A method for screening drought-resistant tea tree germplasms and its application

Abstract

The present invention relates to a method for screening drought-resistant tea germplasms and its application. The method for screening drought-resistant tea germplasms includes: Step 1: After repeatedly subjecting tea plants to drought treatment, measuring the phenotypic, metabolome data, and transcriptome data of the samples; Step 2: Screening from the metabolome data differential metabolites with an expression pattern of not responding or having no significant response when first subjected to drought, but having a significant response after multiple repeated droughts as "memory" metabolites; Step 3: Screening and analyzing differential genes in tea plants subjected to repeated drought treatment: Screening out memory key genes from the differential genes; Step 4: According to the transcriptional level of the memory key genes in tea leaves, determining those with a high transcriptional level as drought-resistant tea germplasms. The present invention verifies 9 memory key genes, and the results show that the selected genes are consistent with the transcriptome sequencing results, and they have good sensitivity, specificity, and accuracy in screening drought-resistant tea plants or improving the drought resistance of tea plants.

Description

Technical Field

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

[0002] Drought is an important environmental factor restricting 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 recurring and continuous event. In this environmental context, plants must evolve more efficient drought tolerance mechanisms to cope with this recurring drought stress. The expression patterns of genes under repeated stress are the "stress memory" of plants. Genes that respond to stress and show similar responses in each stress are defined as "non-memory" genes, while those that show different responses in 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 the drought tolerance of tea trees often regards drought stress as a single event that occurs only once, and there is no prior art on the response of tea trees to periodic drought stress. For example, the prior art CN115948602A discloses screening differentially expressed genes based on the gene expression levels involved in a single drought stress of tea trees to obtain the sequences of differentially expressed genes; associating SSR and ILP molecular marker screening with the differentially expressed genes of tea tree drought stress to obtain the molecular marker primers for tea tree drought stress. Summary of the Invention

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

[0005] To achieve the above purpose, the present invention adopts the following technical solutions:

[0006] A method for screening drought-resistant tea germplasms, comprising the following steps:

[0007] Step 1: After subjecting tea trees to repeated drought treatment, taking the new shoot leaves as samples; measuring the phenotypic, metabolomic, and transcriptomic data of the samples;

[0008] Step 2: Screening from the metabolomic data the differential metabolites with an expression pattern of not responding or having no significant response when first subjected to drought, but having a significant response after multiple repeated droughts as "memory" metabolites;

[0009] Step 3: Screening and analyzing the differential genes in tea trees under repeated drought treatment: screening out the memory key genes from the differential genes;

[0010] Step 4: Determine the tea tree germplasms with high transcriptional levels as drought-resistant ones according to the transcriptional levels of memory key genes in tea tree leaves.

[0011] In one preferred embodiment, the repeated drought treatment means that after the drought treatment reduces the soil water content by no more than 10%, rehydration treatment is carried out; after the rehydration treatment, the next round of drought treatment is carried out; the number of repeated treatments is not less than 2 times.

[0012] In one preferred embodiment, in Step 2, when screening for 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 normalized by z-score, and then K-means clustering analysis is carried out; KEGG enrichment analysis is carried out on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways involved by the differential metabolites.

[0013] In one preferred embodiment, in Step 2, it also includes aligning the transcriptome data with the reference genome and evaluating the quality; the steps for aligning the transcriptome data with the reference genome are: using HISAT2 to perform sequence alignment of Clean Reads with the reference genome to obtain the position information on the reference genome or gene, as well as the sequence characteristic information unique to the sequencing sample.

[0014] In Step 2, the result of the transcriptome data quality assessment can reflect the quality level of this transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be carried out.

[0015] In one preferred embodiment, in Step 3, the conditions for screening differential genes are |log2Fold Change| > 1 and FDR < 0.05.

[0016] In one preferred embodiment, in Step 3, the analysis of differential genes is: performing K-means clustering analysis, GO enrichment analysis and KEGG enrichment analysis on the differentially expressed genes; more specifically, it means screening out GO and gene key gene pathways with P values less than 0.05. GO annotation illustrates the basic functions of genes, and KEGG illustrates the metabolic pathways involved by genes.

[0017] In one preferred embodiment, in Step 3, the screening criteria for memory key genes are: genes that do not respond when the tea tree is first subjected to drought but have a significant response after multiple repeated droughts.

[0018] In one preferred embodiment, in step 3, it further includes verifying the memory key 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 the memory key genes under drought; using R language to analyze the reliability verification of the memory key genes as markers.

[0019] In one preferred embodiment, the memory key genes are photosynthesis-related genes, and the photosynthesis-related genes are selected from any one or several of the sequences SEQ ID NO.1 - SEQ ID NO.9.

[0020] The present invention excavates the "memory key genes" in tea plants that did not respond when first subjected to drought but showed significant responses after multiple repeated drought stresses through setting multiple rounds of repeated drought stress treatments, and verifies these genes as drought markers for application in screening drought-resistant tea varieties.

[0021] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.1 is greater than 13.01, the tea plant is determined to be a drought-resistant tea plant.

[0022] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.2 is greater than 15.14, the tea plant is determined to be a drought-resistant tea plant.

[0023] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.3 is greater than 14.18, the tea plant is determined to be a drought-resistant tea plant.

[0024] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.4 is greater than 13.65, the tea plant is determined to be a drought-resistant tea plant.

[0025] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.5 is greater than 14.05, the tea plant is determined to be a drought-resistant tea plant.

[0026] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.6 is greater than 13.54, the tea plant is determined to be a drought-resistant tea plant.

[0027] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.7 is greater than 14.40, the tea plant is determined to be a drought-resistant tea plant.

[0028] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.8 is greater than 15.63, the tea plant is determined to be a drought-resistant tea plant.

[0029] In one preferred embodiment, when the transcription level of the gene shown in SEQ ID NO.9 is greater than 14.36, the tea plant is determined to be a drought-resistant tea plant.

[0030] Based on the same inventive concept, the present invention also claims to protect a method for screening key memory genes responsive to periodic drought stress, comprising the following steps:

[0031] Step 1: After subjecting the tea plant to repeated drought treatment, take the new shoot leaves as samples; measure the phenotypes, metabolome data, and transcriptome data of the samples;

[0032] Step 2: Screen from the metabolome data differential metabolites with an expression pattern of non-response or non-significant response when first subjected to drought, but with a significant response after multiple repeated droughts as "memory" metabolites;

[0033] Step 3: Screen and analyze the differential genes in the tea plant subjected to repeated drought treatment: screen out the key memory genes from the differential genes.

[0034] In one preferred embodiment, in Step 2, when screening for 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 subjected to z-score normalization, followed by K-means clustering analysis; perform KEGG enrichment analysis on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways involved in the differential metabolites.

[0035] In one preferred embodiment, in Step 2, it further 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 perform sequence alignment of Clean Reads with the reference genome to obtain the position information on the reference genome or gene, as well as the sequence characteristic information unique to the sequencing sample.

[0036] In Step 2, the result of the transcriptome data quality assessment can reflect the quality of this transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be carried out.

[0037] In one preferred embodiment, in Step 3, the conditions for screening differential genes are |log2Fold Change| > 1 and FDR < 0.05.

[0038] In one preferred embodiment, in step 3, the analysis of differential genes is as follows: perform K-means clustering analysis, GO enrichment analysis, and KEGG enrichment analysis on differentially expressed genes; more specifically, it means: screening out GO and gene key gene pathways with a P-value less than 0.05. GO annotation explains the basic functions of genes, and KEGG explains the metabolic pathways involved by genes.

[0039] In one preferred embodiment, in step 3, the screening criteria for memory key genes are: genes in tea plants that do not respond when first exposed to drought but have a significant response after multiple repeated droughts.

[0040] In one preferred embodiment, in step 3, it also includes the verification of memory key genes. 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 memory key genes under drought; using R language to analyze the reliability verification of memory key genes as markers.

[0041] In one preferred embodiment, the memory key genes are photosynthesis-related genes, and the photosynthesis-related genes are selected from any one or several of the sequences SEQ ID NO.1 - SEQ ID NO.9.

[0042] Based on the same inventive concept, the present invention also claims the application of photosynthesis-related genes in screening drought-resistant tea plants, wherein the photosynthesis-related genes are selected from any one or several of the sequences SEQ ID NO.1 - SEQ ID NO.9.

[0043] In one preferred embodiment, the photosynthesis-related gene is the gene shown in SEQ ID NO.4.

[0044] In one preferred embodiment, the photosynthesis-related gene is the gene shown in SEQ ID NO.7.

[0045] A kit for screening drought-resistant tea plants includes reagents for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are selected from any one or several of the sequences SEQ ID NO.1 - SEQ ID NO.9.

[0046] In one preferred embodiment, the reagents for detecting the transcription level of photosynthesis-related genes include primers, TaqMan probes, or gene chips for detecting photosynthesis-related genes.

[0047] In one preferred embodiment, the primers for detecting photosynthesis-related genes include any one or several pairs of the following primer pairs:

[0048] Primer pairs for detecting the gene shown in SEQ ID NO.1: SEQ ID NO.10: AGGCTTTTGCAGAGCTCAAG; SEQ ID NO.11: CATAAGCCCATGCATTGTTG;

[0049] Primer pairs for detecting the gene shown in SEQ ID NO.2: SEQ ID NO.12: TTGTATTTGGGCCCACTCTC; SEQ ID NO.13: CACCGAACTTGACACCATTG;

[0050] Primer pairs for detecting the gene shown in SEQ ID NO.3: SEQ ID NO.14: GTTCGGTGAGGCTGTATGGT; SEQ ID NO.15: TAGAGTGGGTCGGTCACCTC;

[0051] Primer pairs for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16: TTGAATTCCTTGCCATCTCC; SEQ ID NO.17: AACACAGATCCCCACGAAAG;

[0052] Primer pairs for detecting the gene shown in SEQ ID NO.5: SEQ ID NO.18: AGCCTATGGCGAGATCTTCA; SEQ ID NO.19: GGGTCAGCCCAATAGTCGTA;

[0053] Primer pairs for detecting the gene shown in SEQ ID NO.6: SEQ ID NO.20: ATATTCTCGGCGGTTCCTTT; SEQ ID NO.21: AGCCGTAGTCCCCTGGTAGT;

[0054] Primer pairs for detecting the gene shown in SEQ ID NO.7: SEQ ID NO.22: GGTGTCACCGGAATGCTACT; SEQ ID NO.23: AGGGTAGCCACACTCATTGG;

[0055] Primer pairs for detecting the gene shown in SEQ ID NO.8: SEQ ID NO.24: GTACGGTCCCGACAGAAGAA; SEQ ID NO.25: GGCCACAGTTAGCACCAAAT;

[0056] Primer pair for detecting the gene shown in SEQ ID NO.9: SEQ ID NO.26: GTGTTTGGGTTGCAGAGGTT; SEQ ID NO.27: AAGCTCAGCATTCCTTTGGA.

[0057] In one 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: AACACAGATCCCCACGAAAG.

[0058] In one preferred embodiment, the primers for detecting photosynthesis-related genes include a primer pair for detecting the gene shown in SEQ ID NO.7:

[0059] Primer pair for detecting the gene shown in SEQ ID NO.7: SEQ ID NO.22: GGTGTCACCGGAATGCTACT; SEQ ID NO.23: AGGGTAGCCACACTCATTGG.

[0060] A method for identifying the drought resistance of plant germplasm, which detects the transcription level of the gene shown in any one or more of SEQ ID NO.1 - SEQ ID NO.9 in the plant with the described kit, and the plant germplasm with a high transcription level is a drought-resistant plant.

[0061] Based on the same inventive concept, the present invention also claims a preparation for improving the drought resistance of plants, including at least one of the following i)-v):

[0062] i), a nucleic acid molecule shown in any one or more of SEQ ID NO.1 - SEQ ID NO.9;

[0063] ii), an expression vector containing the nucleic acid molecule shown in any one or more of SEQ ID NO.1 - SEQ ID NO.9;

[0064] iii), a recombinant host containing ii);

[0065] iv), a promoter or enhancer that enhances the expression of the gene shown in any one or more of SEQ ID NO.1 - SEQ ID NO.9;

[0066] v), an inducer that promotes the expression of the gene shown in any one or more of SEQ ID NO.1 - SEQ ID NO.9.

[0067] Based on the same inventive concept, the present invention also claims the use of the said preparation in improving the drought resistance of plants.

[0068] Based on the same inventive concept, the present invention also claims a method for improving the drought resistance of plants, which uses the said preparation to increase the level and / or activity of any one or more of the genes shown in SEQ ID NO.1 - SEQ ID NO.9 in plants.

[0069] According to the embodiments of the present invention, the inhibition of the expression of the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 genes in plants can reduce the drought tolerance of plants. Overexpression of these genes improves the drought tolerance of tea plants. The inhibition of the expression of the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 genes in tea plants by antisense oligonucleotide technology can significantly exacerbate the drought damage of tea plants.

[0070] The beneficial technical effects of the present invention are as follows:

[0071] Based on multi-omics technology, the present invention screens out the "memory key genes" in tea plants that do not respond when first exposed to drought but respond significantly after multiple repeated droughts. Combining the pathways enriched with differential metabolites and the results of bioinformatics analysis of differentially expressed genes, important tea plant drought memory key genes are accurately and efficiently mined and identified. The experimental results show that the metabolic pathways co-enriched with differential metabolites under different rounds of drought treatments are amino acid biosynthesis and aminoacyl-tRNA biosynthesis. The typical tea plant drought memory key genes are mainly the genes shown in SEQ ID NO.1 - SEQ ID NO.9. RT-qPCR verification of nine memory key genes shows that the expression patterns of the selected genes are consistent with the transcriptome sequencing results, indicating that the experimental sequencing results of the present invention and the screened memory key genes are correct. In the present invention, the LHC-type tea plant photosynthesis genes (i.e., LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9) that regulate the response of tea plants to drought stress are cloned and verified for the first time. These genes play a regulatory role in the process of tea plants responding to drought stress and affect the formation of drought resistance of tea plants. The present invention also provides recombinant plasmids and transgenic engineering bacteria containing the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 genes. The present invention lays a foundation for seeking reasonable regulatory measures and screening suitable drought-resistant breeding materials for tea plants suffering from high-frequency and repeated drought stresses. Description of the Drawings

[0072] Figure 1 It is a schematic diagram of the repeated drought stress experiment. In the figure, CYCLE 1 is the first round of drought stress treatment; CYCLE 2 is the second round of drought stress treatment; CYCLE 3 is the third round of drought stress treatment.

[0073] Figure 2 It is a diagram of the anatomical structure and parameters of tea tree leaves under repeated drought stress (×36.5); in the figure, Figure 2 A is the anatomical structure of tea tree leaves in the control group; Figure 2 B is the anatomical structure of tea tree leaves under single drought treatment; Figure 2 C is the anatomical structure of tea tree leaves under two rounds of drought treatment; Figure 2 D is the anatomical structure of tea tree leaves under three rounds of drought treatment.

[0074] Figure 3 It is a diagram of the stomatal morphology and parameters of tea tree leaves under repeated drought stress (×300); in the figure, Figure 3 A is the stomatal morphology of tea tree leaves in the control group; Figure 3 B is the stomatal morphology of tea tree leaves in the single-round drought treatment group; Figure 3 C is the stomatal morphology of tea tree leaves in the two-round drought treatment group; Figure 3 D is the stomatal morphology of tea tree leaves in the three-round drought treatment group; Figure 3 E is the stomatal density of tea tree leaves under repeated drought stress; Figure 3 F is the major axis length of tea tree 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.

[0075] Figure 4 It is a volcano plot of differential metabolites in the new shoots of tea trees in the CK group and the D1 group under repeated drought stress.

[0076] Figure 5 It is a volcano plot of differential metabolites in the new shoots of tea trees when comparing the CK group and the D2 group under repeated drought stress.

[0077] Figure 6 It is a volcano plot of differential metabolites in the new shoots of tea trees in the CK group and the D3 group under repeated drought stress.

[0078] Figure 7 It is a volcano plot of differential metabolites in the new shoots of tea trees in the D1 group and the D2 group under repeated drought stress.

[0079] Figure 8 It is a volcano plot of differential metabolites in the new shoots of tea trees in the D1 group and the D3 group under repeated drought stress.

[0080] Figure 9 It is a volcano plot of differential metabolites in the new shoots of tea trees in the D2 group and the D3 group under repeated drought stress.

[0081] Figure 10 It is the K-means clustering analysis diagram of differential metabolites under repeated drought stress; in the diagram, Figure 10 A are metabolites showing an upward trend under repeated drought stress; Figure 10 B are metabolites showing a downward trend under repeated drought stress; Figure 10 C are metabolites showing a trend of rising first and then falling under repeated drought stress.

[0082] Figure 11 It is the volcano plot of differential genes of tea plants in the CK group and D1 group under repeated drought stress.

[0083] Figure 12 It is the volcano plot of differential genes of tea plants in the CK group and D2 group under repeated drought stress.

[0084] Figure 13 It is the volcano plot of differential genes of tea plants in the CK group and D3 group under repeated drought stress.

[0085] Figure 14 It is the volcano plot of differential genes of tea plants in the D1 group and D2 group under repeated drought stress.

[0086] Figure 15 It is the volcano plot of differential genes of tea plants in the D1 group and D3 group under repeated drought stress.

[0087] Figure 16 It is the volcano plot of differential genes of tea plants in the D2 group and D3 group under repeated drought stress.

[0088] Figure 17 It is the heatmap of differential expression of genes related to photosynthesis and genes related to light-harvesting complex binding proteins; among them, Figure 17 A is the heatmap of differential expression of genes related to photosynthesis; Figure 17 B is the heatmap of differential expression of light-harvesting complex binding protein genes.

[0089] Figure 18 It is the RT-qPCR to verify the expression pattern of key genes for memory under repeated drought stress.

[0090] Figure 19 It is the ROC verification of gene markers for the application of drought memory key genes in the breeding of drought-resistant tea plant varieties. Specific implementation manners

[0091] The present invention is not limited to the following specific implementation manners. Those of ordinary skill in the art can implement the present invention in other various specific implementation manners according to the content disclosed in the present invention, or any implementation manners that make simple changes or modifications by adopting the design structure and idea of the present invention fall within the protection scope of the present invention. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other.

[0092] The genes involved in the present invention are:

[0093] SEQ ID NO.1: LHCP1 (TEA009770)

[0094] ATGGCTGCGTCTACCATGGCTCTCTCTTCACCATCTTTTGCCGGAAAGGCTGTGAAAATTGCCCCGGAGGTTCTTGGTGGTGGAAGGATCAGTATGAGGAAGACCGGCAAGCAAGTCCCATCTGGAAGCCCGTGGTACGGTCCAGACCGAGTCTTGTATTTGGGTCCATTATCCGGTGAGCCCCCATCCTACCTCACTGGGCAATTCCCTGGTGATTATGGTTGGGACACTGCTGGGCTTTCGGCTGATCCAGAAACTTTTGCCAAGAACCGTGAGCTCGAGGTGATTCACTGCAGATGGGCCATGCTTGGAGCTTTGGGTTGCGTCTTCCCCGAGCTTTTGGCCCGCAACGGTGTCAAGTTCGGCGAGGCTGTGTGGTTCAAGGCCGGTGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCCCAAAGCATTCTGGCCATCTGGGCTTGCCAAGTTATCTTGATGGGTGCTGTTGAGGGCTACCGCATTGCTGGTGGACCACTCGGGGAGGTGACCGACCCGCTCTACCCCGGTGGAAGCTTCGACCCATTGGGCCTAGCCGATGATCCAGAGGCTTTTGCAGAGCTCAAGGTGAAGGAGATCAAGAATGGAAGACTGGCCATGTTCTCAATGTTTGGGTTCTTTGTTCAGGCAATTGTGACAGGAAAGGGACCATTGGAGAACCTGGCTGACCACCTTGCTGACCCTGTTAACAACAATGCATGGGCTTATGCCACAAACTTTGTCCCAGGAAAGTGA。

[0095] SEQ ID NO.2: LHCP2 (TEA019232)

[0096] ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0097] SEQ ID NO.3: LHCP3 (TEA001864)

[0098] ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAATATTGTACTGTCTAATGGGTTGTTTGGATACAGGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0099] SEQ ID NO.4: LHCP4 (TEA008963)

[0100] ATGGCTTCCAAAGCCCTAATGAGCTGCGGCATCGCCGCCGTCTGCCCGTCAGTCCTTTCCTCTTCCAAGTCCAAATTTGCCGCCGCGTTGCGGCTTCCAAGTGGTGGTGCCACCGCTACCTCCCGGTTCACCATGACGGCTGACTGGATGCCTGGCGAGCCAAGGCCACCCTATCTTGACGGCTCCGCACCCGGTGATTTCGGGTTCGACCCGCTTCGTCTGGGTGAAGTCCCAGAAAACCTTGAAAGATACAAGGAGTCTGAACTCATTCACTGCAGATGGGCTATGCTTGCTGTTCCAGGGATCCTAGTTCCAGAGGCCTTGGGATTGGGCAACTGGGTACAAGCTCAAGAGTGGGCGGCAATCCCTGGAGGACAAGCCACCTACCTTGGCCAACCTGTCCCATGGGGCACCCTCCCAATCATCTTGGCCATTGAATTCCTTGCCATCTCCTTCGTCGAGCACCAGCGCAGCATGGAAAAGGACCCTGAGAAGAAGAAGTACCCCGGTGGAGCTTTCGACCCATTGGGATACTCCAAAGACCCAGTAAAGTTTGAGGAGAACAAGGTCAAAGAAGTAAAAAATGGCCGGCTTGCCTTGTTGGCTTTCGTGGGGATCTGTGTTCAACAGTCCGCTTACCCAGGGACAGGACCGTTGGAGAACCTGGCAACTCACTTGGCTGATCCATGGCACAACAACATTGGCGATATCATTATCCCTAGATCAATTTCTCCATGA。

[0101] SEQ ID NO.5: LHCP5 (TEA000535)

[0102] ATGGCAACTCAAGCACTGGTGTCTTCATCATCTCTTACCTCCTCAGTGGAGGCTGCAAGGCAGATTCTAGGAGGAAGGCCAGCTACCCATTCTTCAAGAAGGAAGGTCTCTTTTGTTGTTAGGGCAGCTACTACTCCCCCTGTTAAGCAAGGAGCAGATAGACCTCTCTGGTTTGCCTCCAAGCAAAGTCTCTCCTACTTAGATGGCAGCCTGCCCGGCGACTACGGATTCGACCCGCTCGGCTTGTCCGACCCAGAAGGTACCGGAGGCTTCATCGAGCCCAGATGGCTAGCCTATGGCGAGATCTTCAACGGCCGTACCGCCATGGTCGGCTCTATCGGATGCATCGCCCCAGAAATCTTGGGCAAACTCGGCCTAATTCCGCCAGAAACCGCTCTGCCGTGGTTCAAAACAGGCGTGATCCCGCCCGCTGGGACCTACGACTATTGGGCTGACCCATACACTCTTTTTGTTTTCGAATTGGCACTAGTGGGCTTTGCAGAGCACAGGAGGTTCCAGGCTTGGTACAACCCAGGCTCAATGAGTAAACAGTACTTTTTGGGCCTGGAGAAATATTTGGGCGGGACGGATAACCCTGCATACCCTGGTGGGCCACTGTTTAACCCACTTGGGCTTGGAAAGGATGAGAAGTCAATGAGGGATATGAAGTTGAAGGAGGTAAAGAACGGGAGGTTGGCCATGTTGGGTATGTTGGGTTTCTTTGTGCAGGCGTTGGTGACTGGGGTTGGACCCTTCCAGAACCTTCTGGATCATTTGGCTGACCCTGTCAACAACAATGTCTTGACCAACCTCAAGTTCCACTAA。

[0103] SEQ ID NO.6: LHCP6 (TEA015295)

[0104] ATGGCGGAGGATCGTCGGAGGAACGTCGGAGCAAACGGTGGAGGCACGATGGAGGAACGACGGAGTAATGGTGGAGAGATGCTTGTGGGTGTCTTAATATTCTCGGCGGTTCCTTTCACGGCGGTGAAAGCTATAGCCAACAGTCCCCTGGGAGAGTTGCTTCAGAGGCGATTGGAAGAGAAAAAGAAGGATGCCATCGATAATTCTTCCAATTTCAAGGCACTTGCTCAAATGGCTAGAAAGGATAGTTTATGGTATGGAGAGAAGCGTCCCCGTTGGCTTGGTCCAATTTCATATGACTATCCTTCATATCTGACTGGAGAACTACCAGGGGACTACGGCTTTGATATTGCAGGTTTAAGCAGGGATCCTGTGGCTTTCCAGAAATATTTCAACTTTGAAATACTGCATGCTCGCTGGGCCATGCTTGCAGCGCTTGGTGCTCTGATTCCCGAACTATTAGACCTAGTAGGAGCCTTTCACTTTGTTGAGCCGGTCTGGTGGAAAGTTGGATATTCAAAGCTTAAGGGTGACACATTGGACTACCTTGGCATCCCTGGGCTCCACTTAGCTGGAAGTCAAGGAGTGATTGTTATAGCTATCTGCCAAGCTCTTCTGATGGTTGGACCGGAATATGCAAGATATTGTGGCATTGAGGCTCTCGAGCCTTTAGGAATTTACTTGCCTGGGGATATCAATTATCCTGGAGGTGCACTTTTCGATCCCTTGAATCTCTCTAAAGACCCGGTATCTTTTGAGGACTTGAAGGTGAAAGAGATAAAAAATGGGCGCTTAGCAATGATTGCATGGTTAGGATTTTACACGCAAGCTGCCCTAACGGGGAAAGGGCCTGTGCAAAACCTTCTTGACCACATCTTGGATCCTTTTCATAATAACCTGCTTTCCATTCTGAAATTCATGTGA。

[0105] SEQ ID NO.7: LHCP7 (TEA016942)

[0106] ATGGCCACCGTCGCAGCTCAGGCATCCACCACGGTTCTTCGGCCATGTGCCTCGAAATCGAGGTTCCTTACCGGTTCTTCCGGTAAGCTAAACCGAGTAATCTCATTTAAACCGACATCACCTTCCTCACTCAGCTCATTCAAAGTTGAAGCCAAGAAAGGAGAATGGTTACCGGGCTTGGCCTCCCCAGGCTATCTTAACGGCAGCCTACCTGGTGACAATGGGTTCGATCCCTTGGGGCTAGCCGAGGACCCAGAGAACTTGAAATGGTTCATCCAGGCCGAGCTTGTGAACAGTCGGTGGGCCATGTTGGGTGTCACCGGAATGCTACTGCCGGAAGTGTTGAGCAGTGTCGGAATAATCAACGTTCCAAAATGGTACGATGCAGGAAAATCCGAATACTTTGCATCATCATCGACACTTTTCGTGATCGAGTTCATCTTGTTTCACTACGTGGAGATCAGACGGTGGCAAGACATCAAGAACCCTGGAAGTGTCAACCAAGATCCTATCTTCAAGAGCTATAGCTTGCCTCCCAATGAGTGTGGCTACCCTGGTGGCATTTTTAACCCCCTCAACTTTGCTCCCACTGAGGAGGCCAAAGAGAAGGAGCTCGCTAATGGGAGATTGGCAATGTTGGCATTCTTGGGGTTTGTGGTTCAGCACAATGTGACTGGAAAAGGGCCATTTGACAACCTCTTGCAGCACATCTCTGATCCATGGCACAACACAATTATTCAAACAATCAGAGGTTACTAA。

[0107] SEQ ID NO.8: LHCP8 (TEA023017)

[0108] ATGGCTTCACTGGCAGCATCAACGGCGGCTGCCTCCCTTGGCATGTCAGAAATGCTCGGAAACCCTCTCCGGAGTGGCGTAACGAGATCGGCACCTCCTCCCACCGCGACATCTAGCCCTGCCACCTTCAAGACCGTCGCACTTTTCTCCAAGAAGAAGGCTGCACCTCCCAAAAAGGCTGTCGTCTCCCCTGTTGATGACGAGCTCGCCAAGTGGTACGGTCCCGACAGAAGAATTTTCTTGCCGGAGGGGCTGTTGGACCGATCAGAAATTCCGGCATACCTCACCGGAGAAGTCCCTGGAGATTATGGTTACGATCCTTTTGGGCTTAGCAAGAAACCAGATGACTTTGCCAAGTACCAAGCATATGAGCTAATTCATGGCAGGTGGGCAATGTTGGGGGCTGCGGGCTTCATCATCCCTGAGGCCTTCAACAAATTTGGTGCTAACTGTGGCCCTGAAGCTGTTTGGTTCAAGACAGGAGCTCTACTCCTAGATGGTAACACACTGAATTACTTTGGAAAGAACATCCCCATTAATCTTATATTCGCTGTCATCGCTGAAGTTGTTCTTGTTGGTGGTGCTGAATACTACAGAATCATCAATGGATTGAATTTGGAGGACAAGCTTCACCCAGGCGGTCCATTTGATCCATTGGGGCTTGCAAAGGATCCAGACCAGGCTGCAATACTGAAGGTGAAGGAGATCAAGAACGGTAGACTTGCAATGTTTGCAATGCTCGGTTTCTTCATCCAAGCTTATGTAACGGGAGAAGGTCCAGTTGAAAACCTCGCCAAACATCTAAGCGATCCGTTTGCAAACAACTTGCTCACTGTGCTTGCTGGATCTGCTGAAAGAGCTCCTACCCTGTGA。

[0109] SEQ ID NO.9: LHCP9 (TEA026680)

[0110] ATGGCCGCAACCACCGCCGCCGCCGCTGCCGCCACATCATCATTTCTAGGCACCCGCCTCGCCGACCTATATTCCGGTTCGGGCCGGGTCCAGGCCCGGTTCGGATTCGGACGCAAAAAGGCTCCACCAAAGAAGATTGCGAAGCAGGGCTTTGACCGCCCACTTTGGTACCCGGGAGCGAAAGCGCCCGAATGGTTAGATGGGAGTCTTGTTGGGGATTACGGGTTTGACCCGTTCGGGTTGGGTAAACCGGCCGAGTACTTGCAATTTGATTTGGACTCGTTGGATCAGAACTTGGCTAAGAACTCGGCGGGTGATGTAATCGGGACCCGGTTCGAGAGCGCGGATGTGAAGTCGACGCCGTTTCAGCCGTACACAGAGGTGTTTGGGTTGCAGAGGTTTAGGGAGTGTGAGCTGATTCATGGAAGGTGGGCTATGTTGGCTACGCTCGGCGCGCTTACTGTTGAGTGGCTCACTGGTGTTACGTGGCAAGACGCTGGAAAGGTGGAGCTAATTGAAGGTTCATCCTACCTTGGCCAACCACTTCCATTTTCCATAACCACATTGATATGGATTGAGGTCATAGTCATTGGATACATAGAGTTCCAAAGGAATGCTGAGCTTGACCCGGAAAAGAGGCTCTACCCGGGTGGAAAATTCTTCGACCCGCTTGGTTTGGCCTCGGACCCAGAGAAGAAGGCAACCCTCCAATTGGCGGAGATCAAGCATGCCCGCCTTGCCATGGTAGCCTTCCTAGGTTTTGCCGTCCAAGCTGCTGTCACCGGCAAAGGGCCACTCAACAACTGGGCGACCCATTTGAGTGACCCGCTCCACACAACCATTATAGACACCTTTTTCTCTTGA。

[0111] Example 1

[0112] A method for mining key genes of drought memory in tea plants based on multi-omics, specifically as follows:

[0113] 1 Repeated drought treatment of tea plants

[0114] Tea seedlings with consistent growth and no pests and diseases were selected for the experiment. As Figure 1 shown, 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 every two days to keep the soil water content at 25%-35%. During the drought treatment of the treatment groups, watering was not carried out. After the soil water content was reduced to 10%, fresh leaves of the new shoots of the tea trees were collected or rehydration treatment was selected. After rehydration treatment, the next round of drought treatment was carried out. The single drought treatment group of tea trees was denoted as D1, the two-round drought treatment group was denoted as D2, and the three-round drought treatment group was denoted as D3.

[0115] 2 Phenotypic analysis of tea tree leaves

[0116] Randomly selected leaves of the new shoots after treatment, and tissue blocks of about 5 mm×10 mm were taken on both sides of the main vein of the leaves. They were fixed with FAA solution (a mixture of 5 mL of formalin, 90 mL of 70% ethanol, and 5 mL of glacial acetic acid), dehydrated with gradient ethanol (gradient: 70% ethanol → 85% ethanol → 95% ethanol → 100% ethanol, and the soaking time for each level was about 1-2 hours, adjusted according to the tissue size), cleared with gradient xylene (ethanol-xylene mixture, ethanol-xylene 1:1 mixture → pure xylene, and the transition of the mixture took about 30 minutes - 1 hour to reduce the direct stimulation of xylene on the tissue), embedded in paraffin, sectioned with a microtome, double-stained with safranin and fast green, and sealed with neutral gum to make permanent sections. Observation and photography were carried out under an optical microscope. The results were as Figure 2 shown. The cells of the tea tree leaves were arranged neatly and tightly, and the intercellular spaces of the spongy tissue cells were small ( Figure 2 A); the cells of the tea tree leaves treated with single drought showed shortening, the intercellular spaces of the spongy tissue cells were larger, and the arrangement was loose and uneven ( Figure 2 B); the number of layers of palisade tissue cells in the tea tree leaves treated with two rounds of drought increased, and the arrangement of the spongy tissue cells was relatively tight ( Figure 2 C); the cells of the tea tree leaves treated with three rounds of drought were arranged neatly and tightly ( Figure 2 D). The Case Viewer software was used to observe and measure indexes such as the upper and lower epidermal thickness (TU, TL), palisade tissue thickness (PP), spongy tissue thickness (SP), and leaf thickness (LT) of the tea tree leaves. The results are as follows.

[0117] Among them, UE: upper epidermis; LE: lower epidermis; PP: palisade tissue; SP: spongy tissue.

[0118] As can be seen from Table 1, compared with the control group, the thickness of the upper epidermis, palisade tissue, spongy tissue, lower epidermis and leaf of tea tree leaves under single drought were all significantly reduced; compared with single-round drought, the thickness of the upper epidermis, palisade tissue, spongy tissue, lower epidermis, leaf and the ratio of palisade tissue to spongy tissue of tea tree leaves under two-round drought were all significantly increased; compared with single-round drought, the thickness of the palisade tissue, lower epidermis and the ratio of palisade tissue to spongy tissue of tea tree leaves under three-round drought stress were significantly increased, while the thickness of the spongy tissue was significantly decreased. This indicates that tea trees adapt to the environment by adjusting the change strategies of different leaf anatomical structures under drought stress of different rounds.

[0119] Randomly select the new shoot leaves after treatment and observe their upper epidermis and lower epidermis. Wash the leaves to be observed with ultrapure water, cut off the middle part avoiding the veins and their edges as the observation materials, and prepare samples by sputter coating with a vacuum coater. Observe the samples with a SEM-6380LV type scanning electron microscope and randomly select five points for taking pictures. The results are as Figure 3 shown. In the figure, 3A is the stomatal morphology of tea tree leaves in the control group; 3B is the stomatal morphology of tea tree leaves in the single-round drought treatment group; 3C is the stomatal morphology of tea tree leaves in the two-round drought treatment group; 3D is the stomatal morphology of tea tree leaves in the three-round drought treatment group; 3E is the stomatal density of tea tree leaves under repeated drought stress; 3F is the long axis length of tea tree 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 development characteristics of leaf stomata ( Figure 3 A, 3B, 3C, 3D). Stomatal density is the number of unit stomata in each microscopic image (about 0.13 mm2), and the long axis of the stoma represents the stoma size. The number of unit stomata increases with the increase of drought rounds, and the stomatal aperture decreases ( Figure 3 A, 3B, 3C, 3D). Use Image-Pro Plus 6.0 software to process the images and measure the distances. Measure the length of leaf stomata using the scale and calculate the stomatal density. The results are as Figure 3 shown in E and 3F. Under drought stress, the stomatal density of tea tree leaves is higher than that of the control group, and the stomatal aperture becomes smaller; under repeated drought stress, the stomatal density and stomatal size show different trends of change. With the increase of drought times, the stomatal density of leaves shows an upward trend, while the stomatal size shows a gradually decreasing trend. Compared with the control group, under single drought, the stomatal density increases and the stomatal size decreases, but neither reaches a significant level, and is similar to the stomatal density and aperture under two drought treatments. Under three-round drought, significant changes occur in the stomatal density and aperture of tea tree leaves. Compared with the control group, the stomatal density increases significantly by 40%, and the stomatal aperture decreases significantly by 48%.

[0120] 3 Metabolome detection

[0121] 3.1 Metabolite Extraction and Sample Preparation

[0122] Fresh leaf samples from the control group and the experimental group were subjected to freeze-drying, grinding, dissolution, centrifugation, filtration, etc. The obtained filtered samples were used for UPLC-MS / MS analysis. The specific process of sample extraction is as follows:

[0123] (1) Fresh tea leaves were placed in a freeze dryer (Scientz-100F) for vacuum freeze-drying;

[0124] (2) Ground to a powder using a grinder (MM 400, Retsch) (30 Hz, 1.5 minutes);

[0125] (3) Weigh 100 mg of the powder and dissolve it in 1.2 mL of 70% methanol extraction solution;

[0126] (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;

[0127] (5) After centrifugation (rotation speed 12000 rpm, 10 min), aspirate the supernatant, filter the sample with a microporous membrane (0.22 μm pore size), and store it in an injection vial for UPLC-MS / MS analysis.

[0128] The mass spectrometry data were processed using the software Analyst 1.6.3, and based on the metabolic database, qualitative and quantitative mass spectrometry analysis of the metabolites in the samples was performed to obtain metabolome data.

[0129] 3.2 Screening and Analysis of Differential Metabolites

[0130] A method combining the Fold Change and the VIP value of the OPLS-DA model was adopted to screen for differential metabolites. Metabolites with Fold Change > 1.5 or Fold Change < 0.67 and VIP > 1 were defined as differential metabolites. A volcano plot was used to visually display the overall distribution of differential metabolites between the two groups. The results are as Figures 4 - 9 shown. In the figure, Figure 4 is the volcano plot of differential metabolites in the new shoots of tea plants in the CK group and the D1 group under repeated drought stress; Figure 5 is the volcano plot of differential metabolites in the comparison of new shoots of tea plants in the CK group and the D2 group under repeated drought stress; Figure 6 is the volcano plot of differential metabolites in the new shoots of tea plants in the CK group and the D3 group under repeated drought stress; Figure 7 is the volcano plot of differential metabolites in the new shoots of tea plants in the D1 group and the D2 group under repeated drought stress; Figure 8Volcano plot of differential metabolites in the new shoots of tea plants in D1 group and D3 group under repeated drought stress; Figure 9 Volcano plot of differential metabolites in the new shoots of tea plants in D2 group and D3 group 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: Red dots in the figure represent up-regulated differential metabolites, green dots represent down-regulated differential metabolites, and gray dots represent 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 respectively, with 265, 363, and 270 being up-regulated and 142, 156, and 118 being down-regulated respectively. D1_vs_D2 ( Figure 7 ), D1_vs_D2 ( Figure 8 ), and D2_vs_D3 ( Figure 9 ) screened out 374, 350, and 208 differential metabolites respectively, with 221, 249, and 142 being up-regulated and 153, 101, and 66 being down-regulated respectively.

[0131] 3.3 KEGG enrichment analysis of differential metabolites

[0132] Differential metabolites interact with each other in organisms to form different pathways. The KEGG database was used to annotate and display the differential metabolites. The results showed that the metabolic pathways co-enriched by the differential metabolites in 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 metabolism pathway and flavonoid pathway.

[0133] 3.4 Trend analysis of differential metabolites and screening of memory metabolites

[0134] To study the relative content change trends of metabolites in different drought treatments, the relative contents of all differential metabolites identified according to the screening criteria in all group comparisons were z-score standardized, and then K-Means clustering analysis was performed. As Figure 10As shown in the figure, in the figure, 10A is a metabolite showing an upward trend under repeated drought stress; 5B is a metabolite showing a downward trend under repeated drought stress; 5C is a metabolite showing an initial upward and then downward trend under repeated drought stress. It can be seen that 798 differential metabolites can be divided into 3 different expression patterns, denoted as Sub Class 1, Sub Class 2, and SubClass 3. The metabolites clustered in Sub Class 1 with the largest abundance increased under drought stress and increased with the increase in the number of drought rounds, actively responding to repeated drought stress ( Figure 10 A). Sub Class 3 contains 458 substances. The metabolites in Sub Class 1 include flavonoids, phenolic acids, amino acids and their derivatives, lipids, and alkaloids, with 112, 79, 57, 57, and 38 species respectively. Sub Class 2 contains 190 substances whose contents decreased under drought stress ( Figure 10 B). Sub Class 3 contains 150 substances whose contents first increased and then decreased under drought stress ( Figure 10 C). The secondary metabolites of plants are the products of plants' adaptation to the environment during growth. Under drought stress, the concentration of secondary metabolites usually increases, such as alkaloids, flavonoids, terpenoids, etc. Repeated drought stress can induce an increase in the active substances of tea plants, thereby inducing tea plants to actively respond and improving the drought tolerance of tea plants. In the present invention, metabolites that did not respond significantly under the first drought stress but responded significantly with the increase in the number of drought rounds were screened out among the metabolites clustered in SubClass 1, which belong to the "memory" metabolites under drought stress.

[0135] 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.

[0136] According to the functions of the 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 adjustment functions to help tea plants maintain cell homeostasis under drought. The accumulation of memory metabolites is an important mechanism for tea plants to adapt to repeated drought stress. In addition, under multiple rounds of drought stress, the content of carbohydrate substances showed a decreasing trend, while secondary metabolites such as flavonoids and phenolic acids accumulated significantly. When tea plants are exposed to drought stress again after a single drought stress, the carbon source is preferentially allocated to the synthesis of secondary metabolites through carbon fixation in photosynthesis, promoting the transformation of primary metabolism to secondary metabolism to enhance the drought resistance of plants.

[0137] 4 Transcriptome Sequencing

[0138] First, the total RNA of fresh tea leaves samples was extracted using the Trizol method. The specific process is as follows:

[0139] 1. Preparation of materials and reagents. Samples: Fresh young tea leaves (immediately frozen in liquid nitrogen 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 tubes (RNase-free), low-temperature centrifuge, vortex mixer, pipette.

[0140] 2. Experimental steps. Sample grinding: Take about 100 mg of leaves, grind them into powder in liquid nitrogen, and quickly transfer them to 1 mL of Trizol, then mix well by vortexing. Lysis and stratification: Let it stand at room temperature for 5 minutes, add 0.2 mL of chloroform, shake vigorously for 15 seconds, and let it stand at room temperature for 3 minutes. Centrifuge at 4 °C (12,000×g, 15 minutes), and it is divided into three layers (the upper aqueous phase contains RNA). RNA precipitation: Pipette the upper aqueous phase into a new tube, add 0.5 mL of isopropanol, invert and mix well, and let it stand at room temperature for 10 minutes. Centrifuge at 4 °C (12,000×g, 10 minutes), discard the supernatant, and retain the RNA precipitate. Washing and dissolution: Wash the precipitate with 1 mL of 75% ethanol, centrifuge at 4 °C (7,500×g, 5 minutes), discard the ethanol, and air dry for 5 minutes. Add 50 μL of RNase-free water to dissolve the RNA and store it at -80 °C.

[0141] Second, agarose gel electrophoresis was used to analyze the integrity of RNA and the presence of DNA contamination. The specific process is as follows:

[0142] 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).

[0143] 2. Experimental steps. Gel preparation: 1.2% agarose gel (dissolved in 1×TAE), melt it in a microwave oven, add EB, and pour it into the gel plate. Loading and electrophoresis: Mix 2 μL of RNA sample with 1 μL of loading buffer, and load the mixture into the gel wells. Electrophoresis at 80 - 100 V for 20 - 30 minutes.

[0144] 3. Result analysis. Integrity: The 28S and 18S rRNA bands are clearly visible (the brightness of 28S is about twice that of 18S). DNA contamination: If there are diffused bands or trailing near the loading well, it indicates the residual genomic DNA. Use the Qubit 2.0 fluorometer to measure the RNA concentration with high precision, and use the Agilent 2100 bioanalyzer to accurately detect the RNA integrity. After passing the inspection, library construction is carried out. The qualified inspection indicators for RNA quality are concentration and integrity. Concentration: Qubit 2.0 detection: ≥50 ng / μL (suitable for 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 (the ideal value is ≥8.0). Electrophoresis: The brightness ratio of 28S:18S bands ≈ 2:1, without degradation trailing. Detect the library quality. After passing the library inspection, sequencing is performed using the Illumina HiSeq platform. The qualified inspection criteria for library inspection are concentration and fragment size. Concentration: Qubit or qPCR detection, the effective library concentration ≥2 nM (meeting the Illumina loading requirements). Fragment size: Agilent 2100 detection, the main peak of the library is at 300 - 500 bp (insert fragment + adapter), without adapter dimers (<100 bp miscellaneous peaks).

[0145] 4.1 Sequence alignment and data analysis

[0146] Filter the raw data obtained from sequencing to get Clean Data. The filtering criteria are as follows: Remove the reads with adapters; When the N content in any sequencing read exceeds 10% of the base number of this read, remove this paired reads; When the number of low-quality (Q≤20) bases in any sequencing read exceeds 50% of the base number of this read, remove this paired reads.

[0147] Use HISAT2 to align the Clean Reads with the genome of Camellia sinensis 'Shuchazao' to obtain the position information on the reference genome or gene, as well as the sequence characteristic information unique to the sequencing sample. Use FPKM (Fragments Per Kilobase of transcript per Million fragments mapped) as an indicator to measure the expression level of transcripts or genes.

[0148] 4.2 Screening of differentially expressed genes

[0149] Differential expression analysis of all expressed genes was performed using the DESeq2 method. DESeq2 is based on the negative binomial distribution model and identifies differentially expressed genes between samples through normalization, dispersion estimation, and statistical testing. The specific process is as follows:

[0150] 1. Data input and preprocessing. Input data: Raw RNA-seq read count matrix (Raw Counts), where rows represent genes and columns represent samples.

[0151] 2. Data normalization. Purpose: To eliminate the effects of sequencing depth and gene length on gene expression levels. First, calculate size factors (normalization factors for each sample): Based on the geometric mean method (Median of Ratios), eliminate differences in sequencing depth between samples.

[0152] 3. Dispersion estimation. Purpose: To quantify the degree of variation in gene expression values (variation between biological replicates). It is divided into three steps: ① Gene-specific dispersion: Estimate the dispersion for each gene individually (based on the negative binomial distribution). ② Fit a trend line: Fit a smooth curve through the mean-dispersion relationship of gene expression levels. ③ Empirical Bayes shrinkage: Shrink the gene-specific dispersion towards the trend line to avoid overfitting in small samples.

[0153] 4. Differential expression analysis. Model construction: Use the generalized linear model (GLM) for hypothesis testing (default Wald test). Calculate the statistical quantity Log2 Fold Change: The logarithmic change value of gene expression fold. p-value: Based on the Wald test or likelihood ratio test (LRT) of the negative binomial distribution.

[0154] 5. Multiple testing correction. Purpose: To control the false discovery rate (False Discovery Rate, FDR). Benjamini-Hochberg (BH) correction: Screening criterion: FDR < 0.05. The screening conditions for differentially expressed genes are |log2FoldChange| > 1 and FDR < 0.05.

[0155] Use a volcano plot to visually display the overall distribution of differentially expressed genes in two groups of samples. The results are as Figures 11 - 16 shown Figure 11 is the volcano plot of differentially expressed genes in tea plants of the CK group and D1 group under repeated drought stress; Figure 12 is the volcano plot of differentially expressed genes in tea plants of the CK group and D2 group under repeated drought stress; Figure 13 is the volcano plot of differentially expressed genes in tea plants of the CK group and D3 group under repeated drought stress; Figure 14 is the volcano plot of differentially expressed genes in tea plants of the D1 group and D2 group under repeated drought stress; Figure 15Volcano plot of differentially expressed genes in tea plants of D1 group and D3 group under repeated drought stress; Figure 16 Volcano plot of differentially expressed genes in tea plants of D2 group and D3 group 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: Red dots in the figure represent up-regulated differentially expressed genes, green dots represent down-regulated differentially expressed genes, and gray dots represent genes with no significant difference. The Readcount in gene expression analysis was normalized using the DESeq2 software, and then the Benjamini-Hochberg method was used to correct the multiple hypothesis test probability (P-value) to obtain the false discovery rate (FDR). The screening criteria for significantly differentially expressed genes (DEGs) were |log2Fold Change| >= 1 and FDR < 0.05. The overall distribution of differentially expressed genes can be directly seen from the volcano plot. The three comparison groups CK_vs_D1 ( Figure 11 ), CK_vs_D2 ( Figure 12 ), and CK_vs_D3 ( Figure 13 ) identified 4669, 3811, and 3694 up-regulated DEGs respectively, and 6069, 4404, and 4901 down-regulated DEGs respectively. D1_vs_D2 ( Figure 14 ), D1_vs_D ( Figure 15 ), and D2_vs_D3 ( Figure 16 ) identified 156, 780, and 51 up-regulated DEGs respectively, and 82, 1039, and 258 down-regulated DEGs respectively.

[0156] 4.3 K-means analysis of differentially expressed genes and screening of key genes

[0157] To study the gene expression patterns under repeated drought treatment, the FPKM of genes was first centralized and standardized, and then K-means clustering analysis was performed. 15,463 differentially expressed genes could be divided into 10 different expression patterns. Genes with similar changing trends might have similar functions. Group 1 contained 547 genes, showing a trend of first increasing and then decreasing. Group 2 contained 1,879 genes, showing a trend of first increasing, then decreasing, and then increasing again. Group 3 contained 1,669 genes, showing a trend of first decreasing and then increasing. Group 4 contained 1,248 genes, showing a trend of first increasing, then decreasing, and then increasing again. Group 5 contained 5,406 genes, showing a trend of first decreasing and then remaining unchanged. Group 6 contained 873 genes, showing a trend of first decreasing, then remaining unchanged, and then decreasing again. Group 7 contained 552 genes, showing a trend of first remaining unchanged and then increasing. Group 8 contained 1,068 genes, showing a trend of first increasing and then remaining unchanged. Group 9 contained 840 genes, showing a trend of first increasing, then decreasing, and then remaining unchanged. Group 10 contained 1,381 genes, showing a trend of first increasing, then remaining unchanged, and then decreasing.

[0158] Genes that did not respond significantly to single drought but had significantly higher expression levels under subsequent repeated drought than under single drought were defined as key genes for drought memory. They were mainly distributed in the photosynthesis pathways of photosystem I (PS I) and photosystem II (PS II), and there were 9 of them (see Table 3).

[0159] These genes included: photosystem I chlorophyll-binding protein genes, photosystem II chlorophyll-binding protein genes, and photosystem II oxygen-evolving enhancer protein genes. The light reaction pathway was the pathway where the key genes for drought memory were most enriched.

[0160] 4.4 Functional analysis of differentially expressed genes

[0161] To better elucidate the functions of differentially expressed genes, GO and KEGG enrichment analyses were performed on the differentially expressed genes, with a screening criterion of P value less than 0.05. Among the 15,463 differentially expressed genes, 10,791 genes were annotated to GO, accounting for 69.79% of all genes. The top 50 most significantly enriched GO-Terms were selected to create a bar chart. The results showed that compared with the control group, the GO-Terms with significant differences (p < 0.05) and a relatively large number of enriched genes under single drought were photosynthesis (GO:0015979), anion transport (GO:0006820), UDP-glucosyltransferase activity (GO:0035251), anion transmembrane transporter activity (GO:0008509), and secondary metabolite biosynthesis (GO:0044550), etc. Moreover, the number of down-regulated genes annotated to each function was more than that of up-regulated genes. Compared with single drought, the GO-Terms with significant differences ( p <0.05) and a relatively large number of enriched genes under two rounds of drought were photosynthesis, photosystem (GO:0009521), chromophore-protein linkage (GO:0018298), and chlorophyll binding (GO:0016168), etc. Moreover, the number of up-regulated genes annotated to each function was more than that of down-regulated genes. Compared with single drought, the GO-Terms with significant differences (p < 0.05) and a relatively large number of enriched genes under three rounds of drought were anion transport, photosynthesis, secondary metabolite biosynthesis, response to wounding (GO:0009611), and anion transmembrane transporter activity, etc. Among them, the number of up-regulated genes annotated to photosynthesis, photosystem, photosystem I, and photosystem II was more than that of down-regulated genes. Compared with two rounds of drought, among the 50 GO-Terms with the lowest q-value in the enrichment analysis results under three rounds of drought, 49 were sub-items in biological processes. The GO-Terms with significant differences (p < 0.05) and a relatively large number of enriched genes were carbohydrate catabolic process (GO:0016052), organic phosphate catabolic process (GO:0046434), response to extracellular stimulus (GO:0009991), response to nutrient level (GO:0031667), and response to starvation (GO:0042594), etc. The number of down-regulated genes annotated to each function was more than that of up-regulated genes.

[0162] Meanwhile, the top 20 most significantly enriched KEGG signaling pathways among groups were also tested. The differentially expressed genes under repeated drought treatment were commonly 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.

[0163] Photosystem I (PS I) and Photosystem II (PS II) are the main sites for the light reaction, each having its own core protein and antenna protein complex. The antenna protein complex captures light energy and transfers it to the core protein to generate high-energy electrons, and then the electron transport chain transfers the high-energy electrons to the final electron acceptor, and ATP and NADPH are generated during this process. Make heatmaps of photosynthesis and photosynthesis-antenna protein pathways and differential expression of related genes, such as Figure 17 shown. It shows that the expression levels of some key genes in the photosynthesis and photosynthesis-antenna protein pathways did not respond significantly under the first drought stress, but the response levels under repeated drought stress were significantly higher than those under single drought stress. These genes belong to the key genes of drought memory. Analysis of the photosynthesis pathway under single drought stress ( Figure 17 A), the photosystem I protein genes psaG, psaH, psaO and the photosystem II genes psbB, psbO, psbP, psbQ1, psbQ2, psb27-1, psb28 and the F-type H+-transporting ATPase gene ATPF1G, ferredoxin-NADP+ reductase gene PetH were significantly down-regulated; compared with single drought, the PS I protein genes psaA, psaB, psaG, psaH, psaO and the PS II protein genes psbB, psbO, psbP and the gene PetH were significantly up-regulated under two rounds of drought; under three rounds of drought, the PS II protein genes psbP, psbQ1, psbQ2, psb27-1, psb28 and the gene ATPF1G were significantly up-regulated; compared with two rounds of drought, the F-type H+-transporting ATPase α-subunit gene ATPF0A and the cytochrome b6 gene petB were significantly down-regulated under three rounds of drought. As shown by Figure 17 B, under drought stress, the light-harvesting complex I chlorophyll a / b-binding protein genes Lhca1-Lhca4 and the light-harvesting complex II chlorophyll a / b-binding protein genes Lhcb1-Lhcb3, Lhcb6 were significantly down-regulated; compared with single drought, the genes Lhca2, Lhca4, Lhcb1, Lhcb2, Lhcb3 and Lhcb6 were significantly up-regulated under two rounds of drought; under three rounds of drought, the genes Lhca1, Lhca3, Lhca4, Lhcb1-Lhcb3 were significantly up-regulated. Under drought stress, the light-harvesting pigment protein complex is damaged, and the up-regulated expression of the LHCA and LHCB protein genes in tea plants is induced under repeated drought stress, improving the light-capturing ability.

[0164] Construct a heatmap of the differential expression of genes in the dark reaction pathway of tea plants. The dark reaction uses ATP and NADPH formed in the light reaction to fix CO2 and synthesize carbohydrates for respiration. To explore whether the dark reaction process in tea plants is consistent with the light reaction response pattern under repeated drought stress, differential genes in the dark reaction process under different rounds of drought stress treatment were analyzed. The results showed that under single drought, the phosphoribulokinase gene PRK was significantly up-regulated, and the ribulose-1,5-bisphosphate carboxylase gene RBCS, fructose-1,6-bisphosphatase gene FBP, sedoheptulose-1,7-bisphosphatase gene SBPase, and ribulose-5-phosphate isomerase gene rpiA were significantly down-regulated. Compared with single drought, the ribulose-1,5-bisphosphate carboxylase large chain gene rbcL was significantly up-regulated in two rounds of drought, and RBCS, FBP, SBPase, rpiA, PRK, and glyceraldehyde-3-phosphate dehydrogenase GAPC2 were significantly up-regulated in three rounds of drought, while the 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 triose phosphate isomerase gene TPI were significantly down-regulated under three rounds of drought. This indicates that after single drought stress, when drought stress is experienced again, most genes related to the dark reaction are up-regulated, and the increasing trend of genes related to the dark reaction pathway is more significant under three rounds of drought.

[0165] Meanwhile, a heatmap of the expression of the abscisic acid synthesis and signal transduction pathway was analyzed. The results showed that in the ABA metabolic pathway, there are four key regulatory enzymes: 9-cis-epoxycarotenoid dioxygenase (NCED), zeaxanthin epoxidase (ZEP), aldehyde oxidase (AAO), and cytochrome oxidase, among which cytochrome oxidase plays a major role in regulating the decomposition of abscisic acid. The gene expression levels in the abscisic acid pathway showed an upward trend with the increase in the number of drought stress rounds after the first drought. Under single drought stress, ZEP1 、 NCED1 and the rate-limiting enzyme gene ABA2 were significantly up-regulated, and the cytochrome oxidase gene CYP707A4 was significantly down-regulated; compared with single drought, NECD1 and CYP707A4 was significantly up-regulated and ABA2 was significantly down-regulated under two rounds of drought; ZEP1 、 NCED1 、 ABA2 and AAO3 were significantly down-regulated under three rounds of drought. Compared with two rounds of drought, NCED1 was significantly down-regulated under three rounds of drought.

[0166] 4.5 RT-qPCR verification of transcriptome data

[0167] To verify the accuracy of RNA-Seq data, nine 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 included 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 experiment process included:

[0168] 1. First, total RNA was extracted from tea leaves using the FastPure Universal Plant Total RNA Isolation Kit (Vazyme, China). First-strand cDNA was synthesized using the 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 of 2× SYBR Green Premix, 0.4 μL of each primer (forward and reverse), 1 μL of cDNA template, and RNase-free water, with a final volume of 20 μL. The cycling conditions were: 95 °C for 30 s, 95 °C for 5 s, 60 °C for 30 s, for 40 cycles.

[0169] The primers for 9 genes include the primers for detecting the gene shown in SEQ ID NO.1: forward primer AGGCTTTTGCAGAGCTCAAG (SEQ ID NO.10), reverse primer CATAAGCCCATGCATTGTTG (SEQ ID NO.11); the primers for detecting the gene shown in SEQ ID NO.2: forward primer TTGTATTTGGGCCCACTCTC (SEQ ID NO.12), reverse primer CACCGAACTTGACACCATTG (SEQ ID NO.13); the primers for detecting the gene shown in SEQ ID NO.3: forward primer GTTCGGTGAGGCTGTATGGT (SEQ ID NO.14), reverse primer TAGAGTGGGTCGGTCACCTC (SEQ ID NO.15); the primers for detecting the gene shown in SEQ ID NO.4: forward primer TTGAATTCCTTGCCATCTCC (SEQ ID NO.16), reverse primer AACACAGATCCCCACGAAAG (SEQ ID NO.17); the primers for detecting the gene shown in SEQ ID NO.5: forward primer AGCCTATGGCGAGATCTTCA (SEQ ID NO.18), reverse primer GGGTCAGCCCAATAGTCGTA (SEQ ID NO.19); the primers for detecting the gene shown in SEQ ID NO.6: forward primer ATATTCTCGGCGGTTCCTTT (SEQ ID NO.20), reverse primer AGCCGTAGTCCCCTGGTAGT (SEQ ID NO.21); the primers for detecting the gene shown in SEQ ID NO.7: forward primer GGTGTCACCGGAATGCTACT (SEQ ID NO.22), reverse primer AGGGTAGCCACACTCATTGG (SEQ ID NO.23); the primers for detecting the gene shown in SEQ ID NO.8: forward primer GTACGGTCCCGACAGAAGAA (SEQ ID NO.24), reverse primer GGCCACAGTTAGCACCAAAT (SEQ ID NO.25); the primers for detecting the gene shown in SEQ ID NO.9: forward primer GTGTTTGGGTTGCAGAGGTT (SEQ ID NO.26), reverse primer AAGCTCAGCATTCCTTTGGA (SEQ ID NO.27). Normalize gene expression to the internal reference β-actin, and use the method to calculate the relative expression level. Verification criterion: If the RT-qPCR result is consistent with the RNA-Seq trend (upregulation / downregulation), it is regarded as successful verification. Such as Figure 18As shown, the expression trends of these 9 genes detected by RT-qPCR were consistent with the trends of RNA-Seq data, indicating that the results of transcriptome sequencing were accurate and reliable, and it was suitable to perform biological significance analysis using RNA-Seq data.

[0170] 5 Gene Structure Analysis of Tea Plant LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9

[0171] The tea plant LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes are tea plant LHC transcription factor genes. Their cloning and sequence structure analysis are as follows: The tea plant variety is the national fine variety Xiangfeicui, which is planted in the Chang'an Tea Base of Hunan Agricultural University in Changsha, Hunan Province. Young tea plant leaves are taken for RNA extraction. Total RNA is extracted using the Fastpure Universal Plant Total RNA Isolation kit (Vazyme, China), and the operation is carried out according to the kit instructions. The RNA content and quality are detected using a spectrophotometer. Then, the first strand of cDNA is generated by reverse transcription: Take 1 μg as the template. According to PrimeScript II 1 stOperate according to the instructions of the Strand cDNA Synthesis Kit (Aikrui, China). Add 5 μL of Oligo dT Primer, 1 μL of dNTP Mix (10 mM each), and make up to 10 μL with RNase Free dH2O. Denature at 65 °C for 5 min and immediately place on ice. Then add 4 μL of 5X RTase Plus Reaction Buffer, 0.5 μL of RNase Inhibitor (40U), 1 μL of Evo M-MLV Plus RTase (200U) to the above reaction solution, and make up to 20 μL with ddH2O. Incubate at 30 °C for 10 min, 42 °C for 60 min, and inactivate the reverse transcriptase by incubating at 95 °C for 5 min to obtain the first-strand cDNA of the reverse transcription product. Take an appropriate amount of the first-strand cDNA of the reverse transcription product for subsequent PCR. Use the first-strand cDNA as the RT-PCR template and perform PCR amplification of the gene sequence by the conventional method. Upstream primer for LHCP1: (5’-ATGAACCCAAATGATGAATCCTTG-3’, SEQ ID NO.31), downstream primer: (5’-CTATGACTTGCGCCTCTGTATGC-3’, SEQ ID NO.32). Upstream primer for LHCP2: (5’-ATGGCTGCGTCTACCATGGC-3’, SEQ ID NO.33), downstream primer: (5’-TCACTTTCCTGGGACAAAGTTTG-3’, SEQ ID NO.34). Upstream primer for LHCP3: (5’-ATGGCTGCCTCTACAATGGC-3’, SEQ ID NO.35), downstream primer: (5’-TCACTTTCCGGGAACAAAGTTAG-3’, SEQ ID NO.36). Upstream primer for LHCP4: (5’-ATGGCTTCCAAAGCCCTAATG-3’, SEQ ID NO.37), downstream primer: (5’-TCATGGAGAAATTGATCTAGGGATAA-3’, SEQ ID NO.38). Upstream primer for LHCP5: (5’-ATGGCAACTCAAGCACTGGTG-3’, SEQ ID NO.39), downstream primer: (5’-TTAGTGGAACTTGAGGTTGGTCAA-3’, SEQ ID NO.40).LHCP6 forward primer: (5’-ATGGCGGAGGATCGTCGG-3’, SEQ ID NO.41), reverse primer: (5’-TCACATGAATTTCAGAATGGAAAGC-3’, SEQ ID NO.42). LHCP7 forward primer: (5’-ATGGCCACCGTCGCAGCT-3’, SEQ ID NO.43), reverse primer: (5’-TTAGTAACCTCTGATTGTTTGAATAATTG-3’, SEQ ID NO.44). LHCP8 forward primer: (5’-ATGGCTTCACTGGCAGCATC-3’, SEQ ID NO.45), reverse primer: (5’-TCACAGGGTAGGAGCTCTTTCAG-3’, SEQ ID NO.46). LHCP9 forward primer: (5’-ATGGCCGCAACCACCGCC-3’, SEQ ID NO.47), reverse primer: (5’-TCAAGAGAAAAAGGTGTCTATAATGGTT-3’, SEQ ID NO.48). The reaction system was: 2×Phanta Max buffer 25 μl, dNTP Mix 1 μL, 2 μL each of forward and reverse primers, Phanta Max Supper-Fidelity DNA Polymerase 1 μL, template 2 μL, ddH2O 17 μL. The reaction procedure was as follows: 95 °C for 3 min, 95 °C for 15 sec, 58 °C for 15 sec, 72 °C for 30 sec, 30 cycles, 72 °C for 5 min, ending at 4 °C. The PCR products LHCP1-P9 were obtained. After purification and recovery of the LHCP1-P9 genes, they were ligated to the pMD19-T Vector (Takara, China) to obtain the plasmids pMD19-T::LHCP1 - pMD19-T::LHCP9, which were transformed into Escherichia coli competent cells DH5α (Vidy, China). After positive clones grew out and were verified by colony PCR, the correctly verified monoclonal colonies were picked for sequencing. The sequencing was sent to Wuhan Seqhealth Medical 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, Bovine Parvovirus, and Bovine Viral Diarrhea Virus Using a Gold Nanoparticle-Assisted PCR Assay With a Dual-Priming Oligonucleotide System. Front Microbiol. 2019;10:2884.

[0172] Functional verification of overexpressed Arabidopsis thaliana of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9

[0173] 6.1 Vector construction

[0174] Construction of the LHCP1-PCAMBIA1300 vector

[0175] Using the pMD19-T::LHCP1 plasmid as a template, the primers are:

[0176] Forward primer: (5’-gtcccagactacgctggatccATGAACCCAAATGATGAATCCTTG-3’, SEQ ID NO.49);

[0177] Reverse primer: (5’-gctcaccatggtaccggatccCTATGACTTGCGCCTCTGTATGC-3’, SEQ ID NO.50).

[0178] Construction of the LHCP2-PCAMBIA1300 vector

[0179] Using the pMD19-T::LHCP2 plasmid as a template, the primers are:

[0180] Forward primer: (5’-gtcccagactacgctggatccATGGCTGCGTCTACCATGGC-3’, SEQ ID NO.51);

[0181] Reverse primer: (5’-gctcaccatggtaccggatccTCACTTTCCTGGGACAAAGTTTG-3’, SEQ ID NO.52).

[0182] Construction of LHCP3-PCAMBIA1300 Vector

[0183] Using the pMD19-T::LHCP3 plasmid as a template, the primers are as follows:

[0184] Forward primer: (5’-gtcccagactacgctggatccATGGCTGCCTCTACAATGGC-3’, SEQ ID NO.53),

[0185] Reverse primer: (5’-gctcaccatggtaccggatccTCACTTTCCGGGAACAAAGTTAG-3’, SEQ ID NO.54).

[0186] Construction of LHCP4-PCAMBIA1300 Vector

[0187] Using the pMD19-T::LHCP4 plasmid as a template, the primers are as follows:

[0188] Forward primer: (5’-gtcccagactacgctggatccATGGCTTCCAAAGCCCTAATG-3’, SEQ ID NO.55);

[0189] Reverse primer: (5’-gctcaccatggtaccggatccTCATGGAGAAATTGATCTAGGGATAA-3’, SEQ ID NO.56).

[0190] Construction of LHCP5-PCAMBIA1300 Vector

[0191] Using the pMD19-T::LHCP5 plasmid as a template, the primers are as follows:

[0192] Forward primer: (5’-gtcccagactacgctggatccATGGCAACTCAAGCACTGGTG-3’, SEQ ID NO.57);

[0193] Reverse primer: (5’-gctcaccatggtaccggatccTTAGTGGAACTTGAGGTTGGTCAA-3’, SEQ ID NO.58).

[0194] Construction of LHCP6-PCAMBIA1300 Vector

[0195] Using the pMD19-T::LHCP6 plasmid as a template, the primers are as follows:

[0196] Forward primer: (5’-gtcccagactacgctggatccATGGCGGAGGATCGTCGG-3’, SEQ ID NO.59);

[0197] Reverse primer: (5’-gctcaccatggtaccggatccTCACATGAATTTCAGAATGGAAAGC-3’, SEQ ID NO.60).

[0198] Construction of the LHCP7-PCAMBIA1300 vector

[0199] Using the pMD19-T::LHCP7 plasmid as a template, the primers are:

[0200] Forward primer: (5’-gtcccagactacgctggatccATGGCCACCGTCGCAGCT-3’, SEQ ID NO.61);

[0201] Reverse primer: (5’-gctcaccatggtaccggatccTTAGTAACCTCTGATTGTTTGAATAATTG-3’, SEQ ID NO.62).

[0202] Construction of the LHCP8-PCAMBIA1300 vector

[0203] Using the pMD19-T::LHCP8 plasmid as a template, the primers are:

[0204] Forward primer: (5’-gtcccagactacgctggatccATGGCTTCACTGGCAGCATC-3’, SEQ ID NO.63);

[0205] Reverse primer: (5’-gctcaccatggtaccggatccTCACAGGGTAGGAGCTCTTTCAG-3’, SEQ ID NO.64).

[0206] Construction of the LHCP9-PCAMBIA1300 vector

[0207] Using the pMD19-T::LHCP9 plasmid as a template, the primers are:

[0208] Forward primer: (5’-gtcccagactacgctggatccATGGCCGCAACCACCGCC-3’, SEQ ID NO.65);

[0209] Downstream primer: (5’-gctcaccatggtaccggatccTCAAGAGAAAAAGGTGTCTATAATGGTT-3’, SEQ ID NO.66).

[0210] Perform PCR amplification successively.

[0211] The PCR product was confirmed for specificity by 1% agarose gel electrophoresis, and then purified and eluted to obtain the purified PCR product.

[0212] The PCAMBIA1300 vector (Novopro, China) was digested with a single enzyme to linearize it, using BamH1 as the digestion site. The digestion system was: 41.4 μL of ddH2O, 5 μL of 10x Buffer, 1.6 μL of PCAMBIA1300 Vector, and 1 μL of BamH1, and incubated in a water bath at 37 °C for 30 min. The digested product was confirmed to be 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 subjected to a recombination reaction using the ClonExpress®II One Step Cloning Kit (Vazyme, China). The reaction system was: 4 μL of PCR product, 2 μL of Exnase II, 3.5 μL of Vector, and made up to 20 μL with ddH2O, and incubated at 37 °C for 30 min. Finally, it was transformed into Escherichia coli competent cells DH5α (Weidi, China). After positive clones grew out and were verified by colony PCR, the correctly verified monoclonal was picked for sequencing. The sequencing was sent to Wuhan SeqHealth Medical Laboratory Co., Ltd. to obtain the CsLHC-PCAMBIA1300 vector. The steps of transformation and PCR verification refer to the prior art Wang M, Yan Y, Wang R, et al. Simultaneous Detection of Bovine Rotavirus, Bovine Parvovirus, and Bovine Viral Diarrhea Virus Using a Gold Nanoparticle-Assisted PCR Assay With a Dual-Priming Oligonucleotide System. Front Microbiol. 2019;10:2884. doi:10.3389 / fmicb.2019.02884.

[0213] 6.2 Arabidopsis thaliana genetic transformation

[0214] Take an appropriate amount of wild-type Arabidopsis thaliana seeds and add deionized water. After vernalization for 72 h, sow the seeds. Cover the sown seeds with plastic wrap and place them under suitable conditions (humidity 70%; temperature 22 °C; photoperiod 10 h light / 14 h dark) and wait for germination. After the seeds germinate, select seedlings of the same size for transplantation and cultivate them normally. Transform the CsLHC-PCAMBIA1300 vector into Agrobacterium tumefaciens GV3101 by the freeze-thaw method and identify positive clones by colony PCR. The methods of transformation and identification are the same as above. Pick 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 h; add the 50 mL of the cultured bacterial solution to 200 mL of fresh LB liquid medium containing antibiotics and continue shaking culture for 6 - 8 h until the OD600 is about 1.0. Centrifuge to collect the bacterial cells, resuspend the bacterial cells with 5% sucrose solution, with a final concentration of OD600 about 0.8, add 0.1% silwet L-77 (Yeasen, China), and shake well to obtain the transformation solution. Arabidopsis thaliana is planted for about one month, and the plants begin to flower successively. Select healthy plants as the plants to be transformed. Continuously remove the apical inflorescence before transformation to make the plants produce more flower buds. The plants to be transformed need to be fully watered one day before transformation. Put the prepared transformation solution in a container and gently immerse the Arabidopsis thaliana inflorescence in the transformation solution for about 60 sec, then place it in the dark for 24 h, and then culture it normally to harvest seeds. Put the harvested Arabidopsis thaliana seeds in a centrifuge tube, sterilize them with 1 mL of 75% ethanol for 1 min, then sterilize them with 10% NaClO for 5 min, and then rinse them with sterile water 5 - 6 times. Use a pipette tip to pick up the seeds and sow them on 1 / 2 MS solid medium containing hygromycin. Vernalize them at 4 °C in the dark for 72 h, transfer them to the culture room, and culture them under the conditions of temperature 22 °C; photoperiod 10 h light / 14 h dark. After about two weeks, select resistant plants with green leaves and normal root development and transplant them into the cultivation substrate for continued cultivation. The cultivation substrate should be fully hydrated before transplantation. Cover it with plastic wrap after transplantation and remove it after about 3 d. The subsequent management is the same as above. Harvest the T2 generation seeds for experiments. Extract the RNA of Arabidopsis thaliana seedlings and use gene-specific primers to perform PCR to detect the expression of the target gene. The methods of extraction and identification are the same as in 4.5. Subject the transgenic plants to natural drought treatment for 10 d, set up a control group, and observe the survival of the seedlings after 10 d.

[0215] Results showed that by analyzing the expression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 in Arabidopsis overexpression lines and wild-type plants, as well as the phenotypes of overexpression lines under drought treatment, it was shown that in the Arabidopsis overexpression experiment, LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 were integrated into Arabidopsis plants by PCR identification. The expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, 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, LHCP9 were respectively treated with drought for 10 days. Compared with wild-type Arabidopsis, the survival rate of transgenic lines increased significantly, and the conductivity decreased significantly, indicating that overexpression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 improved the drought tolerance of plants.

[0216] Functional verification of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 genes in tea plants

[0217] 7.1 In vivo antisense oligonucleotide inhibition experiment

[0218] Synthesize oligonucleotide antisense primers according to the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, LHCP9 sequences. The primer sequences are as follows: 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’, SEQ ID NO.70); LHCP5, (5’-ACACTCTTTTTGTTTTCGA-3’, SEQ ID NO.71); LHCP6, (5’-TTTCATATGACTATCCTTC-3’, SEQ ID NO.72); LHCP7, (5’-GCCGAGGACCCAGAGAA-3’, SEQ ID NO.73); LHCP8, (5’-TGGAGATTATGGTTACGATC-3’, SEQ ID NO.74); LHCP9, (5’-GAGGTTTAGGGAGTGTGA-3’, SEQ ID NO.75). Dissolve them with sterilized water to prepare an in vitro oligonucleotide antisense inhibitory primer solution, with the blank being sterilized water. Cut off the one-bud-two-leaf tea shoots that are basically the same in size, bright in color, healthy in color and luster, and free of pests and diseases, and insert them into 1.5 mL centrifuge tubes containing 1 mL of the 20-in-one in vitro oligonucleotide antisense inhibitory primer solution, ensuring that the tails of the one-bud-two-leaf tea shoots are immersed in the solution. Place the centrifuge tubes in a light incubator for light cultivation according to 16 h of light / 8 h of darkness, and the temperature of the incubator is 25 °C. After 0 h, 6 h, 12 h, and 36 h of treatment respectively, store the one-bud-two-leaf tea shoots in a -80 °C refrigerator with liquid nitrogen. Subsequently, sample the primer treatment group and the control group respectively for gene expression analysis.

[0219] 7.2 Analysis of the effect of in vivo oligonucleotide antisense inhibition on tea tree gene expression

[0220] Total RNA was extracted from the treated samples and control samples respectively, followed by reverse transcription to synthesize the first-strand cDNA, and quantitative PCR was used to detect the expression of related genes. The gene expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in the control and treated samples were detected by the same method as above. The results showed that the antisense inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 oligonucleotides could significantly interfere with the expression level of the target gene, and the inhibitory effect of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 was the best at 6 h.

[0221] 7.3 In vitro antisense inhibition of oligonucleotides, drought treatment of samples and determination of biochemical indexes

[0222] To study the role of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in drought stress of tea plants, the samples treated with the antisense inhibition primer solution of oligonucleotides for 6 h were drought-treated for 12 h, and the change in the Fv / Fm value was observed. The determination of malondialdehyde content after drought treatment was detected according to the method of the kit (product number: BC0020) (Solarbio, China). The results showed that in the in vitro antisense inhibition experiment of oligonucleotides, compared with the control, the expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in the samples were inhibited. After drought treatment, compared with the control group, the plants with inhibited LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 were more damaged, the Fv / Fm ratio was significantly decreased, and the malondialdehyde content was significantly increased, indicating that the in vivo expression inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes could significantly reduce the drought tolerance of tea plants.

[0223] 8 Verification of gene marker ROC for key genes of drought memory in the breeding application of drought-resistant tea varieties

[0224] Tea tree leaf samples were collected from 100 tea tree varieties during the summer drought period (June - August) at the Gaoqiao Tea Experiment Base in Hunan Province (113°08′E, 28°20′N), including: drought - resistant varieties (50): screened by continuous 3 - year drought - resistance phenotypic identification (proportion of drought - damaged leaves ≤ 30%), non - drought - resistant varieties (50): confirmed by the same identification (proportion 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 IDNO.8), LHCP9 (TEA026680, SEQ ID NO.9) (i.e., the 9 key genes for drought memory screened in Example 1).

[0225] First, RT - qPCR analysis was performed on the expression levels of 9 genes in different samples. Total RNA was extracted from tea tree leaves using the FastPure Universal Plant Total RNA Isolation Kit (Vazyme, China). First - strand cDNA was synthesized using the Evo M - MLV Reverse Transcription Premix (Agbio, China). RT - qPCR was carried out 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 of cDNA template, and RNase - free water, with a final volume of 20 μL. The cycling conditions were: 95 °C for 30 s, 95 °C for 5 s, 60 °C for 30 s, for 40 cycles. The primers for the 9 genes were the same as in step 4.5. The gene expression was normalized to the internal reference β - actin, and the method was used to calculate the relative expression levels. ROC analysis was performed using R language (v4.3.1) and the pROC package (v1.18.4). According to the sensitivity and specificity data of each gene, an ROC curve was independently plotted for each gene, and the area under the curve (AUC) was calculated. Significance criterion: AUC > 0.85, indicating that the gene is a tea tree marker under drought and can be applied to the screening of drought - resistant tea tree varieties.

[0226] The results are asFigure 19 As shown, the area under the curve (AUC) of the 9 key drought memory genes is greater than 0.85, indicating that each gene has a high predictive ability for drought resistance classification and meets the excellent diagnostic criteria. The results include 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 has the highest AUC value (0.9), indicating that it performs the best in distinguishing drought sensitivity. LHCP7 follows closely, with an AUC value of 0.899, also showing good discrimination ability. The AUC values of the 6 genes, namely LHCP1, LHCP6, LHCP8, LHCP2, LHCP3, and LHCP9, are all around 0.89 to 0.896, showing good performance. The AUC value of LHCP5 (0.878) is relatively good in distinguishing drought sensitivity. Conclusion: From the AUC values, LHCP4 and LHCP7 perform the best in predicting drought sensitivity, especially when combined, their predicted AUC value is higher. The other 7 genes perform relatively well in predicting drought sensitivity. These genes play important roles in the biological mechanism of drought sensitivity. In summary, these results can be applied to the actual breeding process to screen drought-resistant tea tree varieties using these 9 key drought memory gene markers.

[0227] Verification of the accuracy of gene markers of 6 key drought memory genes in the breeding application of drought-resistant tea tree varieties

[0228] 100 tea tree varieties were sampled from the Gaoqiao Tea Experimental Base in Hunan Province (113°08′ E, 28°20′ N) during the summer drought period (June - August). They included: drought - resistant varieties (50): screened through continuous 3 - year drought - resistance phenotypic identification (proportion of drought - damaged leaves ≤ 30%); non - drought - resistant varieties (50): confirmed through the same identification (proportion 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 key genes for drought memory screened in 4.3 of Example 1). At the same time, the present invention selected a gene TCP8 (TEA003322) that responded in the first drought but did not respond in the second and third droughts as a control.

[0229] The sequence of TCP8 (TEA003322) is as follows:

[0230]

[0231] 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 primers for the newly added TCP8 were: forward primer: GTTTTGATGGCCGATCTCAT (SEQ ID NO.29), reverse primer: GGTGGAGGTGGTTGAAGTGT (SEQ ID NO.30). The gene expression was normalized to the internal reference β-actin, and the method was used to calculate the relative expression level. According to the results of RT-qPCR analysis, it was determined whether the plant was drought-resistant, and then compared with the actual results to obtain the data of accuracy and recall rate. Among them, the definition of accuracy was: among all the samples predicted as "drought-resistant type", the higher the proportion of the true drought-resistant type. For example, the accuracy of LHCP1 was 0.94, indicating that when the expression level of this gene exceeded the threshold, in 94% of the cases, it was indeed drought-resistant, which could reduce false positives. The definition of recall rate was: among all the samples that were truly drought-resistant, the proportion of the correctly identified ones. For example, the recall rate of LHCb4 was 0.952, indicating that when the expression level of this gene exceeded the threshold, 95.2% of the true drought-resistant samples could be detected. The data of the optimal threshold, accuracy, and recall rate of 10 genes are shown in Table 4.

[0232] Note: Significance criterion: An AUC greater than 0.85 indicates good model prediction performance.

[0233] The higher the accuracy, the more credible the prediction result; the higher the recall rate, the higher the proportion of correctly identified true drought-resistant samples, indicating that this gene is a tea tree marker under drought and can be applied to 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 criteria. Characteristics of LHCP1: When the expression level is greater than 13.01, the precision can reach 94% and the recall rate can reach 73.4%; Characteristics of LHCP2: When the expression level is greater than 15.14, the precision can reach 76% and the recall rate can reach 95%; Characteristics of LHCP3: When the expression level is greater than 14.18, the precision can reach 82% and the recall rate can reach 89.1%; Characteristics of LHCa6: When the expression level is greater than 13.65, the precision can reach 86% and the recall rate can reach 84.3%; Characteristics of LHCa3: When the expression level is greater than 14.05, the precision can reach 82% and the recall rate can reach 85.4%; Characteristics of LHCb7: When the expression level is greater than 13.54, the precision can reach 88% and the recall rate can reach 73.3%; Characteristics of LHCP4: When the expression level is greater than 14.40, the precision can reach 80% and the recall rate can reach 87%; Characteristics of LHCb5: When the expression level is greater than 15.63, the precision can reach 80% and the recall rate can reach 95.2%; Characteristics of LHCb4: When the expression level is greater than 14.36, the precision 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 and does not belong to the key memory gene. Its prediction effect is extremely poor, and the precision is 50% and the recall rate is 50%, indicating that its drought sensitivity prediction ability is extremely low. This indirectly shows the accuracy and necessity of the nine key memory genes screened by the present invention through multiple repeated droughts. It shows that this result can be applied to the actual breeding process, and these nine drought memory key gene markers can be used to screen drought-resistant tea tree varieties.

[0234] Therefore, the present invention starts from the inherent genetic genes of tea trees, analyzes the metabolites and genes of tea trees in response to drought stress based on metabolomics and transcriptomics, screens out the key genes for tea tree drought memory, and applies them to the screening of drought-resistant tea trees, providing support for further exploring drought-resistant germplasm resources of tea trees and cultivating new drought-resistant varieties.

[0235] It should be noted that the above embodiments are merely examples for clearly illustrating the present invention and are not intended to limit the implementation manners of the present invention. For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the implementation manners here. Any obvious changes or modifications derived from the technical solution of the present invention still fall within the protection scope of the present invention.

Claims

1. The use of a reagent for detecting the transcription level of photosynthesis-related genes in screening drought-resistant tea trees, characterized in that: The photosynthesis-related gene is shown in the sequence SEQ ID NO.

9.

2. An application of a kit in 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.

3. The use according to claim 2, 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.

4. The use according to claim 3, 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.

5. A method for identifying drought resistance of plant germplasm, characterized in that: The kit according to any one of claims 2 to 4 is used to detect the transcription level of the gene shown in SEQ ID NO.9 in the plant. When the transcription level of the gene shown in SEQ ID NO.9 is greater than 14.36, the tea plant is determined to be a drought-resistant tea plant.

6. 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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