old and 6-year-old ginseng (Fig. 1). Therefore, the differential expression of genes in the MAPK signaling pathway regulated the biosynthesis and accumulation of the secondary metabolites of ginsenosides to some extent.

The DEGs of our compared groups were also significantly enriched in the ABC transporter pathway (Fig. 2 a-c). ABC transporter proteins represent one of the most widely distributed protein families in all domains of life; they regulate plant growth and development and root morphology and play an important role in stress resistance. In addition, this gene family is also involved in the transport and accumulation of secondary metabolites from source organs to storage organs [37]. Studies have shown that the accumulation and transport of terpenoids are strongly associated with ABC transporter proteins, and the ABC transporter protein gene PgPDR3 in ginseng was cloned and validated to be involved in the accumulation of ginsenosides, while another 12 Pg PDR genes in the transcriptome of ginseng were found to be related to the transport of ginsenosides [9, 38, 39]. In this study, we found that the content of ginsenosides increased in the underground part, the root, and decreased in the aboveground tissues, the stem and the leaf, with increasing growth years, which further suggests that the differences in ginsenoside content in ginseng with different growth years are relevant to the existence of transport between different tissues associated with ABC transport proteins.

The regulation of ginsenoside synthesis genes and their potential applications

Gene expression regulation is a complex multilevel regulatory mechanism (genetic, transcriptional, and translational) in eukaryotes, and transcriptional-level regulation is the main mechanism responsible for regulating gene expression [40, 41]. Transcriptional regulation is a complex and dynamic process involving concerted modulation of TFs, and a combinatorial regulation network of gene expression is a critical mechanism for plant life activities [42]. TFs are responsible for the initiation of transcription, are indispensable for gene expression, and can enhance the yield of target products in plants by regulating the gene expression of secondary metabolite synthesis enzymes [43]. The synthesis of artemisinin in Artemisia annua, for instance, is influenced by TFs, including MYB, WRKY, and ERF, and the construction of overexpression vectors for these TFs in plants could enhance the expression of genes in the terpenoid synthesis pathway and increase the accumulation of artemisinin [44-46]. Comparable studies in Catharanthus roseus have shown that ERF and WRKY increase the expression of terpenoid synthesis pathway genes and improve the production of terpenoid indole alkaloids [47, 48]. WRKY, bHLH, and ERF were also identified to be involved in the regulation of Taxol biosynthesis to enhance the content of diterpenoids in Taxus cuspidata [49]. WRKY and MYB have been validated for their ability to promote terpenoid synthesis gene expression and enhance terpenoid accumulation in different species of plants. In our study, the expression of WRKY and MYB was extremely correlated with UGTs (Fig. 4 e) (Pearson correlation coefficient, r> 0.97, p < 0.01); apart from this, the gene expression patterns of transcription factors such as B3, SET, MYB, and Tify were also associated with the expression of UGTs in this regulatory network (Fig. 4 d-f) (Pearson correlation coefficient, r> 0.75, p < 0.01). These results suggest that UGTs, the last step in ginsenoside biosynthesis, are regulated by a number of transcription factors, and there is a high possibility of a potential regulatory relationship between them that warrants further study. Therefore, the scientific investigation of WRKY and MYB in the regulation of terpenoid synthesis gene expression and enhancement of ginsenoside content is a sensible approach that could also be applied to genetic engineering to increase the yield of natural ginsenosides.

As invaluable and diversified posttranscriptional regulation mechanisms in higher eukaryotes, AS events play a crucial role in the life course of plants, including flowering induction, circadian rhythms, and stress resistance; in turn, terpenoid synthesis and accumulation are often associated with stress resistance, and terpenoid biosynthetic genes are regulated by AS events (Fig. 3 c) [50-52]. HMGCS and HMGCR were identified to undergo AS events in the leaves of Ginkgo biloba at different stages of development, and HMGCR is a key enzyme in the MVA upstream pathway of ginsenoside biosynthesis [24] [53]. In our study, differential AS events occurred in 51 genes belonging to 10 enzymes in ginseng with different growth years across 3 tissues; in particular, DDS (EVM0017889) experienced differential AS events and differed in expression between the ONE_vs_SIX group of ginseng roots (Fig. 3 c and Fig. 2 d). In addition, DXS (EVM0006073), which also underwent differential AS events, showed a high correlation (r > 0.75) with the expression of HSF (Fig. 3 c and Fig. 4 f), revealing that the biosynthetic process of ginsenosides is likely to be highly complex and regulated by multiple factors. In general, the TFs related to ginsenoside synthesis identified in this study will be important targets for genetic modification and molecular breeding in the future. Here, we propose that a regulatory network of ginsenoside synthesis genes is more susceptible to regulation by AS events and TFs, and the study of key factors in the regulatory network that directly regulate ginsenoside synthesis gene expression might be helpful for improving ginsenoside production.

By combined transcriptional and metabolic analysis, a multivariate regulatory network involving AS events and TFs related to ginsenoside synthesis was identified within our results. These findings will help to explore the gene expression of ginsenoside biosynthesis in ginseng with different growth years, provide a molecular basis for the complicated regulatory network of ginsenoside biosynthesis, and supply implications for future breeding strategies aimed at improving the production of ginsenosides.

Plant materials and sample preparation

The cultivated ginseng samples grown for 1 year, 3 years, 5 years, and 6 years (marked as ONE, THR, FIV and SIX) were collected from Tonghua County, Jilin Province, northeast China, in August 2021. Samples were harvested from the same woodland at the same time, and biological replicates (n=3) were taken. Fresh ginseng was cleaned rapidly with distilled water, and three tissues (roots, stems, and leaves) were collected as the experimental materials. Then, the tissues of each sample were divided into two parts with a knife. One part was frozen in liquid nitrogen and stored at -80 °C for RNA extraction, and the other was ground into powder for ginsenoside extraction after drying at 55 °C.

Extraction and determination of ginsenosides

A total of 50 mg of ginseng powder was extracted with 500 μL methanol (Sigma Aldrich, Schnelldorf, Germany). The suspension was treated with ultrasound (Kun Shan Ultrasonic Instruments Co., Ltd., Shanghai, China) for 30 min, which was repeated twice, and the extraction solutions were filtered with a 0.22-μm syringe membrane filter. Then, the extraction was analyzed by high-performance liquid chromatography (HPLC) (SHIMADZU, Kyoto, Japan) for ginsenoside quantitative determination. For all the experiments, the column temperature was set at room temperature, the flow rate was 1.0 mL/min, and the injection volume was 20 μL. The UV absorbance detection wavelength was 203 nm, and the gradient mobile phase of (A) deionized water and (B) acetonitrile was as follows: 81% A and 19% B in the beginning, maintained for 30 min, changed to 76% A at 35 min, and 60% A at 60 min [54, 55]. A total of eight common ginsenoside standards (Rb₁, Rb₂, Rb₃, Rc, Rd, Re, Rf, and Rg₁) (Solarbio, Beijing, China) were used as controls in this experiment. Each sample was analyzed three times, and three biological replicates were prepared in this experiment.

Transcriptome profiling and analysis

Total RNA was extracted using TRIzol reagent (Invitrogen, Waltham, MA, USA) according to the manufacturer's protocol. After RNA quantification and qualification, the library preparations were sequenced on an Illumina NovaSeq platform, and 150 bp paired-end reads were generated.

After sequencing, raw data in fastq format were processed. In this step, clean data were obtained by removing reads containing adapters, reads containing poly-N sequences, and low-quality reads from raw data by Trimmomatic (v0.39), and then FastQC (v0.11.9) was used to check the quality control of the clean data [56, 57]. All the clean data were mapped to the ginseng reference genome using HISAT2 (v2.1.0) with default parameters, and RSEM (v1.3.0) was used to count the read numbers mapped to each gene [58-60]. Then, the fragments per kilobase of exon model per million mapped fragments (FPKM) of each gene was calculated based on the length of the gene; this value represents the amount of expression of each gene.

Identification of DEGs

Differential expression analysis was performed by the DESeq2 (v1.28.1) package in R (v4.0.2) [61]. A corrected p value of 0.05 and absolute fold change of 2 were set as the thresholds for significant differential expression. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of differentially expressed genes (DEGs) was performed using ClusterProﬁler (v3.16.1) in R (v4.0.2) [62].

Alternative splicing analysis

Alternative splicing (AS) is an important mechanism for regulating the structures of genes, and we analyzed five major types of alternative splicing events (alternative 3' splice site (A3SS), alternative 5' splice site (A5SS), mutually exclusive exons (MXE), retained intron (RI), and skipped exon (SE)) in our samples. rMATS (v3.2.5) was used to detect differential splicing events, which were defined as those with a p value less than 0.05. After comparing the bam files and using rMATS to perform the differential splicing events, an overview visual bar chart of differential alternative splicing genes (DASGs) was generated by Excel [63].

Weighted gene correlation network analysis

Weighted gene coexpression network analysis (WGCNA) was performed to construct coexpression networks between genes that were expressed in different ginseng growth years and ginsenoside contents using the WGCNA package (v.1.70-3) in R (v4.0.2) [64]. An unsigned gene correlation matrix with soft thresholding (power= 17, 24, 22) was used to build the topological overlap matrix (TOM). Similar clusters were merged using blockwiseModules, and modules were divided with deepSplit = 2 and minModuleSize = 50. Module members that strongly correlated with the ginsenoside monomer content (r > 0.7, p < 0.05) were selected. Regulatory network diagrams were visualized by Cytoscape (v3.8.0), and the top 10% of the genes based on degree were used as hub genes [65].

Statistical analysis

The difference in ginsenoside content among ginseng across 4 growth years (ONE, THR, FIV, and SIX) was analyzed by analysis of variance (ANOVA), and the column stacking chart of ginsenoside content and heatmap of ginsenoside synthesis gene expression were drawn using the ggplot2 (v.3.3.6) package in R (v4.0.2) [66, 67]. Correlation between the expression levels of transcription factors and ginsenoside biosynthesis genes was performed by the corrplot (v.0.92) package in R (v4.0.2) [68].

KEGG: Kyoto Encyclopedia of Genes and Genomes; DEGs: Differentially expressed genes; MAPK: Mitogen-activated protein kinase; ABC transporters: Adenosine triphosphate-binding cassette (ABC) transporters; DASGs: Differential alternative splicing genes; WGCNA: Weighted gene coexpression network analysis; UGTs: UDP-glycosyltransferases; HMGCR: Hydroxymethylglutaryl-coenzyme A reductase; SQE: Squalene epoxidase; IPP: Derivescisopentenyl pyrophosphate; DMAPP: Dimethylallyl pyrophosphate; MVA: Mevalonic acid; MEP: 2-C-methyl-D-erythritol-4-phosphate; CoA: Acetyl-coenzyme A; ACAT: Acetyl-CoA C-acetyltransferase; HMGCS: Hydroxymethylglutaryl-CoA synthase; MVK: Mevalonate kinase; PMK: Phosphomevalonate kinase; MVP: Mevalonate phosphate; MVPP: Mevalonate pyrophosphate; MVD: Diphosphomevalonate decarboxylase; PA: Pyruvic acid; PGAL: 3-phosphoglyceraldehyde; DXP: 1-deoxy-D-xylulose-5-phosphate; DXS: 1-deoxy-D-xylulose-5-phosphate synthase; DXR: 1-deoxy-D-xylulose-5-phosphate reductoisomerase; ispD: 2-C-methyl-D-erythritol 4-phosphate cytidylyltransferase; ispE: 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase; ispF: 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; ispG: 4-hydroxy-3-methylbut-2-enyl-diphosphate synthase; ispH: 4-hydroxy-3-methylbut-2-en-1-yl diphosphate reductase; IDI: Isopentenyl-diphosphate delta-isomerase; GPS: Geranyl diphosphate synthase; GPP: GGeranyl pyrophosphate; FPP: Farnesyl pyrophosphate; FPS: Farnesyl diphosphate synthase; SS: Farnesyl-diphosphate farnesyltransferase; OA: Oleanane; β-AS: β-amyrin synthase; DDS: Dammarenediol synthase; PPDS: Protopanaxadiol synthase; PPD: Protopanaxadiol; PPT: PProtopanaxatriol; PPTS: Protopanaxatriol synthase; HPLC: High-performance liquid chromatography; FPKM: Fragments per kilobase of exon model per million mapped fragments; AS: Alternative splicing; A3SS: Alternative 3' splice site; A5SS: AAlternative 5' splice site; MXE: Mutually exclusive exons; RI: Retained intron; SE: Skipped exon; TFs: Transcription factors.

Acknowledgements

We thank reviewers and editors for their constructive comments that greatly improved our manuscript.

Authors’ contributions

Conception, charts and draft writing, H.X.X and H.Y.W; final writing; Data analysis, X.T.Z and X.X.F; Experiments, Y.B.T and M.F.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by National Natural Science Foundation of China (31770243).

Data Availability and materials

All of the raw reads generated in this study have been deposited in NCBI and can be accessed in the Sequence Read Achieve (SRA) Sequence Database under the bioproject accession number PRJNA903492 (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA903492).

Ethics approval and consent to participate

All cultivated ginseng were collected from the woodland of Jingyu Binghua Chinese Medicines Development Co., Ltd. Jilin Province and we were obtained the permission from the company to collect ginseng. The research conducted in this study neither required approval from an ethics committee, nor involved any human or animal subjects. We complied with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Yun TK: Brief introduction of Panax ginseng CA Meyer. JOURNAL OF KOREAN MEDICAL SCIENCE 2001, 16:S3-S5.
Kim D-H: Chemical Diversity of Panax ginseng, Panax quinquifolium, and Panax notoginseng. JOURNAL OF GINSENG RESEARCH 2012, 36(1):1-15.
Ratan ZA, Haidere MF, Hong YH, Park SH, Lee J-O, Lee J, Cho JY: Pharmacological potential of ginseng and its major component ginsenosides. JOURNAL OF GINSENG RESEARCH 2021, 45(2):199-210.
Shin B-K, Kwon SW, Park JH: Chemical diversity of ginseng saponins from Panax ginseng. JOURNAL OF GINSENG RESEARCH 2015, 39(4):287-298.
Son K-J, Choi KR, Lee SJ, Lee H: Immunogenic Cell Death Induced by Ginsenoside Rg3: Significance in Dendritic Cell-based Anti-tumor Immunotherapy. IMMUNE NETWORK 2016, 16(1):75-84.
Lee SY, Kim GT, Roh SH, Song J-S, Kim H-J, Hong S-S, Kwon SW, Park JH: Proteome changes related to the anti-cancer activity of HT29 cells by the treatment of ginsenoside R-d. PHARMAZIE 2009, 64(4):242-247.
Briskin DP: Medicinal plants and phytomedicines. Linking plant biochemistry and physiology to human health. PLANT PHYSIOLOGY 2000, 124(2):507-514.
Boopathi V, Subramaniyam S, Mathiyalagan R, Yang DC: Till 2018: a survey of biomolecular sequences in genus Panax. Journal of Ginseng Research 2020, 44(1):33-43.
Cao H, Nuruzzaman M, Xiu H, Huang J, Wu K, Chen X, Li J, Wang L, Jeong J-H, Park S-J et al: Transcriptome Analysis of Methyl Jasmonate-Elicited Panax ginseng Adventitious Roots to Discover Putative Ginsenoside Biosynthesis and Transport Genes. INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES 2015, 16(2):3035-3057.
Lee YS, Park H-S, Lee D-K, Jayakodi M, Kim N-H, Koo HJ, Lee S-C, Kim YJ, Kwon SW, Yang T-J: Integrated Transcriptomic and Metabolomic Analysis of Five Panax ginseng Cultivars Reveals the Dynamics of Ginsenoside Biosynthesis. FRONTIERS IN PLANT SCIENCE 2017, 8:1048.
Fan H, Li K, Yao F, Sun L, Liu Y: Comparative transcriptome analyses on terpenoids metabolism in field- and mountain-cultivated ginseng roots. BMC PLANT BIOLOGY 2019, 19:82.
Yang J-LHZ-FZT-TGA-DGTZP: Progress on the Studies of the Key Enzymes of Ginsenoside Biosynthesis.Molecules 23(3):589 In., vol. 23; 2018.
Zhao M, Lin Y, Wang Y, Li X, Han Y, Wang K, Sun C, Wang Y, Zhang M: Transcriptome analysis identifies strong candidate genes for ginsenoside biosynthesis and reveals its underlying molecular mechanism in Panax ginseng CA Meyer. SCIENTIFIC REPORTS 2019, 9:615.
Kong L-T, Wang Q, Xiao B-X, Liao Y-H, He X-X, Ye L-H, Liu X-M, Chang Q: Different pharmacokinetics of the two structurally similar dammarane sapogenins, protopanaxatriol and protopanaxadiol, in rats. Fitoterapia 2013, 86:48-53.
Park SB, Chun JH, Ban YW, Han JY, Choi YE: Alteration of Panax ginseng saponin composition by overexpression and RNA interference of the protopanaxadiol 6-hydroxylase gene (CYP716A53v2). J Ginseng Res 2016, 40(1):47-54.
Kim D, Kim M, Rana GS, Han J: Seasonal Variation and Possible Biosynthetic Pathway of Ginsenosides in Korean Ginseng Panax ginseng Meyer. Molecules 2018, 23(7):1824.
Soldati F, Tanaka O: PANAX-GINSENG - RELATION BETWEEN AGE OF PLANT AND CONTENT OF GINSENOSIDES. PLANTA MEDICA 1984, 50(4):351-352.
Shi W, Wang Y, Li J, Zhang H, Ding L: Investigation of ginsenosides in different parts and ages of Panax ginseng. Food Chemistry 2007, 102(3):664-668.
Zhang J-J, Su H, Zhang L, Liao B-S, Xiao S-M, Dong L-L, Hu Z-G, Wang P, Li X-W, Huang Z-H et al: Comprehensive Characterization for Ginsenosides Biosynthesis in Ginseng Root by Integration Analysis of Chemical and Transcriptome. MOLECULES 2017, 22(22(6):889):889.
Lv C, Lu J: Research progress on the distribution of ginsenosides in different commercial ginseng. Chinese Traditional and Herbal Drugs 2021, 52(17):5329-5338.
Hui F, Liu X, Li Z, Liu F, Yang S: Application of transcriptome sequencing in study of medicinal plants. Chinese Traditional and Herbal Drugs 2019, 50(24):6149-6155.
Lin F, Liu J, Chen HJJoJFe, Technology: The effect of cultivation years on saponin synthetic gene expression quantity in northeast Panax ginseng. Journal of Jilin Forestry Science and Technology 2016, 45:19-22.
Jayakodi M, Lee S-C, Lee YS, Park H-S, Kim N-H, Jang W, Lee HO, Joh HJ, Yang T-J: Comprehensive analysis of Panax ginseng root transcriptomes. BMC PLANT BIOLOGY 2015, 15.
Shen G, Pang Y, Wu W, Liao Z, Zhao L, Sun X, Tang K: Cloning and characterization of a root-specific expressing gene encoding 3-hydroxy-3-methylglutaryl coenzyme a reductase from Ginkgo biloba. MOLECULAR BIOLOGY REPORTS 2006, 33(2):117-127.
Fernandez-Calvo P, Chini A, Fernandez-Barbero G, Chico J-M, Gimenez-Ibanez S, Geerinck J, Eeckhout D, Schweizer F, Godoy M, Manuel Franco-Zorrilla J et al: The Arabidopsis bHLH Transcription Factors MYC3 and MYC4 Are Targets of JAZ Repressors and Act Additively with MYC2 in the Activation of Jasmonate Responses. PLANT CELL 2011, 23(2):701-715.
Chen W, Prabhu B, I. PDGT: Changes of Ginsenoside Composition in the Creation of Black Ginseng Leaf. In: Molecules 25(12):2809. vol. 25; 2020: 2809.
Wang H, Peng D, Xie J: Ginseng leaf-stem: bioactive constituents and pharmacological functions. Chinese Medicine 2009, 4(1):20.
Li WL, Zheng HC, Bukuru J, De Kimpe N: Natural medicines used in the traditional Chinese medical system for therapy of diabetes mellitus. JOURNAL OF ETHNOPHARMACOLOGY 2004, 92(1):1-21.
Kim Y-J, Joo SC, Shi J, Hu C, Quan S, Hu J, Sukweenadhi J, Mohanan P, Yang D-C, Zhang D: Metabolic dynamics and physiological adaptation of Panax ginseng during development. PLANT CELL REPORTS 2018, 37(3):393-410.
Demissie ZA, Tarnowycz M, Adal AM, Sarker LS, Mahmoud SS: A lavender ABC transporter confers resistance to monoterpene toxicity in yeast. Planta 2019, 249(1):139-144.
Campbell EJ, Schenk PM, Kazan K, Penninckx IAMA, Anderson JP, Maclean DJ, Cammue BPA, Ebert PR, Manners JM: Pathogen-Responsive Expression of a Putative ATP-Binding Cassette Transporter Gene Conferring Resistance to the Diterpenoid Sclareol Is Regulated by Multiple Defense Signaling Pathways in Arabidopsis. Plant Physiology 2003, 133(3):1272-1284.
Jasiński M, Stukkens Y, Degand H, Purnelle B, Marchand-Brynaert J, Boutry M: A Plant Plasma Membrane ATP Binding Cassette–Type Transporter Is Involved in Antifungal Terpenoid Secretion. The Plant Cell 2001, 13(5):1095-1107.
Sözen C, Schenk ST, Boudsocq M, Chardin C, Almeida-Trapp M, Krapp A, Hirt H, Mithöfer A, Colcombet J: Wounding and insect feeding trigger two independent MAPK pathways with distinct regulation and kinetics. Plant Cell 2020, 32(6):1988-2003.
Zhou J, Mu Q, Wang X, Zhang J, Yu H, Huang T, He Y, Dai S, Meng X: Multilayered synergistic regulation of phytoalexin biosynthesis by ethylene, jasmonate, and MAPK signaling pathways in Arabidopsis. PLANT CELL 2022, 34:3066-3087.
Fang X, Wang M, Zhou X, Wang H, Wang H, Xiao H: Effects of growth years on ginsenoside biosynthesis of wild ginseng and cultivated ginseng. BMC GENOMICS 2022, 23(1):325.
Bian X, Zhao Y, Xiao S, Yang H, Han Y, Zhang L: Metabolome and transcriptome analysis reveals the molecular profiles underlying the ginseng response to rusty root symptoms. BMC PLANT BIOLOGY 2021, 21(1):215.
Liu N, Sun H, Liu Z, Shao C, Zhang L: Advances in the study of ATP-binding cassette transporter proteins and secondary metabolite transport in medicinal plants. China Journal of Chinese Materia Medica 2021, 46(01):52-56.
Zhang R, Huang J, Zhu J, Xie X, Tang Q, Chen X, Luo J, Luo Z: Isolation and characterization of a novel PDR-type ABC transporter gene PgPDR3 from Panax ginseng CA Meyer induced by methyl jasmonate. MOLECULAR BIOLOGY REPORTS 2013, 40(11):6195-6204.
Zhang R, Zhu J, Cao H-Z, An Y-R, Huang J-J, Chen X-H, Mohammed N, Afrin S, Luo Z-Y: Molecular cloning and expression analysis of PDR1-like gene in ginseng subjected to salt and cold stresses or hormonal treatment. PLANT PHYSIOLOGY AND BIOCHEMISTRY 2013, 71:203-211.
Li J, Farmer AD, Lindquist IE, Dukowic-Schulze S, Mudge J, Li T, Retzel EF, Chen C: Characterization of a set of novel meiotically-active promoters in Arabidopsis. BMC Plant Biology 2012, 12(1):104.
Cao Q, Yang W, Ji X, Wang W: Long Non-coding RNA ST8SIA6-AS1 Promotes Lung Adenocarcinoma Progression Through Sponging miR-125a-3p. Front Genet 2020, 11:597795.
Lin Z, Guo Z, Xu Y, Zhao X: Identification of a secondary promoter of CASP8 and its related transcription factor PURα. Int J Oncol 2014, 45(1):57-66.
Yao L, Wang J, Sun J, He J, Paek K-Y, Park S-Y, Huang L, Gao W: A WRKY transcription factor, PgWRKY4X, positively regulates ginsenoside biosynthesis by activating squalene epoxidase transcription in Panax ginseng. Industrial Crops and Products 2020, 154:112671.
Han J, Wang H, Lundgren A, Brodelius PE: Effects of overexpression of AaWRKY1 on artemisinin biosynthesis in transgenic Artemisia annua plants. Phytochemistry 2014, 102:89-96.
Matías-Hernández L, Jiang W, Yang K, Tang K, Brodelius PE, Pelaz S: AaMYB1 and its orthologue AtMYB61 affect terpene metabolism and trichome development in Artemisia annua and Arabidopsis thaliana. The Plant Journal 2017, 90(3):520-534.
Li Q, Gao X, Chen W, Tan H: Progress in the study of transcription factors related to artemisinin biosynthesis in Artemisia annua. Chinese Traditional and Herbal Drugs 2021, 52(06):1827-1833.
Yang C-Y, Hsu F-C, Li J-P, Wang N-N, Shih M-C: The AP2/ERF Transcription Factor AtERF73/HRE1 Modulates Ethylene Responses during Hypoxia in Arabidopsis. PLANT PHYSIOLOGY 2011, 156(1):202-212.
Suttipanta N, Pattanaik S, Kulshrestha M, Patra B, Singh SK, Yuan L: The Transcription Factor CrWRKY1 Positively Regulates the Terpenoid Indole Alkaloid Biosynthesis in Catharanthus roseus. PLANT PHYSIOLOGY 2011, 157(4):2081-2093.
Kuang X: Biosynthesis and regulation of the diterpenoids paclitaxel and tanshinone. Peking Union Medical College; 2020.
Qin N, Zhang R, Zhang M, Niu Y, Fu S, Wang Y, Wang D, Chen Y, Zhao C, Chen Q et al: Global Profiling of Dynamic Alternative Splicing Modulation in Arabidopsis Root upon Ralstonia solanacearum Infection. Genes (Basel) 2020, 11(9):1078.
Zhao L: Study of aphid-resistant volatile terpenoids and their regulatory genes in wheat. Shandong Agricultural University; 2020.
Xu B, Meng Y, Jin Y: RNA structures in alternative splicing and back-splicing. Wiley Interdiscip Rev RNA 2021, 12(1):e1626.
He B, Han X, Liu H, Bu M, Cui P, Xu L-a: Deciphering alternative splicing patterns in multiple tissues of Ginkgo biloba important secondary metabolites. INDUSTRIAL CROPS AND PRODUCTS 2022, 181:114812.
Liang Q, Zhang J, Su X, Meng Q, Dou J: Extraction and separation of eight ginsenosides from flower buds of Panax ginseng using aqueous ionic liquid-based ultrasonic-assisted extraction coupled with an aqueous biphasic system. Molecules 2019, 24(4):778.
Hsu BY, Jen CT, Inbaraj BS, Chen BH: A Comparative Study on Analysis of Ginsenosides in American Ginseng Root Residue by HPLC-DAD-ESI-MS and UPLC-HRMS-MS/MS. Molecules 2022, 27(10):3071.
Bolger AM, Lohse M, Usadel B: Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30(15):2114-2120.
Wingett S, Andrews S: FastQ Screen: A tool for multi-genome mapping and quality control F1000Res 2018, 7(1338):1338.
Li B, Dewey CN: RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 2011, 12:323.
Kim D, Paggi JM, Park C, Bennett C, Salzberg SL: Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature Biotechnology 2019, 37(8):907-915.
Wang Z-H, Wang X-F, Lu T, Li M-R, Jiang P, Zhao J, Liu S-T, Fu X-Q, Wendel JF, Van de Peer Y et al: Reshuffling of the ancestral core-eudicot genome shaped chromatin topology and epigenetic modification in Panax. Nature Communications 2022, 13(1):1902.
Love MI, Huber W, Anders S: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 2014, 15(12):550.
Yu G, Wang LG, Han Y, He QY: ClusterProfiler: An R package for comparing biological themes among gene clusters. OMICS A Journal of Integrative Biology 2012, 16(5):284-287.
Shen S, Park JW, Lu ZX, Lin L, Henry MD, Wu YN, Zhou Q, Xing Y: rMATS: Robust and flexible detection of differential alternative splicing from replicate RNA-Seq data. Proceedings of the National Academy of Sciences of the United States of America 2014, 111(51):E5593-E5601.
Langfelder P, Horvath S: WGCNA: An R package for weighted correlation network analysis. BMC Bioinformatics 2008, 9:559.
Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: A software Environment for integrated models of biomolecular interaction networks. Genome Research 2003, 13(11):2498-2504.
Anderson MJ: A new method for non-parametric multivariate analysis of variance. Austral Ecology 2001, 26(1):32-46.
Kahle D, Wickham H: ggmap: Spatial visualization with ggplot2. R Journal 2013, 5(1):144-161.
McKenna S, Meyer M, Gregg C, Gerber S: s-CorrPlot: An Interactive Scatterplot for Exploring Correlation. Journal of Computational and Graphical Statistics 2016, 25(2):445-463.

No competing interests reported.

Download PDF

Version 1

posted

You are reading this latest preprint version

Integrative metabolome and transcriptome analyses reveal the regulatory network of ginsenoside synthesis in ginseng

Status:

Version 1

Abstract

Figures

Background

Results

Discussion

Conclusion

Methods

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1