Traditional Chinese medicinal component content regulation gene of Tabebuia chrysantha and identification method thereof

Abstract

The invention discloses a traditional Chinese medicine component content regulation gene of Tabebuia rubra and an identification method thereof. The medicinal component is a flavonoid compound or a quinone compound; wherein the genes for regulating and controlling the content of the flavonoid compounds comprise CDL1206902, CDL1211941, CDL1201396 and nove l.4709, and the genes for regulating and controlling the content of the flavonoid compounds comprise CDL1206902, CDL1211941, CDL1201396 and The gene for regulating and controlling the content of the quinone compounds comprises a CDL1225459 gene, a CDL1208649 gene, a CDL1208581 gene and a CDL1216197 gene. The invention also discloses an identification method of the traditional Chinese medicinal component content regulation gene of the Tabebuia rubra. The gene related to the contents of the flavonoid compounds and the quinones compounds in the Tabebuia chrysantha provided by the invention provides a scientific basis for metabolic regulation and molecular breeding of medicinal components.

Description

Technical Field

[0001] The present invention relates to the technical field of biosynthesis of plant secondary metabolites. Specifically, it is about genes for regulating the content of medicinal components in Handroanthus impetiginosa and methods for their identification. Background Art

[0002] Handroanthus impetiginosa, belonging to the Bignoniaceae family, is an ornamental plant native to South America and also has important medicinal value. Its main medicinal components include flavonoid compounds and quinone compounds. Flavonoid compounds have various biological activities such as antioxidant, anti-inflammatory, antibacterial, and anti-tumor activities, and are widely used in the fields of anti-cancer drugs, antioxidants, and health products; quinone compounds (such as β-lapachone) have attracted much attention for their significant anti-tumor and anti-parasitic activities.

[0003] In existing research, there has been some analysis of the chemical components and verification of the pharmacological activities of Handroanthus impetiginosa, but there is a lack of systematic analysis of its key metabolic pathways and related genes in vivo. And currently, the research on the biosynthesis pathways and their regulatory mechanisms of these medicinal components in Handroanthus impetiginosa is still in its infancy, which limits the development and utilization of its medicinal potential.

[0004] The synthesis of plant secondary metabolites is usually co-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 its regulatory mechanisms of secondary metabolites. However, the synthesis of flavonoid compounds and quinone compounds is regulated by multiple genes and transcription factors, and the specific genes and regulatory mechanisms involved have not been fully elucidated. In-depth research on the synthesis regulatory pathways and related genes of these metabolites, and finding a key gene and transcription factor for regulating the synthesis of these metabolites, and then realizing the regulation of the content of these medicinally valuable metabolites in Handroanthus impetiginosa, is of great significance for developing drug resources based on Handroanthus impetiginosa and improving the germplasm of medicinal plants. Summary of the Invention

[0005] For this reason, the technical problem to be solved by the present invention is to provide a gene for regulating the content of medicinal components in Handroanthus impetiginosa and a method for its identification. By combining transcriptome and metabolome analysis, genes related to the content of flavonoid compounds and quinone compounds in Handroanthus impetiginosa are identified, providing a scientific basis for the metabolic regulation and molecular breeding of medicinal components.

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

[0007] Genes regulating the content of medicinal components in Tabebuia pentaphylla, where the medicinal components are flavonoid compounds or quinone compounds; among them, the genes regulating the content of flavonoid compounds include: CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709; the genes regulating the content of quinone compounds include CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197. Among them, the sequences of CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709 are shown in sequence listing SEQ ID NO.1, 2, 3 and 4 respectively; the sequences of CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197 are shown in sequence listing SEQ ID NO.5, 6, 7 and 8 respectively. These genes regulate the content (i.e., the accumulation amount) of corresponding substances in Tabebuia pentaphylla by regulating the biosynthesis of flavonoid compounds, or regulating the biosynthesis of quinone compounds, or simultaneously regulating the biosynthesis of flavonoid and quinone compounds.

[0008] "Flavonoid biosynthesis" is equivalent to the biosynthesis of flavonoid compounds.

[0009] Method for identifying genes regulating the content of medicinal components in Tabebuia pentaphylla, comprising the following steps:

[0010] S1. Sampling the leaves, roots, bark and xylem tissues of Tabebuia pentaphylla;

[0011] S2. Through metabolome analysis, investigating the species and content differences of flavonoid compounds and quinone compounds in the leaves, roots, bark and xylem tissues to obtain differentially accumulated metabolites; through transcriptome analysis, investigating the gene expression pattern differences in the leaves, roots, bark and xylem tissues to obtain differentially expressed genes;

[0012] S3. Conducting a combined analysis of the differentially expressed genes and differentially accumulated metabolites to identify the genes regulating the content of medicinal components in Tabebuia pentaphylla.

[0013] In the above identification method, in step S2, when performing metabolome analysis, the leaves, roots, bark and xylem tissues of Tabebuia pentaphylla are respectively prepared into test samples for ultra-high performance liquid chromatography-tandem mass spectrometry analysis, and ultra-high performance liquid chromatography-tandem mass spectrometry is used to analyze the test samples to obtain the species and content of flavonoid compounds and quinone compounds in the leaves, roots, bark and xylem; using variable importance projection VIP>1 and |log2 FC|≥1 as the screening criteria, screening the flavonoid compounds and quinone compounds with significant content differences in the 4 tissues as differentially accumulated metabolites.

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

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

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

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

[0018] The technical solution of the present invention has achieved the following beneficial technical effects:

[0019] 1. The present invention combines the combined analysis of transcriptome and metabolome for the first time, and systematically analyzes the synthesis pathways of flavonoids and quinones and their key regulatory genes in Tabebuia pentaphylla. This work provides a solid scientific basis for deeply understanding the metabolic regulation mechanism of the medicinal components of Tabebuia pentaphylla, and lays a theoretical foundation for the subsequent functional research and development of medicinal components.

[0020] 2. The present invention optimizes the sample pretreatment process during metabolomics analysis, including the selection of extraction solvents, the determination of extraction ratios, and the optimization of chromatographic and mass spectrometry conditions, to efficiently and accurately analyze the differentially accumulated metabolites in various tissues of Tabebuia pentaphylla, providing a basis for the combined analysis of metabolomics and transcriptomics. Further, the differentially expressed genes in the transcriptome data are correlated with the differentially accumulated metabolites. Using WGCNA analysis, genes with similar expression patterns are divided into several modules. Based on the module connectivity and the correlation between the modules and phenotypes (flavonoid compounds, quinone compounds), differentially expressed genes are screened, efficiently and significantly reducing the number of differentially expressed genes that need to be subjected to correlation analysis and improving the analysis efficiency. On this basis, the screened differentially expressed genes and differentially accumulated metabolites are mapped to the KEGG database to find the genes and metabolites involved in the biosynthesis pathways of the same flavonoid compound and the same quinone compound, excluding the genes and metabolites involved in different biosynthesis pathways - there is theoretically no correlation between this part of genes and metabolites, improving the accuracy of the screening results. Finally, using the Pearson correlation analysis method, genes and metabolites closely related to the synthesis of flavonoid and quinone compounds are successfully identified. This identification method provides an important reference basis for subsequent gene function verification and molecular breeding, helping to discover new regulatory factors and improve the medicinal value of Tabebuia pentaphylla. Description of the Drawings

[0021] Figure 1 The types and proportions of metabolites identified in the leaf, root, bark, and xylem tissues of Tabebuia pentaphylla in the examples of the present invention;

[0022] Figure 2 Principal component analysis result diagram of metabolites in different tissue parts of Tabebuia pentaphylla;

[0023] Figure 3 Cluster analysis result diagram of metabolites in different tissue parts of Tabebuia pentaphylla;

[0024] Figure 4 Quantitative statistics of differentially accumulated metabolites between different tissue parts of Tabebuia pentaphylla;

[0025] Figure 5 K-means cluster analysis result diagram of all detected metabolites in different tissues of Tabebuia pentaphylla. The heat map shows the relative abundances of each metabolite in different tissues, and the line graph shows the accumulation patterns of metabolites in each cluster;

[0026] Figure 6 Cluster analysis result diagram of flavonoid compounds in different tissues of Tabebuia pentaphylla;

[0027] Figure 7 Cluster analysis result diagram of quinone compounds in different tissues of Tabebuia pentaphylla;

[0028] Figure 8 Venn diagram of gene annotation based on eight gene databases in the embodiments of the present invention;

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

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

[0031] Figure 11 Statistical analysis of the family distribution of some transcription factor genes and transcription factor genes belonging to differentially expressed genes in transcriptome data;

[0032] Figure 12 Statistical analysis of the number of genes contained in 13 out of 14 gene modules divided by WGCNA;

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

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

[0035] Figure 15 Hierarchical clustering tree diagram of WGCNA modules in the embodiments of the present invention;

[0036] Figure 16 Correlation analysis diagram of differentially expressed genes in each gene module and the content of flavonoid compounds;

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

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

[0039] Figure 19 Correlation analysis diagram of differentially expressed genes in each gene module and the content of quinone compounds;

[0040] Figure 20 Correlation analysis diagram of differentially expressed genes in the Blue module and the content of quinone compounds;

[0041] Figure 21 Correlation analysis diagram of differentially expressed genes in the Brown module and the content of quinone compounds;

[0042] Figure 22 Diagram of the flavonoid biosynthesis pathway in Handroanthus impetiginosus, which includes heatmaps of the expression levels of differentially expressed genes and heatmaps of the accumulation levels of differentially accumulated metabolites, showing the flavonoid biosynthesis pathway and the expression and accumulation of key genes and metabolites;

[0043] Figure 23 Correlation analysis of differentially expressed genes and differentially accumulated metabolites during flavonoid biosynthesis in Handroanthus impetiginosus;

[0044] Figure 24 Correlation analysis of differentially expressed genes and differentially accumulated metabolites during quinone biosynthesis in Handroanthus impetiginosus;

[0045] Figure 25 Statistics of the number of differentially expressed genes among different tissues of Handroanthus impetiginosus;

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

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

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

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

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

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

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

[0053] Figure 26hComparison results of qRT-PCR data and RNA-seq data of the CDL12_16599 gene in four tissues of Tabebuia pentaphylla Detailed implementation manners

[0054] 1. Plant materials

[0055] In June 2023, three Tabebuia pentaphylla plants with consistent growth status and no pests and diseases were selected in the Tabebuia germplasm garden of the National Forest Seedling Demonstration Base in the South. Four tissues, namely leaves (L), bark (B), xylem (X), and roots (R), were collected as experimental materials. After quick-freezing in liquid nitrogen, they were stored frozen at -80°C. Each tissue was divided into 6 parts, of which 3 parts were used as 3 biological replicates of the metabolome samples, and the other 3 parts were used as 3 biological replicates of the transcriptome samples.

[0056] 2. Sample preparation

[0057] The metabolome samples were made into samples for ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS / MS) analysis. The specific method was as follows: The samples were freeze-dried in vacuo and ground into powder (using a MM 400 grinder produced by Retsch company, grinding at 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%). It was vortexed once every 30 minutes for 30 seconds each time, for a total of 6 times. After centrifugation (at a speed of 12,000 rpm for 3 minutes), the supernatant was aspirated and filtered through a microporous membrane (pore size 0.22 μm) to obtain the sample.

[0058] 3. Data collection instrument

[0059] The instrument system designed for UPLC-MS / MS analysis consisted of an ultra-high 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 4500 QTRAP). The chromatographic column used was an Agilent SB-C18 column (particle size 1.8 μm, specification 2.1 mm × 100 mm). The flow rate was set at 0.35 mL / min, the column temperature was maintained at 40°C, and the injection volume was 4 μL.

[0060] The mobile phase composition was as follows: Phase A was ultrapure water (containing 0.1% formic acid), and Phase B was acetonitrile (containing 0.1% formic acid). The elution gradient conditions were as follows:

[0061] At 0.00 minutes, the proportion of phase B (volume fraction) is 5%; within 0.00 to 9.00 minutes, the proportion of phase B linearly increases to 95%; from 9.00 to 10.00 minutes, the proportion of phase B remains at 95%; from 10.00 to 11.10 minutes, the proportion of phase B linearly decreases to 5%; from 11.10 to 14.00 minutes, the proportion of phase B remains at 5% to balance the system.

[0062] The mass spectrometry conditions mainly include:

[0063] Electrospray Ionization (ESI) source, with its temperature set at 550 °C. The ion spray voltage (IS) is 5500 V in positive ion mode and -4500 V in negative ion mode.

[0064] Ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) are set at 50 psi, 60 psi and 25 psi respectively. The Collision Induced Dissociation (CID) parameter is set to high. The mass spectrometry scan uses the Multiple Reaction Monitoring (MRM) mode, and the collision gas (nitrogen) is set to medium intensity. Further, through the optimization of the Declustering Potential (DP) and Collision Energy (CE), the settings of DP and CE for each MRM ion pair are completed.

[0065] Based on the self-built database MWDB (Metware Database) of Metware Biotechnology Co., Ltd., the sample is qualitatively analyzed for substances (commissioned by Metware Biotechnology Co., Ltd.), that is, to determine which chemical substances are contained in the sample. When qualitatively analyzing substances, the secondary spectrum information is used for analysis. During the analysis process, the signals of isotope ions (such as K + , Na + , NH4 + ) and the fragment ion signals of repetitive substances with larger molecular weights are removed. The quantitative analysis of metabolites is carried out through the Multiple Reaction Monitoring (MRM) mode of the triple quadrupole mass spectrometer.

[0066] 4. Results of metabolomics analysis

[0067] The results of the qualitative analysis of the sample substances showed that in this example, a total of 1,201 metabolites were identified from the tissues of different parts of Tabebuia pentaphylla. These 1,201 metabolites included: 230 flavonoids (accounting for 19.15%), 183 phenolic acids (accounting for 15.24%), 159 lipids (accounting for 13.24%), 132 terpenoids (accounting for 10.99%), 105 amino acids and derivatives (accounting for 8.74%), 84 organic acids (accounting for 6.99%), 67 nucleotides and their 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 Figure 1 shown.

[0068] There were also differences in the types of metabolites contained in the four tissues of leaf (L), bark (B), xylem (X) and root (R). Principal component analysis PCA was performed on the peak areas of the metabolites in the four tissues, and the results were as Figure 2 shown. PC1 and PC2 explained 47.69% and 20.87% of the total variance of the samples respectively, and the cumulative contribution rate reached 68.56%. The four tissues showed an obvious separation trend in PCA. Among them, the bark, root and xylem were clustered on one side, far from the leaf, indicating that the metabolomic profiles of the bark, root and xylem were more similar, while the metabolomic profile of the leaf was quite different from those of the bark, root and xylem.

[0069] Cluster analysis was performed on the accumulation patterns of the above 1,201 metabolites in the four different tissues of leaf (L), bark (B), xylem (X) and root (R), and the results were as Figure 3 shown. There were significant differences in the types and contents of metabolites in the four different tissues. Metabolites were more abundant in the leaf, followed by the bark.

[0070] Using VIP > 1 and |Log2 FC| ≥ 1 as the screening criteria, differentially changed metabolites (DCMs) with significant content differences in the four tissues were screened. A total of 1,106 differentially accumulated metabolites were screened out in the 5 comparison groups. Figure 4 For the screening results.

[0071] By Figure 4It can be seen that in the comparison group composed of two tissues, namely the leaf (L) and the xylem (X), the largest number of differentially accumulated metabolites was screened out (814 species). Among the 814 differentially accumulated metabolites, 164 metabolites were up-regulated (i.e., the content in the xylem was higher than that in the leaf), and 650 metabolites were down-regulated (i.e., the content in the xylem was lower than that in the leaf). The metabolites with the largest down-regulation were mainly flavonoids, that is, there were significant differences in the accumulation of flavonoids between the leaf and the xylem, and the accumulation of flavonoids in the xylem was significantly less than that in the leaf.

[0072] In the comparison group composed of two tissues, namely the root (R) and the bark (B), the smallest number of differentially accumulated metabolites was screened out (580 species). Among them, 381 metabolites were up-regulated (indicating that the content in the bark was greater than that in the root), and 199 metabolites were down-regulated (indicating that the content in the bark was less than that in the root). The metabolites with the largest down-regulation were mainly phenolic acids.

[0073] The distribution of 1201 metabolites was grouped and classified by K-means mean clustering analysis. The content data of 1201 metabolites were divided into 8 clusters, and each cluster represented a group of metabolites with similar distribution or distribution trend. The results of the mean clustering analysis are as Figure 5 shown.

[0074] It can be seen from Figure 5 that the metabolites contained in clusters 2, 5, and 8 had high contents in the leaf. These 3 clusters contained a total of 604 metabolites, among which there were 202 flavonoids, indicating that flavonoids were mainly concentrated in the leaf.

[0075] In the bark, the clusters with higher metabolite contents were clusters 3 and 4. Clusters 3 and 4 contained a total of 289 metabolites, and these metabolites mainly included phenolic acids (58 species), terpenoids (54 species), and lipids (52 species).

[0076] In the root, the clusters with higher metabolite contents were clusters 4, 5, and 7. These 3 clusters contained a total of 284 metabolites, mainly including lipids (69 species), terpenoids (64 species), and phenolic acids (37 species).

[0077] In the xylem, the cluster with higher metabolite content was cluster 1. Cluster 1 contained a total of 127 metabolites, mainly including amino acids and their derivatives (27 species).

[0078] The distribution of quinone compounds in each cluster was analyzed. In this example, a total of 15 quinone compounds were isolated from each tissue. These 15 quinone compounds were mainly naphthoquinones and anthraquinones, and were distributed in clusters except clusters 5 and 8.

[0079] The content clustering analysis of flavonoids in each tissue was carried out, and the results are as Figure 6As shown in Figure 6 It can be seen that the content of flavonoids in the leaves is relatively high. The flavones and their glycosides detected in the four tissues mainly include quercetin (38 species), kaempferol (30 species, 2 species of kaempferide), luteolin (16 species), apigenin (13 species) and isorhamnetin (13 species). Most of these compounds exist in the form of glycosides. Quercetin, kaempferol and isorhamnetin and their glycosides are the three most important flavonoid compounds.

[0080] The results of cluster analysis of the content of quinone compounds in each tissue are as shown in Figure 7 As shown in Figure 7 It can be seen that quinone compounds are mainly distributed in the roots and bark.

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

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

[0083] During transcriptome analysis, an RNA extraction kit (TIANGEN Biotech product, catalog number DP441) was used to extract the total RNA of four tissues of Tabebuia pentaphylla according to the kit instructions. The NanoDrop 2000 spectrophotometer (Thermo Fisher Scientific product) was used to detect the concentration and purity of the RNA samples. After quality control, the VAHTS mRNA-seq V3 Library Prep Kit for the kit was used to purify mRNA and construct cDNA libraries for the RNA samples, and the constructed cDNA libraries were subjected to PCR amplification and enrichment. Subsequently, paired-end sequencing was performed using the Illumina NovaSeq 6000 platform (Agilent Technologies, CA, USA), and three technical replicates were carried out.

[0084] The raw data (Raw reads) obtained from transcriptome sequencing are first processed to remove adapter sequences and low-quality sequences, and clean reads are obtained after filtering. The Tophat software is used to align the Clean reads with the genome sequence of Tabebuia pentaphylla, and accurate genomic location information is obtained (the genome sequence of Tabebuia pentaphylla is obtained from https: / / academic.oup.com / gigascience / article / 7 / 1 / gix125 / 4739364). The sequences of novel genes are extracted from the genome, and Diamond is used to align the novel genes with the 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 gene annotation results (E-value < 1×10 -5 ).

[0085] The Cufflinks software is used, and FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) is adopted as an indicator to measure the gene expression level, and the gene expression levels in different samples are compared. The DESeq2 software is used to screen for differentially expressed genes (Differentially Expressed Genes, DEG, that is, genes with different expression levels) among different tissues, and the screening criteria are |log2 FC| ≥ 1 and FDR < 0.05. To further obtain detailed information on the differentially expressed genes, the KEGG database is used for functional annotation and enrichment analysis of the differentially expressed genes.

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

[0087] To analyze the relationship between the differential gene expression levels and the accumulation levels of metabolites, the R package WGCNA was used to construct a gene co-expression network. During this process, for all genes extracted from the transcriptome data, the varFilter function in the genefilter package of R language was used to remove genes with low or stable expression levels in all tissues, so as to improve the accuracy of network construction. Finally, 25,120 genes were selected for constructing gene modules. Then, the soft-threshold selection function was used to generate co-expression modules. By calculating the adjacency matrix under the soft threshold, the topological overlap matrix (TOM) reflecting the similarity of gene co-expression relationships was further developed. Finally, the hierarchical clustering tree of differentially expressed genes (DEGs) was constructed using the hierarchical clustering method.

[0088] To identify the differentially expressed genes and differentially accumulated metabolites related to the flavonoid biosynthesis pathway and quinone biosynthesis pathway in Tabebuia pentaphylla, KEGG pathway analysis was performed. At the same time, to better understand the relationship between the transcriptome and metabolome, the differentially expressed genes and differentially accumulated metabolites were mapped to the KEGG pathway database to obtain their common pathway information. If there is a common pathway participated by both differentially expressed genes and differentially accumulated metabolites, it is considered that the differentially expressed genes are very likely to regulate the content of differentially accumulated metabolites by participating in this common pathway.

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

[0090] 7. Real-time quantitative PCR analysis

[0091] The above differentially expressed genes were obtained based on transcriptome data analysis. To verify whether there are indeed differences in the expression levels of these differentially expressed genes obtained from transcriptome data analysis among various tissues and to verify the reliability of the transcriptome data, further, real-time quantitative PCR was used to detect the expression levels of the differentially expressed genes. Eight differentially expressed genes were randomly selected from the above differentially expressed genes for real-time quantitative PCR (qRT-PCR) analysis. Primer design was performed using Primer5.0 software. Total RNA was isolated from the samples using an RNA extraction kit (Invitrogen, TRIzol), and the first-strand cDNA was synthesized using the TransScript First-Strand cDNA Synthesis SuperMix (product of TransGen Biotech, catalog number AT301-02) kit according to the method provided in the kit instructions. The qRT-PCR reaction was carried out using a SYBR green PCR kit (product of Qiagen, catalog number 204054), and the specific operation method was in accordance with the kit instructions. All eight differentially expressed genes were subjected to real-time quantitative PCR in three biological replicates of the transcriptome, and each biological replicate included three technical replicates. The relative expression levels of the differentially expressed genes were calculated using the 2 -△△Ct method. The calculated results of the relative expression levels (i.e., qRT-PCR data) and the expression level data obtained from the transcriptome sequencing results (i.e., RNA-seq data) were normalized using log2FC to eliminate experimental errors and differences between samples, so that data from different sources could be compared on the same scale. The R software package 3.1.3 was used to perform a correlation analysis on the RNA-seq data and qRT-PCR data.

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

[0093] 8. Quality analysis of transcriptome sequencing results

[0094] Twelve transcriptome samples from four tissues of Tabebuia pentaphylla, namely leaves (L), bark (B), xylem (X), and roots (R) (3 samples were provided for each tissue, i.e., 3 biological replicates), were subjected to transcriptome sequencing. After removing adapters, N-ends, and low-quality reads from the sequencing results, 116.26 Gb of clean data was obtained. The clean data of each sample reached 7 Gb, the proportion of N-ends 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 Results of quality analysis of transcriptome sequencing of Tabebuia pentaphylla

[0096]

[0097] The above results indicate that high-quality sequencing results were obtained from transcriptome sequencing.

[0098] 9. Transcriptome analysis results

[0099] Among the above 12 samples, a total of 38,740 genes were detected, including 8,130 novel genes. These 38,740 genes were subjected to BLAST alignment using 8 public databases (i.e., the 8 public databases mentioned in "5. Methods used in transcriptome analysis"), and 30,355, 26,647, 21,411, 36,960, 29,191, 26,524, 2,455, and 36,945 genes were annotated in the GO, KEGG, KOG, NR, Pfam, Swiss-Prot, TF, and TrEMBL databases respectively, as Figure 8 shown in the Venn diagram of gene annotation for the eight gene databases. The functional annotation of these genes provides a basis for further identifying genes related to the regulation of medicinal component synthesis in Tabebuia pentaphylla.

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

[0101] Figure 9 shows the results of functional annotation of 30,355 genes using the GO database. Figure 10 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 together provide a high-quality transcriptome database, which will be used for differential expression gene analysis.

[0102] 10. Differential expression gene analysis results

[0103] The DESeq2 software was used to analyze the differentially expressed genes among 12 samples from four tissue sources: leaf (L), bark (B), xylem (X), and root (R). During screening, 6 comparison groups were set up: L_vs_X, L_vs_R, R_vs_B, R_vs_X, B_vs_X, and L_vs_B. According to the screening criteria: |log2 Fold Change|≥1 and false discovery rate FDR<0.05, the expression level differences of each gene in each comparison group were compared. Among the 12 samples, a total of 22,448 differentially expressed genes were screened, as Figure 25 shown. The expression levels of the differentially expressed genes were different in the two tissues of the comparison group, which is very likely the reason for the differences in the types and contents of metabolites in the two tissues.

[0104] The largest number of differentially expressed genes was screened out in the L_vs_R group (14,750), including 6,961 up-regulated genes, which had higher expression levels in the R group; and 7,789 down-regulated genes, which had higher expression levels in the L group. Followed by the L_vs_X and L_vs_B groups, 14,127 and 14,049 differentially expressed genes were screened out respectively, with the number of up-regulated genes being 5,597 and 5,942 respectively, and the number of down-regulated genes being 8,530 and 8,107 respectively. The B_vs_X group screened out the fewest differentially expressed genes, which was 8,518, including 3,824 up-regulated genes and 4,694 down-regulated genes.

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

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

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

[0108] Among the above differentially expressed genes, there are structural genes and transcription factor genes. Among them, the transcription factors encoded by the transcription factor genes can activate the co-expression of multiple genes by binding to the structural genes, thereby effectively initiating the secondary metabolic pathway. In the transcriptome data of 12 samples, a total of 1,852 transcription factor genes were identified, belonging to 67 transcription factor families. The transcription factor families with the largest number include MYB (142), bHLH (137), AP2 (127), ERF-ERF (127), followed by C2H2 (105), NAC (100) and MYB-related (86). Among the comparison groups, a total of 1,285 transcription factor genes showed significant expression differences, and the transcription factor genes of the bHLH, MYB, AP2, and ERF-ERF families had the largest number of differentially expressed genes. For example, Figure 11 is the family distribution statistics of some transcription factor genes and transcription factor genes belonging to differentially expressed genes.

[0109] According to the results of KEGG analysis, in the transcriptome data of 12 samples, a total of 21 transcription factor genes related to the flavonoid biosynthesis pathway were identified, among which 14 transcription factor genes were differentially expressed. These 14 transcription factors belong to 5 transcription factor families, specifically including: bHLH (5), MYB (2), NAC (3), TCP (3) and C3H (1). The expression patterns of these transcription factor genes are different in different tissue parts. 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), and CDL12_21823b (HLH), CDL12_17637 (NAC), CDL12_04759 (bHLH) are highly expressed in bark (B). For example, Figure 13 is the heat map of the expression levels of these 14 transcription factor genes belonging to differentially expressed genes in different tissue parts.

[0110] In the transcriptome data of 12 samples, a total of 14 transcription factors related to quinone synthesis were identified, including Trihelix (11), C2H2 (2), and MYB-related (1). Among them, 8 transcription factor genes of the Trihelix family were differentially expressed genes. Among these 8 differentially expressed genes, except for CDL12_18640 and CDL12_27183, all were highly expressed in roots (R). As Figure 14 Figure 283 is a heatmap showing the expression levels of these 8 transcription factor genes that are differentially expressed genes in different tissue parts.

[0111] The above differentially expressed genes were analyzed and screened based on the transcriptome sequencing results. To verify whether the screening of these differentially expressed genes was accurate, 8 differentially expressed genes were randomly selected for real-time quantitative PCR (qRT-PCR), and the correlation between the qRT-PCR data and the RNA-seq data (which had been normalized) was analyzed. As Figure 26a - Figure 26h Figure 286 shows the comparison results of the qRT-PCR data and the RNA-seq data of 8 genes, namely CDL12_30090, CDL12_12708, CDL12_18673, CDL12_23401, CDL12_28394, CDL12_11650, CDL12_10798, and CDL12_16599, in four tissues of Tabebuia pentaphylla. It can be seen from the figure that between the four tissues, the high and low trends of the qRT-PCR data and the RNA-seq data of the 8 genes were consistent. The correlation analysis results using the R package showed that there was a strong correlation between the qRT-PCR data and the RNA-seq data, and the correlation coefficient r was 0.804, indicating a significant consistency between the qRT-PCR data and the RNA-seq data, further verifying the effectiveness of the transcriptome data.

[0112] It can be seen from the metabolome analysis results that the content of flavonoids in leaves was significantly higher than that in the other three tissues, and the content of quinones in roots and bark was significantly higher than that in the other two tissues. It can be seen from the transcriptome analysis results that the genes related to flavonoid synthesis were highly expressed in leaves, and the genes related to quinone synthesis were highly expressed in roots. That is to say, the genes related to the flavonoid biosynthesis pathway that were highly expressed in leaves regulated the biosynthesis of flavonoids in Tabebuia pentaphylla. The same is true for quinones.

[0113] To further identify the genes regulating the synthesis of flavonoids or quinones, it is necessary to conduct a comprehensive analysis of the transcriptome and metabolome to find the correlation between differentially accumulated metabolites and differentially expressed genes.

[0114] 11. Construction Results of WGCNA Gene Co-Expression Network

[0115] Using weighted gene co-expression network analysis (WGCNA), gene modules were constructed for the 25,120 genes screened in the above "6. Tools and Methods Used in the Comprehensive Analysis of Transcriptome and Metabolome" (i.e., dividing the 25,120 genes into different gene modules), and a gene co-expression network was further constructed. Different branches of the WGCNA analysis clustering tree represent different gene modules. Genes within a module have a high degree of co-expression, while genes in different modules have a low degree of co-expression. By performing WGCN analysis, the association between modules and specific phenotypes can be explored to identify target genes and gene networks for the purpose of identification.

[0116] First, based on the expression levels of each gene obtained from the transcriptome analysis, Pearson correlation tests were used to calculate the correlation coefficients between the expression levels of any two genes to measure whether the two genes have similar expression patterns. Genes with similar expression patterns were grouped into the same gene module.

[0117] WGCNA divided the 25,120 genes into 14 different gene modules, as Figure 12 shown. Each gene module contains a different number of genes. The Turquoise module contains the most genes, a total of 6,494, followed by the Blue module (3,733 genes) and the Brown module (3,200 genes), while the Grey module (not shown in the figure) has the fewest genes, only 33 genes.

[0118] After dividing the genes into different modules, the module eigengenes of each module were calculated. The module eigengenes are used to represent the overall level of gene expression within the module. Then, the module characteristic genes of each module were calculated. The module characteristic genes best represent the expression patterns of the genes in the module.

[0119] Based on the similarity of the module eigengenes, a module tree with similar gene expression patterns was constructed, as Figure 15 shown. Each branch of the dendrogram in the figure represents a gene, and each color indicates that each gene corresponding to the color on the clustering tree belongs to the same module.

[0120] To identify the modules related to flavonoid biosynthesis and quinone biosynthesis, Pearson correlation analysis was performed on the expression levels of the module characteristic genes and the accumulation levels of 7 flavonoid compounds (Chalcones, Flavanones, Flavanonols, Flavones, Flavonols, Flavanols, and Other Flavonoids). The higher the accumulation level, the more active the flavonoid biosynthesis was considered). The results are as Figure 16As shown. The Turquoise module is positively correlated with the biosynthesis of flavonoids ( Figure 18 ), while the Yellow module is negatively correlated with the content of flavonoids ( Figure 17 ). In these two modules, a total of 177 differentially expressed genes are related to the biosynthesis of flavonoids, 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 results of Pearson correlation analysis of the expression levels of module characteristic genes and the accumulation levels of quinone compounds (the higher the accumulation level, the more active the biosynthesis of quinone compounds is considered) are as Figure 19 shown. The Blue module and the Brown module are closely and positively correlated with the synthesis of quinone compounds ( Figure 20 and Figure 21 ). In these two modules, 50 differentially expressed genes are related to the biosynthesis of quinone compounds, including 2 transcription factors related to quinone synthesis, CDL12_08581 (Trihelix) and CDL12_16197 (Trihelix).

[0122] Pearson correlation analysis 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 that the differentially expressed genes participated in the same flavonoid synthesis pathway.

[0123] Figure 22 The biosynthesis pathway of flavonoids in Tabebuia pentaphylla is shown. The figure includes a heatmap of the expression levels of differentially expressed genes and a heatmap of the accumulation levels of differentially accumulated metabolites. The 4 columns from left to right in the heatmap represent L, B, X, and R respectively. The names of metabolites are within the boxes (metabolites with gray names are metabolites not detected in L, B, X, and R, and metabolites with black names are metabolites detected in at least one of L, B, X, and R). In the heatmap of the accumulation levels of differentially accumulated metabolites, red indicates a significant up-regulation of the accumulation level of the compound, and blue indicates a significant down-regulation of the accumulation level of the compound; in the heatmap of the expression levels of differentially expressed genes, red indicates a significant up-regulation of the gene expression level, and green indicates a significant down-regulation of the gene expression level.

[0124] Among the genes involved in the flavonoid biosynthesis pathway (phenylalanine metabolism, flavonoid synthesis, isoflavonoid synthesis, and flavone and flavene synthesis) given by KEGG analysis, a total of 109 genes showed significantly different expression levels among roots, leaves, bark, and xylem, 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 Figure 22 ). In L (leaves), 31 genes were significantly upregulated compared to B (bark), X (xylem), and R (roots); in B (bark), 16 genes were significantly upregulated compared to L (leaves), X (xylem), and R (roots); in R (roots), 14 genes were significantly upregulated compared to other parts; in X (xylem), 18 genes were significantly upregulated compared to other parts. Another 10 genes showed differences in all six comparison groups.

[0125] The differentially expressed genes mentioned above include the PAL gene. In Tabebuia pentaphylla, the PAL gene converts phenylalanine into cinnamoyl-CoA and is then catalyzed by cinnamate-4-hydroxylase C4H (CYP73A) to convert it into p-coumaroyl-CoA, and finally, through the catalysis of multiple enzymes (such as 4CL, CHS, CHI, F3H, DFR, ANR, and HCT, etc.), flavonoids are synthesized ( Figure 22 ). These enzymes and the compounds they catalyze show different expression patterns in different parts of Tabebuia pentaphylla, indicating that they play important roles in the synthesis of flavonoids.

[0126] According to the results of the combined analysis of transcriptome and metabolome, the 31 genes significantly upregulated in leaves were related to the increased content of 230 flavonoids in leaves. Among them, there was a significant correlation (r > 0.999 and P < 0.05) between the content of 74 flavonoids and the expression levels of 14 genes (all of these 14 genes are genes encoding related enzymes in the flavonoid biosynthesis pathway and all belong to structural genes). These 14 genes are the key genes for flavonoid synthesis and play a crucial role in regulating the content of flavonoid metabolites in Tabebuia pentaphylla.

[0127] The 14 key genes for flavonoid synthesis are CDL12_06902 (the sequence is shown in Sequence Listing SEQ ID NO.1), CDL12_11586, CDL12_11941 (the sequence is shown in 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 relationships between these 14 genes and 74 flavonoid compounds are shown in Table 3.

[0128] These 14 key genes for flavonoid synthesis belong to 8 gene families, namely ANS, CHS, C4H, CCoAOMT, DFR, HCT, F3H, and FLS, and there is a significant correlation with the synthesis of Flavones (37 species), Flavonols (23 species), Flavanones (9 species), Chalcones (3 species), and Flavanonols (2 species) Figure 23 ).

[0129] Among the 14 key genes for flavonoid synthesis, the correlation between CDL12_06902 and Hispidulin and Kaempferide, between CDL12_11941 and dimethoxyflavone, and between novel.889 and Eupatilin, Chrysosplenetin, and Chrysin exceeds 0.9999, further confirming the role of these key genes for flavonoid synthesis in the synthesis of flavonoid compounds.

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

[0131] In addition to these 14 key genes for flavonoid synthesis, the two transcription factor genes CDL12_01396 (the sequence is shown in Sequence Listing SEQ ID NO.3) and novel.4709 (the sequence is shown in Sequence Listing SEQ ID NO.4), which are highly expressed in leaves, also have a correlation coefficient r greater than 0.99 with the accumulation amount of flavonoid compounds respectively.

[0132] Pearson correlation analysis was used to identify the differentially expressed genes related to the biosynthesis of quinone compounds and the metabolites related to the differentially expressed genes in four different tissues (L, B, X, and R). Among the genes included in the quinone biosynthesis pathway, a total of 160 differentially expressed genes were identified to have a significant correlation with 13 differentially accumulated metabolites (r > 0.9 and P < 0.05).

[0133] The most medicinally valuable quinone compounds in Handroanthus impetiginosus include lapachol and β-lapachone. Further analyze the genes related to the accumulation levels of these two quinone compounds. Among the 160 differentially expressed genes, a total of 34 differentially expressed genes were identified to have a strong correlation with the accumulation levels of lapachol and β-lapachone (r > 0.9 and P < 0.05). Among the 34 differentially expressed genes, the expression levels of 28 differentially expressed genes were significantly correlated with the accumulation level of lapachol, and 21 differentially expressed genes were significantly correlated with β-lapachone, as Figure 24 shown. Among the 34 genes, the correlation of CDL12_25459 (sequence shown in Sequence Listing SEQ ID NO.5) and CDL12_08649 (sequence shown in Sequence Listing SEQ ID NO.6) with the accumulation level of β-lapachone is greater than 0.99.

[0134] The 34 genes also include 4 transcription factors, CDL12_08581 (Trihelix, sequence shown in Sequence Listing SEQ ID NO.7), CDL12_16197 (Trihelix, sequence shown in Sequence Listing SEQ ID NO.8), CDL12_03395 (Trihelix), and CDL12_21229 (Trihelix). Among them, CDL12_08581 (Trihelix) and CDL12_16197 (Trihelix) are present in the key module related to quinone compounds in WGCNA. Therefore, CDL12_08581 and CDL12_16197 are closely related to the biosynthesis of lapachol and β-lapachone and can be used as key genes for research.

[0135] The corresponding relationships between these genes and quinone compounds are shown in Table 2.

[0136] Table 2 Correlations between 34 genes and the biosynthesis of lapachol and β-lapachone in Handroanthus impetiginosus

[0137]

[0138]

[0139] Through transcriptome analysis and transcriptome-metabolome integrated analysis, the gene information regulating flavonoid synthesis and quinone synthesis identified from the genome of Handroanthus impetiginosus and the metabolites regulated by these genes are shown in Table 3.

[0140] Table 3

[0141]

[0142]

[0143]

[0144] Obviously, the above embodiments are merely examples for clear illustration and not limitations on the implementation manners. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all implementation manners here. And the obvious changes or modifications derived therefrom are still within the protection scope of the claims of this patent application.

Claims

1. A gene for regulating the content of medicinal components in Handroanthus impetiginosus, characterized in that, The medicinal components are flavonoid compounds or quinone compounds; among them, the genes regulating the content of flavonoid compounds include: CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709; the genes regulating the content of quinone compounds include CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197; Among them, the sequences of CDL12_06902, CDL12_11941, CDL12_01396 and novel.4709 are shown in Sequence Listing SEQ ID NO.1, 2, 3 and 4 respectively; the sequences of CDL12_25459, CDL12_08649, CDL12_08581 and CDL12_16197 are shown in Sequence Listing SEQ ID NO.5, 6, 7 and 8 respectively.

2. The gene for regulating the content of medicinal components in Handroanthus impetiginosus according to claim 1, characterized in that, The genes regulating the content of flavonoid compounds regulate the content of flavonoid compounds in the leaves of Tabebuia pentaphylla; when the expression level of the genes regulating the content of flavonoid compounds in the leaves of Tabebuia pentaphylla is up-regulated, the content of flavonoid compounds in the leaves increases.

3. The gene for regulating the content of medicinal components in Handroanthus impetiginosus according to claim 2, characterized in that, Flavonoid compounds include Hispidulin, Kaempferide, dimethoxyflavone, Eupatilin, Chrysosplenetin and Chrysin; Among the genes regulating the content of flavonoid compounds, the CDL12_06902 gene regulates the content of Hispidulin and Kaempferide, and the CDL12_11941 gene regulates the content of dimethoxyflavone.

4. The gene for regulating the content of medicinal components in Handroanthus impetiginosus according to claim 2, wherein The genes regulating the content of quinone compounds regulate the content of quinone compounds in the roots of Tabebuia pentaphylla; when the expression level of the genes regulating the content of quinone compounds in the roots of Tabebuia pentaphylla is up-regulated, the content of quinone compounds in the roots increases.

5. The gene for regulating the content of medicinal components in Handroanthus impetiginosus according to claim 4, characterized in that, The quinone compound is lapachol or a combination of lapachol and β-lapachone.

6. A method for identifying genes regulating the content of medicinal components in Handroanthus impetiginosus, characterized in that, It includes the following steps: S1. Sampling the leaves, roots, bark and xylem tissues of Tabebuia pentaphylla; S2. Through metabolome analysis, investigating the types and content differences of flavonoid compounds and quinone compounds in the leaves, roots, bark and xylem tissues to obtain differentially accumulated metabolites; through transcriptome analysis, investigating the gene expression pattern differences in the leaves, roots, bark and xylem tissues to obtain differentially expressed genes; S3. Conducting a joint analysis of the differentially expressed genes and differentially accumulated metabolites to identify the genes regulating the content of medicinal components in Tabebuia pentaphylla as described in Claim 1.

7. The identification method according to claim 6, characterized in that, In step S2, when performing metabolome analysis, the leaf, root, bark, and xylem tissues of Tabebuia pentaphylla were separately prepared into test samples for ultra-high performance liquid chromatography-tandem mass spectrometry (UHPLC-MS / MS) analysis. UHPLC-MS / MS was used to analyze the test samples to obtain the types and contents of flavonoids and quinones in the leaves, roots, bark, and xylem. Using variable importance in projection VIP>1 and |log2 FC|≥1 as the screening criteria, flavonoids and quinones with significantly different contents among the leaf, root, bark, and xylem tissues were screened as differentially accumulated metabolites.

8. The identification method according to claim 7, wherein In step S2, when preparing the test samples for UHPLC-MS / MS analysis, the tissues of Tabebuia pentaphylla were vacuum freeze-dried, ground into powder, placed in the extraction solution, vortexed, centrifuged, and the supernatant was taken, filtered and the filtrate was retained. During UHPLC-MS / MS analysis, the multi-reaction monitoring mode of a triple quadrupole mass spectrometer was used to measure the contents of various compounds in the test samples, and the MWDB database was used to analyze the types of flavonoids and quinones in the test samples.

9. The identification method according to claim 8, characterized in that, In step S2, the RNA of the leaf, root, bark, and xylem tissues of Tabebuia pentaphylla was separately extracted, and cDNA libraries were separately constructed. The Illumina NovaSeq 6000 platform was used to sequence the cDNA libraries to obtain the transcriptome sequencing results. Genes were extracted from the transcriptome sequencing results, and the gene expression levels were calculated using FPKM. Then, differentially expressed genes among groups were screened based on the gene expression levels. The screening criteria for differentially expressed genes were |log2 FC|≥1 and false discovery rate FDR<0.

05.

10. The identification method according to claim 9, characterized in that, In step S3, when performing a joint analysis of differentially expressed genes and differentially accumulated metabolites, the R language package WGCNA was used to analyze the genes extracted from the transcriptome sequencing results, and gene modules related to flavonoid biosynthesis and quinone biosynthesis, as well as the differentially expressed genes in these modules, were screened. The differentially accumulated metabolites and the differentially expressed genes screened in this step were mapped to the KEGG pathway database to obtain their common pathway information. For the differentially expressed genes and differentially accumulated metabolites involved in the same flavonoid synthesis pathway, and the differentially expressed genes and differentially accumulated metabolites involved in the same quinone synthesis pathway, the Pearson correlation analysis method was used for joint analysis to calculate the correlation coefficient between the expression levels of the differentially expressed genes in the four Tabebuia pentaphylla tissues and the accumulation levels of the differentially accumulated metabolites in the same pathway as the differentially expressed genes. Using P value<0.05 and |r|>0.9 as the identification criteria for strong correlation, the differentially expressed genes strongly correlated with the differentially accumulated metabolites were identified as the genes regulating the contents of medicinal components in Tabebuia pentaphylla.

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