A screening method for wheat seed storage-related genes

Abstract

The application provides a screening method for a wheat seed storage-related gene, and belongs to the field of agricultural biotechnology.The screening method comprises the following steps: 1) subjecting seeds of a storage-tolerant variety N38 and a storage-intolerant variety N5 to artificial aging treatment, taking the seed embryos after germination; 2) subjecting the seed embryos to metabolomics analysis to obtain differential metabolites; 3) extracting total RNA of the seed embryos, performing transcriptome sequencing, and screening candidate differential expression genes; and 4) screening the candidate differential expression genes associated with the obtained differential metabolites to obtain final screening differential expression genes.The application subjects known storage-tolerant and storage-intolerant wheat varieties to artificial aging, performs metabolomics detection and transcriptome sequencing, screens differential metabolites and candidate differential expression genes, and further analyzes to obtain a wheat seed storage-related gene, thereby providing key gene resources and technical support for wheat storage-tolerant molecular breeding.

Description

Technical Field

[0001] This invention belongs to the field of agricultural biotechnology, and in particular relates to a method for screening genes related to wheat seed storage tolerance. Background Technology

[0002] Wheat is an important staple food crop, accounting for over 22% of total grain production annually. Its storage performance directly affects seed viability and sowing quality, as well as processing and food quality. The annual loss rate of wheat during storage due to aging, mold, and pests is as high as 6.62%, with losses being even more severe in hot and humid regions. Wheat seed storage tolerance refers to its ability to maintain viability and resist deterioration under certain storage conditions, and is one of the core agronomic traits of wheat. Traditional storage techniques (controlled atmosphere storage, ventilation) can only delay deterioration and cannot address seed aging at the genetic level. Therefore, identifying key genes for storage tolerance and cultivating new storage-tolerant varieties are fundamental ways to reduce post-harvest losses and ensure food security.

[0003] Currently, the screening of wheat varieties with storage tolerance mainly relies on phenotypic screening. Phenotypic screening involves artificial aging treatment with high temperature and humidity, followed by measurement of indicators such as germination rate, vigor index, and electrical conductivity to screen for storage-tolerant materials. Its advantages are simplicity and intuitiveness; however, its disadvantages include only screening materials, not locating genes, and significant susceptibility to environmental influences, long processing time, and low efficiency. Candidate gene screening is based on storage tolerance homologous genes from model plants such as Arabidopsis and rice, screening for candidate genes in wheat such as LOX, LEA, SOD, POD, and DNA repair. Among these, lipoxygenase (LOX) has been studied most extensively; silencing LOX can significantly reduce lipid oxidation and prolong storage tolerance. However, this method relies on known genes, making it difficult to discover new genes, and it ignores intergenetic synergistic regulation. Summary of the Invention

[0004] In view of this, the purpose of this invention is to provide a method for screening wheat seed storage-related genes. This invention involves artificially aging known storage-resistant and non-storage-resistant wheat varieties, collecting embryo samples from seeds aged at different times, performing metabolomics sequencing and transcriptomics sequencing, screening for differentially expressed metabolites and candidate differentially expressed genes, and obtaining wheat seed storage-related genes through further analysis.

[0005] This invention provides a method for screening genes related to wheat seed storage tolerance, comprising the following steps: 1) The seeds of the storage-resistant variety N38 and the non-storage-resistant variety N5 were subjected to artificial aging treatment, and the embryos were taken after germination; 2) Using the embryos described in step 1) as samples, perform metabolomics analysis to obtain differentially expressed metabolites; 3) Extract total RNA from the embryos described in step 1), perform transcriptome sequencing, screen for candidate differentially expressed genes, and perform enrichment analysis and functional annotation of candidate differentially expressed genes based on KEGG and GO. 4) Screening candidate differentially expressed genes associated with the differentially expressed metabolites obtained in step 2) to obtain the final screened differentially expressed genes.

[0006] Preferably, in step 2), metabolomics detection is performed using a UPLC-ESI-MS system and a tandem mass spectrometry system for PLC-MS / MS; metabolomics data are obtained, and principal component analysis and hierarchical cluster analysis are performed on the obtained metabolomics data.

[0007] Preferably, metabolites with a VIP value > 1 and |Log2FC| ≥ 1.0 are selected as differential metabolites.

[0008] Preferably, the screening criteria for candidate differentially expressed genes in step 3) are: |log2 (FC)|≥1 and false discovery rate FDR≤0.05.

[0009] Preferably, the temperature of the artificial aging treatment in step 1) is 38~42℃, the relative humidity is 72%~77%, and the time of the artificial aging treatment is 0~22 days.

[0010] Compared with existing technologies, this invention has the following beneficial effects: This invention provides a method for screening wheat seed storage-related genes. By artificially aging known storage-tolerant and non-storage-tolerant wheat varieties, collecting embryo samples from seeds aged at different times, performing metabolomics detection and transcriptome sequencing, screening for differentially expressed metabolites and candidate differentially expressed genes, and obtaining wheat seed storage-related genes through further analysis. This invention is a multi-omics integrated, epigenetic-gene linkage, precise, efficient, and scalable method for screening storage-tolerant genes, providing key gene resources and technical support for molecular breeding of wheat storage tolerance. Attached Figure Description

[0011] Figure 1 A classification diagram of differentially metabolites; Figure 2 KEGG enrichment bubble chart for N38-90_vs_N38-Ck; Figure 3 The results of qRT-PCR for differentially expressed genes in each N5 treatment; Figure 4 The results of qRT-PCR for differentially expressed genes in different N38 treatments are shown. Detailed Implementation

[0012] The technical solutions provided by the present invention will be described in detail below with reference to the embodiments, but they should not be construed as limiting the scope of protection of the present invention.

[0013] This invention provides a method for screening genes related to wheat seed storage tolerance, comprising the following steps: 1) artificially aging seeds of storage-tolerant variety N38 and non-storage-tolerant variety N5, and extracting embryos after germination; 2) performing metabolomics analysis on the embryos obtained in step 1) to obtain differentially expressed metabolites; 3) extracting total RNA from the embryos obtained in step 1), performing transcriptome sequencing, screening candidate differentially expressed genes, and performing enrichment analysis and functional annotation of candidate differentially expressed genes based on KEGG and GO; 4) screening candidate differentially expressed genes associated with the differentially expressed metabolites obtained in step 2) to obtain the final screened differentially expressed genes.

[0014] In this invention, seeds of the storage-resistant variety N38 and the non-storage-resistant variety N5 are subjected to artificial aging treatment, and after germination, the embryos are collected. In this invention, the temperature for the artificial aging treatment is preferably 38-42℃, more preferably 39-41℃, and most preferably 40℃; the relative humidity is preferably 72%-77%, more preferably 73%-76%, and most preferably 75%. The artificial aging treatment time is 0 days, 13 days, 16 days, and 22 days. In this invention, the germination temperature is 18-22℃, more preferably 19-21℃, and most preferably 20℃; the germination time is preferably 20-28 hours, more preferably 22-26 hours, and most preferably 24 hours. After germination, the embryos are harvested, flash-frozen in liquid nitrogen, and then stored in a -80℃ freezer.

[0015] In this invention, the embryos are used as samples for metabolomics analysis to obtain differentially expressed metabolites. Metabolomics detection is performed using a UPLC-ESI-MS system and a tandem mass spectrometry system (PLC-MS / MS). Analyst 1.6.3 software based on the Metawell database is preferred for qualitative and quantitative analysis. The metabolomics data obtained are then subjected to principal component analysis (PCA) and hierarchical cluster analysis. Principal component analysis (PCA) is performed using the statistical function prcomp in R (w-project.org). Prior to PCA analysis, the data are preferably standardized to unit variance. Hierarchical clustering analysis (HC) and Pearson correlation coefficient (PCC) were performed using the R package ComplexHeatmap to explore metabolite abundance patterns. This invention uses orthogonal partial least squares discriminant analysis on metabolomics data to screen metabolites with a VIP value > 1 and |Log2FC| ≥ 1.0 as differential metabolites. The VIP values ​​used in this invention are extracted from the OPLS-DA results, which also include score plots and permutation plots generated by the R package MetaboAnalystR. Before OPLS-DA analysis, the data underwent Log2 transformation and mean centering. To avoid overfitting, a permutation test (200 permutations) was performed.

[0016] In this invention, total RNA is extracted from the embryos, and transcriptome sequencing is performed to screen for candidate differentially expressed genes. Enrichment analysis and functional annotation of these candidate genes are then conducted using KEGG and GO. Preferably, the transcriptome sequencing is performed by a contract biotechnology company. The sequencing results are then compared with the reference genome Triticum_aestivum.IWGSC.dna.toplevel.fa.gz, and functional annotation is performed.

[0017] In this invention, PCA analysis was performed on the transcriptome of all samples. DEGs were analyzed using DESeq2 software, with |log2(FC)|≥1 and a false discovery rate (FDR)≤0.05 as the screening criteria for differentially expressed genes. Enrichment analysis and functional annotation were performed on the selected DEGs based on the GO and KEGG databases.

[0018] Based on the differentially expressed metabolites and differentially expressed genes identified in this invention, KEGG pathways co-annotated by both omics studies were found. If both metabolomics and transcriptomics studies had MetMap pathway annotation (enrichment) results, the co-annotated MetMap pathway results were added to the shared KEGG pathway analysis results. Then, KEGG pathway analysis was performed. For KEGG or MetMap pathways co-annotated by the same comparative group, differentially expressed metabolites and differentially expressed genes were simultaneously mapped onto the pathway map to better understand the relationship between genes and metabolites.

[0019] This invention screens candidate differentially expressed genes associated with the aforementioned differentially expressed metabolites, and then screens lipoxygenase-related genes from them to obtain the final screened differentially expressed genes.

[0020] Example 1

[0021] I. Experimental Materials

[0022] Screening of experimental materials: Three hundred and fifty wheat samples were subjected to 12 days of accelerated aging in a high-temperature and high-humidity environment (temperature: 40℃, relative humidity: 75%), followed by germination. Based on the changes in germination rate, two samples were selected as resistant to storage and two as intolerant to storage. Four samples were then subjected to accelerated aging again in a high-temperature and high-humidity environment (temperature: 40℃, relative humidity: 75%) for 0, 3, 6, 9, 12, 15, 18, 21, 24, 27, and 30 days. Based on the germination rate of the aged samples at different durations, the intolerant variety Duofeng 2000 and the resistant variety Yumai 11 were identified as having significant differences in germination rate.

[0023] Two wheat seed samples (Duofeng 2000, hereinafter referred to as the non-storage-resistant N5, and Yumai 11, hereinafter referred to as the storage-resistant N38) were artificially aged (40℃, 75%RH) to obtain two germplasm CK samples (aging 0 days) and N5 samples with germination rates of 85% (aging 10 days), 50% (aging 13 days), and 10% (aging 16 days); and N38 samples with germination rates of 90% (aging 13 days), 85% (aging 16 days), and 50% (aging 22 days). Approximately 500 seeds from each gradient of N5 and N38 were divided into three replicates, placed in germination boxes with three layers of filter paper, and germinated for 24 hours in an artificial climate chamber at 20℃. After germination, the embryos were extracted. The seeds were fixed with tweezers, and the seed coat was carefully cut open with a scalpel to extract the wheat seed embryos. The embryos were placed in 2ml centrifuge tubes, flash-frozen in liquid nitrogen, and then stored at -80℃.

[0024] II. Metabolomics Sequencing

[0025] To ensure data consistency and experimental traceability, both RNA-seq and metabolite analyses used the same batch of artificially aged seed samples. Two g of seed embryo material from each gradient (N5 and N38) stored at -80°C was weighed and divided into three equal replicates. Metabolomics analysis was performed by Wuhan Metawell Biotechnology Co., Ltd. (Wuhan, China). Vacuum freeze-drying was employed. Biological samples were placed in a freeze dryer (Ccientz-100F) and then ground into powder using a grinder (MM 400, Retsch) (30 Hz, 1 min). Subsequently, 50 mg of sample powder was weighed using an electronic balance (MS105DM) and 1200 μl of 70% methanol aqueous internal standard extraction buffer pre-cooled to -20°C was added (the ratio was 1200 μL of extractant per 50 mg of sample; the amount was reduced proportionally if the sample volume was less than 5 mg). The sample was vortexed for 30 seconds every 30 minutes for a total of 6 times. After centrifugation (12000 rpm for 3 minutes), the supernatant was aspirated, filtered through a 0.22 μm microporous membrane, and transferred to a sample vial. PLC-MS / MS analysis was performed using a UPLC-ESI-MS system (UPLC, ExionLC™ AD, https: / / sciex.com.cn / ) and a tandem mass spectrometry system (https: / / sciex.com.cn / ). Analyst 1.6.3 software based on the Metawell database was used for qualitative and quantitative analysis. Principal component analysis (PCA) was performed using the statistical function prcomp in R (w-project.org). Data were standardized to unit variance before PCA. Hierarchical cluster analysis (HC) and Pearson correlation coefficient (PCC) were performed using the R package ComplexHeatmap to explore metabolite abundance patterns. Differential metabolites were determined by VIP values ​​(VIP>1) and absolute Log2 FC values ​​(|Log2FC|≥1.0). VIP values ​​were extracted from the OPLS-DA results, which also included score plots and permutation plots, generated by the R package MetaboAnalystR. Prior to OPLS-DA analysis, the data underwent Log2 transformation and mean centering. To avoid overfitting, permutation tests (200 permutations) were performed.

[0026] Principal component analysis of the eight sample groups (three replicates per group) showed that the repetitive sequences of each variety clustered together, indicating good reproducibility. A clear separation trend was observed between groups, and significant differences in metabolite distribution were found among different groups. Qualitative and quantitative analyses detected a total of 2058 metabolites from the eight sample groups, encompassing 13 categories. These metabolites included 365 flavonoids, 293 amino acids and their derivatives, 221 lipids, 95 nucleotides and their derivatives, 250 alkaloids, 107 organic acids and their derivatives, 185 phenolic acids, and 122 terpenoids.

[0027] Before screening for differentially accumulated metabolites (DAMs), an OPLS-DA analysis was performed on eight groups. Pairwise comparisons using the OPLS-DA method showed significant differences in metabolites among the eight groups (Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA)). OPLS-DA validation showed that the Q2 values ​​were all greater than 0.9, indicating that the model has very good predictive ability, and the VIP value can be used to screen for metabolites with significant differences between different groups. Metabolites meeting the criteria of VIP ≥ 1.0 and Fold Change ≥ 2 or Fold Change ≤ 0.05 were considered DAMs.

[0028] A total of 908 metabolites were identified between aged and non-aged samples of the non-storage-tolerant germplasm N5, and 794 metabolites were identified between aged and non-aged samples of the storage-tolerant germplasm N38. These metabolites were classified into 13 categories, mainly including amino acids and their derivatives, flavonoids, alkaloids, lipids, phenolic acids, terpenes, nucleotides and their derivatives, quinones, lignans and coumarins, other metabolites, tannins, organic acids, and steroids. A total of 724 metabolites in 12 categories were identified from the eight samples of N5 and N38, including 155 flavonoids, 106 amino acids and their derivatives, 75 phenolic acids, 22 nucleotides and their derivatives, 4 quinones, 24 lignans and coumarins, 99 other metabolites, 1 tannin, 101 alkaloids, 33 terpenes, 36 organic acids, and 68 lipids. The 155 flavonoid metabolites included 7 chalcones, 12 dihydroflavones, 2 dihydroflavonols, 4 anthocyanins, 84 flavones, 29 flavonols, 1 flavanol, 7 other flavonoids, and 9 isoflavones. 584 key differentially expressed metabolites were found between samples N5 and N38. These differentially expressed metabolites are considered valuable candidate metabolites related to storage tolerance.

[0029] K-means analysis of differential metabolites

[0030] To further understand the changes in differential metabolites between storable and non-storable varieties under aging conditions, the relative contents of differential metabolites among all samples were standardized, centralized, and subjected to K-means cluster analysis. These differential metabolites were divided into four subclasses. The trends of change varied between different subclasses, but a consistent trend was observed within each subclass. Subclass 1 consisted of 296 metabolites, with flavonoids accounting for the largest proportion (60 species, 20.2%), followed by other metabolites at 14.5%, then amino acids and their derivatives, and alkaloids, each accounting for 12.8%. Subclass 2 consisted of 402 metabolites, with amino acids and their derivatives accounting for the largest proportion (98 species, 24.3%), followed by flavonoids at 17.1%. Category 3 consists of 563 metabolites, with lipids accounting for the largest proportion at 111 (19.7%), followed by other metabolites at 16.8%, and then alkaloids and amino acids and their derivatives at 10.4% and 10.1%, respectively. Category 4 consists of 334 metabolites, with flavonoids accounting for the largest proportion at 94 (28.1%), followed by alkaloids at 16.7%.

[0031] III. Transcriptome Sequencing

[0032] 0.6g of seed embryos from each gradient (N5 and N38) stored at -80℃ were weighed and divided into three equal replicates. Transcriptome sequencing was commissioned to Wuhan Maiwei Biotechnology Co., Ltd. The simplified procedure is as follows: Total RNA was isolated from the samples using Oligo(dT) magnetic beads, and then the mRNA was randomly fragmented. Using the fragmented mRNA as a template, double-stranded cDNA was synthesized under reverse transcriptase, followed by end repair and the addition of base A to form adapters. Library enrichment was performed by PCR amplification, and library preparation was carried out. The constructed libraries were then sequenced. Data containing adapters, N content exceeding 10% of the bases, and low-quality (Q≤20) bases exceeding 50% were filtered out to obtain high-quality sequencing results. Gene expression levels were expressed as FPKM (female-to-kilobase-per-million-bases) readings. The sequencing results were compared with the reference genome Triticum_aestivum.IWGSC.dna.toplevel.fa.gz (downloadable from: https: / / ftp.ensemblgenomes.ebi.ac.uk / pub / plants / release-56 / fasta / triticum_aestivum / dna / ) and functional annotations were performed.

[0033] PCA analysis was performed on the transcriptomes of all samples. DEGs were analyzed using DESeq2 software, with |log2(FC)|≥1 and false discovery rate (FDR)≤0.05 as the screening criteria for differentially expressed genes. Enrichment analysis and functional annotation were performed on the selected DEGs based on the GO and KEGG databases.

[0034] After filtering, a total of 2,852,931,624 clean reads were obtained from the transcriptome sequencing results of all samples. The alignment rate of each sample to the reference genome was 95.50%–96.26%. Each sample contained more than 14G of clean bases, and the GC content and Q30 base percentage were 52.05–53.97% and 96.44–96.81%, respectively, indicating that the transcriptome sequencing quality was good and could meet the requirements for subsequent data analysis.

[0035] Based on RNA-seq results, differentially expressed genes (DEGs) under artificial aging stress were screened from samples in different groups and comparisons using the screening criteria of |log2(FC)| ≥ 1 and false discovery rate (FD ≤ 0.05), resulting in a total of 3428 DEGs. These DEGs were identified in the following comparisons: N38-CK vs. N5-CK, N5-85 vs. N5-CK, N50 vs. N5-CK, N5-10 vs. N5-CK, N38-90 vs. N38-CK, N38-85 vs. N38-CK, N38-50 vs. N38-CK, N38-85 vs. N5-85, and N38-50 vs. N38-CK. In the N5-50 comparison, 6670 DEGs (3299 up, 3371 down), 15791 DEGs (6547 up, 9246 down), 17901 DGs (7344 up, 10557 down), 22181 DEGs (9755 up, 12426 down), 6904 DEGs (1989 up, 4915 down), 11479 DEGs (40 up, 7359 down), 20734 DEGs (8769 up, 11965 down), 858 DEGs (4040 up, 4818 down), and 7947 DEGs (3850 up, 67 down) were identified.

[0036] Furthermore, Venn diagrams revealed common and specific differentially expressed genes (DEGs) across the six comparison groups of N38, showing that only 250 DEGs were present in all six groups; similarly, for the six comparison groups of N5, only 7 DEGs were present in all six groups. The smaller number of common DEGs compared to specific DEGs indicates a distinct gene expression profile between storage-tolerant (ST) and non-storage-tolerant (SS) genotypes under artificial aging conditions, confirming their different response mechanisms.

[0037] 4. KEGG analysis of differentially expressed genes

[0038] To investigate the metabolic pathways involved by differentially expressed genes regulating wheat seed storage tolerance under different aging gradients, KEGG enrichment analysis was performed on differentially expressed genes in each group. For example, the differentially expressed genes in N38-CK vs N5-CK were mainly enriched in glycine, serine and threonine metabolism (ko00260), base excision repair (ko03410), amino acid biosynthesis (ko01230), alanine, aspartate and glutamate metabolism (ko00250), inositol phosphate metabolism (ko00562), flavonoid biosynthesis (ko00941), glutathione metabolism (ko00480), DNA replication (ko03030), and pyrimidine metabolism (ko00240). metabolism), protein processing in endoplasmic reticulum (ko04141).

[0039] A total of 908 differentially expressed metabolites were identified in the N5-CK vs N5-85 vs N5-50 vs N5-10 assay, mainly including amino acids and their derivatives, lipids, flavonoids, alkaloids, terpenes, phenolic acids, nucleotides and their derivatives, organic acids, lignans, and coumarins. A total of 794 differentially expressed metabolites were identified in the N38-CK vs N5-90 vs N5-85 vs N5-50 assay, mainly including amino acids and their derivatives, lipids, flavonoids, alkaloids, terpenes, phenolic acids, nucleotides and their derivatives, organic acids, lignans, and coumarins. Using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, possible metabolic pathways and their corresponding regulatory proteins were linked together, using metabolic reactions as clues. Based on the main types of differentially expressed metabolites, and after reviewing relevant literature on seed storage tolerance, differentially expressed genes were mainly distributed in metabolic pathways such as lipoxygenase, phenylpropanoid biosynthesis, glutathione metabolism, flavonoid biosynthesis, pyruvate metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, cysteine ​​and methionine metabolism, and arginine and proline metabolism. Genes related to lipoxygenase were screened, and a total of two differentially expressed genes were obtained, as shown in Table 1.

[0040] Table 1 List of 2 key genes

[0041] Verification experiment: In order to compare sequencing results using qRT-PCR expression profiling, this invention validates two selected key candidate genes using qRT-PCR.

[0042] Based on the combined analysis of transcriptional sequencing and metabolomics, two genes corresponding to wheat storage tolerance (genes related to lipoxygenase synthesis) were selected for quantitative real-time PCR (qRT-PCR) detection, and their gene expression levels were detected using the qRT-PCR method.

[0043] The 2-ΔΔCt method was used to process the qRT-PCR results, and the relative expression levels of genes were calculated. The doubling changes in candidate gene expression levels observed in qRT-PCR differed from those in transcriptome data, which is due to differences between the two detection methods. The expression trends of the two selected candidate genes in the qRT-PCR results are shown below. Figure 3 and Figure 4 The expression trend is consistent with that in the transcriptome data, indicating the reliability of the transcriptome data.

[0044] Seed lipoxygenase (LOX) has been reported in literature for its association with storage tolerance regulation in wheat, rice, soybean, and other crops. In wheat seeds, LOX catalyzes the oxidation of unsaturated fatty acids, producing hydroperoxides during seed storage, leading to a decrease in seed vigor and nutritional quality. This invention screens two genes related to LOX synthesis (…). TraesCS5D02G388700 and TraesCS5D02G513100) This gene will be used as an important candidate gene for further analysis to explore the molecular mechanisms related to wheat storage tolerance.

[0045] As can be seen from the above embodiments, the present invention provides a method for screening wheat seed storage-related genes. This method involves artificially aging known storage-tolerant and non-storage-tolerant wheat varieties, collecting embryo samples from seeds aged at different times, performing metabolomics analysis and transcriptome sequencing, screening for differentially expressed metabolites and candidate differentially expressed genes, and obtaining wheat seed storage-related genes through further analysis. The screening method described in this invention is a multi-omics integration and epigenetic linkage approach, enabling precise, efficient, and scalable screening of storage-tolerant genes, providing key gene resources and technical support for molecular breeding of wheat with storage tolerance.

[0046] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for screening genes related to wheat seed storage tolerance, characterized in that, Includes the following steps: 1) The seeds of the storage-resistant variety N38 and the non-storage-resistant variety N5 were subjected to artificial aging treatment, and the embryos were taken after germination; 2) Using the embryos described in step 1) as samples, perform metabolomics analysis to obtain differentially expressed metabolites; 3) Extract total RNA from the embryos described in step 1), perform transcriptome sequencing, screen for candidate differentially expressed genes, and perform enrichment analysis and functional annotation of candidate differentially expressed genes based on KEGG and GO. 4) Screening candidate differentially expressed genes associated with the differentially expressed metabolites obtained in step 2) to obtain the final screened differentially expressed genes.

2. The screening method according to claim 1, characterized in that, Step 2) Metabolomics detection was performed using a UPLC-ESI-MS system and a tandem mass spectrometry system for PLC-MS / MS; metabolomics data were obtained, and principal component analysis and hierarchical cluster analysis were performed on the obtained metabolomics data.

3. The screening method according to claim 2, characterized in that, Metabolites with a VIP value > 1 and |Log2FC| ≥ 1.0 are considered differential metabolites.

4. The screening method according to claim 1, characterized in that, The screening criteria for candidate differentially expressed genes in step 3) are: |log2 (FC)|≥1 and false discovery rate FDR≤0.

05.

5. The screening method according to claim 1, characterized in that, Step 1) The temperature of the artificial aging treatment is 38~42℃, the relative humidity is 72%~77%, and the time of the artificial aging treatment is 0~22 days.

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