Key gene for biosynthesis of flavonoids in crataegus maximus bge and screening method and application thereof

Abstract

The present application belongs to the technical field of plant secondary metabolite biosynthesis, and particularly relates to a key gene for biosynthesis of flavonoid compounds in Crataegus maximus and a screening method and application thereof. The present application screens a key gene combination comprising 7 structural genes and 3 transcription factor genes through joint analysis of metabolome and transcriptome. The expression of these genes is significantly positively correlated with the accumulation of target flavonoid metabolite [6]-gingerol, and is verified by experiments to be a core factor for regulating the synthesis of flavonoid compounds in Crataegus maximus. The present application first systematically identifies the key genes for regulating the synthesis of flavonoid substances such as [6]-gingerol in the fruit of Crataegus maximus, and provides a target for analyzing the quality formation mechanism from the molecular level. The gene can be used for molecular marker assisted breeding to cultivate new varieties of Crataegus maximus with high flavonoid content, and can also provide gene resources and technical support for the development of functional food and health products.

Description

Technical Field

[0001] This invention belongs to the field of plant secondary metabolite biosynthesis technology, specifically relating to a key gene for the biosynthesis of flavonoids in large-fruited hawthorn, its screening method, and its application. Background Technology

[0002] Large-fruited hawthorn refers to the fruit of two species, *Malus doumeri* (Bois) Chev. and *Malus leiocalyca* SZHuang, both belonging to the genus *Malus* in the Rosaceae family. These plants are distributed in southern my country, Vietnam, and Laos. Jingxi County in Guangxi Province has the longest history of cultivation, the widest distribution, the largest cultivation area, and the largest fruit production, and it has been recognized as a geographical indication product. Large-fruited hawthorn is a fruit with both edible and medicinal uses. The fruit can be eaten raw, made into preserves, or used to make wine, while the leaves can be used to make tea. Dried hawthorn can be used medicinally, possessing various pharmacological effects and health benefits, including regulating qi and strengthening the spleen, aiding digestion, inhibiting bacteria, and providing antioxidants. It has been included in the standards for medicinal materials. Currently, the market benefits of large-fruited hawthorn are continuously increasing, and its resource utilization and development have become one of the directions driving local industrial development. Therefore, conducting more in-depth research is meaningful and necessary.

[0003] Existing studies have used continuously optimized processes such as high performance liquid chromatography, ultrasound, and microwave to extract functional components from the fruit, leaves, and processed products of large-fruited hawthorn, such as flavonoids, triterpenes, phenols, amino acids, organic acids, and vitamins. The differences in component content at different maturity stages and production areas have been compared, and antioxidant capacity and antibacterial activity have been verified. It has been found that there are certain differences in functional components among different germplasms. However, there is currently little research on large-fruited hawthorn at the molecular biology level, especially a lack of systematic detection and identification of key genes for the biosynthesis of flavonoids. Furthermore, there are no reports of using multi-omics combined analysis strategies to screen key genes that regulate the accumulation of specific flavonoid active components (such as [6]-gingerol). Therefore, if multi-omics and other technical means can be integrated to elucidate the molecular mechanisms and regulatory mechanisms of the main functional components of large-fruited hawthorn at the molecular mechanism level, it will provide a better theoretical basis for the genetic improvement of varieties.

[0004] Plants are rich in various flavonoids, which can influence plant color and flavor characteristics, regulate cell growth, attract pollinating insects, and defend against related stresses. For humans, flavonoids possess a variety of health benefits and pharmacological activities, including antioxidant, anti-inflammatory, and immunomodulatory effects. Due to their excellent functional properties, flavonoids are frequently used in the food, cosmetic, and pharmaceutical industries. Currently, numerous studies have comprehensively described the biosynthetic pathways and molecular regulatory mechanisms of flavonoids and other functional components. However, the biosynthetic pathways, key enzyme genes, and transcriptional regulators of flavonoids in hawthorn berries remain unclear, which severely restricts the progress of breeding programs aimed at improving the nutritional value of their fruits through molecular means.

[0005] It is worth noting that [6]-gingerol, as a flavonoid compound with important health benefits, has been mainly studied in ginger family plants. Whether it is synthesized and how it is regulated in Rosaceae fruit trees, especially large-fruited hawthorn, remains a blank. Therefore, using multi-omics integrated analysis techniques such as metabolomics and transcriptomics to systematically screen and identify key genes that regulate the biosynthesis of flavonoid compounds (including [6]-gingerol) in large-fruited hawthorn fruit is of urgent theoretical and practical significance for fundamentally analyzing its quality formation mechanism, cultivating new varieties with high flavonoid content, and expanding the application of this species in the development of functional foods. Summary of the Invention

[0006] The present invention aims to solve the above-mentioned technical problems, provide a key gene combination for regulating the biosynthesis of flavonoids in hawthorn fruit and its screening method, and elaborate on its application.

[0007] The technical solution of this invention is as follows:

[0008] A key gene for the biosynthesis of flavonoids in large-fruited hawthorn, the key gene comprising:

[0009] (a) Structural genes: gene-LOC103405591, gene-LOC114821133, gene-LOC114821135, gene-LOC103403337, gene-LOC103409539, gene-LOC103454980, gene-LOC103426517;

[0010] (b) Transcription factor genes: gene-LOC103427630, gene-LOC103434665 and gene-LOC103422512;

[0011] The sequences of gene-LOC103405591, gene-LOC114821133, gene-LOC114821135, gene-LOC103403337, gene-LOC103409539, gene-LOC103454980, and gene-LOC103426517 are shown in SEQ ID NO. 1, 2, 3, 4, 5, 6, and 7, respectively; the sequences of gene-LOC103427630, gene-LOC103434665, and gene-LOC103422512 are shown in SEQ ID NO. 8, 9, and 10, respectively.

[0012] Furthermore, the key gene is used to increase the content of flavonoid compound [6]-gingerol in hawthorn fruit.

[0013] A method for screening key genes in the biosynthesis of flavonoids from large-fruited hawthorn includes the following steps:

[0014] S1. Select at least two large-fruited hawthorn germplasms with significant differences in total flavonoid content as experimental materials and collect their fruit samples.

[0015] S2. Perform metabolomics analysis of flavonoids on the fruit samples, identify and screen flavonoid metabolites with significant differences in content among the germplasms, and use them as differential accumulation metabolites.

[0016] S3. Perform transcriptomic analysis on the fruit samples to identify and screen genes that show significant differences in expression among the germplasms, and designate them as differentially expressed genes.

[0017] S4. Perform joint analysis on the differentially accumulated metabolites obtained in step S2 and the differentially expressed genes obtained in step S3 to screen out key genes that are related to the biosynthetic pathway of flavonoids and whose expression patterns are consistent with the accumulation trend of the target differentially expressed metabolites.

[0018] Furthermore, in step S2, the metabolomics analysis employs ultra-high performance liquid chromatography-mass spectrometry; the criteria for screening differentially accumulating metabolites are: variable importance projection score ≥1, |log2(FC)| ≥1, and P value <0.05.

[0019] Furthermore, in step S3, the transcriptomics analysis is based on RNA-Seq technology; the criteria for screening differentially expressed genes are: |log2(FC)|≥1, and the corrected P value <0.05.

[0020] Further, in step S4, the joint analysis includes:

[0021] (1) Map differentially expressed genes and differentially accumulated metabolites to the KEGG metabolic pathway database to identify the common enriched flavonoid biosynthetic pathways.

[0022] (2) Correlation analysis was performed on differentially expressed genes and differentially accumulated metabolites enriched in the same pathway, and the Pearson correlation coefficient between gene expression level and metabolite accumulation was calculated;

[0023] (3) Differentially expressed genes that are significantly positively correlated with the target differential metabolites are identified as candidate key genes that regulate the biosynthesis of the flavonoids.

[0024] Further, the target differential metabolite is [6]-gingerol; the key genes include at least one of gene-LOC103409539, gene-LOC103405591, gene-LOC103403337, gene-LOC114821135, gene-LOC114821133, gene-LOC103454980, gene-LOC103426517, gene-LOC103427630, gene-LOC103434665 and gene-LOC103422512.

[0025] This invention provides the application of the above-mentioned key genes in the cultivation of large-fruited hawthorn varieties with high flavonoid content.

[0026] This invention also provides the application of the above-mentioned key genes in the improvement of the nutritional quality of hawthorn fruit or the development of functional products.

[0027] Due to the adoption of the above technical solution, the beneficial effects of the present invention are as follows:

[0028] 1. This invention, through multi-omics joint analysis, screened out key genes including 7 structural genes (gene-LOC103409539, gene-LOC103405591, gene-LOC103403337, gene-LOC114821135, gene-LOC114821133, gene-LOC103454980, gene-LOC103426517) and 3 transcription factor genes (gene-LOC103427630, gene-LOC103434665, gene-LOC103422512). The expression patterns of these genes are highly consistent with the accumulation trend of the target metabolite ([6]-gingerol), and were verified by qRT-PCR, providing a clear target for analyzing the synthesis mechanism of flavonoids, especially [6]-gingerol, in hawthorn at the molecular level.

[0029] 2. The metabolomics and transcriptomics combined analysis screening method established in this invention can rapidly identify candidate key genes closely related to target traits (such as high flavonoid content) by performing KEGG pathway co-enrichment analysis and correlation network construction on differentially accumulated metabolites and differentially expressed genes. This method overcomes the limitations of single-mic studies, significantly improves the efficiency and accuracy of gene screening, and can be widely applied to the study of biosynthetic regulation of other plant secondary metabolites.

[0030] 3. The key genes identified in this invention, especially the hydroxycinnamoyltransferase (HCT) gene and the 4-coumaryl-CoA ligase (4CL) gene, as well as transcription factors such as WRKY, MYB, and bHLH, can be directly used as molecular markers or genetic manipulation targets. By regulating these genes through genetic engineering methods (such as overexpression or gene editing), it is possible to directionally improve large-fruited hawthorn varieties, cultivate new varieties with significantly increased content of flavonoids (especially high-value gingerol [6]), and enhance their nutritional quality and market competitiveness.

[0031] 4. This invention is the first to identify and correlate [6]-gingerol and its potential regulatory genes in hawthorn fruit, breaking through the traditional understanding that this active ingredient is mainly found in ginger family plants. This provides a new resource clue and scientific basis for the development of fruits as a source of novel natural [6]-gingerol and other flavonoids, and helps to promote its innovative application in functional foods, health products and pharmaceutical raw materials. Attached Figure Description

[0032] Figure 1 The images shown are photographs of the fruit appearance of two large-fruited hawthorn germplasms (G8 and G9) and a bar chart comparing their total flavonoid content in this embodiment of the invention. A represents a photograph of the fruit appearance of large-fruited hawthorn germplasm G8; B represents a photograph of the fruit appearance of large-fruited hawthorn germplasm G9; and C represents a bar chart comparing the total flavonoid content of G8 and G9. Experimental data are expressed as the mean ± standard error (SEM) of three biological replicates. A t-test was used to analyze the significance of differences, with **** representing a significance level of P < 0.0001.

[0033] Figure 2 The graphs shown are multivariate statistical analysis graphs of flavonoid metabolites from two germplasms (G8 and G9) in this embodiment of the invention. A is the orthogonal partial least squares discriminant analysis score graph; B is the model permutation test graph; and C is the hierarchical clustering analysis heatmap of flavonoid metabolite content.

[0034] Figure 3 The diagram shows the analysis of flavonoid metabolites with significant differences between the two germplasms (G8 and G9) in this embodiment of the invention. A is a volcano diagram of differentially accumulated flavonoids; B is a heatmap of hierarchical cluster analysis.

[0035] Figure 4 The following are multivariate statistical analysis plots of transcriptome data from two germplasms (G8 and G9) in this embodiment of the invention. A is a heatmap of correlation analysis between samples; B is a volcano plot of differentially expressed genes; and C is a cluster analysis plot of K-means of differentially expressed genes.

[0036] Figure 5The diagrams show the functional enrichment analysis of differentially expressed genes in two germplasms (G8 and G9) in this embodiment of the invention. A is the GO enrichment analysis diagram, and B is the KEGG enrichment analysis diagram.

[0037] Figure 6 The diagram shows the KEGG pathway and heatmap of key differentially expressed genes related to the biosynthesis of the target metabolite [6]-gingerol in this embodiment of the invention. The enzymes marked in red have their encoding genes significantly upregulated in G9; the enzymes marked in gray have their encoding genes with no significant difference in expression.

[0038] Figure 7 The following is a multivariate statistical analysis diagram of differentially expressed transcription factors in two germplasms (G8 and G9) in the embodiments of the present invention. A is a volcano diagram of differentially expressed transcription factors; B is a network diagram of the correlation of transcription factors co-expressed with structural genes related to flavonoid synthesis; C is a network diagram of the correlation of core transcription factors (WRKY, MYB, and bHLH, one each) significantly associated with the HCT gene (gene-LOC103454980) and [6]-gingerol.

[0039] Figure 8 This is a bar chart showing the relative expression levels of some key genes (including 7 structural genes and 3 transcription factors) screened by qRT-PCR in G8 and G9 in this embodiment of the invention. Detailed Implementation

[0040] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0041] Example

[0042] 1. Introduction

[0043] This embodiment aims to verify the function of the key gene combination described in this invention in regulating the biosynthesis of flavonoids (especially [6]-gingerol) in hawthorn by combining metabolomics and transcriptomics analysis.

[0044] 2. Materials and Methods

[0045] 2.1. Plant materials

[0046] Mature hawthorn fruit samples were collected from Bashu, Jingxi City, Baise City, Guangxi Zhuang Autonomous Region (23°34'N, 106°28'E, altitude 798.8 meters). Several fresh fruits of uniform size and without mechanical damage were randomly selected from the upper part of the tree crown. The fruits were washed with ultrapure water to remove impurities and dried. Then, the pits were removed, the fruits were cut into pieces, and immediately flash-frozen in liquid nitrogen and stored at -80°C for later use.

[0047] 2.2. Determination of total flavonoid content

[0048] Large-fruited hawthorn samples (2.1 cm in diameter) were coarsely ground and extracted with 70% ethanol at a liquid-to-material ratio of 1:18 using ultrasonic extraction (300W, 30 min, repeated twice). The supernatant was collected. The total flavonoid content in the samples was determined using the aluminum nitrate colorimetric method. The absorbance was measured at 510 nm using a TecanInfinite 2000pro multi-functional microplate reader, and a standard curve was plotted. The total flavonoid content of several germplasms was compared using IBM SPSS Statistics 27 with a t-test and a significance level set at P<0.05. Finally, two germplasms, G8 and G9, with significant differences in flavonoid content were selected for further research.

[0049] 2.3. Extraction, separation, and metabolomics data processing of flavonoid metabolites

[0050] The metabolite extraction procedure was as follows: The fruit sample was homogenized using a grinder. 100 mg of sample was accurately weighed and mixed with 1 mL of extraction solvent (methanol:acetonitrile:water = 2:2:1, v / v). The mixture was sonicated in an ice-water bath for 10 minutes, then rapidly frozen in liquid nitrogen for 1 minute. This process was repeated three times. The sample was then stored at -20°C for 1 hour, followed by centrifugation. The supernatant was collected, dried under a gentle nitrogen stream, and reconstituted with 100 μL of acetonitrile:water (1:1, v / v). The reconstituted sample was sonicated again in an ice-water bath for 15 minutes, centrifuged, and the supernatant was collected for mass spectrometry analysis.

[0051] The chromatographic-mass spectrometry (HPLC-MS) detection conditions were as follows: An ultra-high performance liquid chromatography-mass spectrometry (ULCS-MS) system equipped with a Waters UPLC BEH Amide column (2.1 × 100 mm, 1.7 μm) was used for detection. The injection volume was 5 μL, and the chromatographic conditions were as follows: column temperature: 55 °C; mobile phase: water (A) containing 25 mM ammonium acetate and 25 mM ammonium hydroxide, and 100% acetonitrile (B); gradient elution program: 0–1 min: 85% B, 1–12 min: 65% B, 12–12.1 min: 40% B, 12.1–15 min: 40% B, 15–15.1 min: 85% B, 15.1–20 min: 85% B; flow rate: 0.3 mL / min. The mass spectrometry (MS) data acquisition conditions were as follows: electrospray ionization source; ion source voltage: 4500 V or 5500 V; curtain gas: 20 psi; nebulizer gas and auxiliary gas: 60 psi each.

[0052] For data processing, ProteoWizard was used to convert the raw UPLC-MS data to .mzXML format, and then the SCIEXOS software package was used for peak alignment, retention time correction, and peak area extraction. Metabolite identification was performed using primary and secondary mass spectrometry matching (mass error within 25 ppm), referencing proprietary databases from Guangzhou Weiyu Zhihe Technology Co., Ltd., China, and public metabolomics databases. Metabolite quantification was performed using MRM mode, and data standardization was performed by peak area integration.

[0053] Based on this, orthogonal partial least squares discriminant analysis and hierarchical cluster analysis were used to preliminarily compare the flavonoid metabolic characteristics of G8 and G9. The fold difference (FC) was calculated, and the p-value was calculated by t-test. Metabolites with significant differences were screened based on the criteria of variable importance projection score ≥1, |log2(FC)| ≥1, and p-value <0.05.

[0054] 2.4. RNA-Seq and Analysis

[0055] Total RNA was extracted from hawthorn tissue using the TRIzol method, and RNA quality was initially assessed using spectrophotometry, agarose gel electrophoresis, and an Agilent 2100 bioanalyzer. Subsequently, mRNA was enriched using an Oligo(dT) magnetic rack and fragmented using fragmentation buffer. For library construction, cDNA libraries were constructed using the TruSeq™ RNA Sample Preparation Kit. After adapter ligation, fragments were purified and selected for size using Agencourt AMPure XP Beads, followed by PCR amplification. After purification, the libraries were quantified and quality controlled using a Quantus™ fluorometer and a QuantiFluor® dsDNA detection system. Sequencing platforms included Illumina HiSeq Xten, NovaSeq 6000, or T7 sequencers.

[0056] After sequencing, the raw data were first quality-controlled using FASTP. Then, HiSat2 and TopHat2 were used to align the quality-controlled effective reads with the reference genome (Malus domestica PRJNA1194184) to obtain data for subsequent transcript assembly and expression level calculations. Simultaneously, the alignment results of this transcriptome sequencing were quality-assessed. Based on this, StringTie software was used to quantitatively analyze gene and transcript expression levels, and edgeR and DEGseq were used to analyze gene expression differences between samples. Differentially expressed genes were screened based on |log2(FC)|≥1 and padj<0.05. For the screened differentially expressed genes (DEGs), GO enrichment analysis was performed using Goatools, with Fisher's exact test applied. Four multiple test methods were used to correct the P-value to control the false positive rate; a corrected P-value ≤0.05 was considered to indicate significant GO enrichment. KEGG channel enrichment analysis was performed using KOBAS, with Fisher's exact test and multiple tests using the BH (FDR) method. A corrected p-value < 0.05 was used as the threshold, defining KEGG pathways significantly enriched in differentially expressed genes. Furthermore, transcription factors in differentially expressed genes were identified using the PlantTFDB (Plant Transcription Factor Database) and AnimalTFDB (Animal Transcription Factor Database), with |log2(FC)| ≥ 1 and padj < 0.05 as the screening criteria. Based on this, using the Malus reference genome as a reference, the measured transcriptome reads of the G9 fruit corresponding to the screened key genes were compared and corrected to obtain their actual gene sequences in Hawthorn berries. These sequences were named gene-LOC103405591, gene-LOC114821133, gene-LOC114821135, gene-LOC103403337, gene-LOC103409539, gene-LOC103454980, gene-LOC103426517, gene-LOC103427630, gene-LOC103434665, and gene-LOC103422512, respectively. Their nucleotide sequences are shown in SEQ ID NO.1-SEQ ID NO.10. The sequence of the internal reference gene β-actin, shown in SEQ ID NO.31, was obtained using the same method.

[0057] 2.5. Real-time quantitative PCR verification and absolute quantitative detection of [6]-gingerol

[0058] The methods for total RNA extraction and cDNA reverse transcription were as described above. Ten genes related to flavonoid metabolite synthesis selected above were used for qRT-PCR validation, with β-actin as the internal reference gene (nucleotide sequence shown in SEQ ID NO. 31). Specific primers were designed based on the reference genome using PrimerPremier5 software (primer sequences are shown in Table 1). qRT-PCR reactions were performed using the Ultra SYBR Mix kit amplification system on a QuantReady K9600 real-time quantitative PCR analyzer. Relative quantification was performed using 2... -△△CT The calculations were performed using the method described above. IBM SPSS Statistics 27 software was used to analyze the qRT-PCR data. The significance level was set at P<0.05 for the test.

[0059] Table 1. Primer sequences required for real-time quantitative PCR to verify genes

[0060]

[0061] Note: SEQ ID NO.1-SEQ ID NO.10 and SEQ ID NO.31 are the actual gene sequences obtained in this study based on transcriptome sequencing data, assembled using the Malus reference genome as a template, and verified by measured data from Malus doumeri fruit. The above qRT-PCR primers were designed based on the corresponding sequences in the Malus reference genome. Comparison with the actual gene sequences obtained in this invention (SEQ ID NO.1-SEQ ID NO.10 and SEQ ID NO.31) revealed some naturally occurring single nucleotide polymorphisms in the corresponding regions of the primers. This was verified by qRT-PCR experiments (see [link to qRT-PCR documentation]). Figure 8 The primer still exhibits good amplification efficiency in the experimental materials of this invention and is suitable for the expression detection of the above-mentioned genes in hawthorn.

[0062] 3. Results

[0063] 3.1. Analysis of total flavonoid content and metabolomics analysis of flavonoid compounds

[0064] In the initial stage of this experiment, the total flavonoid content of multiple germplasms was compared, revealing that G9 and G8 had significantly higher and lower content, respectively, with G9 containing approximately 3.5 times more total flavonoids than G8. Therefore, we selected G8 and G9 for further investigation. Figure 1 Images A and B in the image show the fruit appearance of two large-fruited hawthorn cultivars, G8 and G9, respectively. Figure 1 The bar chart in Figure C shows a comparison of the total flavonoid content of G8 and G9. Figure 1 Metabolomics analysis of G8 and G9 cells targeting flavonoid compounds identified a total of 103 flavonoids, mainly including 63 flavonoids (61.17%), 16 isoflavones (15.53%), and 7 linear 1,3-diarylpropanes (6.80%). Figure 2 C). OPLS-DA analysis of total metabolites showed that all model parameters were close to 1, indicating a good model fit. Figure 2 A and B). HCA results showed that the relative high expression of flavonoid metabolites in the two germplasms differed to some extent ( Figure 2 C).

[0065] Metabolites with significant differential regulation were screened using VIP≥1, |log2(FC)|≥1, and P<0.05 as criteria. A total of 26 significantly differentially regulated metabolites were identified (comparing G8 and G9, 11 were upregulated and 15 were downregulated). Figure 3 A). Among the significantly differentially expressed metabolites, those significantly upregulated in G9 included 13 flavonoids, 5 isoflavones, 3 phenols, 2 organic oxygen compounds, and 1 each of benzopyran, coumarin and its derivatives, and linear 1,3-diarylpropane (…). Figure 3 B).

[0066] 3.2. Transcriptomics Analysis

[0067] 3.2.1. Transcriptomics and Differential Gene Expression Analysis

[0068] Sequencing data quality assessment results showed that an average of 46,837,115 valid sequences were obtained from the G8 and G9 libraries, representing 99.77% of the total. The Q30 percentage for each library was above 95%, the average GC content was 47.02%, and an average of 81.49% of the valid sequences mapped to the reference genome (Table 2). To analyze differentially expressed genes, preliminary screening and correlation analysis were performed on biological duplicates. The results showed that the R... 2 The values ​​are all close to 1, indicating good sample repeatability, and subsequent difference analysis can be performed. Figure 4 A). Subsequently, differentially expressed genes were screened based on |log2(FC)|≥1 and a corrected P-value <0.05, resulting in 2065 differentially expressed genes (comparing G8 and G9, of which 992 were upregulated and 1073 were downregulated). Figure 4 B). Further K-means analysis divided the differentially expressed genes into 6 clusters, and the results showed that there was a large amount of differential expression among the biological replicates of all samples (B). Figure 4 C).

[0069] Table 2. Statistical summary of RNA sequencing results from Hawthorn berries (Rhizoma Cypripedii).

[0070]

[0071] Functional annotation and enrichment analysis were performed on differentially expressed genes. According to GO annotation, differentially expressed genes were mainly enriched in the categories of "biological processes" and "cellular processes" within the biological process category, "cellular components" within the cellular component category, and "molecular function" and "catalytic activity" within the molecular function category. Figure 5 A). KEGG annotation results showed that differentially expressed genes were mainly annotated in the MAPK signaling pathway, NF-kappaB signaling pathway, and neurotrophic factor signaling pathway. Notably, the phenylpropane biosynthesis and flavonoid and flavonol biosynthesis pathways also showed significant enrichment. Figure 5 B).

[0072] 3.2.2. Differential expression of related pathways and regulatory genes

[0073] Based on the enriched KEGG pathway and gene function annotation, significantly differentially expressed genes involved in encoding related pathways were further identified (Table 3). Specifically, 28 differentially expressed genes were identified in the phenylpropane biosynthesis pathway, including 2 4CLs, 3 CCRs, 2 CADs, 4 COMTs, 4 K22395s, 5 E1.11.1.7s, 1 UGT72E, 1 F5H, and 6 HCTs. Among them, the expression of 4CL was significantly upregulated in G9; the expression of 2 CCRs, 2 CADs, 3 COMTs, 2 K22395s, 1 E1.11.1.7, and 1 UGT72E was significantly downregulated in G9. This expression pattern may directly or indirectly promote the accumulation of flavonoid precursors. In the flavonoid biosynthesis pathway, 17 differentially expressed genes were identified, including 6 HCTs, 4 C12RT1s, 3 FLSs, 1 LAR, and 2 PGT1s. Among them, 6 HCT and 1 LAR genes were significantly upregulated in G9, suggesting that they may directly promote the accumulation of flavonoids. In addition, 2 differentially expressed genes were identified in the isoflavone biosynthesis pathway, 1 CYP81E and 1 PTS, with PTS being significantly upregulated in G9; 4 differentially expressed genes were identified in the flavonoid and flavonol biosynthesis pathways, all of which were C12RT1, and all of them were significantly downregulated in G9.

[0074] Table 3. List of differentially expressed structural genes in flavonoid-related pathways in G8 and G9

[0075]

[0076] 3.3. Combined Transcriptome and Metabolome Analysis

[0077] To gain a comprehensive understanding of the biosynthetic mechanisms of flavonoids, a combined analysis of differentially expressed genes and metabolites was conducted. Specifically, based on the KEGG database, association analysis was performed on genes and metabolites involved in the same metabolic pathway. The results showed that flavonoid metabolites were mainly enriched in five pathways: flavonoid biosynthesis, isoflavone biosynthesis, flavonoid and flavonol biosynthesis, stilbene, diarylheptane and gingerol biosynthesis, and flavonoid degradation pathway (Table 4).

[0078] Table 4. KEGG pathway enriched with differentially expressed flavonoid metabolites by combined transcriptomic and metabolomic analysis

[0079]

[0080] In the phenylpropane biosynthesis pathway, the expression of one 4CL was significantly upregulated in G9 (approximately 2.62-fold); the expression of two CCRs, two CADs, three COMTs, two K22395s, one E1.11.1.7, and one UGT72E was significantly downregulated in G9. This expression pattern may directly or indirectly promote the accumulation of flavonoid precursors such as [6]-gingerol (Table 3). Specifically, the synthetic precursor p-coumaryl-CoA enters the biosynthetic pathways of stilbene, diarylheptanes, and gingerol, successively via HCT, CCoAOMT generates feruloyl-CoA, which is then converted to gingerol by polyketide synthase and reductase [6].

[0081] In the flavonoid biosynthesis pathway, the enzyme genes (PGT1, C12RT1, FLS, LAR) that share the precursor p-coumaryl-CoA are all downregulated in G9, thus relatively reducing the competitive pressure from precursor substances. Notably, six HCTs (gene-LOC103409539, gene-LOC103405591, gene-LOC103403337, gene-LOC114821135, gene-LOC114821133, gene-LOC103454980) are simultaneously enriched in the biosynthesis pathways of stilbenes, diarylheptanes and gingerols, flavonoids, and phenylpropanes. All six HCTs and [6]-gingerol were significantly upregulated in G9 (HCTs were upregulated by approximately 4.80, 2.85, 2.57, 3.32, 3.38, and 3.11 times, respectively; [6]-gingerol was upregulated by approximately 2.00 times), showing a highly consistent expression trend. Figure 6 (See Tables 3 and 4). In summary, the above-mentioned HCT and 4CL (gene-LOC103426517) may be involved in the biosynthesis of flavonoids such as gingerol[6], which leads to the higher content of flavonoid metabolites in G9 than in G8.

[0082] 3.4. Transcription Factor Analysis

[0083] Compared with G9, a total of 1097 differentially expressed transcription factors were identified in G8 (494 upregulated and 603 downregulated). The differentially expressed transcription factors mainly included bHLH (116, 10.57%), MYB (101, 9.21%), NAC (94, 8.57%), WRKY (61, 5.56%), ERF (60, 5.47%), etc. Figure 7 A).

[0084] To investigate the regulatory role of transcription factors in flavonoid synthesis, correlation analysis was performed on some structural genes related to flavonoid synthesis and transcription factors. Structural genes significantly upregulated in G9 were selected, and further screening was conducted using a correlation coefficient |R|>0.90 and P<0.01 as the threshold. The results showed that the structural gene HCT (including 7 genes such as gene-LOC103454980) was significantly correlated with 1105 transcription factors; 4CL (gene-LOC103426517) was significantly correlated with 167 transcription factors; PAL (gene-LOC103440652) was significantly correlated with 33 transcription factors; and LAR (gene-LAR1) was significantly correlated with 74 transcription factors. Figure 7 B).

[0085] Based on this, and combined with the correlation analysis between transcription factors and metabolites, the following transcription factors were further screened using P<0.01 as the criterion: WRKY (gene-LOC103427630), MYB (gene-LOC103434665), and bHLH (gene-LOC103422512). All three were significantly correlated with HCT (gene-LOC103454980) and [6]-gingerol. All of the above transcription factors were significantly upregulated in G9 ( Figure 7 C and Table 5).

[0086] Table 5. Screening information for key transcription factors

[0087]

[0088] 3.5. qRT-PCR verification of transcriptome data and verification of gingerol content [6].

[0089] To verify the reliability of key transcriptome sequencing results, seven differentially expressed genes (including six HCT genes and one 4CL gene) and three transcription factor genes (one each of WRKY, MYB, and bHLH) were selected, and their expression levels at G8 and G9 were analyzed by qRT-PCR. Figure 8The results showed that the expression patterns of the above genes were highly consistent with the RNA-Seq results, with most of the correlation coefficients R² > 0.7 (Table 6).

[0090] Table 6. Comparison of qRT-PCR and RNA sequencing data of G8 and G9 related genes based on Pearson correlation coefficient.

[0091]

[0092] 4. Discussion

[0093] 4.1. Identification and differential analysis of flavonoid metabolites in large-fruited hawthorn fruit

[0094] To clarify the composition of flavonoid metabolites in hawthorn fruits and their germplasm differences, this study used UPLC-MS to perform metabolomics analysis on two germplasms, G8 and G9, which showed significant differences in total flavonoid content. A total of 103 flavonoid compounds were identified, mainly including flavonoids, isoflavones, and linear 1,3-diarylpropane. PCA and HCA analyses showed significant differences in the accumulation patterns of flavonoid metabolites between G8 and G9, and further screening revealed 26 significantly differentially expressed metabolites. Among these, the majority of upregulated substances in G9 were flavonoids, indicating that germplasm G9 may be more conducive to the accumulation of specific flavonoids.

[0095] 4.2. Analysis of genes and transcription factors related to flavonoid biosynthesis

[0096] To further elucidate the molecular basis of the aforementioned metabolic differences, this study performed transcriptomic analysis on G8 and G9, identifying a total of 2065 significantly differentially expressed genes. Functional annotation and enrichment analysis of these differentially expressed genes revealed that they were mainly enriched in the KEGG pathway, including phenylpropane biosynthesis, flavonoid and flavonol biosynthesis. In addition, this study identified 1097 differentially expressed transcription factors, involving multiple families such as MYB, bHLH, WRKY, and AP2.

[0097] In the G9 germplasm, more than half of the genes involved in the flavonoid biosynthesis pathway were systematically upregulated at the transcriptional level. This synergistic enhancement of gene expression is the core molecular mechanism driving G9 to synthesize more flavonoids, ultimately resulting in a significantly higher total flavonoid content than G8.

[0098] Based on this, in order to clarify the association between related genes and metabolites, this study further conducted a combined metabolomics and transcriptomics analysis. The results showed that flavonoid metabolites were enriched in pathways such as phenylpropane biosynthesis, flavonoid biosynthesis, isoflavone biosynthesis, flavonoid and flavonol biosynthesis, stilbene, diarylheptane, and gingerol biosynthesis. In the above enrichment pathways, six HCT genes and one 4CL gene related to the synthesis of the flavonoid compound [6]-gingerol were significantly upregulated in G9, and their expression trends were highly consistent with the accumulation trend of [6]-gingerol. This indicates that the upregulation of the expression levels of these genes enhances the flux of related metabolic pathways, thereby promoting the biosynthesis of flavonoids such as [6]-gingerol.

[0099] Furthermore, co-expression correlation analysis was performed on structural genes and transcription factors related to flavonoid synthesis, and the results showed that a large number of structural genes and transcription factors were significantly correlated. Combined with the correlation analysis between transcription factors and metabolites, and using P < 0.01 as the standard, one WRKY, one MYB and one bHLH transcription factor were further screened out, all of which were significantly correlated with HCT (gene-LOC103454980) and [6]-gingerol. The above results indicate that the high expression of these transcription factors in G9 may affect the biosynthesis of flavonoids such as [6]-gingerol by directly or indirectly regulating the transcriptional activity of HCT.

[0100] 4.3. qRT-PCR verification and quantitative analysis of [6]-gingerol

[0101] To verify the reliability of the above screening results, this study selected seven structural genes and three transcription factor genes as candidate key genes, and validated their expression using qRT-PCR. The results showed that the expression trends of the six HCT genes (gene-LOC103409539, gene-LOC103405591, gene-LOC103403337, gene-LOC114821135, gene-LOC114821133, gene-LOC103454980), one 4CL gene (gene-LOC103426517), and transcription factors WRKY (gene-LOC103427630), MYB (gene-LOC103434665), and bHLH (gene-LOC103422512) upregulated in G9 were consistent with the RNA-Seq results. These validation results further support the crucial role of these genes in the biosynthesis of flavonoids.

[0102] 5. Conclusion

[0103] This invention conducted metabolomics and transcriptomics comparative analyses on two large-fruited hawthorn germplasms, G8 and G9, with significant differences in total flavonoid content, screening out significantly differentially expressed metabolites, genes, and transcription factors, and revealing the associations among them through joint analysis. Finally, after screening and verification by qRT-PCR, it was confirmed that 6 HCT genes and 1 4CL gene are involved in and actively affect the biosynthesis of [6]-gingerol. At the same time, 1 each of WRKY, MYB, and bHLH transcription factor genes were identified, which may promote the biosynthesis of [6]-gingerol by activating the expression of HCT (gene-LOC103454980). This study provides gene resources and theoretical basis for elucidating the molecular mechanism of flavonoid biosynthesis in large-fruited hawthorn, carrying out excellent breeding, and developing new health care products.

[0104] The above description is a detailed description of the preferred embodiments of the present invention. However, the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modifications made under the technical spirit of the present invention should fall within the patent scope covered by the present invention.

Claims

1. A key gene for the biosynthesis of flavonoids in large-fruited hawthorn, characterized in that, The key genes mentioned include: (a) Structural genes: gene-LOC103405591, gene-LOC114821133, gene-LOC114821135, gene-LOC103403337, gene-LOC103409539, gene-LOC103454980, gene-LOC103426517; (b) Transcription factor genes: gene-LOC103427630, gene-LOC103434665 and gene-LOC103422512; The sequences of gene-LOC103405591, gene-LOC114821133, gene-LOC114821135, gene-LOC103403337, gene-LOC103409539, gene-LOC103454980, and gene-LOC103426517 are shown in SEQ ID NO. 1, 2, 3, 4, 5, 6, and 7, respectively; the sequences of gene-LOC103427630, gene-LOC103434665, and gene-LOC103422512 are shown in SEQ ID NO. 8, 9, and 10, respectively. The key gene described above is used to increase the content of flavonoid compound [6]-gingerol in hawthorn fruit.

2. The application of the key gene described in claim 1 in screening large-fruited hawthorn varieties with high flavonoid content, wherein the flavonoid is [6]-gingerol.

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