Method for identifying gene for regulating content of medicinal ingredient in delonix regia

Abstract

The application discloses a medicinal ingredient content regulation gene of Morus notabilis and a method for identifying the same. The medicinal ingredient is a flavonoid compound or a quinone compound; wherein the genes for regulating the content of the flavonoid compound include CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709; the genes for regulating the content of the quinone compound include CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197. The application also discloses a method for identifying the medicinal ingredient content regulation gene of Morus notabilis. The genes related to the content of the flavonoid compound and the quinone compound in Morus notabilis provided by the application provide a scientific basis for metabolic regulation and molecular breeding of medicinal ingredients.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of plant secondary metabolite biosynthesis. Specifically, it is a Handroanthus impetiginosa medicinal ingredient content regulation gene and its identification method. BACKGROUND

[0002] Handroanthus impetiginosa, belonging to Bignoniaceae, is an ornamental plant originally from South America, and has important medicinal value. Its main medicinal ingredients include flavonoids and quinones. Flavonoids have various biological activities such as antioxidant, anti-inflammatory, antibacterial, and anti-tumor activities, and are widely used in the fields of anticancer drugs, antioxidants, and health products; quinones (such as β-lapachone) are of great concern due to their significant anti-tumor and anti-parasitic activities.

[0003] In existing research, some studies have analyzed the chemical components of Handroanthus impetiginosa and verified its pharmacological activities, but there is a lack of systematic analysis of its key metabolic pathways and related genes in vivo. At present, the research on the biosynthetic pathways and regulation mechanisms of these medicinal ingredients in Handroanthus impetiginosa is still in its early stages, which limits the development and utilization of its medicinal potential.

[0004] The synthesis of plant secondary metabolites is usually regulated by key enzyme genes and transcription factors. The combination of high-throughput sequencing technology and metabolomics analysis methods provides an effective tool for studying the synthesis and regulation mechanism of secondary metabolites. However, the synthesis of flavonoids and quinones is regulated by multiple genes and transcription factors, but the specific genes and regulation mechanisms involved have not been fully elucidated. In-depth research on the synthesis and regulation pathways of these metabolites and related genes, and finding a key gene and transcription factor that regulates the synthesis of these metabolites, can help regulate the content of these metabolites with medicinal value in Handroanthus impetiginosa, which is of great significance for developing drug resources based on Handroanthus impetiginosa and improving medicinal plant germplasm. SUMMARY

[0005] Therefore, the technical problem to be solved by the present application is to provide a Handroanthus impetiginosa medicinal ingredient content regulation gene and its identification method. Through transcriptome and metabolome combined analysis, genes related to the content of flavonoids and quinones in Handroanthus impetiginosa are identified, providing a scientific basis for metabolic regulation and molecular breeding of medicinal ingredients.

[0006] To solve the above technical problems, the present application provides the following technical solutions:

[0007] The content regulation gene of the medicinal ingredient in Delonix regia, the medicinal ingredient is flavonoids or quinones; wherein, the gene for regulating the content of flavonoids includes: CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709; the gene for regulating the content of quinones includes CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197. The sequences of CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709 are shown in SEQ ID NO. 1, 2, 3 and 4 respectively; the sequences of CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197 are shown in SEQ ID NO. 5, 6, 7 and 8 respectively. These genes regulate the content (i.e. accumulation) of the corresponding substances in Delonix regia by regulating the biosynthesis of flavonoids, or regulating the biosynthesis of quinones, or regulating the biosynthesis of both flavonoids and quinones.

[0008] “Flavonoid biosynthesis” is equivalent to the biosynthesis of flavonoids.

[0009] The identification method of the medicinal ingredient content regulation gene in Delonix regia, comprising the following steps:

[0010] S1, sampling the leaves, roots, barks and xylem tissues of Delonix regia;

[0011] S2, investigating the differences in the types and contents of flavonoids and quinones in the leaves, roots, barks and xylem tissues by metabolome analysis, obtaining differential accumulation metabolites; investigating the differences in the gene expression patterns in the leaves, roots, barks and xylem tissues by transcriptome analysis, obtaining differential expression genes;

[0012] S3, performing joint analysis on the differential expression genes and differential accumulation metabolites, and identifying the medicinal ingredient content regulation gene in Delonix regia.

[0013] In the above identification method, in step S2, when performing metabolome analysis, the leaves, roots, barks and xylem tissues of Delonix regia are prepared into test samples for ultra-high performance liquid chromatography-tandem mass spectrometry analysis, and the test samples are analyzed by ultra-high performance liquid chromatography-tandem mass spectrometry to obtain the types and contents of flavonoids and quinones in the leaves, roots, barks and xylem; using variable importance projection VIP>1 and |log2 FC|≥1 as screening criteria, screening flavonoids and quinones with significant content differences in the four tissues as differential accumulation metabolites.

[0014] The abbreviation VIP stands for Variable Importance in Projection, and the abbreviation FC stands for Fold Change. The abbreviation FDR stands for False Discovery Rate. r stands for the correlation coefficient in a Pearson correlation analysis.

[0015] When preparing a sample for ultra-high performance liquid chromatography-tandem mass spectrometry analysis, the extraction liquid is a 70% methanol solution by volume, and the mass / volume ratio of the sample to be tested to the extraction liquid is 50 mg:1.2 mL;

[0016] When performing ultra-high performance liquid chromatography analysis, an Agilent SB-C18 column is used, with a column particle size of 1.8 μm, a specification of 2.1 mm x 100 mm, and a column temperature of 40 DEG C. The A phase in the mobile phase is a 0.1% formic acid solution in ultrapure water, and the B phase in the mobile phase is a 0.1% formic acid solution in acetonitrile. The flow rate is 0.35 mL / min, and the injection volume is 4 μL. The gradient conditions during elution are as follows: 0-9 minutes, the volume fraction of B phase is linearly increased from 5% to 95%; 9-10 minutes, the volume fraction of B phase is maintained at 95%, 10-11.1 minutes, the volume fraction of B phase is linearly reduced from 95% to 5%; 11.1-14 minutes, the volume fraction of B phase is maintained at 5% to balance the system.

[0017] When performing tandem mass spectrometry analysis, the temperature of the electrospray ion source is 550 DEG C, the ion spray voltage is 5500 V in positive ion mode and -4500 V in negative ion mode, ion source gas I is set to 50 psi, ion source gas II is set to 60 psi, and curtain gas is set to 25 psi.

[0018] The technical solution of the present application achieves the following beneficial technical effects:

[0019] 1. The present application first combines the joint analysis of transcriptome and metabolome, and systematically analyzes the synthesis pathways of flavonoids and quinones in Delonix regia and their key regulatory genes. This work provides a solid scientific basis for in-depth understanding of the metabolic regulation mechanism of medicinal ingredients in Delonix regia, and lays a theoretical foundation for subsequent functional research and development of medicinal ingredients.

[0020] 2、The present application optimizes the sample pretreatment process in metabolomics analysis, including the selection of extraction solvent, the determination of extraction ratio and the optimization of chromatography mass spectrometry conditions, efficiently and accurately analyzes the differential accumulation metabolites in each tissue of Delonix regia, and provides a basis for combined analysis of metabolome and transcriptome. Further, the differential expression genes in the transcriptome data are correlated with the differential accumulation metabolites, genes with similar expression patterns are divided into several modules by using WGCNA analysis, the differential expression genes are screened according to the correlation between the module and the phenotype (flavonoid compounds, quinone compounds), the number of differential expression genes needing to be analyzed is efficiently and greatly reduced, and the analysis efficiency is improved. On this basis, the screened differential expression genes and differential accumulation metabolites are mapped into the KEGG database, genes and metabolites participating in the biosynthesis pathways of the same flavonoid compounds and the same quinone compounds are found, genes and metabolites participating in different biosynthesis pathways are excluded, the accuracy of the screening result is improved, and finally, the Pearson correlation analysis method is used to successfully identify the genes and metabolites closely related to the synthesis of flavonoids and quinones. The identification method provides an important reference for subsequent gene function verification and molecular breeding, and helps to find new regulatory factors and improve the medicinal value of Delonix regia. BRIEF DESCRIPTION OF DRAWINGS

[0021] Figure 1 The types and proportions of metabolites identified in the leaf, root, bark and xylem tissues of Delonix regia in the embodiment of the present application;

[0022] Figure 2 The principal component analysis result graph of metabolites in different parts of tissues of Delonix regia;

[0023] Figure 3 The cluster analysis result graph of metabolites in different parts of tissues of Delonix regia;

[0024] Figure 4 The number statistics of differential accumulation metabolites between different parts of tissues of Delonix regia;

[0025] Figure 5 The K-means cluster analysis result graph of all detected metabolites in different tissues of Delonix regia, the heat map shows the relative abundance of each metabolite in different tissues, and the line chart shows the accumulation mode of metabolites in each cluster;

[0026] Figure 6 The cluster analysis result graph of flavonoids in different tissues of Delonix regia;

[0027] Figure 7 The cluster analysis result graph of quinones in different tissues of Delonix regia;

[0028] Figure 8 Gene annotation weinberg plot based on eight gene databases in the embodiment of the present application;

[0029] Figure 9 Result plot of functional annotation of genes using GO database;

[0030] Figure 10 Result plot of functional classification of genes using KOG database;

[0031] Figure 11 Family distribution statistics of part of transcription factor genes and transcription factor genes belonging to differentially expressed genes in transcriptome data;

[0032] Figure 12 Gene number statistics of 13 genes contained in 14 gene modules divided by WGCNA;

[0033] Figure 13 Heat map of expression amount of 14 differentially expressed transcription factor genes belonging to flavonoid biosynthesis pathway in different tissue parts;

[0034] Figure 14 Heat map of expression amount of 8 differentially expressed transcription factor genes related to quinone compound synthesis in different tissue parts;

[0035] Figure 15 WGCNA module hierarchical clustering tree plot in the embodiment of the present application;

[0036] Figure 16 Correlation analysis plot of differentially expressed genes in each gene module and flavonoid compound content;

[0037] Figure 17 Correlation analysis of differentially expressed genes in Yellow module and flavonoid compound content;

[0038] Figure 18 Correlation analysis of differentially expressed genes in Turquoise module and flavonoid compound content;

[0039] Figure 19 Correlation analysis plot of differentially expressed genes in each gene module and quinone compound content;

[0040] Figure 20 Correlation analysis plot of differentially expressed genes in Blue module and quinone compound content;

[0041] Figure 21 Correlation analysis plot of differentially expressed genes in Brown module and quinone compound content;

[0042] Figure 22 Figure of biosynthetic pathway of flavonoids in Delonix regia, including heat map of expression of differentially expressed genes and heat map of accumulation of differentially accumulated metabolites, showing the biosynthetic pathway of flavonoids and the expression and accumulation of key genes and metabolites;

[0043] Figure 23 Correlation analysis of differentially expressed genes and differentially accumulated metabolites in the process of flavonoid biosynthesis in Delonix regia;

[0044] Figure 24 Correlation analysis of differentially expressed genes and differentially accumulated metabolites in the process of quinone biosynthesis in Delonix regia;

[0045] Figure 25 Number statistics of differentially expressed genes between different tissues of Delonix regia;

[0046] Figure 26a Comparison results of qRT-PCR data and RNA-seq data of CDL12_30090 gene in four tissues of Delonix regia;

[0047] Figure 26b Comparison results of qRT-PCR data and RNA-seq data of CDL12_12708 gene in four tissues of Delonix regia;

[0048] Figure 26c Comparison results of qRT-PCR data and RNA-seq data of CDL12_18673 gene in four tissues of Delonix regia;

[0049] Figure 26d Comparison results of qRT-PCR data and RNA-seq data of CDL12_23401 gene in four tissues of Delonix regia;

[0050] Figure 26e Comparison results of qRT-PCR data and RNA-seq data of CDL12_28394 gene in four tissues of Delonix regia;

[0051] Figure 26f Comparison results of qRT-PCR data and RNA-seq data of CDL12_11650 gene in four tissues of Delonix regia;

[0052] Figure 26g Comparison results of qRT-PCR data and RNA-seq data of CDL12_10798 gene in four tissues of Delonix regia;

[0053] Figure 26hComparison of qRT-PCR data and RNA-seq data of CDL12_16599 gene in four tissues of L. rubra. DETAILED DESCRIPTION

[0054] 1. Plant material

[0055] In June 2023, three L. rubra plants with consistent growth and no pests and diseases were selected from the L. rubra Germplasm Garden of the National Forest Tree Seedling Demonstration Base in the South, and four tissues, i.e., leaves (L), bark (B), xylem (X), and roots (R), were collected as experimental materials. The samples were quickly frozen in liquid nitrogen and stored at -80°C. Each tissue was divided into six parts, three of which were used as three biological replicates of the metabolome samples, and the other three were used as three biological replicates of the transcriptome samples.

[0056] 2. Sample preparation

[0057] The metabolome samples were prepared for UPLC-MS / MS analysis. Specifically, the samples were vacuum freeze-dried and ground into powder (using a MM 400 grinder from Retsch, 30 Hz for 1.5 minutes), 50 mg of the powder was weighed and dissolved in 1.2 mL of 70% methanol (i.e., a methanol solution with a methanol volume fraction of 70%) extraction solution; vortexed every 30 minutes for 30 seconds, a total of 6 times; after centrifugation (12000 rpm for 3 minutes), the supernatant was aspirated and filtered with a microporous filter (pore size 0.22 μm), and the sample was obtained.

[0058] 3. Data acquisition instrument

[0059] The instrument system designed for UPLC-MS / MS analysis consisted of an ultra-performance liquid chromatograph (Ultra Performance Liquid Chromatography, UPLC, model: ExionLC TM AD) and a tandem mass spectrometer (Tandem Mass Spectrometry, MS / MS, model: Applied Biosystems 4500QTRAP). The chromatographic column used was an Agilent SB-C18 column (particle size 1.8 μm, size 2.1 mm x 100 mm). The flow rate was set to 0.35 mL / min, the column temperature was maintained at 40°C, and the injection volume was 4 μL.

[0060] The mobile phase was composed of A phase (ultra-pure water containing 0.1% formic acid) and B phase (acetonitrile containing 0.1% formic acid). The elution gradient conditions were as follows:

[0061] 0.00 min, the proportion (volume fraction) of phase B was 5%; 0.00-9.00 min, the proportion of phase B was linearly increased to 95%; 9.00-10.00 min, the proportion of phase B was maintained at 95%; 10.00-11.10 min, the proportion of phase B was linearly decreased to 5%; 11.10-14.00 min, the proportion of phase B was maintained at 5% to balance the system.

[0062] The mass spectrometry conditions mainly included:

[0063] Electrospray Ionization (ESI) with a temperature setting of 550°C. The ion spray voltage (IS) was 5500V in positive ion mode and -4500V in negative ion mode.

[0064] Ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) were set to 50 psi, 60 psi and 25 psi, respectively. The collision-induced dissociation (CID) parameter was set to high. The mass spectrometry scan used the Multiple Reaction Monitoring (MRM) mode, and the collision gas (nitrogen) was set to medium intensity. Further optimization of the Declustering Potential (DP) and Collision Energy (CE) was performed to set the DP and CE of each MRM ion pair.

[0065] Metware Database (MWDB) of Metware Metabolomics Co., Ltd. was used for substance qualification (commissioned by Metware Metabolomics Co., Ltd.) to determine which chemical substances were contained in the sample. During substance qualification, secondary spectrum information was used for analysis. During the analysis process, the signals of isotopic ions (such as K + , Na + , NH4 + ) and repeated fragment ion signals of larger molecular weight substances were removed. The quantitative analysis of metabolites was performed by the Multiple Reaction Monitoring (MRM) mode of the triple quadrupole mass spectrometer.

[0066] 4. Metabolomics analysis results

[0067] The results of sample substance qualification show that 1201 metabolites are identified from the tissues of different parts of P. cerasifera in the embodiment. The 1201 metabolites include 230 flavonoids (accounting for 19.15%), 183 phenolic acids (accounting for 15.24%), 159 lipids (accounting for 13.24%), 132 terpenes (accounting for 10.99%), 105 amino acids and derivatives (accounting for 8.74%), 84 organic acids (accounting for 6.99%), 67 nucleotides and derivatives (accounting for 5.58%), 56 lignans and coumarins (accounting for 4.66%), 51 alkaloids (accounting for 4.25%), 15 quinones (accounting for 1.25%), and 119 other compounds (accounting for 9.91%). As shown in Table 1. Figure 1

[0068] The types of metabolites contained in the four tissues of leaves (L), bark (B), xylem (X) and roots (R) also differ. The peak areas of metabolites in the four tissues are subjected to principal component analysis (PCA), and the results are shown in Table 2. Figure 2 PC1 and PC2 explain 47.69% and 20.87% of the total variance of the sample, respectively, and the cumulative contribution rate reaches 68.56%. The four tissues show an obvious separation trend in PCA, among which the bark, roots and xylem are clustered on one side and are far away from the leaves, indicating that the metabolome profiles of the bark, roots and xylem are more similar, while the metabolome profile of the leaves is quite different from those of the bark, roots and xylem.

[0069] The cumulative patterns of the above 1201 metabolites in the four different tissues of leaves (L), bark (B), xylem (X) and roots (R) are subjected to cluster analysis, and the results are shown in Table 3. Figure 3 The types and contents of metabolites in the four different tissues differ significantly, and the metabolites in the leaves are more abundant, followed by the bark.

[0070] Using VIP>1 and |Log2 FC|≥1 as the screening criteria, 1106 differentially accumulated metabolites (DCMs, Differentially Changed Metabolites) with significant content differences in the four tissues are screened out in the five comparison groups. Figure 4 The screening results are shown in Table 4.

[0071] As shown in Table 4, the 1106 DCMs are mainly flavonoids, phenolic acids, lipids, terpenes, amino acids and derivatives, organic acids, nucleotides and derivatives, lignans and coumarins, alkaloids, quinones and other compounds. Figure 4 ​It can be seen that the number of the screened differential accumulation metabolites is the most (814 kinds) in the contrast group of the two tissues of leaf (L) and xylem (X). Among the 814 kinds of differential accumulation metabolites, 164 kinds of metabolites are up-regulated (i.e. the content in xylem is higher than that in leaf), and 650 kinds of metabolites are down-regulated (i.e. the content in xylem is lower than that in leaf). The most different down-regulated metabolites are mainly flavonoids, that is, there is a significant difference in the accumulation amount of flavonoids between leaf and xylem, and the accumulation amount of flavonoids in xylem is significantly less than that in leaf.

[0072] In the contrast group of the two tissues of root (R) and bark (B), the number of the screened differential accumulation metabolites is the least (580 kinds), among which 381 kinds of metabolites are up-regulated (i.e. the content in bark is greater than that in root), and 199 kinds of metabolites are down-regulated (i.e. the content in bark is less than that in root). The most different down-regulated metabolites are mainly phenolic acids.

[0073] The distribution of 1201 kinds of metabolites is grouped and classified by K-means average clustering analysis, and the content data of 1201 kinds of metabolites are divided into 8 clusters, each cluster representing a group of metabolites with similar distribution or distribution trend. The average clustering analysis result is shown in Figure 5

[0074] It can be seen from Figure 5 that the metabolites contained in cluster 2, cluster 5 and cluster 8 have high content in leaf. These three clusters contain a total of 604 kinds of metabolites, among which there are 202 kinds of flavonoids, indicating that flavonoids are mainly concentrated in leaf.

[0075] In bark, the clusters with high metabolite content are cluster 3 and cluster 4. Cluster 3 and cluster 4 together contain 289 kinds of metabolites, which mainly include phenolic acids (58 kinds), terpenes (54 kinds) and lipids (52 kinds).

[0076] In root, the clusters with high metabolite content are cluster 4, cluster 5 and cluster 7, and these three clusters together contain 284 kinds of metabolites, mainly including lipids (69 kinds), terpenes (64 kinds) and phenolic acids (37 kinds).

[0077] In xylem, the cluster with high metabolite content is cluster 1, and cluster 1 contains a total of 127 kinds of metabolites, mainly including amino acids and their derivatives (27 kinds).

[0078] The distribution of quinones in each cluster is analyzed. In this embodiment, a total of 15 kinds of quinones are isolated in each tissue, and these 15 kinds of quinones are mainly naphthaquinones and anthraquinones, which are distributed in clusters except cluster 5 and cluster 8.

[0079] The content clustering analysis of flavonoids in each tissue is carried out, and the result is shown in Figure 6 ​The flavonoids content in leaves was higher than that in other tissues. Figure 6 The flavonoids and their glycosides detected in four tissues mainly included quercetin (38 kinds), kaempferol (30 kinds, 2 kinds of kaempferitrin), luteolin (16 kinds), apigenin (13 kinds) and isorhamnetin (13 kinds). Most of these compounds existed in the form of glycosides. Quercetin, kaempferol and isorhamnetin and their glycosides were the most important three flavonoids.

[0080] The clustering analysis of the content of quinones in each tissue is shown in Table 5. Figure 7 As can be seen from Table 5, quinones are mainly distributed in roots and bark. Figure 7

[0081] From the above metabolomic analysis results, it can be seen that the metabolites of L. formosana contain rich flavonoids, which are mainly distributed in leaves. In addition, the metabolites of L. formosana also contain a certain amount of quinones, which are mainly distributed in roots and bark. These two types of compounds are the main medicinal active substances in the metabolites of L. formosana. In order to further screen and identify the genes that regulate the biosynthesis of flavonoids and quinones (and further regulate the content of corresponding compounds) in L. formosana, transcriptome analysis was performed.

[0082] 5. Tools and methods used in transcriptome analysis

[0083] In the transcriptome analysis, the total RNA of four tissues of L. formosana was extracted using an RNA extraction kit (Tiangen Biological Products, item number DP441) according to the instructions of the kit. The concentration and purity of the RNA sample were detected using a NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific product). After quality control, the VAHTS mRNA-seq V3 Library PrepKit for Illumina NovaSeq6000 (Tiangen Biological Products, item number NLSV3-24) was used to purify the mRNA of the RNA sample and construct the cDNA library. The constructed cDNA library was subjected to PCR amplification and enrichment. Subsequently, the Illumina NovaSeq 6000 platform (Agi lent Technologies, CA, USA) was used for double-end sequencing, and three technical repeats were performed.

[0084] ​​The raw data (Raw reads) obtained by transcriptome sequencing were first removed of adapter sequences and low-quality sequences, and clean sequences (Clean reads) were obtained after filtration. The Clean reads were aligned with the X. corymbosa genome sequence using Tophat software to obtain accurate genomic positioning information (the X. corymbosa genome sequence was obtained from https: / / academic.oup.com / gigascience / article / 7 / 1 / gix125 / 4739364). The sequences of new genes were extracted from the genome, and Diamond was used to align the new genes with KEGG (https: / / www.kegg.jp / kegg / pathway.html), GO (https: / / geneontology.org), NR (https: / / ftp.ncbi.nlm.nih.gov / blast / db / FASTA / ), Swiss-Prot (https: / / www.uniprot.org / uniprotkb?query=&facets=reviewed%3Atrue), TrEMBL (https: / / www.uniprot.org / uniprotkb?query=&facets=reviewed%3Afalse), KOG (ftp: / / ftp.ncbi.nih.gov / pub / COG / KOG / kyva), TF (https: / / planttfdb.gao-lab.org / ) and Pfam (http: / / pfam.xfam.org / ) databases to obtain the annotation results of the genes (E-value < 1 x 10 -5 ).

[0085] The Cufflinks software was used, and FPKM (Fragments Per Ki lobase of transcript per Million fragments mapped) was used as an index for measuring the expression level of genes, and the expression levels of genes in different samples were compared. The DESeq2 screening software was used to screen the differentially expressed genes (Differential ly Expressed Genes, DEG, i.e., genes with different expression levels) between different tissues, and the screening conditions were |log2 FC|≥1 and FDR < 0.05. In order to further obtain detailed information of the differentially expressed genes, the KEGG database was used for functional annotation and enrichment analysis of the differentially expressed genes.

[0086] 6. Tools and methods used in the joint analysis of transcriptome and metabolome

[0087] To analyze the relationship between the difference of gene expression and the accumulation of metabolites, the R package WGCNA was used to construct the gene co-expression network. In this process, for all the genes extracted from the transcriptome data, the varFilter function in the genefilter package of R language was used to remove the genes with low expression or stable expression in all tissues, in order to improve the accuracy of network construction. Finally, 25120 genes were selected for constructing gene modules. Then, the soft threshold selection function was used to generate co-expression modules. By calculating the adjacent matrix under the soft threshold, the topological overlap matrix (TOM) reflecting the similarity of gene co-expression relationship was further developed. Finally, the hierarchical clustering method was used to construct the hierarchical clustering tree of the differentially expressed genes (DEG).

[0088] In order to determine the differentially expressed genes and differentially accumulated metabolites related to flavonoid biosynthesis pathway and quinone biosynthesis pathway in Delonix regia, KEGG pathway analysis was performed. At the same time, in order to better understand the relationship between transcriptome and metabolome, the differentially expressed genes and differentially accumulated metabolites were mapped into the KEGG pathway database to obtain their common pathway information. If there is a common pathway involved between the differentially expressed genes and the differentially accumulated metabolites, it is considered that the differentially expressed genes are most likely to complete the regulation of the content of the differentially accumulated metabolites by participating in the common pathway.

[0089] Further, based on the R package ggplot2, Pearson correlation analysis was performed on the differentially expressed genes and differentially accumulated metabolites involved in the two pathways (flavonoid biosynthesis pathway and quinone biosynthesis pathway). P value < 0.05 and |r| (absolute value of correlation coefficient) > 0.9 were used as the identification standard for strong correlation. For the differentially accumulated metabolites and differentially expressed genes with strong correlation, the network interaction diagram was constructed using Cytoscape-v3.6.1 software.

[0090] 7、Real-time quantitative PCR analysis

[0091] The differentially expressed genes were obtained based on the transcriptome data analysis. In order to verify whether the expression of the differentially expressed genes obtained by the transcriptome data analysis is indeed different between the tissues, and to verify the reliability of the transcriptome data, the expression of the differentially expressed genes was further detected by real-time quantitative PCR. Eight differentially expressed genes were randomly selected from the differentially expressed genes for real-time quantitative PCR (qRT-PCR) analysis. Primer 5.0 software was used for primer design. Total RNA was isolated from the samples by an RNA extraction kit (Invitrogen, TRIzol), and the TransScript First-Strand cDNA Synthesis SuperMix (Fullgen Company product, item number AT301-02) kit was used to synthesize the first-strand cDNA according to the method provided in the kit instructions. The qRT-PCR reaction was performed using the SYBR green PCR kit (Qiagen Company product, item number 204054), and the specific operation method was according to the kit instructions. Real-time quantitative PCR was performed on all eight differentially expressed genes in three biological replicates of the transcriptome, and each biological replicate contained three technical replicates. The relative expression of the differentially expressed genes was calculated by 2 -△△Ct Method calculation, the calculation results of the relative expression (that is, qRT-PCR data) and the expression data obtained from the transcriptome sequencing results (that is, RNA-seq data) were normalized by log2FC to eliminate experimental errors and differences between samples, so that data from different sources can be compared on the same scale. The R software package 3.1.3 was used for correlation analysis of RNA-seq data and qRT-PCR data.

[0092] The above R software package (R package) and software are publicly available and free.

[0093] 8. Quality analysis of transcriptome sequencing results

[0094] Transcriptome sequencing was performed on 12 transcriptome samples derived from four tissues of L. leucantha, including leaves (L), bark (B), xylem (X), and roots (R), with 3 samples provided for each tissue (3 biological replicates). After removing adapters, N-termini, and low-quality reads, 116.26 Gb of clean data was obtained, with each sample reaching 7 Gb. The N-terminal ratio was about 0.02%, the Q20 value of the obtained sequences was greater than 96.96%, the Q30 value was greater than 92.09%, and the GC content was greater than 44.48%, as shown in Table 1.

[0095] Table 1 Quality analysis results of L. leucantha transcriptome sequencing

[0096]

[0097] The above results show that the transcriptome sequencing results are of high quality.

[0098] 9. Transcriptome analysis results

[0099] Among the above 12 samples, a total of 38740 genes were detected, including 8130 novel genes. BLAST comparison was performed on these 38740 genes using 8 public databases (i.e., the 8 public databases mentioned in “5. Methods used in transcriptome analysis”), and 30355, 26647, 21411, 36960, 29191, 26524, 2455 and 36945 genes were annotated in GO, KEGG, KOG, NR, Pfam, Swiss-Prot, TF and TrEMBL databases, respectively, as shown in FIG. 6. Figure 8 FIG. 6 shows the gene annotation Venn diagram of the eight gene databases. The functional annotation of these genes provides a basis for further identifying genes related to the in vivo regulation of the synthesis of medicinal ingredients in P. bungeana.

[0100] According to the annotation results of GO, KOG and KEGG databases, the detected genes were classified.

[0101] Figure 9 FIG. 7 shows the functional annotation results of 30355 genes using the GO database. Figure 10 FIG. 8 shows the classification results of the detected genes using the KOG database. In addition, the genes were classified by enrichment using the KEGG database. These analysis results collectively provide a high-quality transcriptome database that will be used for differential expression gene analysis.

[0102] 10. Differential expression gene analysis results

[0103] DESeq2 software was used to analyze the differential expression genes between the 12 samples from four tissue sources of leaves (L), bark (B), xylem (X) and roots (R). When screening, 6 comparison groups of L_vs_X, L_vs_R, R_vs_B, R_vs_X, B_vs_X and L_vs_B were set respectively, and the expression differences of each gene in each comparison group were compared according to the screening condition: |log2 Fold Change|≥1 and false discovery rate FDR<0.05. Among the 12 samples, a total of 22448 differential expression genes were screened, as shown in FIG. 9. Figure 25 The expression of differential expression genes is different in the two tissues of the comparison group, which is likely to be the cause of the difference in the types and contents of metabolites in the two tissues.

[0104] The L_vs_R group had the most number of differentially expressed genes (14750), of which 6961 were up-regulated, and 7789 were down-regulated. The L_vs_X and L_vs_B groups had the next most number of differentially expressed genes, 14127 and 14049, respectively, of which 5597 and 5942 were up-regulated, and 8530 and 8107 were down-regulated, respectively. The B_vs_X group had the least number of differentially expressed genes, 8518, of which 3824 were up-regulated, and 4694 were down-regulated.

[0105] In the transcriptome data of the 12 samples, 856 genes related to flavonoid biosynthesis pathway and 380 genes related to quinone biosynthesis pathway were identified. After differential expression gene analysis, it was further determined that in each comparison group, a total of 642 flavonoid synthesis pathway-related genes had differences in expression levels, and 35 flavonoid synthesis-related genes showed significant differences in expression levels in the six comparison groups.

[0106] KEGG analysis showed that 309, 163, 99 and 40 genes were involved in phenylpropanoid biosynthesis, flavonoid biosynthesis, isoflavonoid biosynthesis, and flavone and flavonol biosynthesis metabolic pathways, respectively.

[0107] 257 genes related to quinone synthesis pathway had differences in expression levels in some comparison groups. Among these differentially expressed genes related to quinone synthesis pathway, 5 genes showed significant differences in the six comparison groups.

[0108] Among the differentially expressed genes mentioned above, there are structural genes and transcription factor genes. Among them, the transcription factors encoded by the transcription factor genes can activate the coordinated expression of multiple genes by binding to structural genes, thereby effectively starting the secondary metabolic pathway. In the transcriptome data of 12 samples, a total of 1852 transcription factor genes belonging to 67 transcription factor families were identified. The largest number of transcription factor families include MYB (142), bHLH (137), AP2 (127), ERF-ERF (127), followed by C2H2 (105), NAC (100) and MYB-related (86). In each comparison group, a total of 1285 transcription factor genes showed significant expression differences, among which the number of transcription factor genes belonging to the bHLH, MYB, AP2 and ERF-ERF families was the largest. For example Figure 11 The family distribution statistics of part of the transcription factor genes and the transcription factor genes belonging to the differentially expressed genes.

[0109] According to the results of KEGG analysis, a total of 21 transcription factor genes related to flavonoid biosynthesis pathway were identified in the transcriptome data of 12 samples, among which 14 transcription factor genes were differentially expressed. These 14 transcription factors belong to 5 transcription factor families, including bHLH (5), MYB (2), NAC (3), TCP (3) and C3H (1). The expression patterns of these transcription factor genes in different tissue parts are different. For example, novel.4709 (NAC), CDL12_09901 (NAC), CDL12_07208 (bHLH), CDL12_01396 (bHLH), CDL12_01458 (TCP), CDL12_03348 (TCP) and CDL12_22823 (MYB) are highly expressed in leaves (L), while CDL12_27660 (C3H), CDL12_12561 (MYB), CDL12_03348 (TCP), novel.1928b (HLH) are highly expressed in roots (R), CDL12_21823b (HLH), CDL12_17637 (NAC), CDL12_04759 (bHLH) are highly expressed in bark (B). For example Figure 13 The heatmap of the expression amounts of the 14 transcription factor genes belonging to the differentially expressed genes in different tissue parts.

[0110] In the transcriptome data of 12 samples, a total of 14 transcription factors related to quinone compound synthesis were identified, including Trihelix (11), C2H2 (2), MYB-related (1). Among them, 8 transcription factor genes of Trihelix family are differentially expressed genes. Among the 8 differentially expressed genes, except CDL12_18640 and CDL12_27183, all are highly expressed in roots (R). For example Figure 14 The heat map of the expression amounts of the 8 transcription factor genes belonging to differentially expressed genes in different tissue sites.

[0111] The above differentially expressed genes are based on transcriptome sequencing results analysis and screening. In order to verify whether the screening of these differentially expressed genes is accurate, 8 differentially expressed genes are randomly selected for real-time quantitative PCR (qRT-PCR), and the correlation between qRT-PCR data and RNA-seq data (which has been normalized) is analyzed. As shown in Figure 26a to Figure 26h The qRT-PCR data and RNA-seq data of CDL12_30090, CDL12_12708, CDL12_18673, CDL12_23401, CDL12_28394, CDL12_11650, CDL12_10798 and CDL12_16599 in four tissues of Delonix regia are shown. As can be seen from the figure, among the four tissues, the qRT-PCR data and RNA-seq data of the 8 genes are consistent in high and low trends. The correlation analysis results using R package show that there is a strong correlation between qRT-PCR data and RNA-seq data, with a correlation coefficient r of 0.804, indicating that there is a significant consistency between qRT-PCR data and RNA-seq data, further verifying the effectiveness of the transcriptome data.

[0112] From the results of metabolome analysis, it can be seen that the content of flavonoids in leaves is significantly higher than that in other three tissues, and the content of quinones in roots and bark is significantly higher than that in other two tissues. From the results of transcriptome analysis, it can be seen that the genes related to flavonoid synthesis are highly expressed in leaves, and the genes related to quinone synthesis are highly expressed in roots. That is, the genes related to flavonoid biosynthesis that are highly expressed in leaves regulate the biosynthesis of flavonoids in Delonix regia. The same is true for quinones.

[0113] In order to further identify the genes that regulate the synthesis of flavonoids or quinones, it is necessary to comprehensively analyze the transcriptome and metabolome to find the correlation between differentially accumulated metabolites and differentially expressed genes.

[0114] 11. WGCNA gene co-expression network construction results

[0115] The 25120 genes screened in the above "6, tools and methods used in the comprehensive analysis of transcriptome and metabolome" were subjected to weighted gene co-expression network analysis (WGCNA) to construct gene modules (i.e. 25120 genes were divided into different gene modules) and further construct gene co-expression network. Different branches of the WGCNA analysis clustering tree represent different gene modules, and the genes in the module have high co-expression degree, while the genes in different modules have low co-expression degree. By performing WGCN analysis, the correlation between the module and the specific phenotype can be explored to identify the target genes and gene network of the goal.

[0116] First, according to the expression amount of each gene obtained in the transcriptome analysis, the correlation coefficient between the expression amounts of any two genes was calculated using Pearson correlation test to measure whether the two genes have similar expression patterns, and the genes with similar expression patterns were divided into the same gene module.

[0117] The 25120 genes were divided into 14 different gene modules by WGCNA, as shown in Figure 12 , each gene module contains different number of genes. The Turquoise module contains the most genes, a total of 6494, followed by the Blue module (3733 genes) and the Brown module (3200 genes), while the Grey module (not shown in the figure) has the least number of genes, only 33 genes.

[0118] After dividing the genes into different modules, the module eigenvalue of each module was calculated. The module eigenvalue is used to represent the overall level of gene expression in the module. Then the module eigengene of each module was calculated, which best represents the expression pattern of the genes in the module.

[0119] According to the similarity of the module eigenvalue, a module tree of similar gene expression patterns was constructed, as shown in Figure 15 . Each tree diagram branch in the figure represents a gene, and each color represents a color corresponding to each gene on the clustering tree belonging to the same module.

[0120] In order to determine the modules related to flavonoid biosynthesis and quinone biosynthesis, Pearson correlation analysis was performed between the expression amount of the module eigengene and the accumulation amount of 7 kinds of flavonoids (Chalcones, Flavanones, Flavanonols, Flavones, Flavonols, Flavanols and Other Flavonoids), and the accumulation amount (the higher the accumulation amount, the more active the flavonoid biosynthesis) was considered. The results are shown in Figure 16The Turquoise module was positively correlated with flavonoid biosynthesis Figure 18 , while the Yellow module was negatively correlated with flavonoid content Figure 17 . Among these two modules, 177 differentially expressed genes were related to flavonoid biosynthesis, including 7 transcription factor genes: CDL12_01396 (bHLH), CDL12_01458 (TCP), CDL12_03208 (TCP), CDL12_07208 (bHLH), CDL12_09901 (NAC), novel.4709 (NAC), CDL12_17637 (NAC).

[0121] In addition, the Pearson correlation analysis results between the expression levels of the module characteristic genes and the accumulation levels of quinones (the higher the accumulation level, the more active the quinone biosynthesis) are shown in Figure 19 . The Blue module and the Brown module were positively correlated with quinone biosynthesis Figure 20 and Figure 21 . Among these two modules, 50 differentially expressed genes were related to quinone biosynthesis, including 2 transcription factors related to quinone biosynthesis, CDL12_08581 (Trihelix) and CDL12_16197 (Trihelix).

[0122] The Pearson correlation analysis method was used to identify the correlation between the expression levels of differentially expressed genes related to flavonoid biosynthesis in four different tissues (L, B, X, and R) and the accumulation levels of differentially accumulated metabolites involved in the same flavonoid synthesis pathway.

[0123] Figure 22 The biosynthesis pathway of safflower tree flavonoids is shown, which includes the expression level heat map of differentially expressed genes and the accumulation level heat map of differentially accumulated metabolites. The 4 columns from left to right in the heat map represent L, B, X, and R. The box in the figure is the name of the metabolite (the metabolite with gray name is not detected in L, B, X, and R, and the metabolite with black name is detected in at least one of L, B, X, and R). In the accumulation level heat map of differentially accumulated metabolites, red indicates that the accumulation level of the compound is significantly up-regulated, and blue indicates that the accumulation level of the compound is significantly down-regulated; in the expression level heat map of differentially expressed genes, red indicates that the expression level of the gene is significantly up-regulated, and green indicates that the expression level of the gene is significantly down-regulated.

[0124] Among the genes involved in the flavonoid biosynthesis pathway (phenylalanine metabolism, flavonoid biosynthesis, isoflavonoid biosynthesis, and flavone and flavene biosynthesis) given by KEGG analysis, 109 genes had significantly different expression levels among roots, leaves, barks, and xylems, including PAL (6), 4CL (13), ANR (4), ANS (2), CHS (4), C4H (6), DER (6), CCoAOMT (7), HCT (42), F3H (2), FLS (5), C3'H (5), and CHI (7) genes (see Table 1). Figure 22 Among the 31 genes that were significantly up-regulated in L (leaves) compared to B (bark), X (xylem), and R (root), 16 genes were significantly up-regulated in B (bark) compared to L (leaves), X (xylem), and R (root), 14 genes were significantly up-regulated in R (root) compared to the other parts, and 18 genes were significantly up-regulated in X (xylem) compared to the other parts. In addition, 10 genes showed differences in all six comparison groups.

[0125] PAL genes were included in the differentially expressed genes described above. In P. cerasifera, PAL genes convert phenylalanine into cinnamoyl-CoA and catalyze its conversion into p-coumaroyl-CoA by cinnamic acid-4-hydroxylase C4H (CYP73A), and ultimately into flavonoids by the catalysis of multiple enzymes such as 4CL, CHS, CHI, F3H, DFR, ANR, and HCT (see Fig. 1). Figure 22 These enzymes and the compounds they catalyze exhibit different expression patterns in different parts of P. cerasifera, indicating that they play an important role in the synthesis of flavonoids.

[0126] According to the results of the combined transcriptome and metabolome analysis, the 31 genes that were significantly up-regulated in leaves were related to the increased content of 230 flavonoids in leaves. Among them, the content of 74 flavonoids had a significant correlation (r > 0.999, and P < 0.05) with the expression levels of 14 genes (all of which were genes encoding enzymes involved in the flavonoid biosynthesis pathway and belonged to structural genes). These 14 genes were key flavone synthesis genes and played a key role in the regulation of the content of flavonoid metabolites in P. cerasifera.

[0127] The 14 flavone synthesis key genes are CDL12_06902 (see sequence listing SEQ ID NO. 1), CDL12_11586, CDL12_11941 (see sequence listing SEQ ID NO. 2), CDL12_16004, CDL12_18077, CDL12_18270, CDL12_18918, CDL12_18919, CDL12_19611, CDL12_26238, novel.210, novel.2962, novel.7044 and novel.889. The corresponding relationship between the 14 genes and the 74 flavonoids is shown in Table 3.

[0128] The 14 flavone synthesis key genes belong to 8 gene families of ANS, CHS, C4H, CCoAOMT, DFR, HCT, F3H and FLS, and have significant correlation with the synthesis of flavones (37 kinds), flavonols (23 kinds), flavanones (9 kinds), chalcones (3 kinds) and flavanonols (2 kinds). Figure 23

[0129] Among the 14 flavone synthesis key genes, the correlation between CDL12_06902 and hispidulin and kaempferide, CDL12_11941 and dimethoxyflavone, and novel.889 and eupatilin, chrysosplenetin and chrysin is more than 0.9999, further confirming the role of these flavone synthesis key genes in the synthesis of flavonoids.

[0130] Among the 14 flavone synthesis key genes, CDL12_11586 (DFR), CDL12_11941 (CHS) and CDL12_16004 (FLS) also appear in the Turquoise gene module of WGCNA. The 14 flavone synthesis key genes are key regulatory factors for the synthesis of flavonoids.

[0131] In addition to the 14 flavone synthesis key genes, the correlation coefficient r between CDL12_01396 (see sequence listing SEQ ID NO. 3) and novel.4709 (see sequence listing SEQ ID NO. 4), two transcription factor genes highly expressed in leaves, and the accumulation amount of flavonoids is also greater than 0.99.

[0132] ​The differentially expressed genes related to the biosynthesis of quinones and the metabolites related to the differentially expressed genes in four different tissues (L, B, X and R) were identified by Pearson correlation analysis. Among the genes involved in the biosynthesis of quinones, 160 differentially expressed genes were identified to have significant correlation (r>0.9, and P<0.05) with 13 differentially accumulated metabolites.

[0133] The most valuable quinones in Delonix regia include Lapachol and β-lapachone. Further analysis of the genes related to the accumulation of these two quinones was performed. Among the 160 differentially expressed genes, 34 differentially expressed genes were identified to have strong correlation (r>0.9, and P<0.05) with the accumulation of Lapachol and β-lapachone. Among the 34 differentially expressed genes, 28 differentially expressed genes had significant correlation with the accumulation of Lapachol, and 21 differentially expressed genes had significant correlation with β-lapachone, as shown in Table 2. Figure 24 Among the 34 genes, CDL12_25459 (see sequence listing SEQ ID NO. 5) and CDL12_08649 (see sequence listing SEQ ID NO. 6) had correlation with the accumulation of β-lapachone greater than 0.99.

[0134] The 34 genes also included 4 transcription factors, CDL12_08581 (Trihelix, see sequence listing SEQ ID NO. 7), CDL12_16197 (Trihelix, see sequence listing SEQ ID NO. 8), CDL12_03395 (Trihelix), and CDL12_21229 (Trihelix). Among them, CDL12_08581 (Trihelix) and CDL12_16197 (Trihelix) were present in the key module related to quinones in WGCNA, and therefore, CDL12_08581 and CDL12_16197 were closely related to the biosynthesis of Lapachol and β-lapachone, and could be used as key genes for further research.

[0135] The corresponding relationship between these genes and quinones is shown in Table 2.

[0136] Table 2 Correlation of 34 genes with the biosynthesis of Lapachol and β-lapachone in Delonix regia

[0137]

[0138]

[0139] The gene information of the genes regulating flavonoid compound synthesis and quinone compound synthesis and the metabolites regulated by the genes are shown in Table 3 through transcriptome analysis and transcriptome-metabolome combined analysis.

[0140] Table 3

[0141]

[0142]

[0143]

[0144] Obviously, the above examples are merely illustrative examples for the sake of clarity, and are not intended to limit the embodiments. Based on the above description, other different forms of changes or variations can also be made by those of ordinary skill in the art. Here, it is not necessary and impossible to exhaust all the embodiments. The obvious changes or variations derived therefrom are still within the protection scope of the claims of the present patent application.

Claims

1. A gene for regulating the content of medicinal ingredients in Delonix regia, characterized in that, The medicinal ingredients are flavonoids or quinones; wherein, the genes for regulating the content of flavonoids include CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709; the genes for regulating the content of quinones include CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197; The sequences of CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709 are shown in SEQ ID NO. 1, 2, 3 and 4 respectively; the sequences of CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197 are shown in SEQ ID NO. 5, 6, 7 and 8 respectively; The genes for regulating the content of flavonoids regulate the content of flavonoids in the leaves of Morus tatarinowii; when the expression amount of the genes for regulating the content of flavonoids in the leaves of Morus tatarinowii is up-regulated, the content of flavonoids in the leaves is increased; the flavonoids include Hispidulin, Kaempferide, dimethoxyflavone, Eupatilin, Chrysosplenetin and Chrysin; The genes for regulating the content of quinones regulate the content of quinones in the roots of Morus tatarinowii; the quinones are lapachol or lapachol and β-lapachone; when the expression amount of the genes for regulating the content of quinones in the roots of Morus tatarinowii is up-regulated, the content of quinones in the roots is increased.

2. The gene regulating the content of medicinal components in Tabebuia rubra according to claim 1, characterized in that, Among the genes for regulating the content of flavonoids, the CDL12_06902 gene regulates the content of Hispidulin and Kaempferide, and the CDL12_11941 gene regulates the content of dimethoxyflavone.

3. A method for identifying a gene for regulating content of a medicinal ingredient in Delonix regia, characterized in that, The method comprises the following steps: S1, sampling the leaves, roots, barks and xylem tissues of Morus tatarinowii; S2, investigating the differences in the types and contents of flavonoids and quinones in the leaves, roots, barks and xylem tissues through metabolome analysis, obtaining differential accumulation metabolites; investigating the differences in the gene expression patterns in the leaves, roots, barks and xylem tissues through transcriptome analysis, obtaining differential expression genes; S3, performing joint analysis on the differential expression genes and differential accumulation metabolites, and identifying the medicinal ingredient content regulating genes of Morus tatarinowii according to claim 1.

4. The method of claim 3, wherein, In step S2, when performing metabolome analysis, the leaves, roots, barks and xylem tissues of Morus alba L. are prepared into samples to be tested for ultra-high performance liquid chromatography-tandem mass spectrometry analysis, and the samples to be tested are analyzed by using ultra-high performance liquid chromatography-tandem mass spectrometry to obtain the types and contents of flavonoids and quinones in the leaves, roots, barks and xylem; the variable importance projection VIP>1 and |log2FC|≥1 are used as screening criteria to screen flavonoids and quinones with significant content differences among the leaves, roots, barks and xylem tissues as differential accumulation metabolites.

5. The method of claim 4, wherein, In step S2, when preparing the samples to be tested for ultra-high performance liquid chromatography-tandem mass spectrometry analysis, the tissues of Morus alba L. are vacuum freeze-dried, ground into powder, placed in an extraction solution and vortexed, the supernatant is taken after centrifugation, filtered and the filtrate is reserved to obtain the sample to be tested. In the ultra-high performance liquid chromatography-tandem mass spectrometry analysis, the content of each compound in the sample to be tested is measured by using the multiple reaction monitoring mode of a triple quadrupole mass spectrometer, and the types of flavonoids and quinones in the sample to be tested are analyzed by using the MWDB database.

6. The method of claim 5, wherein, In step S2, the RNA of the leaves, roots, barks and xylem tissues of Morus alba L. is extracted respectively, and cDNA libraries are constructed respectively; the cDNA libraries are sequenced by using the Illumina NovaSeq 6000 platform to obtain the transcriptome sequencing results; genes are extracted from the transcriptome sequencing results, the gene expression is calculated by using FPKM, and then the differentially expressed genes between each group are screened according to the gene expression; the screening criteria for the differentially expressed genes are |log2FC|≥1 and false discovery rate FDR<0.

05.

7. The method of claim 6, wherein, In step S3, when performing joint analysis on the differentially expressed genes and the differential accumulation metabolites, the genes extracted from the transcriptome sequencing results are analyzed by using the R language package WGCNA to screen out gene modules related to flavonoid biosynthesis and quinone biosynthesis, and differentially expressed genes in these modules; the differential accumulation metabolites and the differentially expressed genes screened in this step are mapped into the KEGG pathway database to obtain their common pathway information; for the differentially expressed genes and the differential accumulation metabolites involved in the same flavonoid synthesis pathway, and the differentially expressed genes and the differential accumulation metabolites involved in the same quinone compound synthesis pathway, joint analysis is performed by using the Pearson correlation analysis method, the correlation coefficient between the expression amount of the differentially expressed genes and the accumulation amount of the differential accumulation metabolites in the same pathway is calculated, and P value<0.05 and |r|>0.9 are used as the identification criteria for strong correlation, and the differentially expressed genes with strong correlation with the differential accumulation metabolites are identified as the content regulation genes of medicinal components in Morus alba L.

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