Method for screening drought-resistant tea germplasm and application

Abstract

The invention relates to a method for screening drought-resistant tea germplasm and application. The method for screening the drought-resistant tea tree germplasm comprises the following steps: step 1, performing repeated drought treatment on a tea tree, and measuring phenotype, metabolome data and transcriptome data of a sample; 2, screening the differential metabolite from the metabolome data, wherein the expression mode of the differential metabolite is that the differential metabolite does not respond or has no significance in response when the differential metabolite is subjected to drought for the first time and has significant response after multiple times of repeated drought, and taking the differential metabolite as a memory metabolite; 3, screening and analyzing differential genes in the tea trees subjected to repeated drought treatment: screening memory key genes from the differential genes; and step 4, according to the transcriptional level of the memory key genes in the tea tree leaves, determining the key genes with high transcriptional level as the drought-resistant tea tree germplasm. The 9 memory key genes are verified, the result shows that the selected gene is consistent with the transcriptome sequencing result, and the gene has good sensitivity, specificity and accuracy in screening drought-resistant tea trees or improving the drought resistance of the tea trees.

Description

Technical Field

[0001] The invention relates to the technical field of genetic breeding, and in particular to a method for screening drought-resistant tea tree germplasm and its application. Background Art

[0002] Drought is an important environmental factor that restricts the growth of tea trees, and the losses it causes to agriculture are greater than the sum of other environmental stresses. Under natural conditions, drought is a repeated and continuous event. In this environmental context, plants must evolve more efficient drought-tolerant mechanisms to cope with this repeated drought stress. The expression pattern of genes under repeated stress is the "stress memory" of the plant. Genes that respond to stress and respond similarly to each stress are defined as "non-memory" genes, and those that respond differently to subsequent stresses are defined as "memory" genes. Epigenetics is an important mechanism for the formation of "stress memory". Chromatin remodeling also plays an important role in the "memory" mechanism of plants' response to repeated stress.

[0003] However, the existing research on tea tree drought tolerance often regards drought stress as a single event that only occurs once, and there is no prior art that studies tea tree response to periodic drought stress. For example, the prior art CN115948602A discloses screening differentially expressed genes and obtaining differentially expressed gene sequences based on the gene expression levels involved in a drought stress of tea trees; SSR and ILP molecular marker screening is associated with differentially expressed genes of tea tree drought stress, and drought stress molecular marker primers of tea trees are obtained. Summary of the invention

[0004] The purpose of the invention is to provide a method for screening drought-resistant tea tree germplasm and its application.

[0005] In order to achieve the above object, the present invention adopts the following technical solution: A method for screening drought-resistant tea tree germplasm, comprising the following steps: Step 1: After the tea trees are repeatedly subjected to drought treatment, new shoot leaves are taken as samples; the phenotype, metabolome data, and transcriptome data of the samples are measured; Step 2: Screen the metabolites from the metabolome data for differential metabolites whose expression patterns showed no response or no significant response to the first drought, but showed significant response after repeated droughts as “memory” metabolites; Step 3: Screening and analyzing differentially expressed genes in tea plants subjected to repeated drought treatments: Screening out key memory genes from differentially expressed genes; Step 4: Based on the transcription level of the key memory gene in tea leaves, determine that the tea germplasm with high transcription level is drought-resistant tea germplasm.

[0006] In one preferred embodiment, repeated drought treatment means that after the drought treatment reduces the soil moisture content by no more than 10%, rehydration treatment is performed; after the rehydration treatment, the next round of drought treatment is performed; the number of repeated treatments is no less than 2 times.

[0007] In one preferred embodiment, in step 2, when screening differential metabolites, metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 are defined as differential metabolites, and the relative contents of all differential metabolites are z-score standardized, followed by K-means cluster analysis; KEGG enrichment analysis is performed on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways in which the differential metabolites are involved.

[0008] In one of the preferred embodiments, step 2 also includes aligning the transcriptome data with the reference genome and evaluating the quality; the step of aligning the transcriptome data with the reference genome is: using HISAT2 to align the Clean Reads with the reference genome to obtain the position information on the reference genome or gene, as well as the sequence feature information specific to the sequencing sample.

[0009] In step 2, the result of the transcriptome data quality assessment can reflect the quality of the transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be performed.

[0010] In one preferred embodiment, in step 3, the conditions for differential gene screening are |log2Fold Change|>1 and FDR<0.05.

[0011] In one preferred embodiment, in step 3, the analysis of differentially expressed genes is: performing K-means cluster analysis, GO enrichment analysis and KEGG enrichment analysis on the differentially expressed genes; further specifically, it refers to: screening out GO and gene key gene pathways with P values ​​less than 0.05, GO annotations explain the basic functions of genes, and KEGG explains the metabolic pathways in which genes participate.

[0012] In one preferred embodiment, in step 3, the screening criteria for key memory genes are: genes that do not respond when the tea tree is subjected to drought for the first time, but have a significant response after repeated droughts.

[0013] In one of the preferred embodiments, step 3 also includes verification of key memory genes, and the verification includes selecting genes for RT-qPCR to verify the reliability of transcriptome data; using the RT-qPCR method to analyze the expression levels of key memory genes under drought; and using R language to analyze the reliability verification of key memory genes as markers.

[0014] In a preferred embodiment, the key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

[0015] The present invention sets up multiple rounds of repeated drought stress treatment to dig out "memory key genes" in tea trees that did not respond when they were first subjected to drought but only responded significantly after multiple repeated droughts, and verifies these genes as drought markers for application in the screening of drought-resistant tea tree varieties.

[0016] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.1 is greater than 13.01, the tea tree is determined to be a drought-resistant tea tree.

[0017] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO. 2 is greater than 15.14, the tea tree is determined to be a drought-resistant tea tree.

[0018] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO. 3 is greater than 14.18, the tea tree is determined to be a drought-resistant tea tree.

[0019] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.4 is greater than 13.65, the tea tree is determined to be a drought-resistant tea tree.

[0020] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.5 is greater than 14.05, the tea tree is determined to be a drought-resistant tea tree.

[0021] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.6 is greater than 13.54, the tea tree is determined to be a drought-resistant tea tree.

[0022] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.7 is greater than 14.40, the tea tree is determined to be a drought-resistant tea tree.

[0023] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.8 is greater than 15.63, the tea tree is determined to be a drought-resistant tea tree.

[0024] In a preferred embodiment, when the transcription level of the gene shown by SEQ ID NO.9 is greater than 14.36, the tea tree is determined to be a drought-resistant tea tree.

[0025] Based on the same inventive concept, the present invention also claims a method for screening key memory genes that respond to periodic drought stress, comprising the following steps: Step 1: After the tea trees are repeatedly subjected to drought treatment, new shoot leaves are taken as samples; the phenotype, metabolome data, and transcriptome data of the samples are measured; Step 2: Screen the metabolites from the metabolome data for differential metabolites whose expression patterns showed no response or no significant response to the first drought, but showed significant response after repeated droughts as “memory” metabolites; Step 3: Screening and analyzing differentially expressed genes in tea trees subjected to repeated drought treatments: Screening out key memory genes from the differentially expressed genes.

[0026] In one preferred embodiment, in step 2, when screening differential metabolites, metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 are defined as differential metabolites, and the relative contents of all differential metabolites are z-score standardized, followed by K-means cluster analysis; KEGG enrichment analysis is performed on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways in which the differential metabolites are involved.

[0027] In one of the preferred embodiments, step 2 also includes aligning the transcriptome data with the reference genome and evaluating the quality; the step of aligning the transcriptome data with the reference genome is: using HISAT2 to align the Clean Reads with the reference genome to obtain the position information on the reference genome or gene, as well as the sequence feature information specific to the sequencing sample.

[0028] In step 2, the result of the transcriptome data quality assessment can reflect the quality of the transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be performed.

[0029] In one preferred embodiment, in step 3, the conditions for differential gene screening are |log2Fold Change|>1 and FDR<0.05.

[0030] In one preferred embodiment, in step 3, the analysis of differentially expressed genes is: performing K-means cluster analysis, GO enrichment analysis and KEGG enrichment analysis on the differentially expressed genes; further specifically, it refers to: screening out GO and gene key gene pathways with P values ​​less than 0.05, GO annotations explain the basic functions of genes, and KEGG explains the metabolic pathways in which genes participate.

[0031] In one preferred embodiment, in step 3, the screening criteria for key memory genes are: genes that do not respond when the tea tree is subjected to drought for the first time, but have a significant response after repeated droughts.

[0032] In one of the preferred embodiments, step 3 also includes verification of key memory genes, and the verification includes selecting genes for RT-qPCR to verify the reliability of transcriptome data; using the RT-qPCR method to analyze the expression levels of key memory genes under drought; and using R language to analyze the reliability verification of key memory genes as markers.

[0033] In a preferred embodiment, the key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

[0034] Based on the same inventive concept, the present invention also claims to protect the use of photosynthesis-related genes in screening drought-resistant tea trees, wherein the photosynthesis-related genes are selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

[0035] In a preferred embodiment, the photosynthesis-related gene is the gene shown in SEQ ID NO.4.

[0036] In a preferred embodiment, the photosynthesis-related gene is the gene shown in SEQ ID NO.7.

[0037] A kit for screening drought-resistant tea trees comprises a reagent for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

[0038] In a preferred embodiment, the reagent for detecting the transcription level of photosynthesis-related genes includes primers, TaqMan probes or gene chips for detecting photosynthesis-related genes.

[0039] In a preferred embodiment, the primers for detecting photosynthesis-related genes include any one or more of the following primer pairs: Primer pair for detecting the gene shown in SEQ ID NO.1: SEQ ID NO.10: AGGCTTTTGCAGAGCTCAAG; SEQ ID NO.11: CATAAGCCCATGCATTGTTG; Primer pair for detecting the gene shown in SEQ ID NO.2: SEQ ID NO.12: TTGTATTTGGGCCCACTCTC; SEQ ID NO.13: CACCGAACTTGACACCATTG; Primer pair for detecting the gene shown in SEQ ID NO.3: SEQ ID NO.14: GTTCGGTGAGGCTGTATGGT; SEQ ID NO.15: TAGAGTGGGTCGGTCACCTC; Primer pair for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16: TTGAATTCCTTGCCATCTCC; SEQ ID NO.17: AACACAGATCCCCCACGAAAG; Primer pair for detecting the gene shown in SEQ ID NO.5: SEQ ID NO.18: AGCCTATGGCGAGATCTTCA; SEQ ID NO.19: GGGTCAGCCCAATAGTCGTA; Primer pair for detecting the gene shown in SEQ ID NO.6: SEQ ID NO.20: ATATTCTCGGCGGTTCCTTT; SEQ ID NO.21: AGCCGTAGTCCCCTGGTAGT; Primer pair for detecting the gene shown in SEQ ID NO.7: SEQ ID NO.22: GGTGTCACCGGAATGCTACT; SEQ ID NO.23: AGGGTAGCCACACTCATTGG; Primer pair for detecting the gene shown in SEQ ID NO.8: SEQ ID NO.24: GTACGGTCCCGACAGAAGAA; SEQ ID NO.25: GGCCACAGTTAGCACCAAAT; The primer pair for detecting the gene shown in SEQ ID NO.9: SEQ ID NO.26: GTGTTTGGGTTGCAGAGGTT; SEQ ID NO.27: AAGCTCAGCATTCCTTTGGA.

[0040] In a preferred embodiment, the primers for detecting photosynthesis-related genes include a primer pair for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16: TTGAATTCCTTGCCATCTCC; SEQ ID NO.17: AACACAGATCCCCCACGAAAG.

[0041] In a preferred embodiment, the primers for detecting photosynthesis-related genes include a primer pair for detecting the gene shown in SEQ ID NO.7: The primer pair for detecting the gene shown in SEQ ID NO.7 is as follows: SEQ ID NO.22: GGTGTCACCGGAATGCTACT; SEQ ID NO.23: AGGGTAGCCACACTCATTGG.

[0042] A method for identifying the drought resistance of plant germplasm, using the kit to detect the transcription level of any one or several genes shown in SEQ ID NO.1-SEQ ID NO.9 in the plant, and the plant germplasm with high transcription level is a drought-resistant plant.

[0043] Based on the same inventive concept, the present invention also claims a preparation for improving plant drought resistance, comprising at least one of the following i)-v): i) a nucleic acid molecule as shown in any one or more of SEQ ID NO.1 to SEQ ID NO.9; ii) an expression vector comprising any one or more of the nucleic acid molecules shown in SEQ ID NO.1 to SEQ ID NO.9; iii), a recombinant host containing ii); iv), a promoter or enhancer that enhances the expression of any one or more genes shown in SEQ ID NO.1 to SEQ ID NO.9; v) an inducer that promotes the expression of any one or more genes shown in SEQ ID NO.1 to SEQ ID NO.9.

[0044] Based on the same inventive concept, the present invention also claims to protect the use of the preparation in improving the drought resistance of plants.

[0045] Based on the same inventive concept, the present invention also claims a method for improving the drought resistance of plants, using the preparation to improve the level and / or activity of any one or more endogenous genes in SEQ ID NO.1-SEQ ID NO.9.

[0046] According to an embodiment of the present invention, the expression inhibition of the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in plants can reduce the drought tolerance of plants. Overexpression of the gene improves the drought tolerance of tea trees, and inhibiting the expression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in tea trees by antisense oligonucleotide technology can significantly aggravate drought damage to tea trees.

[0047] The beneficial technical effects of the present invention are: The present invention is based on multi-omics technology to screen out "key memory genes" in tea trees that did not respond when they were first subjected to drought, but only responded significantly after repeated droughts. Combined with the results of the pathways enriched with differential metabolites and the bioinformatics analysis of differentially expressed genes, important key genes for drought memory in tea trees are accurately and efficiently mined and identified. The experimental results show that the metabolic pathways for the common enrichment of differential metabolites under different rounds of drought treatment are amino acid biosynthesis and aminoacyl-tRNA biosynthesis. Typical key genes for drought memory in tea trees are mainly the genes shown in SEQ ID NO.1-SEQ ID NO.9. By selecting 9 key memory genes for RT-qPCR verification, it is shown that the expression pattern of the selected genes is consistent with the transcriptome sequencing results, indicating that the sequencing results of the experiment of the present invention and the screened key memory genes are correct. In the present invention, LHC tea photosynthesis genes (i.e., LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9) that regulate tea tree response to drought stress are cloned and verified for the first time. These genes achieve regulatory effects in the process of tea tree response to drought stress, affecting the formation of drought resistance of tea tree. The present invention also provides recombinant plasmids and transgenic engineering bacteria containing LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes. The present invention lays a foundation for seeking reasonable regulatory measures and screening suitable drought-resistant breeding materials for tea trees subjected to high-frequency and repeated drought stress. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 Schematic diagram of repeated drought stress experiment, in which CYCLE 1 is the first round of drought stress treatment; CYCLE2 is the second round of drought stress treatment; CYCLE3 is the third round of drought stress treatment.

[0049] Figure 2 This is a diagram of the anatomical structure and parameters of tea leaves under repeated drought stress (×36.5); in the figure, Figure 2 A is the anatomical structure of tea leaves in the control group; Figure 2 B is the anatomical structure of tea leaves after a single drought treatment; Figure 2 C is the anatomical structure of tea leaves treated with two rounds of drought; Figure 2 D is the anatomical structure of tea leaves after three rounds of drought treatment.

[0050] Figure 3 Figure 3 shows the stomatal morphology and parameters of tea leaves under repeated drought stress (×300). Figure 3 A is the stomatal morphology of tea leaves in the control group; Figure 3 B is the stomatal morphology of tea leaves in the single-round drought treatment group; Figure 3C is the stomatal morphology of tea leaves in the two-round drought treatment group; Figure 3 D is the stomatal morphology of tea leaves in the three-round drought treatment group; Figure 3 E is the stomatal density of tea leaves under repeated drought stress; Figure 3 F is the long axis length of tea leaves under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; D3 is the three-round drought treatment group.

[0051] Figure 4 This is a volcano plot of differential metabolites in tea tree shoots between the CK group and the D1 group under repeated drought stress.

[0052] Figure 5 This is a volcano plot comparing the differential metabolites of tea tree shoots between the CK group and the D2 group under repeated drought stress.

[0053] Figure 6 This is a volcano plot of differential metabolites in tea tree shoots between the CK group and the D3 group under repeated drought stress.

[0054] Figure 7 This is a volcano plot of differential metabolites in tea tree shoots in groups D1 and D2 under repeated drought stress.

[0055] Figure 8 This is a volcano plot of differential metabolites in tea tree shoots in groups D1 and D3 under repeated drought stress.

[0056] Fig. 9 This is a volcano plot of differential metabolites in tea tree shoots in groups D2 and D3 under repeated drought stress.

[0057] Fig.10 This is a K-means cluster analysis diagram of differential metabolites under repeated drought stress; in the figure, Fig.10 A is the metabolite that showed an increasing trend under repeated drought stress; Fig.10 B is a metabolite that showed a decreasing trend under repeated drought stress; Fig.10 C is a metabolite that first increased and then decreased under repeated drought stress.

[0058] Fig.11 This is the volcano map of differentially expressed genes in tea plants of CK and D1 groups under repeated drought stress.

[0059] Fig.12 This is the volcano map of differentially expressed genes in tea plants of CK group and D2 group under repeated drought stress.

[0060] Fig.13 This is the volcano map of differentially expressed genes in tea plants in the CK and D3 groups under repeated drought stress.

[0061] Fig.14 This is the volcano map of differentially expressed genes in tea trees in groups D1 and D2 under repeated drought stress.

[0062] Fig.15 This is the volcano map of differentially expressed genes in tea trees in groups D1 and D3 under repeated drought stress.

[0063] Fig.16 This is the volcano map of differentially expressed genes in tea trees in groups D2 and D3 under repeated drought stress.

[0064] Fig.17 This is a heat map of differential expression of photosynthesis-related genes and light-harvesting complex binding protein-related genes; among them, Fig.17 A is the heat map of differential expression of photosynthesis-related genes; Fig.17 B is the heat map of differential expression of light-harvesting complex binding protein genes.

[0065] Fig.18 RT-qPCR was used to verify the expression pattern of key memory genes under repeated drought stress.

[0066] Fig.19 ROC validation of gene markers for key drought memory genes in the breeding of drought-resistant tea varieties. DETAILED DESCRIPTION

[0067] The present invention is not limited to the following specific embodiments. A person skilled in the art can implement the present invention in various other specific embodiments according to the contents disclosed in the present invention, or any simple changes or modifications made to the design structure and ideas of the present invention fall within the protection scope of the present invention. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0068] The genes involved in the present invention are: SEQ ID NO.1: LHCP1 (TEA009770) ATGGCTGCGTCTACCATGGCTCTCTCTTCACCATCTTTTGCCGGAAAGGCTGTGAAAATTGCCCCGGAGGTTCTTGGTGGTGGAAGGATCAGTATGAGGAAGACCGGCAAGCAAGTCCCATCTGGAAGCCCGTGGTACGGTCCAGACCGAGTCTTGTATTTGGGTCCATTATCCGGTGAGCCCCCATCCTACCTCACTGGGCAATTCCCTGGTGATTATGGTTGGGACACTGCTGGGCTTTCGGCTGATCCAGAAACTTTTGCCAAGAACCGTGAGCTCGAGGTGATTCACTGCAGATGGGCCATGCTTGGAGCTTTGGGTTGCGTCTTCCCCGAGCTTTTGGCCCGCAACGGTGTCAAGTTCGGCGAGGCTGTGTGGTTCAAGGCCGGTGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCCCAAAGCATTCTGGCCATCTGGGCTTGCCAAGTTATCTTGATGGGTGCTGTTGAGGGCTACCGCATTGCTGGTGGACCACTCGGGGAGGTGACCGACCCGCTCTACCCCGGTGGAAGCTTCGACCCATTGGGCCTAGCCGATGATCCAGAGGCTTTTGCAGAGCTCAAGGTGAAGGAGATCAAGAATGGAAGACTGGCCATGTTCTCAATGTTTGGGTTCTTTGTTCAGGCAATTGTGACAGGAAAGGGACCATTGGAGAACCTGGCTGACCACCTTGCTGACCCTGTTAACAACAATGCATGGGCTTATGCCACAAACTTTGTCCCAGGAAAGTGA。

[0069] SEQ ID NO.2:LHCP2(TEA019232) ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0070] SEQ ID NO.3:LHCP3(TEA001864) ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAATATTGTACTGTCTAATGGGTTGTTTGGATACAGGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0071] SEQ ID NO.4:LHCP4(TEA008963) ATGGCTTCCAAAGCCCTAATGAGCTGCGGCATCGCCGCCGTCTGCCCGTCAGTCCTTTCCTCTTCCAAGTCCAAATTTGCCGCCGCGTTGCGGCTTCCAAGTGGTGGTGCCACCGCTACCTCCCGGTTCACCATGACGGCTGACTGGATGCCTGGCGAGCCAAGGCCACCCTATCTTGACGGCTCCGCACCCGGTGATTTCGGGTTCGACCCGCTTCGTCTGGGTGAAGTCCCAGAAAACCTTGAAAGATACAAGGAGTCTGAACTCATTCACTGCAGATGGGCTATGCTTGCTGTTCCAGGGATCCTAGTTCCAGAGGCCTTGGGATTGGGCAACTGGGTACAAGCTCAAGAGTGGGCGGCAATCCCTGGAGGACAAGCCACCTACCTTGGCCAACCTGTCCCATGGGGCACCCTCCCAATCATCTTGGCCATTGAATTCCTTGCCATCTCCTTCGTCGAGCACCAGCGCAGCATGGAAAAGGACCCTGAGAAGAAGAAGTACCCCGGTGGAGCTTTCGACCCATTGGGATACTCCAAAGACCCAGTAAAGTTTGAGGAGAACAAGGTCAAAGAAGTAAAAAATGGCCGGCTTGCCTTGTTGGCTTTCGTGGGGATCTGTGTTCAACAGTCCGCTTACCCAGGGACAGGACCGTTGGAGAACCTGGCAACTCACTTGGCTGATCCATGGCACAACAACATTGGCGATATCATTATCCCTAGATCAATTTCTCCATGA。

[0072] SEQ ID NO.5:LHCP5(TEA000535) ATGGCAACTCAAGCACTGGTGTCTTCATCATCTCTTACCTCCTCAGTGGAGGCTGCAAGGCAGATTCTAGGAGGAAGGCCAGCTACCCATTCTTCAAGAAGGAAGGTCTCTTTTGTTGTTAGGGCAGCTACTACTCCCCCTGTTAAGCAAGGAGCAGATAGACCTCTCTGGTTTGCCTCCAAGCAAAGTCTCTCCTACTTAGATGGCAGCCTGCCCGGCGACTACGGATTCGACCCGCTCGGCTTGTCCGACCCAGAAGGTACCGGAGGCTTCATCGAGCCCAGATGGCTAGCCTATGGCGAGATCTTCAACGGCCGTACCGCCATGGTCGGCTCTATCGGATGCATCGCCCCAGAAATCTTGGGCAAACTCGGCCTAATTCCGCCAGAAACCGCTCTGCCGTGGTTCAAAACAGGCGTGATCCCGCCCGCTGGGACCTACGACTATTGGGCTGACCCATACACTCTTTTTGTTTTCGAATTGGCACTAGTGGGCTTTGCAGAGCACAGGAGGTTCCAGGCTTGGTACAACCCAGGCTCAATGAGTAAACAGTACTTTTTGGGCCTGGAGAAATATTTGGGCGGGACGGATAACCCTGCATACCCTGGTGGGCCACTGTTTAACCCACTTGGGCTTGGAAAGGATGAGAAGTCAATGAGGGATATGAAGTTGAAGGAGGTAAAGAACGGGAGGTTGGCCATGTTGGGTATGTTGGGTTTCTTTGTGCAGGCGTTGGTGACTGGGGTTGGACCCTTCCAGAACCTTCTGGATCATTTGGCTGACCCTGTCAACAACAATGTCTTGACCAACCTCAAGTTCCACTAA。

[0073] SEQ ID NO.6:LHCP6(TEA015295) ATGGCGGAGGATCGTCGGAGGAACGTCGGAGCAAACGGTGGAGGCACGATGGAGGAACGACGGAGTAATGGTGGAGAGATGCTTGTGGGTGTCTTAATATTCTCGGCGGTTCCTTTCACGGCGGTGAAAGCTATAGCCAACAGTCCCCTGGGAGAGTTGCTTCAGAGGCGATTGGAAGAGAAAAAGAAGGATGCCATCGATAATTCTTCCAATTTCAAGGCACTTGCTCAAATGGCTAGAAAGGATAGTTTATGGTATGGAGAGAAGCGTCCCCGTTGGCTTGGTCCAATTTCATATGACTATCCTTCATATCTGACTGGAGAACTACCAGGGGACTACGGCTTTGATATTGCAGGTTTAAGCAGGGATCCTGTGGCTTTCCAGAAATATTTCAACTTTGAAATACTGCATGCTCGCTGGGCCATGCTTGCAGCGCTTGGTGCTCTGATTCCCGAACTATTAGACCTAGTAGGAGCCTTTCACTTTGTTGAGCCGGTCTGGTGGAAAGTTGGATATTCAAAGCTTAAGGGTGACACATTGGACTACCTTGGCATCCCTGGGCTCCACTTAGCTGGAAGTCAAGGAGTGATTGTTATAGCTATCTGCCAAGCTCTTCTGATGGTTGGACCGGAATATGCAAGATATTGTGGCATTGAGGCTCTCGAGCCTTTAGGAATTTACTTGCCTGGGGATATCAATTATCCTGGAGGTGCACTTTTCGATCCCTTGAATCTCTCTAAAGACCCGGTATCTTTTGAGGACTTGAAGGTGAAAGAGATAAAAAATGGGCGCTTAGCAATGATTGCATGGTTAGGATTTTACACGCAAGCTGCCCTAACGGGGAAAGGGCCTGTGCAAAACCTTCTTGACCACATCTTGGATCCTTTTCATAATAACCTGCTTTCCATTCTGAAATTCATGTGA。

[0074] SEQ ID NO.7:LHCP7(TEA016942) ATGGCCACCGTCGCAGCTCAGGCATCCACCACGGTTCTTCGGCCATGTGCCTCGAAATCGAGGTTCCTTACCGGTTCTTCCGGTAAGCTAAACCGAGTAATCTCATTTAAACCGACATCACCTTCCTCACTCAGCTCATTCAAAGTTGAAGCCAAGAAAGGAGAATGGTTACCGGGCTTGGCCTCCCCAGGCTATCTTAACGGCAGCCTACCTGGTGACAATGGGTTCGATCCCTTGGGGCTAGCCGAGGACCCAGAGAACTTGAAATGGTTCATCCAGGCCGAGCTTGTGAACAGTCGGTGGGCCATGTTGGGTGTCACCGGAATGCTACTGCCGGAAGTGTTGAGCAGTGTCGGAATAATCAACGTTCCAAAATGGTACGATGCAGGAAAATCCGAATACTTTGCATCATCATCGACACTTTTCGTGATCGAGTTCATCTTGTTTCACTACGTGGAGATCAGACGGTGGCAAGACATCAAGAACCCTGGAAGTGTCAACCAAGATCCTATCTTCAAGAGCTATAGCTTGCCTCCCAATGAGTGTGGCTACCCTGGTGGCATTTTTAACCCCCTCAACTTTGCTCCCACTGAGGAGGCCAAAGAGAAGGAGCTCGCTAATGGGAGATTGGCAATGTTGGCATTCTTGGGGTTTGTGGTTCAGCACAATGTGACTGGAAAAGGGCCATTTGACAACCTCTTGCAGCACATCTCTGATCCATGGCACAACACAATTATTCAAACAATCAGAGGTTACTAA。

[0075] SEQ ID NO.8:LHCP8(TEA023017) ATGGCTTCACTGGCAGCATCAACGGCGGCTGCCTCCCTTGGCATGTCAGAAATGCTCGGAAACCCTCTCCGGAGTGGCGTAACGAGATCGGCACCTCCTCCCACCGCGACATCTAGCCCTGCCACCTTCAAGACCGTCGCACTTTTCTCCAAGAAGAAGGCTGCACCTCCCAAAAAGGCTGTCGTCTCCCCTGTTGATGACGAGCTCGCCAAGTGGTACGGTCCCGACAGAAGAATTTTCTTGCCGGAGGGGCTGTTGGACCGATCAGAAATTCCGGCATACCTCACCGGAGAAGTCCCTGGAGATTATGGTTACGATCCTTTTGGGCTTAGCAAGAAACCAGATGACTTTGCCAAGTACCAAGCATATGAGCTAATTCATGGCAGGTGGGCAATGTTGGGGGCTGCGGGCTTCATCATCCCTGAGGCCTTCAACAAATTTGGTGCTAACTGTGGCCCTGAAGCTGTTTGGTTCAAGACAGGAGCTCTACTCCTAGATGGTAACACACTGAATTACTTTGGAAAGAACATCCCCATTAATCTTATATTCGCTGTCATCGCTGAAGTTGTTCTTGTTGGTGGTGCTGAATACTACAGAATCATCAATGGATTGAATTTGGAGGACAAGCTTCACCCAGGCGGTCCATTTGATCCATTGGGGCTTGCAAAGGATCCAGACCAGGCTGCAATACTGAAGGTGAAGGAGATCAAGAACGGTAGACTTGCAATGTTTGCAATGCTCGGTTTCTTCATCCAAGCTTATGTAACGGGAGAAGGTCCAGTTGAAAACCTCGCCAAACATCTAAGCGATCCGTTTGCAAACAACTTGCTCACTGTGCTTGCTGGATCTGCTGAAAGAGCTCCTACCCTGTGA。

[0076] SEQ ID NO.9:LHCP9(TEA026680) .

[0077] Example 1 The method for mining key genes of tea tree drought memory based on multi-omics is as follows: 1. Repeated drought treatment of tea trees Tea seedlings with consistent growth and no pests and diseases were selected for the experiment, e.g. Figure 1As shown in the figure, a total of 4 groups were set up, namely 1 control group (CK) and 3 experimental groups (D1, D2, D3). The control group was watered once every two days to maintain the soil moisture content at 25%-35%. During the drought treatment period, the treatment group was not watered. After the soil moisture content dropped to 10%, the new shoots and fresh leaves of the tea trees were collected or rewatering was selected. After rewatering, the next round of drought treatment was carried out. The tea tree single drought treatment group was recorded as D1, the two-round drought treatment group was recorded as D2, and the three-round drought treatment group was recorded as D3.

[0078] 2. Tea leaf phenotypic analysis The new shoot leaves after treatment were randomly selected, and tissue blocks of about 5 mm × 10 mm were taken along both sides of the main vein of the leaves. They were fixed with FAA solution (a mixture of 5 mL formalin, 90 mL 70% ethanol, and 5 mL glacial acetic acid), dehydrated with gradient ethanol (the gradient was 70% ethanol → 85% ethanol → 95% ethanol → 100% ethanol, and the immersion time for each level was about 1-2 hours, which was adjusted according to the size of the tissue), transparentized with gradient xylene (ethanol-xylene mixture, ethanol-xylene 1:1 mixture → pure xylene, the transition of the mixture was about 30 minutes to 1 hour, which reduced the direct stimulation of xylene on the tissue), embedded in paraffin, sliced ​​by a microtome, double stained with safranin and fast green, and sealed with neutral gum to make permanent slices. The slices were observed and photographed under an optical microscope. The results are shown in the figure. Figure 2 As shown, tea leaf cells are arranged neatly and closely, and the intercellular spaces of spongy tissue are small ( Figure 2 A); Tea tree leaf cells that were treated with a single drought showed shortening, and the intercellular spaces in the spongy tissue were larger and arranged loosely and unevenly ( Figure 2 B); The number of palisade tissue cell layers in tea leaves treated with two rounds of drought increased, and the spongy tissue cells were arranged more closely ( Figure 2 C); Tea tree leaf cells after three rounds of drought treatment are arranged neatly and tightly ( Figure 2 D). Case Viewer software was used to observe and measure the upper and lower epidermal thickness (TU, TL), palisade tissue thickness (PP), spongy tissue thickness (SP), leaf thickness (LT) and other indicators of tea leaves. The results are shown below.

[0079] Among them, UE: upper epidermis; LE: lower epidermis; PP: palisade tissue; SP: spongy tissue.

[0080] As shown in Table 1, compared with the control group, the thickness of the upper epidermis, palisade tissue, spongy tissue, lower epidermis and leaf thickness of tea leaves under a single drought were significantly reduced; compared with a single round of drought, the thickness of the upper epidermis, palisade tissue, spongy tissue, lower epidermis, leaf thickness and the ratio of palisade tissue to spongy tissue of tea leaves under two rounds of drought were significantly increased; compared with a single round of drought, the palisade tissue thickness, lower epidermis thickness and the ratio of palisade tissue to spongy tissue of tea leaves under three rounds of drought stress were significantly increased, and the spongy tissue thickness was significantly decreased. This indicates that tea trees adapt to the environment by adjusting different strategies of leaf anatomical structures under different rounds of drought stress.

[0081] Randomly select the new shoot leaves after treatment and observe their upper and lower epidermis. Use ultrapure water to clean the leaves to be observed, avoid the veins and edges, cut off the middle part as the observation material, and use a vacuum coating machine to spray gold to prepare the sample. Use a SEM-6380LV scanning electron microscope to observe the sample and randomly select five points to take pictures. The results are as follows Figure 3 As shown in the figure, 3A is the stomatal morphology of tea leaves in the control group; 3B is the stomatal morphology of tea leaves in the single-round drought treatment group; 3C is the stomatal morphology of tea leaves in the two-round drought treatment group; 3D is the stomatal morphology of tea leaves in the three-round drought treatment group; 3E is the stomatal density of tea leaves under repeated drought stress; 3F is the long axis length of tea leaves under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; D3 is the three-round drought treatment group. Stomatal density and stomatal aperture are used to describe the characteristics of leaf stomatal development ( Figure 3 A, 3B, 3C, 3D). Stomatal density is the number of stomata per unit in each microscopic image (about 0.13 mm2), and the long axis of the stoma represents the size of the stoma. The number of stomata per unit increases with the increase of drought cycles, and the stomatal aperture decreases ( Figure 3 A, 3B, 3C, 3D). Use Image-Pro Plus 6.0 software to process images and measure distances. Use a scale to measure the length of leaf stomata and calculate the stomatal density. The results are shown in Figure 3 As shown in E and 3F, under drought stress, the stomatal density of tea leaves was higher than that of the control group, and the stomatal aperture became smaller; under repeated drought stress, the stomatal density and stomatal size showed different trends. With the increase in the number of droughts, the stomatal density of leaves showed an upward trend, while the stomatal size showed a gradual downward trend. Compared with the control group, under a single drought, the stomatal density increased and the stomatal size decreased, but neither reached a significant level, and was similar to the stomatal density and aperture under two drought treatments. Under three rounds of drought, the stomatal density and aperture of tea leaves changed significantly. Compared with the control group, the stomatal density increased significantly by 40%, and the stomatal aperture decreased significantly by 48%.

[0082] 3 Metabolome testing 3.1 Metabolite extraction and sample preparation Fresh leaf samples of the control group and experimental group were freeze-dried, ground, dissolved, centrifuged, filtered, and then used for UPLC-MS / MS analysis. The specific process of sample extraction is as follows: (1) Fresh tea leaves were placed in a freeze dryer (Scientz-100F) for vacuum freeze drying; (2) Grinding (30 Hz, 1.5 min) into powder using a grinder (MM 400, Retsch); (3) Weigh 100 mg of powder and dissolve it in 1.2 mL of 70% methanol extract; (4) Vortex once every 30 minutes for 30 seconds each time, for a total of 6 times, and place the sample in a 4°C refrigerator overnight; (5) After centrifugation (12,000 rpm, 10 min), aspirate the supernatant, filter the sample using a microporous filter membrane (0.22 μm pore size), and store it in a sample injection bottle for UPLC-MS / MS analysis.

[0083] The mass spectrometry data were processed using the software Analyst 1.6.3, and the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry based on the metabolic database to obtain the metabolome data.

[0084] 3.2 Screening and analysis of differential metabolites The method of combining Fold Change and VIP value of OPLS-DA model was adopted to screen differential metabolites. Metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 were defined as differential metabolites. The volcano plot was used to intuitively display the overall distribution of differential metabolites between the two groups. The results are shown in Figure 2. Figure 4-9 As shown in the figure, Figure 4 This is the volcano plot of differential metabolites of tea tree shoots in CK group and D1 group under repeated drought stress; Figure 5 This is a volcano plot comparing the metabolites of tea shoots between the CK group and the D2 group under repeated drought stress; Figure 6 This is the volcano plot of differential metabolites of tea tree shoots in CK group and D3 group under repeated drought stress; Figure 7 This is the volcano plot of the differential metabolites of tea tree shoots in groups D1 and D2 under repeated drought stress; Figure 8 This is the volcano plot of differential metabolites of tea tree shoots in groups D1 and D3 under repeated drought stress; Fig. 9The volcano plot of the metabolites of tea shoots in groups D2 and D3 under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; D3 is the three-round drought treatment group; Note: The red points in the figure indicate up-regulated metabolites, the green points indicate down-regulated metabolites, and the gray points indicate metabolites with no significant difference; It can be seen that CK_vs_D1 ( Figure 4 )、CK_vs_D2( Figure 5 ) and CK_vs_D3 ( Figure 6 ) screened out 407, 519 and 488 differential metabolites, of which 265, 363 and 270 were upregulated, and 142, 156 and 118 were downregulated. Figure 7 )、D1_vs_D2( Figure 8 ) and D2_vs_D3( Fig. 9 ) screened out 374, 350 and 208 differential metabolites, respectively, of which 221, 249 and 142 were upregulated and 153, 101 and 66 were downregulated, respectively.

[0085] 3.3 KEGG enrichment analysis of differential metabolites Differential metabolism interacts with each other in the organism to form different pathways. The KEGG database was used to annotate and display the differential metabolites. The results showed that the metabolic pathways commonly enriched in the differential metabolites of the three comparison groups CK_vs_D1, D1_vs_D2 and D1_vs_D3 were amino acid biosynthesis and aminoacyl-tRNA biosynthesis, and D2_vs_D3 was mainly enriched in the phenylpropanoid metabolic pathway and flavonoid pathway.

[0086] 3.4 Differential metabolite trend analysis and memory metabolite screening In order to study the relative content trends of metabolites under different drought treatments, the relative contents of all differential metabolites identified according to the screening criteria in all group comparisons were z-score standardized, followed by K-Means cluster analysis. Fig.10 As shown in the figure, 10A is a metabolite that shows an upward trend under repeated drought stress; 5B is a metabolite that shows a downward trend under repeated drought stress; 5C is a metabolite that first increases and then decreases under repeated drought stress. It can be seen that the 798 differential metabolites can be divided into 3 different expression patterns, recorded as Sub Class 1, Sub Class 2 and SubClass 3. The metabolites clustered by Sub Class 1 with the largest abundance increase under drought stress and increase with the increase of drought cycles, actively responding to repeated drought stress ( Fig.10A). Sub Class 3 contains 458 substances. The metabolites in Sub Class 1 include flavonoids, phenolic acids, amino acids and their derivatives, lipids, and alkaloids, which are 112, 79, 57, 57, and 38, respectively. Sub Class 2 contains 190 substances whose contents decrease under drought stress ( Fig.10 B). Sub Class 3 contains 150 substances whose contents first increase and then decrease under drought stress ( Fig.10 C). Plant secondary metabolites are products of plant adaptation to the environment during growth. Under drought stress, the concentration of secondary metabolites usually increases, such as alkaloids, flavonoids, terpenes, etc. Repeated drought stress can induce the increase of active substances in tea trees, thereby inducing tea trees to respond positively and improving the drought tolerance of tea trees. The present invention screened out metabolites that did not respond significantly under the first drought stress, but responded significantly with the increase of drought cycles, from the metabolites clustered by SubClass 1. These metabolites are "memory" metabolites under drought stress.

[0087] Under repeated drought stress, “memory” metabolites were screened out: 20 amino acids and their derivatives, 7 phenolic acids, 6 alkaloids, 3 flavonoids and 2 organic acids. The specific results are shown in Table 2 .

[0088] According to the functions of memory metabolites, L-proline, L-lysine, L-leucine, L-valine and L-isoleucine play a role in maintaining the homeostasis of photosynthesis and respiration; L-asparagine, proline, L-glutamine and GABA have osmotic regulation functions, helping tea trees maintain cell homeostasis under drought. The accumulation of memory metabolites is an important mechanism for tea trees to adapt to repeated drought stress. In addition, under multiple rounds of drought stress, sugar substances showed a decreasing trend, while secondary metabolites such as flavonoids and phenolic acids accumulated significantly. When tea trees encounter drought stress again after a single drought stress, they preferentially allocate carbon sources to the synthesis of secondary metabolites through carbon fixation in photosynthesis, promote the transition from primary metabolism to secondary metabolism, and enhance the drought resistance of plants.

[0089] 4 Transcriptome sequencing First, the total RNA of tea fresh leaf samples was extracted using the Trizol method. The specific process is as follows: 1. Preparation of materials and reagents. Samples: fresh tea leaves (quickly frozen in liquid nitrogen immediately after picking and stored at -80°C). Reagents: TRIzol reagent, chloroform, isopropanol, 75% ethanol (prepared with DEPC water), RNase-free water, liquid nitrogen. Equipment: pre-cooled mortar, centrifuge tube (RNase-free), low-temperature centrifuge, vortexer, pipette.

[0090] 2. Experimental steps. Sample grinding: Take about 100 mg of leaves, grind into powder in liquid nitrogen, quickly transfer to 1 mL TRIzol, and vortex to mix. Lysis and layering: Let stand at room temperature for 5 minutes, add 0.2 mL chloroform, shake vigorously for 15 seconds, and let stand at room temperature for 3 minutes. Centrifuge at 4 ℃ (12,000×g, 15 minutes), separate into three layers (the upper aqueous phase contains RNA). RNA precipitation: Pipette the upper aqueous phase into a new tube, add 0.5 mL isopropanol, invert and mix, and let stand at room temperature for 10 minutes. Centrifuge at 4 ℃ (12,000×g, 10 minutes), discard the supernatant, and retain the RNA precipitate. Washing and dissolving: Wash the precipitate with 1 mL 75% ethanol, centrifuge at 4 ℃ (7,500×g, 5 minutes), discard the ethanol, and dry for 5 minutes. Add 50μL RNase-free water to dissolve the RNA and store at -80℃.

[0091] Second, use agarose gel electrophoresis to analyze the integrity of the RNA and whether there is DNA contamination. The specific process is as follows: 1. Reagents and equipment: 1×TAE electrophoresis buffer, 1.2% agarose gel (containing 0.5 μg / mL ethidium bromide, EB), 6× RNA loading buffer, DNA / RNA Marker (such as Thermo Scientific™ RNA Ladder).

[0092] 2. Experimental steps. Gel preparation: 1.2% agarose gel (dissolved in 1×TAE), melt in a microwave oven, add EB, and pour into the gel preparation plate. Sample spotting and electrophoresis: Mix 2 μL RNA sample with 1 μL loading buffer and spot on the gel well. Electrophoresis at 80-100 V for 20-30 minutes.

[0093] 3. Result analysis. Integrity: 28S and 18S rRNA bands are clearly visible (the brightness of 28S is about twice that of 18S). DNA contamination: If there are diffuse bands or tails near the sample well, it indicates that genomic DNA is residual. Use Qubit2.0 fluorometer to measure RNA concentration with high precision, and use Agilent2100 bioanalyzer to accurately detect RNA integrity. After passing the test, library construction is carried out. RNA quality inspection indicators are concentration and integrity. Concentration: Qubit 2.0 detection: ≥50ng / μL (applicable to Illumina library construction), OD260 / OD280 ratio: 1.8-2.2 (purity, excluding protein contamination), OD260 / OD230 ratio: ≥2.0 (excluding polysaccharide / phenol contamination). Integrity: Agilent 2100 RIN value (RNA Integrity Number) ≥7.0 (ideal value ≥8.0). Electrophoresis: 28S:18S band brightness ratio ≈2:1, no degradation tailing. The library quality was tested. After the library was qualified, it was sequenced using the Illumina HiSeq platform. The qualified library inspection criteria were concentration and fragment size. Concentration: Qubit or qPCR detection, effective library concentration ≥ 2 nM (must meet Illumina machine requirements). Fragment size: Agilent 2100 detection, the main peak of the library was 300-500 bp (insert fragment + adapter), and there was no adapter dimer (<100 bp impurity peak).

[0094] 4.1 Sequence alignment and data analysis The raw data obtained by sequencing were filtered to obtain Clean Data. The filtering criteria were as follows: remove reads with adapters; remove paired reads when the N content in any sequencing read exceeded 10% of the base number of the read; remove paired reads when the number of low-quality (Q≤20) bases in any sequencing read exceeded 50% of the base number of the read.

[0095] HISAT2 was used to align the Clean Reads with the genome of the tea plant 'Shucha Zao' to obtain the position information on the reference genome or gene, as well as the sequence feature information unique to the sequenced sample. FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) was used as an indicator to measure the transcript or gene expression level.

[0096] 4.2 Screening of differentially expressed genes The DESeq2 method was used to perform differential expression analysis on all expressed genes. DESeq2 is based on the negative binomial distribution model and identifies differentially expressed genes between samples through standardization, dispersion estimation, and statistical tests. The specific process is as follows: 1. Data input and preprocessing Input data: raw RNA-seq read matrix (Raw Counts), rows represent genes, columns represent samples.

[0097] 2. Data normalization. Purpose: To eliminate the effects of sequencing depth and gene length on gene expression. First, calculate the size factors (normalization factors for each sample): Based on the geometric mean method (Median of Ratios), eliminate the differences in sequencing depth between samples.

[0098] 3. Dispersion estimation. Purpose: To quantify the degree of variation in gene expression values ​​(variation between biological replicates). Three steps: ① Gene-specific dispersion: Estimate the dispersion of each gene separately (based on negative binomial distribution). ② Fit trend line: Fit a smooth curve through the mean-dispersion relationship of gene expression levels. ③ Empirical Bayesian shrinkage: Shrink the gene-specific dispersion toward the trend line to avoid overfitting under small samples.

[0099] 4. Differential expression analysis. Model construction: Use generalized linear model (GLM) for hypothesis testing (default Wald test). Calculate the statistic Log2 Fold Change: the logarithmic change value of gene expression fold. p value: Wald test or likelihood ratio test (LRT) based on negative binomial distribution.

[0100] 5. Multiple testing correction. Purpose: To control the false discovery rate (FDR). Benjamini-Hochberg (BH) correction: Screening criteria: FDR<0.05. The screening conditions for differentially expressed genes were |log2FoldChange|>1 and FDR<0.05.

[0101] The volcano plot is used to visually display the overall distribution of differentially expressed genes in the two groups of samples. Figure 11-16 As shown, Fig.11 The volcano map of differentially expressed genes between tea plants in the CK and D1 groups under repeated drought stress; Fig.12 The volcano map of differentially expressed genes between tea plants in the CK and D2 groups under repeated drought stress; Fig.13 The volcano map of differentially expressed genes in tea plants between the CK group and the D3 group under repeated drought stress; Fig.14 The volcano map of differentially expressed genes between tea plants in groups D1 and D2 under repeated drought stress; Fig.15The volcano map of differentially expressed genes in tea plants in groups D1 and D3 under repeated drought stress; Fig.16 This is a volcano plot of differentially expressed genes in tea trees in groups D2 and D3 under repeated drought stress; CK: control group; D1: single-round drought treatment group; D2: two-round drought treatment group; D3: three-round drought treatment group; Note: The red dots in the figure indicate up-regulated differentially expressed genes, the green dots indicate down-regulated differentially expressed genes, and the gray dots indicate genes with no significant differences. The DESeq2 software was used to standardize the Readcount in the gene expression analysis, and then the Benjamini-Hochberg method was used to correct the hypothesis test probability (P-value) for multiple hypothesis testing to obtain the false discovery rate (FDR). The screening conditions for significantly differentially expressed genes (DEGs) were |log2Fold Change|>=1, and FDR<0.05. The overall distribution of differentially expressed genes can be intuitively seen from the volcano plot. Three comparison groups CK_vs_D1 ( Fig.11 )、CK_vs_D2( Fig.12 ) and CK_vs_D3 ( Fig.13 ) identified 4669, 3811, and 3694 upregulated DEGs, and 6069, 4404, and 4901 downregulated DEGs, respectively. D1_vs_D2 ( Fig.14 )、D1_vs_D( Fig.15 ) and D2_vs_D3( Fig.16 ) identified 156, 780, and 51 upregulated DEGs, and 82, 1039, and 258 downregulated DEGs, respectively.

[0102] 4.3 K-means analysis of differentially expressed genes and screening of key memory genes In order to study the expression pattern of genes under repeated drought treatment, the FPKM of the genes was first centered and standardized, and then K-means cluster analysis was performed. The 15,463 differentially expressed genes can be divided into 10 different expression patterns. Genes with similar change trends may have similar functions. Group 1 contains 547 genes, which show a trend of first rising and then falling. Group 2 contains 1,879 genes, which show a trend of first rising, then falling, and then rising. Group 3 contains 1,669 genes, which show a trend of first falling and then rising. Group 4 contains 1,248 genes, which show a trend of first rising, then falling, and then rising. Group 5 contains 5,406 genes, which show a trend of first falling and then unchanged. Group 6 contains 873 genes, which show a trend of first falling, then unchanged, and then falling. Group 7 contains 552 genes, which show a trend of first unchanged and then rising. Group 8 contains 1,068 genes, which show a trend of first rising and then unchanged. Group 9 contains 840 genes, showing a trend of first increasing, then decreasing, and then remaining unchanged. Group 10 contains 1381 genes, showing a trend of first increasing, then remaining unchanged, and then decreasing.

[0103] The genes that did not respond significantly to a single drought but whose expression levels under subsequent repeated droughts were significantly higher than those under a single drought were defined as key drought memory genes. They were mainly distributed in the photosynthesis pathways operated by photosystem I (PSⅠ) and photosystem II (PSⅡ), and there were 9 of them (see Table 3).

[0104] These genes include: photosystem I chlorophyll binding protein gene, photosystem II chlorophyll binding protein gene, photosystem II oxygen evolution enhancing protein gene. The light response pathway is the pathway with the most enriched drought memory key genes.

[0105] 4.4 Analysis of differentially expressed genes’ functions In order to better explain the functions of differentially expressed genes, GO and KEGG enrichment analysis was performed on the differentially expressed genes, and the P value was less than 0.05 as the screening standard. Among the 15,463 differentially expressed genes, 10,791 genes were annotated to GO, accounting for 69.79% of all genes. The top 50 GO-Terms with the most significant enrichment were selected to make a bar graph. The results showed that compared with the control group, the GO-Terms with significant differences (p<0.05) and a large number of enriched genes under single drought included photosynthesis (GO:0015979), anion transport (GO:0006820), UDP-glucosyltransferase activity (GO:0035251), anion transmembrane transporter activity (GO:0008509) and secondary metabolite biosynthesis (GO:0044550), and the number of down-regulated genes annotated to each function was greater than the number of up-regulated genes. Compared with a single drought, the difference was significant under two rounds of drought (p <0.05) and the number of GO-Terms enriched with a large number of genes included photosynthesis, photosynthetic system (GO:0009521), chromophore-protein connection (GO:0018298) and chlorophyll binding (GO:0016168), and the number of up-regulated genes annotated to each function was greater than the number of down-regulated genes. Compared with a single drought, the GO-Terms with significant differences (p<0.05) and a large number of enriched genes under three rounds of drought included anion transport, photosynthesis, secondary metabolite biosynthesis, response to damage (GO:0009611) and anion transmembrane transporter activity. Among them, the number of up-regulated genes annotated to photosynthesis, photosynthetic system, photosynthetic system I and photosynthetic system II was greater than the number of down-regulated genes. Compared with the two rounds of drought, 49 of the 50 GO-Terms with the lowest q-value in the enrichment analysis results under the three rounds of drought were sub-items in biological processes, among which the GO-Terms with significant differences (p<0.05) and a large number of enriched genes included carbohydrate decomposition metabolism process (GO:0016052), organic phosphate decomposition metabolism process (GO:0046434), response to extracellular stimulation (GO:0009991), response to nutrient level (GO:0031667) and response to starvation (GO:0042594). The number of down-regulated genes annotated to each function was greater than the number of up-regulated genes.

[0106] At the same time, the 20 most significantly enriched KEGG signal pathways between the groups were also tested. The differentially expressed genes under repeated drought treatment were annotated to pathways related to photosynthesis. Therefore, photosynthesis is closely related to the response of tea plants to repeated drought stress, that is, the key genes of drought memory.

[0107] Photosystem I (PSⅠ) and Photosystem II (PSⅡ) are the main sites for light reactions. They have their own core proteins and antenna protein complexes, respectively. The antenna protein complex captures light energy and transfers it to the core protein to produce high-energy electrons. The electron transport chain then transfers the high-energy electrons to the final electron acceptor, and ATP and NADPH are produced in the process. Make a heat map of the differential expression of photosynthesis and photosynthesis-antenna protein pathway and related genes, such as Fig.17 As shown. It shows that the expression levels of some key genes in photosynthesis and photosynthesis-antenna protein pathways did not respond significantly to the first drought stress, but the response levels under repeated drought stress were significantly higher than the expression levels under single drought stress. These genes are key genes for drought memory. Analysis of photosynthesis pathways under single drought stress ( Fig.17A), photosystem I protein genes psaG, psaH, psaO and photosystem II genes psbB, psbO, psbP, psbQ1, psbQ2, psb27-1, psb28 and F-type H+-transporting ATPase gene ATPF1G, ferredoxin-NADP+ reductase gene PetH were significantly down-regulated; compared with a single drought, PSⅠ protein genes psaA, psaB, psaG, psaH, psaO and PSⅡ protein genes psbB, psbO, psbP and gene PetH were significantly up-regulated under two rounds of drought, and PSⅡ protein genes psbP, psbQ1, psbQ2, psb27-1, psb28 and gene ATPF1G were significantly up-regulated under three rounds of drought; compared with two rounds of drought, F-type H+-transporting ATPase α subunit gene ATPF0A and cytochrome b6 gene petB were significantly down-regulated under three rounds of drought. Fig.17 B shows that under drought stress, the light-harvesting complex I chlorophyll a / b binding protein genes Lhca1~Lhca4 and light-harvesting complex II chlorophyll a / b binding protein genes Lhcb1~Lhcb3, Lhcb6 were significantly down-regulated; compared with a single drought, the genes Lhca2, Lhca4, Lhcb1, Lhcb2, Lhcb3 and Lhcb6 were significantly up-regulated under two rounds of drought; and the genes Lhca1, Lhca3, Lhca4, Lhcb1~Lhcb3 were significantly up-regulated under three rounds of drought. The light-harvesting pigment protein complex was destroyed under drought stress, and repeated drought stress induced the up-regulated expression of LHCA and LHCB protein genes in tea plants, improving the ability to capture light.

[0108] A heat map of the dark response pathway and related gene differential expression in tea trees was made. The dark reaction uses ATP and NADPH formed by the light reaction to fix CO2 and synthesize carbohydrates for respiration. In order to explore whether the dark reaction process of tea trees under repeated drought stress is consistent with the light reaction response pattern, the differential genes in the dark reaction process under different rounds of drought stress were analyzed. The results showed that under a single drought, the expression of the ribulose kinase gene PRK was significantly upregulated, and the ribulose-1,5-bisphosphate carboxylase gene RBCS, the fructose-1,6-bisphosphatase gene FBP, the sedoheptulose-1, 7-bisphosphatase gene SBPase and the 5-phosphoribosyl isomerase gene rpiA were significantly downregulated. Compared with a single drought, the ribulose-1,5-bisphosphate carboxylase large chain gene rbcL was significantly up-regulated in two rounds of drought, RBCS, FBP, SBPase, rpiA, PRK and glyceraldehyde-3-phosphate dehydrogenase GAPC2 were significantly up-regulated in three rounds of drought, and phosphoglycerate kinase gene PGK and glyceraldehyde-3-phosphate dehydrogenase GAPCP2 were significantly down-regulated; compared with two rounds of drought, GAPC2 was significantly up-regulated, and rbcL and triosephosphate isomerase gene TPI were significantly down-regulated under three rounds of drought. This indicates that after a single drought stress, when experiencing drought stress again, most dark response-related genes are up-regulated, and the increase trend of dark response pathway-related genes under three rounds of drought is more significant.

[0109] At the same time, the expression heat map of abscisic acid synthesis and signal transduction pathways was analyzed. The results showed that in the ABA metabolic pathway, there are four key regulatory enzymes: 9-cis-epoxycarotenoid oxygenase (NCED), zeaxanthin oxidase (ZEP), acetate aldehyde oxidase (AAO) and cytochrome oxidase, among which cytochrome oxidase plays a major role in regulating the decomposition of abscisic acid. The expression of genes in the abscisic acid pathway showed an upward trend with the increase of drought stress cycles after the first drought. Under a single drought stress, ZEP1 , NCED1 and rate-limiting enzyme genes ABA2 Significantly up-regulated expression of cytochrome oxidase gene CYP707A4 Compared with a single drought, the NECD1 and CYP707A4 Significantly increased, ABA2 Significantly down-regulated expression; three rounds of drought ZEP1 , NCED1 , ABA2 and AAO3 Compared with the two rounds of drought, the three rounds of drought NCED1 Significantly downgraded.

[0110] 4.5 RT-qPCR validation of transcriptome data To verify the accuracy of RNA-Seq data, 9 differentially expressed genes were randomly selected from the key pathways and memory key genes of tea plants adapting to repeated drought stress for RT-qPCR verification. The plant materials used in the verification experiment include the control group: CK, and the experimental groups: D1, D2 and D3 (D1: single-round drought treatment group; D2: two-round drought treatment group; D3: three-round drought treatment group). The verification test process includes: 1. First, total RNA was extracted from tea leaves using the FastPure Universal Plant Total RNA Isolation Kit (Vazyme, China). The first-strand cDNA was synthesized using Evo M-MLV Reverse Transcription Premix (Agbio, China). RT-qPCR was performed on a QuantStudio 3 system (Thermo Fisher Scientific, USA) using the SYBR Green PremixPro Taq HS qPCR Kit (Agbio, China). Each 20 μL reaction included 10 μL 2× SYBR Green Premix, 0.4 μL of each primer (forward and reverse), 1 μL cDNA template, and RNase-free water, with a final volume of 20 μL. The cycling conditions were: 95 ℃ 30 s, 95 ℃ 5 s, 60 ℃ 30 s, and 40 cycles.

[0111] The primers for the 9 genes include primers for detecting the gene shown in SEQ ID NO.1: F-terminal primer AGGCTTTTGCAGAGCTCAAG (SEQ ID NO.10), R-terminal primer CATAAGCCCATGCATTGTTG (SEQ ID NO.11); primers for detecting the gene shown in SEQ ID NO.2: F-terminal primer TTGTATTTGGGCCCACTCTC (SEQ ID NO.12), R-terminal primer CACCGAACTTGACACCATTG (SEQ ID NO.13); primers for detecting the gene shown in SEQ ID NO.3: F-terminal primer GTTCGGTGAGGCTGTATGGT (SEQ ID NO.14), R-terminal primer TAGAGTGGGTCGGTCACCTC (SEQ ID NO.15); primers for detecting the gene shown in SEQ ID NO.4: F-terminal primer TTGAATTCCTTGCCATCTCC (SEQ ID NO.16), R-terminal primer AACACAGATCCCCACGAAAG (SEQ ID NO.17); primers for detecting the gene shown in SEQ ID NO. Primers for detecting the gene shown in SEQ ID NO.5: F-terminal primer AGCCTATGGCGAGATCTTCA (SEQ ID NO.18), R-terminal primer GGGTCAGCCCAATAGTCGTA (SEQ ID NO.19); Primers for detecting the gene shown in SEQ ID NO.6: F-terminal primer ATATTCTCGGCGGTTCCTTT (SEQ ID NO.20), R-terminal primer AGCCGTAGTCCCCTGGTAGT (SEQ ID NO.21); Primers for detecting the gene shown in SEQ ID NO.7: F-terminal primer GGTGTCACCGGAATGCTACT (SEQ ID NO.22), R-terminal primer AGGGTAGCCACACTCATTGG (SEQ ID NO.23); Primers for detecting the gene shown in SEQ ID NO.8: F-terminal primer GTACGGTCCCGACAGAAGAA (SEQ ID NO.24), R-terminal primer GGCCACAGTTAGCACCAAAT (SEQ ID NO.25); Primers for detecting the gene shown in SEQ ID NO. Primers for the gene shown in NO.9: F-terminal primer GTGTTTGGGTTGCAGAGGTT (SEQ ID NO.26), R-terminal primer AAGCTCAGCATTCCTTTGGA (SEQ ID NO.27). Gene expression was normalized to the internal reference β-actin, and Relative expression was calculated by the method. Verification criteria: RT-qPCR results were considered to be successful if they were consistent with the RNA-Seq trend (up / down regulation). Fig.18As shown in the figure, the expression trends of the nine genes detected by RT-qPCR are consistent with the changing trends of RNA-Seq data, indicating that the transcriptome sequencing results are accurate and reliable, and RNA-Seq data are suitable for biological analysis.

[0112] 5 Analysis of the gene structure of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8 and LHCP9 in tea plants Tea tree LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 genes are tea tree LHC transcription factor genes. Their cloning and sequence structure analysis are as follows: The tea tree variety is the national fine variety Xiangfeicui, planted in the Chang'an Tea Base of Hunan Agricultural University in Changsha, Hunan Province. Young leaves of tea trees were used for RNA extraction. Total RNA was extracted using the Fastpure Universal Plant Total RNA Isolation Kit (Vazyme, China) according to the instructions of the kit, and the RNA content and quality were detected by a spectrophotometer. Then reverse transcription was used to generate the first chain of cDNA: 1 μg was taken as a template and the RNA was extracted according to PrimeScript II 1 stStrand cDNA Synthesis Kit (Aikerui, China) was operated according to the instructions. Oligo dT Primer 5 μL, dNTP Mix (10 mM each) 1 μL, RNase Free dH2O was added to 10 μL, denatured at 65 °C for 5 min, and immediately placed on ice. Then, 5X RTase Plus Reaction Buffer 4 μL, RNase Inhibitor (40U) 0.5 μL, Evo M-MLV Plus RTase (200U) 1 μL, ddH2O water was added to 20 μL, incubated at 30 °C for 10 min, incubated at 42 °C for 60 min, and incubated at 95 °C for 5 min to inactivate the reverse transcriptase to obtain the first strand of the reverse transcribed product cDNA. An appropriate amount of the first strand of the reverse transcribed product cDNA was used for subsequent PCR. The first strand of cDNA was used as the RT-PCR template, and the gene sequence was amplified by PCR using the conventional method. LHCP1 upstream primer: (5'-ATGAACCCAAATGATGAATCCTTG-3', SEQ ID NO.31), downstream primer: (5'-CTATGACTTGCGCCTCTGTATGC-3', SEQ ID NO.32). LHCP2 upstream primer: (5'-ATGGCTGCGTCTACCATGGC-3', SEQ ID NO.33), downstream primer: (5'-TCACTTTCCTGGGACAAAGTTTG-3', SEQ ID NO.34). LHCP3 upstream primer: (5'-ATGGCTGCCTCTACAATGGC-3', SEQ ID NO.35), downstream primer: (5'-TCACTTTCCGGGAACAAAGTTAG-3', SEQ ID NO.36). LHCP4 upstream primer: (5'-ATGGCTTCCAAAGCCCTAATG-3', SEQ ID NO.37), downstream primer: (5'-TCATGGAGAAATTGATCTAGGGATAA-3', SEQ ID NO.38). LHCP5 upstream primer: (5'-ATGGCAACTCAAGCACTGGTG-3', SEQ ID NO.39), downstream primer: (5'-TTAGTGGAACTTGAGGTTGGTCAA-3', SEQ ID NO.40).LHCP6 upstream primer: (5'-ATGGCGGAGGATCGTCGG-3', SEQ ID NO.41), downstream primer: (5'-TCACATGAATTTCAGAATGGAAAGC-3', SEQ ID NO.42). LHCP7 upstream primer: (5'-ATGGCCACCGTCGCAGCT-3', SEQ ID NO.43), downstream primer: (5'-TTAGTAACCTCTGATTGTTTGAATAATTG-3', SEQ ID NO.44). LHCP8 upstream primer: (5'-ATGGCTTCACTGGCAGCATC-3', SEQ ID NO.45), downstream primer: (5'-TCACAGGGTAGGAGCTCTTTCAG-3', SEQ ID NO.46). LHCP9 upstream primer: (5'-ATGGCCGCAACCACCGCC-3', SEQ ID NO.47), downstream primer: (5'-TCAAGAGAAAAAGGTGTCTATAATGGTT-3', SEQ ID NO.48). The reaction system is: 2×Phanta Maxbuffer 25μl, dNTP Mix 1 μL, upstream and downstream primers 2 μL each, Phanta Max Supper-Fidelity DNA Polymerase 1 μL, template 2 μL, ddH2O 17 μL. The reaction program is as follows: 95 ℃ 3 min, 95 ℃ 15sec, 58 ℃ 15 sec, 72 ℃ 30 sec, 30 cycles, 72 ℃ 5 min, 4℃ end. The PCR product LHCP1-P9 was obtained. After purification and recovery, the LHCP1-P9 gene was ligated to the pMD19-T Vector vector (Takara, China) to obtain the pMD19-T::LHCP1- pMD19-T::LHCP9 plasmid, which was transformed into Escherichia coli competent cells DH5α transformation (Weidi, China). After the positive clones grew out and were verified by colony PCR, the correct single clones were picked for sequencing and sent to Wuhan Hope Group Medical Testing Laboratory Co., Ltd. to obtain the nucleotide sequences of the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes.The steps of gene purification, recovery, ligation, transformation, and PCR verification refer to the prior art Wang M, Yan Y, Wang R, et al. Simultaneous Detection of Bovine Rotavirus, BovineParvovirus, and Bovine Viral Diarrhea Virus Using a Gold Nanoparticle-Assisted PCR Assay With a Dual-Priming Oligonucleotide System. FrontMicrobiol. 2019;10:2884.

[0113] 6 Functional verification of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 overexpression in Arabidopsis 6.1 Vector construction LHCP1-PCAMBIA1300 vector construction Using pMD19-T::LHCP1 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGAACCCAAATGATGAATCCTTG-3', SEQ ID NO.49); Downstream primer: (5'-gctcaccatggtaccggatccCTATGACTTGCGCCTCTGTATGC-3', SEQ ID NO.50).

[0114] LHCP2-PCAMBIA1300 vector construction Using pMD19-T::LHCP2 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCTGCGTCTACCATGGC-3', SEQ ID NO.51); Downstream primer: (5'-gctcaccatggtaccggatccTCACTTTCCTGGGACAAAGTTTG-3', SEQ ID NO.52).

[0115] LHCP3-PCAMBIA1300 vector construction Using pMD19-T::LHCP3 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCTGCCTCTACAATGGC-3', SEQ ID NO.53), Downstream primer: (5'-gctcaccatggtaccggatccTCACTTTCCGGGAACAAAGTTAG-3', SEQ ID NO. 54).

[0116] LHCP4-PCAMBIA1300 vector construction Using pMD19-T::LHCP4 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCTTCCAAAGCCCTAATG-3', SEQ ID NO.55); Downstream primer: (5'-gctcaccatggtaccggatccTCATGGAGAAATTGATCTAGGGATAA-3', SEQ ID NO.56).

[0117] LHCP5-PCAMBIA1300 vector construction Using pMD19-T::LHCP5 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCAACTCAAGCACTGGTG-3', SEQ ID NO.57); Downstream primer: (5'-gctcaccatggtaccggatccTTAGTGGAACTTGAGGTTGGTCAA-3', SEQ ID NO.58).

[0118] LHCP6-PCAMBIA1300 vector construction Using pMD19-T::LHCP6 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCGGAGGATCGTCGG-3', SEQ ID NO.59); Downstream primer: (5'-gctcaccatggtaccggatccTCACATGAATTTCAGAATGGAAAGC-3', SEQ ID NO.60).

[0119] LHCP7-PCAMBIA1300 vector construction Using pMD19-T::LHCP7 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCCACCGTCGCAGCT-3', SEQ ID NO.61); Downstream primer: (5'-gctcaccatggtaccggatccTTAGTAACCTCTGATTGTTTGAATAATTG-3', SEQ ID NO.62).

[0120] LHCP8-PCAMBIA1300 vector construction Using pMD19-T::LHCP8 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCTTCACTGGCAGCATC-3', SEQ ID NO.63); Downstream primer: (5'-gctcaccatggtaccggatccTCACAGGGTAGGAGCTCTTTCAG-3', SEQ ID NO.64).

[0121] LHCP9-PCAMBIA1300 vector construction Using pMD19-T::LHCP9 plasmid as template, the primers are: Upstream primer: (5'-gtcccagactacgctggatccATGGCCGCAACCACCGCC-3', SEQ ID NO.65); Downstream primer: (5'-gctcaccatggtaccggatccTCAAGAGAAAAAGGTGTCTATAATGGTT-3', SEQ ID NO.66).

[0122] PCR amplification was performed sequentially.

[0123] The specificity of the PCR product was confirmed by 1% agarose gel electrophoresis, and then purified and eluted to obtain the purified PCR product.

[0124] The PCAMBIA1300 vector (Novopro, China) was linearized by single restriction digestion, with BamH1 as the restriction site. The restriction digestion system was: ddH2O 41.4 μL, 10x Buffer 5 μL, PCAMBIA1300 Vector 1.6 μL, BamH1 1 μL, and the reaction was carried out in a water bath at 37 ℃ for 30 min. The digestion product was confirmed to have been cut by 1% agarose gel electrophoresis, and then purified and eluted to obtain the linearized PCAMBIA1300 vector. The linearized PCAMBIA1300 vector and the purified PCR product were recombined using the ClonExpress®II One Step Cloning Kit (Vazyme, China). The reaction system was: PCR product 4 μL, Exnase II 2 μL, Vector 3.5 μL, ddH2O was added to 20 μL system, and the reaction was carried out at 37 ℃ for 30 min. Finally, the competent E. coli cells DH5α were transformed (Weidi, China), and after the positive clones were grown and verified by colony PCR, the correct single clones were selected for sequencing, and the sequencing was sent to Wuhan Hope Medical Laboratory Co., Ltd. to obtain the CsLHC-PCAMBIA1300 vector. The transformation and PCR verification steps refer to the prior art Wang M, Yan Y, Wang R, et al. Simultaneous Detection of Bovine Rotavirus, Bovine Parvovirus, and BovineViral Diarrhea Virus Using a Gold Nanoparticle-Assisted PCR Assay With aDual-Priming Oligonucleotide System. Front Microbiol. 2019;10:2884. doi:10.3389 / fmicb.2019.02884.

[0125] 6.2 Arabidopsis genetic transformation Take an appropriate amount of wild-type Arabidopsis seeds and add deionized water, and sow them after 72 hours of vernalization. Cover with plastic wrap after sowing and place under appropriate conditions (humidity 70%; temperature 22 ℃; photoperiod 10 h light / 14 h dark) to wait for germination. After the seeds germinate, select seedlings of uniform size for transplanting and culture normally. Transform the CsLHC-PCAMBIA1300 vector into GV3101 Agrobacterium by freeze-thaw method, and identify positive clones by colony PCR method. The transformation and identification methods are the same as above. Pick the positive colonies containing the target gene and culture them in 50 mL of LB liquid medium containing antibiotics at 28 °C and 200 r / min for about 24 hours; add 50 ml of the cultured bacterial solution to 200 mL of fresh LB liquid medium containing antibiotics, continue shaking culture for 6-8 hours until OD600 is about 1.0, collect the bacteria by centrifugation, resuspend the bacteria with 5% sucrose solution, and the final concentration of OD600 is about 0.8. Add 0.1% silwet L-77 (Yeasen, China) and shake well to obtain the transformation solution. Arabidopsis thaliana was planted for about one month, and the plants began to bloom one after another. Select the plants with strong growth as the plants to be transformed. Before transformation, the top inflorescences were constantly removed to make the plants produce more flower buds. The plants to be transformed should be fully watered the day before transformation. Put the prepared transformation solution in a container, gently soak the Arabidopsis inflorescence in the transformation solution for about 60 seconds, then place it in the dark for 24 hours, then culture normally and harvest the seeds. The harvested Arabidopsis seeds were placed in a centrifuge tube, sterilized with 1 mL 75% ethanol for 1 min, then sterilized with 10% NaClO for 5 min, then rinsed with sterile water 5-6 times, and the seeds were sucked with a pipette tip and sown on 1 / 2 MS solid medium containing hygromycin. Vernalization was carried out for 72 h under dark conditions at 4 ℃, and the seeds were transferred to the culture room at 22 ℃; the photoperiod was 10 h light / 14 h dark. About two weeks later, resistant plants with green leaves and normal root development were selected and transplanted into the cultivation medium for further cultivation. The cultivation medium was fully watered before transplantation, and the plastic wrap was covered after transplantation, which was removed after about 3 days. The subsequent management was the same as above, and the T2 generation seeds were harvested for experiments. Arabidopsis seedling RNA was extracted, and the expression of the target gene was detected by PCR using gene-specific primers. The extraction and identification methods were the same as 4.5. The transgenic plants were cultured under natural drought for 10 days, and a control group was set up. The survival of the seedlings was observed after 10 days.

[0126] The results showed that by analyzing the expression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in Arabidopsis overexpression strains and wild types, as well as the phenotypes of the overexpression strains under drought treatment, it was shown that LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in the overexpression Arabidopsis experiment were integrated into Arabidopsis plants by PCR identification, and the expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in transgenic (OE) plants were significantly higher than those in wild-type (WT) plants. The overexpression lines of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 were treated with drought for 10 days. Compared with the wild-type Arabidopsis, the survival rate of the transgenic lines was significantly increased and the electrical conductivity was significantly decreased, indicating that overexpression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 improved the plant's tolerance to drought.

[0127] 7 Functional verification of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in tea plants 7.1 In vivo antisense oligonucleotide inhibition assay According to the sequences of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8 and LHCP9, the primers for synthesizing antisense oligonucleotides were designed. The primer sequences are as shown below: LHCP1, (5'-TTTTGGCCCGCAACGGTG-3', SEQ ID NO.67); LHCP2, (5'-AGGTGATCCATTGCAGATGG-3', SEQ ID NO.68); LHCP3, (5'-GACCTCGTTGGCAGAGGA-3', SEQ ID NO.69); LHCP4, (5'-CGCACCCGGTGATTTCGGG-3', SEQID NO.70); LHCP5, (5'-ACACTCTTTTTGTTTTCGA-3', SEQ ID NO.71); LHCP6, (5'-TTTCATATGACTATCCTTC-3', SEQ ID NO.72); LHCP7, (5'-GCCGAGGACCCAGAGAA-3', SEQID NO.73); LHCP8, (5'-TGGAGATTATGGTTACGATC-3', SEQ ID NO.74); LHCP9, (5'-GAGGTTTAGGGAGTGTGA-3', SEQ ID NO.75), were dissolved in sterile water to prepare an in vitro oligonucleotide antisense inhibitory primer solution, and the blank was sterile water; one bud and two leaves of basically the same size, bright color, healthy color, and no insects or diseases were cut with scissors, and were inserted into 1.5 mL centrifuge tubes containing 1 ml of 20-in vitro oligonucleotide antisense inhibitory primer solution respectively, and it was ensured that the tail of one bud and two leaves was immersed in the solution. The centrifuge tube was placed in a light incubator for light culture at 16 h / darkness for 8 h, and the incubator temperature was 25 °C. After 0 h, 6 h, 12 h, and 36 h of treatment, one bud and two leaves were stored in a -80 °C refrigerator with liquid nitrogen. Subsequently, samples were taken from the primer treatment group and the control group for gene expression analysis.

[0128] 7.2 Analysis of the effects of oligonucleotide antisense inhibition on gene expression in tea plants Total RNA was extracted from the treated samples and control samples, and then reverse transcribed to synthesize the first-strand cDNA, and the expression of related genes was detected by quantitative PCR. The gene expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in the control and treated samples were detected using the same method as above. The results showed that the oligonucleotide antisense inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 could significantly interfere with the expression level of the target gene, and the inhibition effect of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 was the best at 6 h.

[0129] 7.3 In vitro oligonucleotide antisense inhibition sample drought treatment and biochemical index determination In order to study the role of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in tea drought stress, the samples treated with oligonucleotide antisense inhibitory primer solution for 6 h were subjected to drought treatment for 12 h, and the changes in Fv / Fm values ​​were observed. The malondialdehyde content after drought treatment was determined according to the kit (Cat. No.: BC0020) (Solarbio, China). The results showed that in the in vitro oligonucleotide antisense inhibition experiment, the expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in the samples were inhibited compared with the control. After drought treatment, compared with the control group, plants with inhibited LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 suffered more damage, significantly reduced Fv / Fm ratio, and significantly increased malondialdehyde content, indicating that in vivo expression inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes can significantly reduce the drought tolerance of tea trees.

[0130] 8 ROC validation of gene markers for drought memory key genes in the application of drought-resistant tea variety breeding Tea leaf samples were collected from 100 tea varieties during the summer drought period (June-August) at the Gaoqiao Tea Experimental Base in Hunan Province (113°08′E, 28°20′N), including: drought-resistant varieties (50 samples): screened by drought-resistant phenotype identification for 3 consecutive years (ratio of drought-damaged leaves ≤30%), and non-drought-resistant varieties (50 samples): confirmed by the same identification (ratio of drought-damaged leaves ≥85%). Target genes: LHCP1 (TEA009770, SEQ ID NO.1), LHCP2 (TEA019232, SEQ ID NO.2), LHCP3 (TEA001864, LHCP4 (TEA008963, SEQ ID NO.4), LHCP5 (TEA000535, SEQ ID NO.5), LHCP6 (TEA015295, SEQ ID NO.6), LHCP7 (TEA016942, SEQ ID NO.7), LHCP8 (TEA023017, SEQ ID NO.8), LHCP9 (TEA026680, SEQ ID NO.9) (i.e., the 9 key drought memory genes screened in 4.3 of Example 1).

[0131] First, the expression levels of the nine genes in different samples were analyzed by RT-qPCR. Total RNA was extracted from tea leaves using the FastPure Universal Plant Total RNA Isolation Kit (Vazyme, China). The first-strand cDNA was synthesized using Evo M-MLV Reverse Transcription Premix (Agbio, China). RT-qPCR was performed on a QuantStudio 3 system (Thermo Fisher Scientific, USA) using the SYBR Green Premix Pro Taq HS qPCR Kit (Agbio, China). Each 20 μL reaction included 10 μL 2× SYBR Green Premix, 0.4 μL of each primer (forward and reverse), 1 μL cDNA template, and RNase-free water, with a final volume of 20 μL. The cycling conditions were: 95 ℃ 30 s, 95 ℃ 5 s, 60 ℃ 30 s, and 40 cycles. The primers for the nine genes were the same as in step 4.5. Gene expression was normalized to the internal reference β-actin and the expression of the genes was normalized to the internal reference β-actin using The relative expression was calculated by the ROC method. ROC analysis was performed using R language (v4.3.1) and pROC package (v1.18.4). According to the sensitivity and specificity data of each gene, the ROC curve was drawn independently for each gene, and the area under the curve (AUC) was calculated. Significant criterion: AUC>0.85, indicating that the gene is a marker for tea trees under drought and can be used for the screening of drought-resistant tea varieties.

[0132] The results are as follows Fig.19 As shown, the area under the curve (AUC) of the nine key genes of drought memory were all greater than 0.85, indicating that each gene had a high predictive ability for drought resistance classification and reached the excellent diagnostic standard. The results included LHCP1: AUC = 0.896; LHCP2: AUC = 0.89; LHCP3: AUC = 0.895; LHCP4: AUC = 0.9; LHCP5: AUC = 0.878; LHCP6: AUC = 0.898; LHCP7: AUC = 0.899; LHCP8: AUC = 0.896; LHCP9: AUC = 0.89. Analysis: LHCP4: had the highest AUC value (0.9), indicating that it performed best in distinguishing drought sensitivity. LHCP7: followed closely with an AUC value of 0.899, also showing good discrimination ability. The AUC values ​​of the six genes LHCP1, LHCP6, LHCP8, LHCP2, LHCP3, and LHCP9 were all around 0.89 to 0.896, which performed well. The AUC value of LHCP5 (0.878) was relatively good in distinguishing drought sensitivity. Conclusion: From the perspective of AUC values, LHCP4 and LHCP7 performed best in predicting drought sensitivity, especially when combined, the predicted AUC value was higher. The other seven genes performed relatively well in predicting drought sensitivity. These genes play an important role in the biological mechanism of drought sensitivity. In summary, the results can be applied to the actual breeding process, using these nine drought memory key gene markers to screen drought-resistant tea varieties.

[0133] Validation of the accuracy of gene markers for 6 drought memory key genes in the breeding of drought-resistant tea varieties Tea leaf samples were collected from 100 tea varieties during the summer drought period (June-August) at the Gaoqiao Tea Experimental Base in Hunan Province (113°08′E, 28°20′N), including: drought-resistant varieties (50 samples): screened by drought-resistant phenotype identification for 3 consecutive years (ratio of drought-damaged leaves ≤30%), and non-drought-resistant varieties (50 samples): confirmed by the same identification (ratio of drought-damaged leaves ≥85%). Target genes: LHCP1 (TEA009770, SEQ ID NO.1), LHCP2 (TEA019232, SEQ ID NO.2), LHCP3 (TEA001864, SEQ ID NO.3), LHCa6 (TEA008963, SEQ ID NO.4), LHCa3 (TEA000535, SEQ ID NO.5), LHCb7 (TEA015295, SEQ ID NO.6), LHCP4 (TEA016942, SEQ ID NO.7), LHCb5 (TEA023017, SEQ ID NO.8), LHCb4 (TEA026680, SEQ ID NO.9) (i.e., the 9 drought memory key genes screened in 4.3 of Example 1). At the same time, the present invention selected a gene TCP8 (TEA003322) that responded to the first drought but did not respond to the second and third droughts as a control.

[0134] The sequence for TCP8 (TEA003322) is as follows:

[0135] First, the expression levels of 10 genes in different samples were blindly tested for RT-qPCR analysis. The detection process and the reagents used for retrieval were the same as above. Among them, the newly added primers for TCP8 were: F-terminal primer: GTTTGATGGCCGATCTCAT (SEQ ID NO.29), R-terminal primer: GGTGGAGGTGGTTGAAGTGT (SEQ ID NO.30). Gene expression was normalized to the internal reference β-actin, and The relative expression was calculated by the method. The results of RT-qPCR analysis were used to determine whether the plant was drought-resistant, and then compared with the actual results to obtain the accuracy and recall data. Among them, the accuracy is defined as: among all samples predicted to be "drought-resistant", the higher the proportion of true drought-resistant samples. For example, the accuracy of LHCP1 is 0.94, which means that when the expression of the gene exceeds the threshold, it is indeed drought-resistant in 94% of cases, which can reduce false positives. The recall rate is defined as: the proportion of correctly identified samples among all true drought-resistant samples. For example, the recall rate of LHCb4 is 0.952, which means that when the expression of the gene exceeds the threshold, 95.2% of the true drought-resistant samples can be detected. The data of the optimal threshold, accuracy and recall rate of the 10 genes are shown in Table 4.

[0136] Note: Significant standard: AUC greater than 0.85 indicates that the model has good prediction effect.

[0137] The higher the accuracy, the more reliable the prediction result; the higher the recall rate, the higher the proportion of correct identification in the real drought-resistant samples, indicating that the gene is a marker of tea trees under drought and can be used for the screening of drought-resistant tea tree varieties. As shown in Table 4, each gene has a high predictive ability for drought resistance classification and meets the excellent diagnostic standard. LHCP1 feature: When the expression level is greater than 13.01, the accuracy can reach 94% and the recall rate can reach 73.4%; LHCP2 feature: When the expression level is greater than 15.14, the accuracy can reach 76% and the recall rate can reach 95%; LHCP3 feature: When the expression level is greater than 14.18, the accuracy can reach 82% and the recall rate can reach 89.1%; LHCa6 feature: When the expression level is greater than 13.65, the accuracy can reach 86% and the recall rate can reach 84.3%; LHCa3 feature: When the expression level is greater than 14.05 , the accuracy can reach 82%, and the recall rate can reach 85.4%; LHCb7 feature: when the expression level is greater than 13.54, the accuracy can reach 88%, and the recall rate can reach 73.3%; LHCP4 feature: when the expression level is greater than 14.40, the accuracy can reach 80%, and the recall rate can reach 87%; LHCb5 feature: when the expression level is greater than 15.63, the accuracy can reach 80%, and the recall rate can reach 95.2%; LHCb4 feature: when the expression level is greater than 14.36, the accuracy can reach 84%, and the recall rate can reach 91.3%. At the same time, the control gene is a gene that responds to the first drought but does not respond to multiple droughts. It does not belong to the key memory gene. Its prediction effect is extremely poor, and its accuracy is 50%, and the recall rate is 50%, indicating that its drought sensitivity prediction ability is extremely low. This aspect illustrates the accuracy and necessity of the 9 key memory genes screened by the present invention through repeated droughts. It shows that the results can be applied to the actual breeding process, using these 9 drought memory key gene markers to screen drought-resistant tea varieties.

[0138] Therefore, the present invention takes the intrinsic genetic genes of tea trees as the starting point, analyzes the metabolites and genes of tea trees in response to drought stress based on the metabolome and transcriptome, screens out the key genes of drought memory of tea trees, and applies them in the screening of drought-resistant tea trees, providing support for further exploring the drought-resistant germplasm resources of tea trees and cultivating new drought-resistant varieties.

[0139] It should be noted that the above embodiments are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. Any obvious changes or modifications derived from the technical solution of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for screening drought-resistant tea germplasm, characterized in that: The following steps are involved: Step 1: After the tea trees are repeatedly subjected to drought treatment, new shoot leaves are taken as samples; Determine the phenotype, metabolome data, and transcriptome data of the samples; Step 2: Screen the metabolites from the metabolome data for differential metabolites whose expression patterns showed no response or no significant response to the first drought exposure, but showed significant response after repeated droughts as "memory" metabolites; Step 3: Screening and analyzing differentially expressed genes in tea plants subjected to repeated drought treatments: Screening out key memory genes from differentially expressed genes; Step 4: Based on the transcription level of the key memory gene in tea leaves, determine that the tea germplasm with high transcription level is drought-resistant tea germplasm.

2. The method according to claim 1, characterized in that The key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.

9.

3. The method according to claim 1, characterized in that: When the transcription level of the gene shown in SEQ ID NO.1 is greater than 13.01, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.2 is greater than 15.14, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.3 is greater than 14.18, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.4 is greater than 13.65, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.5 is greater than 14.05, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.6 is greater than 13.54, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.7 is greater than 14.40, the tea tree is determined to be a drought-resistant tea tree; when the transcription level of the gene shown in SEQ ID NO.8 is greater than 15.63, the tea tree is determined to be a drought-resistant tea tree; When the transcription level of the gene shown in NO.9 is greater than 14.36, the tea tree is determined to be a drought-resistant tea tree.

4. A method for screening key memory genes that respond to periodic drought stress, characterized in that: The following steps are involved: Step 1: After the tea trees are repeatedly subjected to drought treatment, new shoot leaves are taken as samples; Determine the phenotype, metabolome data, and transcriptome data of the samples; Step 2: Screen the metabolites from the metabolome data for differential metabolites whose expression patterns showed no response or no significant response to the first drought exposure, but showed significant response after repeated droughts as "memory" metabolites; Step 3: Screening and analyzing differentially expressed genes in tea trees subjected to repeated drought treatments: Screening out key memory genes from the differentially expressed genes.

5. The application of photosynthesis-related genes in screening drought-resistant tea trees is characterized in that: The photosynthesis-related gene is shown in the sequence SEQ ID NO.

9.

6. Use of a kit for screening drought-resistant tea trees, characterized in that: The kit comprises a reagent for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are shown in the sequence SEQ ID NO.

9.

7. The use according to claim 6, characterized in that: The reagent for detecting the transcription level of photosynthesis-related genes includes primers, TaqMan probes or gene chips for detecting photosynthesis-related genes.

8. The use according to claim 7, characterized in that: The primers for detecting photosynthesis-related genes include the following primer pairs: The primer pair for detecting the gene shown in SEQ ID NO.9 is as follows: SEQ ID NO.26: GTGTTTGGGTTGCAGAGGTT; SEQ ID NO.27: AAGCTCAGCATTCCTTTGGA.

9. A method for identifying drought resistance of plant germplasm, characterized in that: The kit is used to detect the transcription level of the gene shown in SEQ ID NO.9 in the plant, and the plant germplasm with high transcription level is a drought-resistant plant; the kit includes a reagent for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are shown in the sequence SEQ ID NO.

9.

10. A method for improving plant drought resistance, characterized in that: The preparation is used to increase the level and / or activity of the gene shown in SEQ ID NO.9 in the plant; The preparation comprises at least one of the following: i. a nucleic acid molecule as shown in SEQ ID NO.9; ii. an expression vector comprising the nucleic acid molecule shown in SEQ ID NO.9; iii. a recombinant host containing ii; iv. a promoter or enhancer that enhances the expression of the gene shown in SEQ ID NO.9; v. An inducer that promotes the expression of the gene shown in SEQ ID NO.9.

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