Method for screening drought-resistant tea germplasm and application

Abstract

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

Description

Technical Field

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

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

[0003] However, existing research on tea drought tolerance often considers drought stress as a single, one-time event, and no prior art has examined tea's response to periodic drought stress. For example, prior art CN115948602A discloses screening for differentially expressed genes based on the expression levels of genes involved in a single drought stress event in tea plants, obtaining differentially expressed gene sequences. Furthermore, SSR and ILP molecular marker screening was associated with differentially expressed genes in tea plants under drought stress, resulting in the generation of drought stress molecular marker primers for tea plants. Summary of the Invention

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

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

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

[0007] Step 1: After repeated drought treatment of tea plants, new shoot leaves were collected as samples; the phenotype, metabolome data, and transcriptome data of the samples were measured;

[0008] Step 2: Screen the metabolomics data for differentially expressed metabolites that showed no response or no significant response to the first drought exposure, but showed significant response after repeated droughts, as “memory” metabolites.

[0009] Step 3: Screening and analyzing differentially expressed genes in tea plants subjected to repeated drought treatments: Screening out key memory genes from the differentially expressed genes;

[0010] Step 4: Based on the transcription level of key memory genes in tea leaves, determine that the tea germplasm with high transcription level is drought-resistant tea germplasm.

[0011] In one preferred embodiment, repeated drought treatment means that after the drought treatment reduces the soil moisture content by no more than 10%, rehydration treatment is performed; after the rehydration treatment, the next round of drought treatment is performed; and the number of repeated treatments is no less than 2 times.

[0012] In one preferred embodiment, in step 2, when screening differential metabolites, metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 are defined as differential metabolites, and the relative contents of all differential metabolites are z-score standardized, followed by K-means cluster analysis; KEGG enrichment analysis is performed on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways in which the differential metabolites participate.

[0013] In one preferred embodiment, step 2 also includes aligning the transcriptome data with the reference genome and evaluating the quality; the step of aligning the transcriptome data with the reference genome is: using HISAT2 to align the Clean Reads with the reference genome to obtain position information on the reference genome or gene, as well as sequence feature information unique to the sequencing sample.

[0014] In step 2, the results of the transcriptome data quality assessment can reflect the quality of the transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be performed.

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

[0016] In one preferred embodiment, in step 3, the analysis of differentially expressed genes is: performing K-means cluster analysis, GO enrichment analysis and KEGG enrichment analysis on the differentially expressed genes; further specifically, it refers to: screening out GO and gene key gene pathways with a P value less than 0.05, GO annotations explain the basic functions of the genes, and KEGG explains the metabolic pathways in which the genes participate.

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

[0018] In one preferred embodiment, step 3 also includes verification of key memory genes, which includes selecting genes for RT-qPCR verification of the reliability of transcriptome data; using the RT-qPCR method to analyze the expression levels of key memory genes under drought; and using R language to analyze the reliability verification of key memory genes as markers.

[0019] In a preferred embodiment, the key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

[0020] The present invention sets up multiple rounds of repeated drought stress treatment to dig out "memory key genes" in tea trees that do not respond when they are first exposed to drought but only respond significantly after multiple repeated droughts, and verifies these genes as drought markers for application in the screening of drought-resistant tea varieties.

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

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

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

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

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

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

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

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

[0029] In a preferred embodiment, when the transcription level of the gene shown by 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 a method for screening key memory genes that respond to periodic drought stress, comprising the following steps:

[0031] Step 1: After repeated drought treatment of tea plants, new shoot leaves were collected as samples; the phenotype, metabolome data, and transcriptome data of the samples were measured;

[0032] Step 2: Screen the metabolomics data for differentially expressed metabolites that showed no response or no significant response to the first drought exposure, but showed significant response after repeated droughts, as “memory” metabolites.

[0033] Step 3: Screening and analyzing differentially expressed genes in tea plants subjected to repeated drought treatment: Screening out key memory genes from the differentially expressed genes.

[0034] In one preferred embodiment, in step 2, when screening differential metabolites, metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 are defined as differential metabolites, and the relative contents of all differential metabolites are z-score standardized, followed by K-means cluster analysis; KEGG enrichment analysis is performed on the differential metabolites, and the KEGG enrichment analysis illustrates the metabolic pathways in which the differential metabolites participate.

[0035] In one preferred embodiment, step 2 also includes aligning the transcriptome data with the reference genome and evaluating the quality; the step of aligning the transcriptome data with the reference genome is: using HISAT2 to align the Clean Reads with the reference genome to obtain position information on the reference genome or gene, as well as sequence feature information unique to the sequencing sample.

[0036] In step 2, the results of the transcriptome data quality assessment can reflect the quality of the transcriptome sequencing. If the quality of the transcriptome sequencing is high, subsequent bioinformatics analysis can be performed.

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

[0038] In one preferred embodiment, in step 3, the analysis of differentially expressed genes is: performing K-means cluster analysis, GO enrichment analysis and KEGG enrichment analysis on the differentially expressed genes; further specifically, it refers to: screening out GO and gene key gene pathways with a P value less than 0.05, GO annotations explain the basic functions of the genes, and KEGG explains the metabolic pathways in which the genes participate.

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

[0040] In one preferred embodiment, step 3 also includes verification of key memory genes, which includes selecting genes for RT-qPCR verification of the reliability of transcriptome data; using the RT-qPCR method to analyze the expression levels of key memory genes under drought; and using R language to analyze the reliability verification of key memory genes as markers.

[0041] In a preferred embodiment, the key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.9.

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

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

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

[0045] A kit for screening drought-resistant tea trees comprises a reagent for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are selected from any one or more of the sequences SEQ ID NO.1 to SEQ ID NO.9.

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

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

[0048] Primer pair for detecting the gene shown in SEQ ID NO.1: SEQ ID NO.10:

[0049] AGGCTTTTGCAGAGCTCAAG; SEQ ID NO.11: CATAAGCCCATGCATTGTTG;

[0050] Primer pair for detecting the gene shown in SEQ ID NO.2: SEQ ID NO.12:

[0051] TTGTATTTGGGCCCACTCTC;SEQ ID NO.13:CACCGAACTTGACACCATTG;

[0052] Primer pair for detecting the gene shown in SEQ ID NO.3: SEQ ID NO.14:

[0053] GTTCGGTGAGGCTGTATGGT; SEQ ID NO.15: TAGAGTGGGTCGGTCACCTC;

[0054] Primer pair for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16:

[0055] TTGAATTCCTTGCCATCTCC;SEQ ID NO.17:AACACAGATCCCCACGAAAG;

[0056] Primer pair for detecting the gene shown in SEQ ID NO.5: SEQ ID NO.18:

[0057] AGCCTATGGCGAGATCTTCA; SEQ ID NO.19: GGGTCAGCCCAATAGTCGTA;

[0058] Primer pair for detecting the gene shown in SEQ ID NO.6: SEQ ID NO.20:

[0059] ATATTCTCGGCGGTTCCTTT; SEQ ID NO.21: AGCCGTAGTCCCCTGGTAGT;

[0060] Primer pair for detecting the gene shown in SEQ ID NO.7: SEQ ID NO.22:

[0061] GGTGTCACCGGAATGCTACT;SEQ ID NO.23:AGGGTAGCCACACTCATTGG;

[0062] Primer pair for detecting the gene shown in SEQ ID NO.8: SEQ ID NO.24:

[0063] GTACGTCCCGACAGAAGAA; SEQ ID NO.25: GGCCACAGTTAGCACCAAAT;

[0064] Primer pair for detecting the gene shown in SEQ ID NO.9: SEQ ID NO.26:

[0065] GTGTTTGGGTTGCAGAGGTT; SEQ ID NO. 27: AAGCTCAGCATTCCTTTGGA.

[0066] In a preferred embodiment, the primers for detecting photosynthesis-related genes include a primer pair for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16: TTGAATTCCTTGCCATCTCC; SEQ ID NO.17: AACACAGATCCCCACGAAAG.

[0067] In a preferred embodiment, the primers for detecting photosynthesis-related genes include a primer pair for detecting the gene shown in SEQ ID NO.7:

[0068] Primer pair for detecting the gene shown in SEQ ID NO.7: SEQ ID NO.22:

[0069] GGTGTCACCGGAATGCTACT; SEQ ID NO. 23: AGGGTAGCCACACTCATTGG.

[0070] A method for identifying the drought resistance of plant germplasm, wherein the kit is used to detect the transcription level of any one or several genes represented by SEQ ID NO.1-SEQ ID NO.9 in the plant, and the plant germplasm with high transcription level is a drought-resistant plant.

[0071] Based on the same inventive concept, the present invention also claims protection for a formulation for improving plant drought resistance, comprising at least one of the following i)-v):

[0072] i) a nucleic acid molecule as shown in any one or more of SEQ ID NO.1 to SEQ ID NO.9;

[0073] ii) an expression vector comprising any one or more nucleic acid molecules represented by SEQ ID NO.1 to SEQ ID NO.9;

[0074] iii), a recombinant host containing ii);

[0075] iv) a promoter or enhancer that enhances the expression of any one or more genes shown in SEQ ID NO.1 to SEQ ID NO.9;

[0076] v) an inducer that promotes the expression of any one or more genes shown in SEQ ID NO.1 to SEQ ID NO.9.

[0077] Based on the same inventive concept, the present invention also claims protection for the use of the preparation in improving the drought resistance of plants.

[0078] Based on the same inventive concept, the present invention also claims a method for improving the drought resistance of plants, wherein the preparation is used to increase the level and / or activity of any one or more endogenous genes shown in SEQ ID NO.1-SEQ ID NO.9.

[0079] According to embodiments of the present invention, inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 gene expression in plants can reduce drought tolerance. Overexpression of these genes improves drought tolerance in tea plants, and inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 gene expression in tea plants using antisense oligonucleotide technology can significantly exacerbate drought damage in tea plants.

[0080] The beneficial technical effects of the present invention are:

[0081] The present invention is based on multi-omics technology to screen out "key memory genes" in tea trees that did not respond when they were first subjected to drought, but only responded significantly after repeated droughts. Combining the pathways enriched with differential metabolites and the results of bioinformatics analysis of differentially expressed genes, important key drought memory genes of tea trees are accurately and efficiently mined and identified. The experimental results showed that the metabolic pathways in which differential metabolites are commonly enriched under different rounds of drought treatment are amino acid biosynthesis and aminoacyl-tRNA biosynthesis. Typical key drought memory genes of tea trees are mainly genes shown in SEQ ID NO.1-SEQ ID NO.9. By selecting 9 key memory genes for RT-qPCR verification, it was shown that the expression patterns of the selected genes were consistent with the transcriptome sequencing results, indicating that the experimental sequencing results of the present invention and the screened key memory genes are correct. The present invention clones and verifies for the first time the LHC tea photosynthesis genes (i.e., LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9) that regulate the tea plant's response to drought stress. These genes regulate the tea plant's response to drought stress, influencing the development of its drought resistance. The present invention also provides recombinant plasmids and genetically engineered bacteria containing the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes. This invention lays the foundation for seeking rational regulatory measures and screening suitable drought-resistant breeding materials for tea plants subjected to high-frequency, repeated drought stress. BRIEF DESCRIPTION OF THE DRAWINGS

[0082] Figure 1 Schematic diagram of 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.

[0083] Figure 2 The anatomical structure and parameters of tea leaves under repeated drought stress (×36.5); Figure 2 A is the anatomical structure of tea leaves in the control group; Figure 2 B is the anatomical structure of tea leaves after single drought treatment; Figure 2 C is the anatomical structure of tea leaves treated with two rounds of drought; Figure 2 D is the anatomical structure of tea leaves after three rounds of drought treatment.

[0084] Figure 3 Figure 3 is the stomatal morphology and parameters of tea leaves under repeated drought stress (×300); Figure 3 A is the stomatal morphology of tea leaves in the control group; Figure 3 B is the stomatal morphology of tea leaves in the single-round drought treatment group; Figure 3 C is the stomatal morphology of tea leaves in the two-round drought treatment group; Figure 3 D is the stomatal morphology of tea leaves in the three-round drought treatment group; Figure 3 E is the stomatal density of tea leaves under repeated drought stress; Figure 3 F is the long axis length of tea leaves under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; and D3 is the three-round drought treatment group.

[0085] Figure 4 This is a volcano plot of differential metabolites in tea tree shoots between the CK group and the D1 group under repeated drought stress.

[0086] Figure 5 This is a volcano plot comparing the differential metabolites of tea tree shoots between the CK group and the D2 group under repeated drought stress.

[0087] Figure 6 This is a volcano plot of differential metabolites in tea tree shoots between the CK group and the D3 group under repeated drought stress.

[0088] Figure 7 This is a volcano plot of differential metabolites in tea tree shoots in groups D1 and D2 under repeated drought stress.

[0089] Figure 8 This is a volcano plot of differential metabolites in tea tree shoots between groups D1 and D3 under repeated drought stress.

[0090] Figure 9 This is a volcano plot of differential metabolites in tea tree shoots in groups D2 and D3 under repeated drought stress.

[0091] Figure 10 This is the K-means cluster analysis diagram of differential metabolites under repeated drought stress; Figure 10 A represents the metabolite that showed an increasing trend under repeated drought stress; Figure 10 B is the metabolite that showed a decreasing trend under repeated drought stress; Figure 10 C is a metabolite that first increases and then decreases under repeated drought stress.

[0092] Figure 11 This is the volcano map of differentially expressed genes in tea plants between the CK and D1 groups under repeated drought stress.

[0093] Figure 12 This is the volcano map of differentially expressed genes in tea plants between the CK and D2 groups under repeated drought stress.

[0094] Figure 13 This is the volcano map of differentially expressed genes in tea plants between the CK and D3 groups under repeated drought stress.

[0095] Figure 14 This is the volcano map of differentially expressed genes in tea plants in groups D1 and D2 under repeated drought stress.

[0096] Figure 15This is the volcano map of differentially expressed genes in tea plants in groups D1 and D3 under repeated drought stress.

[0097] Figure 16 This is the volcano map of differentially expressed genes in tea plants in groups D2 and D3 under repeated drought stress.

[0098] Figure 17 This is a heat map of differential expression of photosynthesis-related genes and light-harvesting complex binding protein-related genes; Figure 17 A is the heat map of differential expression of photosynthesis-related genes; Figure 17 B is the heat map of differential expression of light-harvesting complex binding protein genes.

[0099] Figure 18 RT-qPCR was used to verify the expression pattern of key memory genes under repeated drought stress.

[0100] Figure 19 ROC validation of gene markers for key drought memory genes in the breeding of drought-resistant tea varieties. DETAILED DESCRIPTION

[0101] The present invention is not limited to the following specific embodiments. Based on the disclosure of the present invention, a person skilled in the art may adopt a variety of other specific embodiments to implement the present invention. Any simple changes or modifications made to the design structure and concept of the present invention fall within the scope of protection of the present invention. It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other unless they conflict.

[0102] The genes involved in the present invention are:

[0103] SEQ ID NO.1: LHCP1(TEA009770)

[0104] ATGGCTGCGTCTACCATGGCTCTCTCTTCACCATCTTTTGCCGGAAAGGCTGTGAAAATTGCCCCGGAGGTTCTTGGTGGTGGAAGGATCAGTATGAGGAAGACCGGCAAGCAAGTCCCATCTGGAAGCCCGTGGTACGGTCCAGACCGAGTCTTGTATTTGGGTCCATTATCCGGTGAGCCCCCATCCTACCTCACTGGGCAATTCCCTGGTGATTATGGTTGGGACACTGCTGGGCTTTCGGCTGATCCAGAAACTTTTGCCAAGAACCGTGAGCTCGAGGTGATTCACTGCAGATGGGCCATGCTTGGAGCTTTGGGTTGCGTCTTCCCCGAGCTTTTGGCCCGCAACGGTGTCAAGTTCGGCGAGGCTGTGTGGTTCAAGGCCGGTGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCCCAAAGCATTCTGGCCATCTGGGCTTGCCAAGTTATCTTGATGGGTGCTGTTGAGGGCTACCGCATTGCTGGTGGACCACTCGGGGAGGTGACCGACCCGCTCTACCCCGGTGGAAGCTTCGACCCATTGGGCCTAGCCGATGATCCAGAGGCTTTTGCAGAGCTCAAGGTGAAGGAGATCAAGAATGGAAGACTGGCCATGTTCTCAATGTTTGGGTTCTTTGTTCAGGCAATTGTGACAGGAAAGGGACCATTGGAGAACCTGGCTGACCACCTTGCTGACCCTGTTAACAACAATGCATGGGCTTATGCCACAAACTTTGTCCCAGGAAAGTGA。

[0105] SEQ ID NO.2:LHCP2(TEA019232)

[0106] ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0107] SEQ ID NO.3:LHCP3(TEA001864)

[0108] ATGGCTGCCTCTACAATGGCTCTCTCTTCTCCATCTTTCGCCGGAAAGGCGATAAAACTCTCTCCTTCCACCCCAGACCTCGTTGGCAGAGGAAGGATCAGCATGAGGAAGACTGGTGGCAAGCCCGTCCGATCCGGTAGCCCATGGTACGGCCCAGACCGAGTCTTGTATTTGGGCCCACTCTCTGGTGACCCCCCATCCTACCTTACTGGAGAATTCCCTGGTGACTACGGTTGGGACACTGCTGGGCTTTCAGCTGACCCAGAAACATTTTCCAAGAACCGTGAGCTCGAGGTGATCCATTGCAGATGGGCCATGCTCGGCGCTCTTGGGTGTGTCTTCCCCGAGCTTTTGGCCCGCAATGGTGTCAAGTTCGGTGAGGCTGTATGGTTCAAAGCCGGGGCCCAAATCTTCAGTGAGGGTGGGCTTGACTACTTGGGCAACCCTAGCTTGATCCATGCTCAAAGCATTTTGGCCATTTGGGCTTGCCAAGTTATCTTGATGGGCGCCGTGGAGGGCTACCGTATTGCAGGTGGGCCGCTCGGTGAGGTGACCGACCCACTCTACCCGGGTGGAAGCTTCGACCCATTGGGCCTTGCCGATGACCCAGAGGCCTTTGCTGAGCTCAAGGTGAAGGAGATCAAGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAATATTGTACTGTCTAATGGGTTGTTTGGATACAGGAATGGTAGACTTGCCATGTTTTCCATGTTTGGATTCTTTGTTCAAGCCATTGTGACTGGAAAGGGACCATTGGAGAACCTTGCTGACCACCTTGCTGATCCAGTGAACAACAATGCCTGGGCTTATGCCACTAACTTTGTTCCCGGAAAGTGA。

[0109] SEQ ID NO.4:LHCP4(TEA008963)

[0110] ATGGCTTCCAAAGCCCTAATGAGCTGCGGCATCGCCGCCGTCTGCCCGTCAGTCCTTTCCTCTTCCAAGTCCAAATTTGCCGCCGCGTTGCGGCTTCCAAGTGGTGGTGCCACCGCTACCTCCCGGTTCACCATGACGGCTGACTGGATGCCTGGCGAGCCAAGGCCACCCTATCTTGACGGCTCCGCACCCGGTGATTTCGGGTTCGACCCGCTTCGTCTGGGTGAAGTCCCAGAAAACCTTGAAAGATACAAGGAGTCTGAACTCATTCACTGCAGATGGGCTATGCTTGCTGTTCCAGGGATCCTAGTTCCAGAGGCCTTGGGATTGGGCAACTGGGTACAAGCTCAAGAGTGGGCGGCAATCCCTGGAGGACAAGCCACCTACCTTGGCCAACCTGTCCCATGGGGCACCCTCCCAATCATCTTGGCCATTGAATTCCTTGCCATCTCCTTCGTCGAGCACCAGCGCAGCATGGAAAAGGACCCTGAGAAGAAGAAGTACCCCGGTGGAGCTTTCGACCCATTGGGATACTCCAAAGACCCAGTAAAGTTTGAGGAGAACAAGGTCAAAGAAGTAAAAAATGGCCGGCTTGCCTTGTTGGCTTTCGTGGGGATCTGTGTTCAACAGTCCGCTTACCCAGGGACAGGACCGTTGGAGAACCTGGCAACTCACTTGGCTGATCCATGGCACAACAACATTGGCGATATCATTATCCCTAGATCAATTTCTCCATGA。

[0111] SEQ ID NO.5:LHCP5(TEA000535)

[0112] ATGGCAACTCAAGCACTGGTGTCTTCATCATCTCTTACCTCCTCAGTGGAGGCTGCAAGGCAGATTCTAGGAGGAAGGCCAGCTACCCATTCTTCAAGAAGGAAGGTCTCTTTTGTTGTTAGGGCAGCTACTACTCCCCCTGTTAAGCAAGGAGCAGATAGACCTCTCTGGTTTGCCTCCAAGCAAAGTCTCTCCTACTTAGATGGCAGCCTGCCCGGCGACTACGGATTCGACCCGCTCGGCTTGTCCGACCCAGAAGGTACCGGAGGCTTCATCGAGCCCAGATGGCTAGCCTATGGCGAGATCTTCAACGGCCGTACCGCCATGGTCGGCTCTATCGGATGCATCGCCCCAGAAATCTTGGGCAAACTCGGCCTAATTCCGCCAGAAACCGCTCTGCCGTGGTTCAAAACAGGCGTGATCCCGCCCGCTGGGACCTACGACTATTGGGCTGACCCATACACTCTTTTTGTTTTCGAATTGGCACTAGTGGGCTTTGCAGAGCACAGGAGGTTCCAGGCTTGGTACAACCCAGGCTCAATGAGTAAACAGTACTTTTTGGGCCTGGAGAAATATTTGGGCGGGACGGATAACCCTGCATACCCTGGTGGGCCACTGTTTAACCCACTTGGGCTTGGAAAGGATGAGAAGTCAATGAGGGATATGAAGTTGAAGGAGGTAAAGAACGGGAGGTTGGCCATGTTGGGTATGTTGGGTTTCTTTGTGCAGGCGTTGGTGACTGGGGTTGGACCCTTCCAGAACCTTCTGGATCATTTGGCTGACCCTGTCAACAACAATGTCTTGACCAACCTCAAGTTCCACTAA。

[0113] SEQ ID NO.6:LHCP6(TEA015295)

[0114] ATGGCGGAGGATCGTCGGAGGAACGTCGGAGCAAACGGTGGAGGCACGATGGAGGAACGACGGAGTAATGGTGGAGAGATGCTTGTGGGTGTCTTAATATTCTCGGCGGTTCCTTTCACGGCGGTGAAAGCTATAGCCAACAGTCCCCTGGGAGAGTTGCTTCAGAGGCGATTGGAAGAGAAAAAGAAGGATGCCATCGATAATTCTTCCAATTTCAAGGCACTTGCTCAAATGGCTAGAAAGGATAGTTTATGGTATGGAGAGAAGCGTCCCCGTTGGCTTGGTCCAATTTCATATGACTATCCTTCATATCTGACTGGAGAACTACCAGGGGACTACGGCTTTGATATTGCAGGTTTAAGCAGGGATCCTGTGGCTTTCCAGAAATATTTCAACTTTGAAATACTGCATGCTCGCTGGGCCATGCTTGCAGCGCTTGGTGCTCTGATTCCCGAACTATTAGACCTAGTAGGAGCCTTTCACTTTGTTGAGCCGGTCTGGTGGAAAGTTGGATATTCAAAGCTTAAGGGTGACACATTGGACTACCTTGGCATCCCTGGGCTCCACTTAGCTGGAAGTCAAGGAGTGATTGTTATAGCTATCTGCCAAGCTCTTCTGATGGTTGGACCGGAATATGCAAGATATTGTGGCATTGAGGCTCTCGAGCCTTTAGGAATTTACTTGCCTGGGGATATCAATTATCCTGGAGGTGCACTTTTCGATCCCTTGAATCTCTCTAAAGACCCGGTATCTTTTGAGGACTTGAAGGTGAAAGAGATAAAAAATGGGCGCTTAGCAATGATTGCATGGTTAGGATTTTACACGCAAGCTGCCCTAACGGGGAAAGGGCCTGTGCAAAACCTTCTTGACCACATCTTGGATCCTTTTCATAATAACCTGCTTTCCATTCTGAAATTCATGTGA。

[0115] SEQ ID NO.7:LHCP7(TEA016942)

[0116] ATGGCCACCGTCGCAGCTCAGGCATCCACCACGGTTCTTCGGCCATGTGCCTCGAAATCGAGGTTCCTTACCGGTTCTTCCGGTAAGCTAAACCGAGTAATCTCATTTAAACCGACATCACCTTCCTCACTCAGCTCATTCAAAGTTGAAGCCAAGAAAGGAGAATGGTTACCGGGCTTGGCCTCCCCAGGCTATCTTAACGGCAGCCTACCTGGTGACAATGGGTTCGATCCCTTGGGGCTAGCCGAGGACCCAGAGAACTTGAAATGGTTCATCCAGGCCGAGCTTGTGAACAGTCGGTGGGCCATGTTGGGTGTCACCGGAATGCTACTGCCGGAAGTGTTGAGCAGTGTCGGAATAATCAACGTTCCAAAATGGTACGATGCAGGAAAATCCGAATACTTTGCATCATCATCGACACTTTTCGTGATCGAGTTCATCTTGTTTCACTACGTGGAGATCAGACGGTGGCAAGACATCAAGAACCCTGGAAGTGTCAACCAAGATCCTATCTTCAAGAGCTATAGCTTGCCTCCCAATGAGTGTGGCTACCCTGGTGGCATTTTTAACCCCCTCAACTTTGCTCCCACTGAGGAGGCCAAAGAGAAGGAGCTCGCTAATGGGAGATTGGCAATGTTGGCATTCTTGGGGTTTGTGGTTCAGCACAATGTGACTGGAAAAGGGCCATTTGACAACCTCTTGCAGCACATCTCTGATCCATGGCACAACACAATTATTCAAACAATCAGAGGTTACTAA。

[0117] SEQ ID NO.8:LHCP8(TEA023017)

[0118] ATGGCTTCACTGGCAGCATCAACGGCGGCTGCCTCCCTTGGCATGTCAGAAATGCTCGGAAACCCTCTCCGGAGTGGCGTAACGAGATCGGCACCTCCTCCCACCGCGACATCTAGCCCTGCCACCTTCAAGACCGTCGCACTTTTCTCCAAGAAGAAGGCTGCACCTCCCAAAAAGGCTGTCGTCTCCCCTGTTGATGACGAGCTCGCCAAGTGGTACGGTCCCGACAGAAGAATTTTCTTGCCGGAGGGGCTGTTGGACCGATCAGAAATTCCGGCATACCTCACCGGAGAAGTCCCTGGAGATTATGGTTACGATCCTTTTGGGCTTAGCAAGAAACCAGATGACTTTGCCAAGTACCAAGCATATGAGCTAATTCATGGCAGGTGGGCAATGTTGGGGGCTGCGGGCTTCATCATCCCTGAGGCCTTCAACAAATTTGGTGCTAACTGTGGCCCTGAAGCTGTTTGGTTCAAGACAGGAGCTCTACTCCTAGATGGTAACACACTGAATTACTTTGGAAAGAACATCCCCATTAATCTTATATTCGCTGTCATCGCTGAAGTTGTTCTTGTTGGTGGTGCTGAATACTACAGAATCATCAATGGATTGAATTTGGAGGACAAGCTTCACCCAGGCGGTCCATTTGATCCATTGGGGCTTGCAAAGGATCCAGACCAGGCTGCAATACTGAAGGTGAAGGAGATCAAGAACGGTAGACTTGCAATGTTTGCAATGCTCGGTTTCTTCATCCAAGCTTATGTAACGGGAGAAGGTCCAGTTGAAAACCTCGCCAAACATCTAAGCGATCCGTTTGCAAACAACTTGCTCACTGTGCTTGCTGGATCTGCTGAAAGAGCTCCTACCCTGTGA。

[0119] SEQ ID NO.9:LHCP9(TEA026680)

[0120] ATGGCCGCAACCACCGCCGCCGCCGCTGCCGCCACATCATCATTTCTAGGCACCCGCCTCGCCGACCTATATTCCGGTTCGGGCCGGGTCCAGGCCCGGTTCGGATTCGGACGCAAAAAGGCTCCACCAAAGAAGATTGCGAAGCAGGGCTTTGACCGCCCACTTTGGTACCCGGGAGCGAAAGCGCCCGAATGGTTAGATGGGAGTCTTGTTGGGGATTACGGGTTTGACCCGTTCGGGTTGGGTAAACCGGCCGAGTACTTGCAATTTGATTTGGACTCGTTGGATCAGAACTTGGCTAAGAACTCGGCGGGTGATGTAATCGGGACCCGGTTCGAGAGCGCGGATGTGAAGTCGACGCCGTTTCAGCCGTACACAGAGGTGTTTGGGTTGCAGAGGTTTAGGGAGTGTGAGCTGATTCATGGAAGGTGGGCTATGTTGGCTACGCTCGGCGCGCTTACTGTTGAGTGGCTCACTGGTGTTACGTGGCAAGACGCTGGAAAGGTGGAGCTAATTGAAGGTTCATCCTACCTTGGCCAACCACTTCCATTTTCCATAACCACATTGATATGGATTGAGGTCATAGTCATTGGATACATAGAGTTCCAAAGGAATGCTGAGCTTGACCCGGAAAAGAGGCTCTACCCGGGTGGAAAATTCTTCGACCCGCTTGGTTTGGCCTCGGACCCAGAGAAGAAGGCAACCCTCCAATTGGCGGAGATCAAGCATGCCCGCCTTGCCATGGTAGCCTTCCTAGGTTTTGCCGTCCAAGCTGCTGTCACCGGCAAAGGGCCACTCAACAACTGGGCGACCCATTTGAGTGACCCGCTCCACACAACCATTATAGACACCTTTTTCTCTTGA。

[0121] Example 1

[0122] Method for mining key genes of drought memory in tea plants based on multi-omics, specifically:

[0123] 1 Repeated drought treatment of tea plants

[0124] Tea seedlings with consistent growth and no pests and diseases were selected for the experiment, e.g. Figure 1 As shown in the figure, a total of four groups were set up, namely one control group (CK) and three experimental groups (D1, D2, and D3). The control group was watered once every two days to maintain the soil moisture content at 25%-35%. The treatment groups were not watered during their drought treatment period. After the soil moisture content dropped to 10%, fresh leaves of the tea tree shoots were collected or rewatered. After rewatering, the next round of drought treatment was carried out. The tea tree single drought treatment group was recorded as D1, the two-round drought treatment group was recorded as D2, and the three-round drought treatment group was recorded as D3.

[0125] 2. Phenotypic analysis of tea leaves

[0126] The treated new shoot leaves were randomly selected, and tissue blocks of about 5 mm × 10 mm were taken along both sides of the main vein of the leaves. They were fixed with FAA solution (a mixture of 5 mL of formalin, 90 mL of 70% ethanol, and 5 mL of glacial acetic acid), dehydrated with gradient ethanol (the gradient was 70% ethanol → 85% ethanol → 95% ethanol → 100% ethanol, and the immersion time for each level was about 1-2 hours, which was adjusted according to the size of the tissue), transparentized with gradient xylene (ethanol-xylene mixture, ethanol-xylene 1:1 mixture → pure xylene, the transition time of the mixture was about 30 minutes to 1 hour to reduce the direct stimulation of xylene on the tissue), embedded in paraffin, sliced ​​with a microtome, double stained with safranin and fast green, and sealed with neutral gum to make permanent slices. The slices were observed and photographed under an optical microscope. The results are as follows: Figure 2 As shown, tea leaf cells are arranged neatly and tightly, and the intercellular spaces between spongy tissue cells are small ( Figure 2 A); Tea tree leaf cells under single drought treatment showed shortening phenomenon, and the intercellular spaces of spongy tissue were larger and arranged loosely and unevenly ( Figure 2 B); The number of palisade tissue cell layers in tea leaves treated with two rounds of drought increased, and the spongy tissue cells were arranged more densely ( Figure 2 C); Tea tree leaf cells after three rounds of drought treatment are arranged neatly and tightly ( Figure 2 D). Case Viewer software was used to observe and measure tea leaves' upper and lower epidermal thickness (TU, TL), palisade tissue thickness (PP), spongy tissue thickness (SP), and leaf thickness (LT). The results are shown below.

[0127] Table 1 Results of upper and lower epidermal thickness, palisade tissue thickness, spongy tissue thickness, and leaf thickness of tea leaves treated in each group

[0128]

[0129] UE: upper epidermis; LE: lower epidermis; PP: palisade tissue; SP: spongy tissue.

[0130] Table 1 shows that compared with the control group, tea leaves subjected to a single drought episode significantly decreased in upper epidermal thickness, palisade tissue thickness, spongy tissue thickness, lower epidermal thickness, and leaf thickness. Compared with a single drought episode, tea leaves subjected to two drought episodes significantly increased in upper epidermal thickness, palisade tissue thickness, spongy tissue thickness, lower epidermal thickness, leaf thickness, and the palisade to spongy tissue ratio. Compared with a single drought episode, tea leaves subjected to three drought episodes significantly increased in palisade tissue thickness, lower epidermal thickness, and the palisade to spongy tissue ratio, while spongy tissue thickness significantly decreased. This suggests that tea plants adapt to different drought episodes by adjusting different leaf anatomical structures.

[0131] Randomly select the new shoot leaves after treatment and observe their upper and lower epidermis. Use ultrapure water to clean the leaves to be observed, avoid the veins and edges, cut off the middle part for observation, and use a vacuum coating machine to spray gold to prepare the sample. Use a SEM-6380LV scanning electron microscope to observe the sample and randomly select five points to take pictures. The results are as follows Figure 3 As shown in the figure, 3A is the stomatal morphology of tea leaves in the control group; 3B is the stomatal morphology of tea leaves in the single-round drought treatment group; 3C is the stomatal morphology of tea leaves in the two-round drought treatment group; 3D is the stomatal morphology of tea leaves in the three-round drought treatment group; 3E is the stomatal density of tea leaves under repeated drought stress; 3F is the long axis length of tea leaves under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; D3 is the three-round drought treatment group. Stomatal density and stomatal aperture are used to describe the characteristics of leaf stomatal development ( Figure 3 A, 3B, 3C, 3D). Stomatal density is the number of stomata per unit area in each microscopic image (approximately 0.13 mm2), and the long axis of the stomata represents the size of the stomata. The number of stomata per unit area increases with the increase of drought cycles, while the stomatal aperture decreases ( Figure 3 A, 3B, 3C, 3D). Use Image-Pro Plus 6.0 software to process images and measure distances. Use a scale to measure the length of leaf stomata and calculate the stomatal density. The results are shown in Figure 2. Figure 3 As shown in Figures E and 3F, under drought stress, the stomatal density of tea leaves was higher than in the control group, while the stomatal diameter decreased. Under repeated drought stress, stomatal density and stomatal size exhibited different trends. With increasing drought episodes, stomatal density increased, while stomatal size gradually decreased. Compared with the control group, a single drought episode increased stomatal density and decreased stomatal size, but neither reached significant levels and remained similar to the stomatal density and diameter after two drought treatments. After three cycles of drought, both stomatal density and diameter of tea leaves changed significantly. Compared with the control group, stomatal density increased by 40% and stomatal diameter decreased by 48%.

[0132] 3 Metabolome testing

[0133] 3.1 Metabolite extraction and sample preparation

[0134] Fresh leaf samples from the control and experimental groups were freeze-dried, ground, dissolved, centrifuged, and filtered. The filtered samples were then used for UPLC-MS / MS analysis. The sample extraction process is as follows:

[0135] (1) Fresh tea leaves were placed in a freeze dryer (Scientz-100F) and vacuum freeze-dried;

[0136] (2) Grinding (30 Hz, 1.5 min) to powder using a grinder (MM 400, Retsch);

[0137] (3) Weigh 100 mg of powder and dissolve it in 1.2 mL of 70% methanol extract;

[0138] (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;

[0139] (5) After centrifugation (12000 rpm, 10 min), the supernatant was aspirated, the sample was filtered through a microporous filter membrane (0.22 μm pore size), and stored in a sample injection bottle for UPLC-MS / MS analysis.

[0140] The mass spectrometry data were processed using Analyst 1.6.3 software, and the metabolites of the samples were qualitatively and quantitatively analyzed by mass spectrometry based on the metabolic database to obtain metabolome data.

[0141] 3.2 Screening and analysis of differential metabolites

[0142] The method of combining Fold Change and VIP value of OPLS-DA model was adopted to screen differential metabolites. Metabolites with Fold Change>1.5 or Fold Change<0.67 and VIP>1 were defined as differential metabolites. The volcano plot was used to visually display the overall distribution of differential metabolites between the two groups. The results are as follows Figure 4-9 As shown in the figure, Figure 4 This is the volcano plot of differential metabolites in tea shoots between the CK group and the D1 group under repeated drought stress; Figure 5 This is a volcano plot comparing the metabolites of tea shoots between the CK group and the D2 group under repeated drought stress; Figure 6 This is the volcano plot of differential metabolites in tea shoots between the CK group and the D3 group under repeated drought stress; Figure 7 This is the volcano plot of differential metabolites in tea shoots between the D1 and D2 groups under repeated drought stress; Figure 8 This is the volcano plot of differential metabolites in tea shoots between the D1 and D3 groups under repeated drought stress; Figure 9 The volcano plot of the metabolites of tea shoots in groups D2 and D3 under repeated drought stress; CK is the control group; D1 is the single-round drought treatment group; D2 is the two-round drought treatment group; D3 is the three-round drought treatment group; Note: The red points in the figure indicate up-regulated metabolites, the green points indicate down-regulated metabolites, and the gray points indicate metabolites with no significant differences; 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, of which 265, 363, and 270 were upregulated, and 142, 156, and 118 were downregulated. Figure 7 )、D1_vs_D2( Figure 8 ) and D2_vs_D3( Figure 9 ) screened out 374, 350, and 208 differential metabolites, respectively, of which 221, 249, and 142 were upregulated, and 153, 101, and 66 were downregulated, respectively.

[0143] 3.3 KEGG enrichment analysis of differential metabolites

[0144] Differential metabolism interacts within organisms, forming distinct pathways. The differential metabolites were annotated and displayed using the KEGG database. The results showed that the differential metabolites in the three comparison groups, CK_vs_D1, D1_vs_D2, and D1_vs_D3, were enriched in the amino acid biosynthesis and aminoacyl-tRNA biosynthesis pathways. D2_vs_D3 was primarily enriched in the phenylpropanoid metabolism pathway and the flavonoid pathway.

[0145] 3.4 Differential metabolite trend analysis and memory metabolite screening

[0146] In order to study the relative content trends of metabolites under different drought treatments, the relative contents of all differential metabolites identified according to the screening criteria in all group comparisons were normalized by z-score, and then K-Means cluster analysis was performed. Figure 10 As shown in the figure, 10A is a metabolite that shows an increasing trend under repeated drought stress; 5B is a metabolite that shows a decreasing trend under repeated drought stress; 5C is a metabolite that first increases and then decreases under repeated drought stress. It can be seen that the 798 differential metabolites can be divided into three different expression patterns, recorded as Sub Class 1, Sub Class 2, and Sub Class 3. The metabolites clustered in Sub Class 1, which has the highest abundance, increase under drought stress and increase with the increase in drought cycles, actively responding to repeated drought stress ( Figure 10A). Subclass 3 contains 458 substances. Metabolites in Subclass 1 include flavonoids, phenolic acids, amino acids and their derivatives, lipids, and alkaloids, which are 112, 79, 57, 57, and 38, respectively. Subclass 2 contains 190 substances whose contents decrease under drought stress ( Figure 10 B). Subclass 3 contains 150 substances whose contents first increase and then decrease under drought stress ( Figure 10 C) Plant secondary metabolites are products of plant adaptation to their environment during growth. Drought stress typically increases the concentration of these secondary metabolites, such as alkaloids, flavonoids, and terpenes. Repeated drought stress can induce an increase in active substances in tea plants, thereby inducing a positive response and improving drought tolerance. The present invention screened metabolites clustered in Subclass 1 for those that did not significantly respond to the first drought stress episode but only showed a significant response with each successive drought cycle. These metabolites are considered "memory" metabolites under drought stress.

[0147] 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.

[0148] Table 2 Memory metabolites in tea leaves under repeated drought stress

[0149]

[0150] According to the functions of memory metabolites, L-proline, L-lysine, L-leucine, L-valine and L-isoleucine play a role in maintaining the homeostasis of photosynthesis and respiration; L-asparagine, proline, L-glutamine and GABA have osmotic regulation functions, helping tea plants maintain cellular 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, sugar substances show a decreasing trend, while secondary metabolites such as flavonoids and phenolic acids accumulate significantly. When tea plants encounter drought stress again after a single drought stress, they preferentially allocate carbon sources to the synthesis of secondary metabolites through carbon fixation in photosynthesis, promote the shift from primary metabolism to secondary metabolism, and enhance the plant's drought resistance.

[0151] 4 Transcriptome sequencing

[0152] First, the total RNA of fresh tea leaf samples was extracted using the Trizol method. The specific process is as follows:

[0153] 1. Preparation of Materials and Reagents. Samples: Fresh young tea leaves (quickly frozen in liquid nitrogen immediately after harvesting and stored at -80°C). Reagents: TRIzol®, chloroform, isopropanol, 75% ethanol (prepared with DEPC water), RNase-free water, liquid nitrogen. Equipment: Pre-chilled mortar, RNase-free centrifuge tubes, refrigerated centrifuge, vortexer, and pipette.

[0154] 2. Experimental Procedure. Sample Grinding: Grind approximately 100 mg of leaves in liquid nitrogen until powdered. Quickly transfer to 1 mL of TRIzol and vortex to mix thoroughly. Lysis and Separation: Let stand at room temperature for 5 minutes, add 0.2 mL of chloroform, shake vigorously for 15 seconds, and let stand at room temperature for 3 minutes. Centrifuge at 4°C (12,000 × g, 15 minutes) to separate into three layers (the upper aqueous phase contains RNA). RNA Precipitation: Pipette the upper aqueous phase into a new tube, add 0.5 mL of isopropanol, invert to mix thoroughly, and let stand at room temperature for 10 minutes. Centrifuge at 4°C (12,000 × g, 10 minutes), discard the supernatant, and retain the RNA pellet. Washing and Solubilization: Wash the pellet 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 at -80°C.

[0155] Second, use agarose gel electrophoresis to analyze the integrity of the RNA and the presence of DNA contamination. The specific process is as follows:

[0156] 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 TM RNA Ladder).

[0157] 2. Experimental Procedure: Prepare gel: Melt 1.2% agarose gel (dissolved in 1× TAE) in a microwave oven, add EB, and pour onto a gel-forming plate. Sample Spotting and Electrophoresis: Mix 2 μL of RNA sample with 1 μL of loading buffer and spot onto the gel wells. Electrophoresis is performed at 80-100 V for 20-30 minutes.

[0158] 3. Result analysis. Integrity: 28S and 18S rRNA bands are clearly visible (the brightness of 28S is about twice that of 18S). DNA contamination: If there are diffuse bands or tails near the sample wells, it indicates that there is genomic DNA residue. Use Qubit2.0 fluorometer to measure RNA concentration with high precision, and use Agilent2100 bioanalyzer to accurately detect RNA integrity. After passing the test, the library is constructed. The RNA quality inspection indicators are concentration and integrity. Concentration: Qubit 2.0 detection:

[0159] ≥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 (ideal value ≥8.0). Electrophoresis: 28S:18S band brightness ratio ≈2:1, no degradation tailing. Library quality is tested, and after passing the test, sequencing is performed on the Illumina HiSeq platform. The test criteria for library passing the test are concentration and fragment size. Concentration: Qubit or qPCR detection, effective library concentration ≥2 nM (must meet Illumina machine requirements). Fragment size: Agilent 2100 detection, the main peak of the library is 300-500 bp (insert fragment + adapter), with no adapter dimers (<100 bp artifacts).

[0160] 4.1 Sequence alignment and data analysis

[0161] Clean data was obtained by filtering the raw data obtained by sequencing. The filtering criteria were as follows: remove reads with adapters; remove paired reads when the N content in any sequencing read exceeded 10% of the total base number of the read; remove paired reads when the number of low-quality (Q≤20) bases in any sequencing read exceeded 50% of the total base number of the read.

[0162] Clean reads were aligned to the 'Shuchazao' tea genome using HISAT2 to obtain positional information on the reference genome or gene, as well as sequence characteristics unique to the sequenced sample. Fragments Per Kilobase of transcript per Million fragments mapped (FPKM) was used as a metric to measure transcript or gene expression levels.

[0163] 4.2 Screening of differentially expressed genes

[0164] 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 standardization, dispersion estimation, and statistical testing. The specific process is as follows:

[0165] 1. Data input and preprocessing: Input data: raw RNA-seq read matrix (Raw Counts), with rows representing genes and columns representing samples.

[0166] 2. Data normalization. Purpose: To eliminate the effects of sequencing depth and gene length on gene expression. First, calculate size factors (normalization factors for each sample): using the geometric mean (median of ratios) method to eliminate differences in sequencing depth between samples.

[0167] 3. Dispersion Estimation. Purpose: To quantify the degree of variation in gene expression values ​​(variation between biological replicates). This method involves three steps: ① Gene-specific dispersion: Estimate the dispersion of each gene individually (based on a negative binomial distribution). ② Trendline Fitting: Fit a smooth curve to the mean-dispersion relationship of gene expression levels. ③ Empirical Bayesian Shrinkage: Shrink the gene-specific dispersion toward the trend line to avoid overfitting in small sample sizes.

[0168] 4. Differential Expression Analysis. Model Construction: Hypothesis testing was performed using a generalized linear model (GLM) (default Wald test). Calculation of the statistic Log2 Fold Change: The logarithmic change in gene expression. P-value: Wald test or likelihood ratio test (LRT) based on a negative binomial distribution.

[0169] 5. Multiple testing correction. Purpose: To control the false positive rate (FDR).

[0170] Benjamini-Hochberg (BH) correction: Screening criteria: FDR < 0.05. The screening conditions for differentially expressed genes were |log2FoldChange|> 1 and FDR < 0.05.

[0171] The volcano plot is used to visually display the overall distribution of differentially expressed genes in the two groups of samples. The results are as follows: Figure 11-16 As shown, Figure 11 This is the volcano map of differentially expressed genes in tea plants between the CK and D1 groups under repeated drought stress; Figure 12 This is the volcano map of differentially expressed genes between the tea plants in the CK and D2 groups under repeated drought stress; Figure 13 This is the volcano map of differentially expressed genes in tea plants between the CK and D3 groups under repeated drought stress; Figure 14 This is the volcano map of differentially expressed genes between the tea plants in groups D1 and D2 under repeated drought stress; Figure 15 This is the volcano map of differentially expressed genes in tea plants between the D1 and D3 groups under repeated drought stress; Figure 16This is a volcano plot of differentially expressed genes in tea trees in groups D2 and D3 under repeated drought stress; CK: control group; D1: single-round drought treatment group; D2: two-round drought treatment group; D3: three-round drought treatment group; Note: The red dots in the figure indicate up-regulated differentially expressed genes, the green dots indicate down-regulated differentially expressed genes, and the gray dots indicate genes with no significant differences. The DESeq2 software was used to standardize the readcount in the gene expression analysis, and then the Benjamini-Hochberg method was used to correct the hypothesis test probability (P-value) for multiple hypothesis testing to obtain the false discovery rate (FDR). The screening conditions for significantly differentially expressed genes (DEGs) were |log2Fold Change| >=1 and FDR<0.05. The overall distribution of differentially expressed genes can be intuitively seen from the volcano plot. 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 upregulated DEGs, and 6069, 4404, and 4901 downregulated DEGs, respectively. Figure 14 )、D1_vs_D( Figure 15 ) and D2_vs_D3( Figure 16 ) identified 156, 780, and 51 upregulated DEGs, and 82, 1039, and 258 downregulated DEGs, respectively.

[0172] 4.3 K-means analysis of differentially expressed genes and screening of key memory genes

[0173] To investigate gene expression patterns under repeated drought conditions, we first centered and normalized the FPKM values ​​of the genes, followed by K-means cluster analysis. The 15,463 differentially expressed genes could be grouped into 10 distinct expression patterns. Genes with similar expression patterns may have similar functions. Group 1, comprising 547 genes, showed an increasing trend followed by a decreasing trend. Group 2, comprising 1,879 genes, showed an increasing trend followed by a decreasing trend followed by an increasing trend. Group 3, comprising 1,669 genes, showed a decreasing trend followed by an increasing trend. Group 4, comprising 1,248 genes, showed an increasing trend followed by a decreasing trend followed by an increasing trend. Group 5, comprising 5,406 genes, showed a decreasing trend followed by a stable trend. Group 6, comprising 873 genes, showed a decreasing trend followed by a stable trend followed by a decreasing trend. Group 7, comprising 552 genes, showed a stable trend followed by an increasing trend. Group 8, comprising 1,068 genes, showed an increasing trend followed by a stable trend. Group 9, which contained 840 genes, showed a trend of first increasing, then decreasing, and then remaining unchanged. Group 10, which contained 1,381 genes, showed a trend of first increasing, then remaining unchanged, and then decreasing.

[0174] The genes that did not respond significantly to a single drought but whose expression levels were significantly higher under repeated drought than that under a single drought were defined as drought memory key genes. They were mainly distributed in the photosynthesis pathways operated by photosystem I (PSI) and photosystem II (PSII), and there were 9 of them (see Table 3).

[0175] Table 3 “Memory” genes of tea plants under repeated drought stress

[0176]

[0177] These genes include: photosystem I chlorophyll-binding protein gene, photosystem II chlorophyll-binding protein gene, photosystem II oxygen-evolution enhancing protein gene. The light response pathway is the pathway with the most enriched drought memory key genes.

[0178] 4.4 Functional analysis of differentially expressed genes

[0179] To better elucidate the functions of differentially expressed genes, GO and KEGG enrichment analysis was performed, with a P value less than 0.05 as the screening criterion. Of the 15,463 differentially expressed genes, 10,791 genes were annotated to the GO, accounting for 69.79% of all genes. The top 50 most significantly enriched GO terms were selected and plotted as a histogram. The results showed that compared with the control, GO terms with significant differences (p < 0.05) and a high number of enriched genes under single drought exposure included photosynthesis (GO:0015979), anion transport (GO:0006820), UDP-glucosyltransferase activity (GO:0035251), anion transmembrane transporter activity (GO:0008509), and secondary metabolite biosynthesis (GO:0044550). Furthermore, the number of downregulated genes annotated to each function was greater than the number of upregulated genes. Compared with a single drought, the GO-terms enriched with a higher number of genes under the two drought cycles showed significant differences (p < 0.05) in photosynthesis, photosynthetic system (GO:0009521), chromophore-protein linkage (GO:0018298), and chlorophyll binding (GO:0016168). The number of up-regulated genes annotated to each function was higher than the number of down-regulated genes. Compared with a single drought, the GO-terms enriched with a higher number of genes under the three drought cycles showed significant differences (p < 0.05) in anion transport, photosynthesis, secondary metabolite biosynthesis, response to wounding (GO:0009611), and anion transmembrane transporter activity. The number of up-regulated genes annotated to photosynthesis, photosynthetic system, photosynthetic system I, and photosynthetic system II was higher than the number of down-regulated genes. Compared with the two drought cycles, the enrichment analysis results for the three drought cycles revealed that 49 of the 50 lowest q-value GO-terms under the three drought cycles were sub-terms related to biological processes. GO-terms with significant differences (p < 0.05) and a higher number of enriched genes included carbohydrate catabolism (GO:0016052), organophosphate catabolism (GO:0046434), response to extracellular stimuli (GO:0009991), response to nutrient levels (GO:0031667), and response to starvation (GO:0042594). The number of downregulated genes annotated to each function was greater than the number of upregulated genes.

[0180] The 20 most significantly enriched KEGG signaling pathways across the groups were also analyzed. The differentially expressed genes under repeated drought treatment were annotated to pathways related to photosynthesis. Therefore, photosynthesis is closely related to the tea plant's response to repeated drought stress, known as drought memory key genes.

[0181] Photosystem I (PS I) and Photosystem II (PS II) are the primary sites for light reactions, each with 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. The electron transport chain then transfers the high-energy electrons to the final electron acceptor, producing ATP and NADPH in the process. Create a heat map of differential expression of photosynthesis and the photosynthesis-antenna protein pathway and related genes, such as Figure 17 As shown in the figure, the expression levels of some key genes in the photosynthesis and photosynthesis-antenna protein pathways did not respond significantly to the first drought stress, but the response levels under repeated drought stress were significantly higher than those under single drought stress. These genes are key genes for drought memory. Analysis of photosynthesis pathway under single drought stress ( Figure 17 A), photosystem I protein genes psaG, psaH, psaO and photosystem II genes psbB, psbO, psbP, psbQ1, psbQ2, psb27-1, psb28 and F-type H+-transporting ATPase gene ATPF1G, ferredoxin-NADP+ reductase gene PetH were significantly down-regulated; compared with a single drought, PSⅠ protein genes psaA, psaB, psaG, psaH, psaO and PSⅡ protein genes psbB, psbO, psbP and gene PetH were significantly up-regulated under two rounds of drought, and PSⅡ protein genes psbP, psbQ1, psbQ2, psb27-1, psb28 and gene ATPF1G were significantly up-regulated under three rounds of drought; compared with two rounds of drought, F-type H+-transporting ATPase α subunit gene ATPF0A and cytochrome b6 gene petB were significantly down-regulated under three rounds of drought. Figure 17 As shown in Figure 2, under drought stress, the genes Lhca1-Lhca4, which are involved in the light-harvesting complex I chlorophyll a / b binding proteins, and Lhcb1-Lhcb3 and Lhcb6, which are involved in the light-harvesting complex II chlorophyll a / b binding proteins, were significantly downregulated. Compared to a single drought, two cycles of drought significantly upregulated the genes Lhca2, Lhca4, Lhcb1, Lhcb2, Lhcb3, and Lhcb6. Three cycles of drought significantly upregulated the genes Lhca1, Lhca3, Lhca4, and Lhcb1-Lhcb3. Drought stress disrupts the light-harvesting pigment protein complex, and repeated drought stress induces upregulated expression of the LHCA and LHCB protein genes in tea plants, improving their light-harvesting ability.

[0182] A heatmap of the dark response pathway and related gene differential expression was created for tea plants. The dark response uses ATP and NADPH generated by the light reaction to fix CO2 and synthesize carbohydrates for respiration. To investigate whether the dark response process of tea plants under repeated drought stress is consistent with the light response pattern, differentially expressed genes in the dark response process under different rounds of drought stress were analyzed. The results showed that after a single drought, the expression of the ribulose phosphorylation kinase gene PRK was significantly upregulated, while the expression of the ribulose-1,5-bisphosphate carboxylase gene RBCS, the fructose-1,6-bisphosphatase gene FBP, the sedoheptulose-1,7-bisphosphatase gene SBPase, and the ribosomal 5-phosphate isomerase gene rpiA were significantly downregulated. Compared with a single drought, the expression of the ribulose-1,5-bisphosphate carboxylase (RBC) gene (large chain) was significantly upregulated in two drought cycles. The expression of RBCS, FBP, SBPase, rpiA, PRK, and glyceraldehyde-3-phosphate dehydrogenase (GAPC2) was significantly upregulated in three drought cycles, while the expression of phosphoglycerate kinase (PGK) and glyceraldehyde-3-phosphate dehydrogenase (GAPCP2) was significantly downregulated. Compared with two drought cycles, GAPC2 was significantly upregulated, while rbcL and triosephosphate isomerase (TPI) were significantly downregulated in three drought cycles. This suggests that after a single drought, the expression of most dark response-related genes is upregulated when drought stress is repeated, and the increase in dark response-related genes is more pronounced in the three drought cycles.

[0183] Furthermore, heat maps of ABA biosynthesis and signaling pathway expression were analyzed. Results revealed four key regulatory enzymes in the ABA metabolic pathway: 9-cis-epoxycarotenoid oxygenase (NCED), zeaxanthin oxidase (ZEP), acetate aldehyde oxidase (AAO), and cytochrome oxidase. Cytochrome oxidase plays a major role in regulating ABA breakdown. Gene expression in the ABA pathway increased with increasing drought stress cycles after the first drought. Under a single drought stress cycle, ZEP1, NCED1, and the rate-limiting enzyme gene ABA2 were significantly upregulated, while the cytochrome oxidase gene CYP707A4 was significantly downregulated. Compared with a single drought, NECD1 and CYP707A4 were significantly upregulated, while ABA2 was significantly downregulated, under two drought cycles. ZEP1, NCED1, ABA2, and AAO3 were significantly downregulated under three drought cycles. Compared with two drought cycles, NCED1 was significantly downregulated under three drought cycles.

[0184] 4.5 RT-qPCR validation of transcriptome data

[0185] To verify the accuracy of the RNA-Seq data, nine differentially expressed genes were randomly selected from key pathways and memory genes involved in tea plant adaptation to repeated drought stress and subjected to RT-qPCR validation. The plant materials used in the validation experiment included a control group (CK) and experimental groups (D1, D2, and D3) (D1: single-cycle drought treatment group; D2: two-cycle drought treatment group; and D3: three-cycle drought treatment group). The validation process included:

[0186] 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 Evo M-MLV Reverse Transcription Premix (Agbio, China). RT-qPCR was performed on a QuantStudio 3 system (Thermo Fisher Scientific, USA) using the SYBR Green Premix Pro Taq HS qPCR Kit (Agbio, China). Each 20 μL reaction included 10 μL of 2× SYBR Green Premix, 0.4 μL of each primer (forward and reverse), 1 μL of cDNA template, and RNase-free water for a final volume of 20 μL. Cycling conditions were: 95°C for 30 s, 95°C for 5 s, 60°C for 30 s, for 40 cycles.

[0187] The primers for the nine genes included primers for detecting the gene shown in SEQ ID NO.1: F-terminal primer AGGCTTTTGCAGAGCTCAAG (SEQ ID NO.10), R-terminal primer

[0188] CATAAGCCCATGCATTGTTG (SEQ ID NO.11); primers for detecting the gene shown in SEQ ID NO.2: F-terminal primer TTGTATTTGGGCCCACTCTC (SEQ ID NO.12), R-terminal primer CACCGAACTTGACACCATTG (SEQ ID NO.13); primers for detecting the gene shown in SEQ ID NO.3: F-terminal primer GTTCGGTGAGGCTGTATGGT (SEQ ID NO.14), R-terminal primer TAGAGTGGGTCGGTCACCTC (SEQ ID NO.15); primers for detecting the gene shown in SEQ ID NO.4: F-terminal primer TTGAATTCCTTGCCATCTCC (SEQ ID NO.16), R-terminal primer AACACAGATCCCCACGAAAG (SEQ ID NO.17); primers for detecting the gene shown in SEQ ID NO.5: F-terminal primer AGCCTATGGCGAGATCTTCA (SEQ ID NO.18), R-terminal primer GGGTCAGCCCAATAGTCGTA (SEQ ID NO.19); primers for detecting the gene shown in SEQ ID NO.6: F-terminal primer ATATTCTCGGCGGTTCCTTT (SEQ ID NO.20), R-terminal primer AGCCGTAGTCCCCTGGTAGT (SEQ ID NO.21); primers for detecting the gene shown in SEQ ID NO.7: F-terminal primer GGTGTCACCGGAATGCTACT (SEQ ID NO.22), R-terminal primer AGGGTAGCCACACTCATTGG (SEQ ID NO.23); primers for detecting the gene shown in SEQ ID NO.8: F-terminal primer GTACGGTCCCGACAGAAGAA (SEQ ID NO.24), R-terminal primer GGCCACAGTTAGCACCAAAT (SEQ ID NO.25); primers for detecting the gene shown in SEQ ID NO.9: F-terminal primer GTGTTGGTTGCAGAGGTT (SEQ ID NO.26), R-terminal primer AAGCTCAGCATTCCTTTGGA (SEQ ID NO.27). Gene expression was normalized to the internal reference β-actin, and 2 -ΔΔCT Relative expression was calculated by the method. Verification criteria: RT-qPCR results were considered successful if they were consistent with the RNA-Seq trend (upregulation / downregulation). Figure 18As shown in the figure, the expression trends of these nine genes detected by RT-qPCR are consistent with the changing trends of RNA-Seq data, indicating that the transcriptome sequencing results are accurate and reliable, and RNA-Seq data are suitable for biological analysis.

[0189] 5 Analysis of the gene structure of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in tea plants

[0190] The tea plant LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes are LHC-class transcription factor genes in tea plants. Their cloning and sequence structure analysis are as follows: The tea plant variety is the national-grade fine tea variety Xiangfeicui, cultivated at the Chang'an Tea Base of Hunan Agricultural University in Changsha, Hunan Province. RNA was extracted from young leaves of tea plants. Total RNA was extracted using the Fastpure Universal Plant Total RNA Isolation Kit (Vazyme, China) according to the kit's instructions. RNA content and quality were determined using a spectrophotometer. First-strand cDNA was then generated by reverse transcription: 1 μg of the template was used and the RNA was analyzed using PrimeScript II 1. stThe Strand cDNA Synthesis Kit (Aicore, China) was used according to the kit's instructions. 5 μL of Oligo dT Primer, 1 μL of dNTP Mix (10 mM each), and 10 μL of RNase-Free dH2O were added to the reaction mixture. Denature at 65°C for 5 minutes and immediately place on ice. Then, 4 μL of 5X RTase Plus Reaction Buffer, 0.5 μL of RNase Inhibitor (40 U), 1 μL of Evo M-MLV Plus RTase (200 U), and 20 μL of ddH2O were added to the reaction mixture. The mixture was incubated at 30°C for 10 minutes, 42°C for 60 minutes, and 95°C for 5 minutes to inactivate the reverse transcriptase. This yielded the first-strand cDNA product. An appropriate amount of the first-strand cDNA was used for subsequent PCR. The cDNA first-strand was used as the RT-PCR template for conventional PCR amplification of the gene sequence. LHCP1 upstream primer: (5'-ATGAACCCAAATGATGAATCCTTG-3', SEQ ID NO.31), downstream primer: (5'-CTATGACTTGCGCCTCTGTATGC-3', SEQ ID NO.32). LHCP2 upstream primer: (5'-ATGGCTGCGTCTACCATGGC-3', SEQ ID NO.33), downstream primer:

[0191] (5'-TCACTTTCCTGGGACAAAGTTTG-3', SEQ ID NO.34) LHCP3 upstream primer:

[0192] (5'-ATGGCTGCCTCTACAATGGC-3', SEQ ID NO.35), downstream primer:

[0193] (5'-TCACTTTCCGGGAACAAAGTTAG-3', SEQ ID NO.36) LHCP4 upstream primer:

[0194] (5'-ATGGCTTCCAAAGCCCTAATG-3', SEQ ID NO.37), downstream primer:

[0195] (5'-TCATGGAGAAATTGATCTAGGGATAA-3', SEQ ID NO.38). LHCP5 upstream primer: (5'-ATGGCAACTCAAGCACTGGTG-3', SEQ ID NO.39), downstream primer:

[0196] (5'-TTAGTGGAACTTGAGGTTGGTCAA-3', SEQ ID NO.40). LHCP6 upstream primer:

[0197] (5'-ATGGCGGAGGATCGTCGG-3', SEQ ID NO.41), downstream primer:

[0198] (5'-TCACATGAATTTCAGAATGGAAAGC-3', SEQ ID NO.42). LHCP7 upstream primer:

[0199] (5'-ATGGCCACCGTCGCAGCT-3', SEQ ID NO.43), downstream primer:

[0200] (5'-TTAGTAACCTCTGATTGTTTGAATAATTG-3', SEQ ID NO.44). LHCP8 upstream primer: (5'-ATGGCTTCACTGGCAGCATC-3', SEQ ID NO.45), downstream primer:

[0201] (5'-TCACAGGGTAGGAGCTCTTTCAG-3', SEQ ID NO.46). LHCP9 upstream primer:

[0202] (5'-ATGGCCGCAACCACCGCC-3', SEQ ID NO.47), downstream primer:

[0203] (5'-TCAAGAGAAAAAGGTGTCTATAATGGTT-3', SEQ ID NO. 48). The reaction system consisted of 25 μL of 2× Phanta Max buffer, 1 μL of dNTP Mix, 2 μL each of upstream and downstream primers, 1 μL of Phanta Max Supper-Fidelity DNA Polymerase, 2 μL of template, and 17 μL of ddH2O. The reaction program was as follows: 95°C for 3 min, 95°C for 15 sec, 58°C for 15 sec, 72°C for 30 sec, 72°C for 5 min, and 4°C for termination. The resulting PCR product, LHCP1-P9, was obtained. After purification and recovery, the LHCP1-P9 gene was ligated into the pMD19-T Vector vector (Takara, China) to obtain the pMD19-T::LHCP1-pMD19-T::LHCP9 plasmid, which was then transformed into Escherichia coli competent cells DH5α (Weidi, China). After positive clones grew and were verified by colony PCR, the correct single clones were picked for sequencing and sent to Wuhan Hope Group Medical Testing Laboratory Co., Ltd. to obtain the nucleotide sequences of the LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes. The steps of gene purification, 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.

[0204] Functional verification of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 overexpression in Arabidopsis

[0205] 6.1 Vector Construction

[0206] LHCP1-PCAMBIA1300 vector construction

[0207] Using the pMD19-T::LHCP1 plasmid as a template, the primers are:

[0208] Upstream primer: (5'-gtcccagactacgctggatccATGAACCCAAATGATGAATCCTTG-3',

[0209] SEQ ID NO.49);

[0210] Downstream primer: (5′-gctcaccatggtaccggatccCTATGACTTGCGCCTCTGTATGC-3′, SEQ ID NO. 50).

[0211] LHCP2-PCAMBIA1300 vector construction

[0212] Using pMD19-T::LHCP2 plasmid as template, the primers are:

[0213] Upstream primer: (5′-gtcccagactacgctggatccATGGCTGCGTCTACCATGGC-3′, SEQ ID NO. 51);

[0214] Downstream primer: (5′-gctcaccatggtaccggatccTCACTTTCCTGGGACAAAGTTTG-3′, SEQ ID NO. 52).

[0215] LHCP3-PCAMBIA1300 vector construction

[0216] Using pMD19-T::LHCP3 plasmid as template, the primers are:

[0217] Upstream primer: (5'-gtcccagactacgctggatccATGGCTGCCTCTACAATGGC-3', SEQ ID NO.53),

[0218] Downstream primer: (5'-gctcaccatggtaccggatccTCACTTTCCGGGAACAAAGTTAG-3', SEQ ID NO. 54).

[0219] LHCP4-PCAMBIA1300 vector construction

[0220] Using pMD19-T::LHCP4 plasmid as template, the primers are:

[0221] Upstream primer: (5′-gtcccagactacgctggatccATGGCTTCCAAAGCCCTAATG-3′, SEQ ID NO. 55);

[0222] Downstream primer: (5′-gctcaccatggtaccggatccTCATGGAGAAATTGATCTAGGGATAA-3′, SEQ ID NO. 56).

[0223] LHCP5-PCAMBIA1300 vector construction

[0224] Using pMD19-T::LHCP5 plasmid as template, the primers are:

[0225] Upstream primer: (5′-gtcccagactacgctggatccATGGCAACTCAAGCACTGGTG-3′, SEQ ID NO. 57);

[0226] Downstream primer: (5'-gctcaccatggtaccggatccTTAGTGGAACTTGAGGTTGGTCAA-3',

[0227] SEQ ID NO.58).

[0228] LHCP6-PCAMBIA1300 vector construction

[0229] Using pMD19-T::LHCP6 plasmid as template, the primers are:

[0230] Upstream primer: (5′-gtcccagactacgctggatccATGGCGGAGGATCGTCGG-3′, SEQ ID NO. 59);

[0231] Downstream primer: (5′-gctcaccatggtaccggatccTCACATGAATTTCAGAATGGAAAGC-3′, SEQ ID NO. 60).

[0232] LHCP7-PCAMBIA1300 vector construction

[0233] Using the pMD19-T::LHCP7 plasmid as a template, the primers are:

[0234] Upstream primer: (5′-gtcccagactacgctggatccATGGCCACCGTCGCAGCT-3′, SEQ ID NO. 61);

[0235] Downstream primer:

[0236] (5'-gctcaccatggtaccggatccTTAGTAACCTCTGATTGTTTGAATAATTG-3', SEQ ID NO. 62).

[0237] LHCP8-PCAMBIA1300 vector construction

[0238] Using pMD19-T::LHCP8 plasmid as template, the primers are:

[0239] Upstream primer: (5′-gtcccagactacgctggatccATGGCTTCACTGGCAGCATC-3′, SEQ ID NO. 63);

[0240] Downstream primer: (5′-gctcaccatggtaccggatccTCACAGGGTAGGAGCTCTTTCAG-3′, SEQ ID NO. 64).

[0241] LHCP9-PCAMBIA1300 vector construction

[0242] Using pMD19-T::LHCP9 plasmid as template, the primers are:

[0243] Upstream primer: (5′-gtcccagactacgctggatccATGGCCGCAACCACCGCC-3′, SEQ ID NO. 65);

[0244] Downstream primer:

[0245] (5'-gctcaccatggtaccggatccTCAAGAGAAAAAGGTGTCTATAATGGTT-3', SEQ ID NO. 66).

[0246] PCR amplification was performed sequentially.

[0247] The specificity of the PCR product was confirmed by 1% agarose gel electrophoresis, and then purified and eluted to obtain the purified PCR product.

[0248] The PCAMBIA1300 vector (Novopro, China) was linearized by single enzyme digestion, using BamH1 as the digestion site. The digestion system was: 41.4 μL ddH2O, 5 μL 10x Buffer, 1.6 μL PCAMBIA1300 Vector, 1 μL BamH1, and incubated in a water bath at 37°C for 30 min. The digestion product was confirmed to have been cleaved by 1% agarose gel electrophoresis and then purified and eluted to obtain the linearized PCAMBIA1300 vector. The linearized PCAMBIA1300 vector and purified PCR product were recombinated using the IIOne Step Cloning Kit (Vazyme, China). The reaction system consisted of 4 μL of PCR product, 2 μL of Exnase II, 3.5 μL of Vector, and ddH2O to a volume of 20 μL. The reaction was incubated at 37°C for 30 min. Finally, competent Escherichia coli DH5α cells (Vazyme, China) were transformed. Positive clones were grown and verified by colony PCR. Correct single clones were selected for sequencing and sent to Wuhan Hope Group Medical Testing 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.

[0249] 6.2 Arabidopsis genetic transformation

[0250] Take an appropriate amount of wild-type Arabidopsis seeds, add deionized water, and sow after vernalization for 72 hours. Cover with plastic wrap after sowing and place under appropriate conditions (70% humidity; 22°C; photoperiod of 10 hours light / 14 hours dark) to wait for germination. After the seeds germinate, select seedlings of uniform size for transplanting and culture normally. Transform the CsLHC-PCAMBIA1300 vector into GV3101 Agrobacterium by freeze-thaw method, and identify positive clones by colony PCR. The transformation and identification methods are the same as above. Positive colonies harboring the target gene were selected and cultured in 50 mL of antibiotic-containing LB liquid medium at 28°C and 200 rpm for approximately 24 hours. 50 mL of the cultured bacterial broth was added to 200 mL of fresh antibiotic-containing LB liquid medium and cultured with shaking for another 6-8 hours until the OD600 reached approximately 1.0. The cells were then collected by centrifugation and resuspended in a 5% sucrose solution to a final OD600 concentration of approximately 0.8. 0.1% Silwet L-77 (Yeasen, China) was added and shaken to obtain the transformation solution. After approximately one month of Arabidopsis thaliana cultivation, when the plants began to flower, robust plants were selected for transformation. Prior to transformation, the top inflorescences were removed to encourage the production of more flower buds. The plants were thoroughly watered the day before transformation. The prepared transformation solution was placed in a container and the Arabidopsis inflorescences were gently immersed in the solution for approximately 60 seconds. The solution was then placed in the dark for 24 hours before normal incubation and seed harvest. Harvested Arabidopsis seeds were placed in a centrifuge tube and sterilized with 1 mL of 75% ethanol for 1 minute, followed by 10% NaClO for 5 minutes. The seeds were then rinsed 5-6 times with sterile water, pipetted, and sown on 1 / 2 MS solid medium containing hygromycin. Vernalize for 72 hours in the dark at 4°C, transfer to a culture room at 22°C, and incubate under a photoperiod of 10 hours light / 14 hours dark. After approximately two weeks, resistant plants with green leaves and normal root development were transplanted into a culture medium for further cultivation. The culture medium was thoroughly hydrated before transplanting. After transplanting, the medium was covered with plastic wrap and removed approximately 3 days later. Subsequent care was maintained as above. T2 seeds were harvested for use in experiments. RNA was extracted from Arabidopsis seedlings, and target gene expression was detected by PCR using gene-specific primers. The extraction and identification procedures were the same as in 4.5. Transgenic plants were subjected to natural drought for 10 days, with a control group established. Seedling survival was observed 10 days later.

[0251] The results showed that by analyzing the expression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in Arabidopsis overexpression lines and wild types, as well as the phenotypes of the overexpression lines under drought treatment, it was shown that LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 were integrated into Arabidopsis plants in the overexpression Arabidopsis experiment, and the expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in transgenic (OE) plants were significantly higher than those in wild-type (WT) plants. The overexpression lines of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 were subjected to drought treatment for 10 days. Compared with wild-type Arabidopsis, the survival rate of the transgenic lines was significantly increased and the electrical conductivity was significantly decreased, indicating that overexpression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 improved the plant's tolerance to drought.

[0252] Functional verification of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes in tea plants

[0253] 7.1 In vivo antisense oligonucleotide inhibition experiments

[0254] According to the sequences of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9, oligonucleotide antisense primers were designed and synthesized. The primer sequences are shown as follows: LHCP1,

[0255] (5'-TTTTGGCCCGCAACGGTG-3', SEQ ID NO. 67); LHCP2,

[0256] (5'-AGGTGATCCATTGCAGATGG-3', SEQ ID NO. 68); LHCP3,

[0257] (5'-GACCTCGTTGGCAGAGGA-3', SEQ ID NO. 69); LHCP4,

[0258] (5'-CGCACCCGGTGATTTCGGG-3', SEQ ID NO.70); LHCP5,

[0259] (5'-ACACTCTTTTTGTTTTCGA-3', SEQ ID NO.71); LHCP6,

[0260] (5'-TTTCATATGACTATCCTTC-3', SEQ ID NO.72); LHCP7,

[0261] (5'-GCCGAGGACCCAGAGAA-3', SEQ ID NO.73); LHCP8,

[0262] (5'-TGGAGATTATGGTTACGATC-3', SEQ ID NO.74); LHCP9,

[0263] Dissolve the primer (5'-GAGGTTTAGGGAGTGTGA-3', SEQ ID NO. 75) in sterile water to prepare an in vitro oligonucleotide antisense inhibitory primer solution. Sterile water was used as a blank. Use scissors to cut one bud and two leaves of roughly uniform size, bright color, healthy appearance, and free of insects and diseases. Each bud and two leaves were inserted into a 1.5mL centrifuge tube containing 1mL of the in vitro oligonucleotide antisense inhibitory primer solution, ensuring that the tail of each bud and two leaves were submerged in the solution. Place the centrifuge tubes in a light incubator with a 16-hour light / 8-hour dark cycle at 25°C. After treatment for 0, 6, 12, and 36 hours, the buds and two leaves were stored in a -80°C freezer covered with liquid nitrogen. Gene expression analysis was subsequently performed on samples from the primer-treated and control groups.

[0264] 7.2 Analysis of the effects of oligonucleotide antisense inhibition on tea plant gene expression in vivo

[0265] Total RNA was extracted from treated and control samples, followed by reverse transcription and first-strand cDNA synthesis. Quantitative PCR was used to analyze the expression of relevant genes. The gene expression levels of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 were measured in both treated and control samples using the same method as above. The results showed that antisense oligonucleotides for LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 significantly disrupted target gene expression, with the highest inhibition observed for LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 at 6 hours.

[0266] 7.3 Drought treatment of samples and biochemical index determination in vitro oligonucleotide antisense inhibition

[0267] To investigate the role of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 in drought stress in tea plants, samples treated with an oligonucleotide antisense inhibitory primer solution for 6 hours were subjected to drought treatment for 12 hours, and changes in Fv / Fm values ​​were observed. Malondialdehyde content after drought treatment was measured using a kit (Cat. No. BC0020) (Solarbio, China). The results showed that in the in vitro oligonucleotide antisense inhibition assay, the expression of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 was suppressed compared to the control. After drought treatment, compared with the control group, plants with inhibited LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 suffered more damage, significantly reduced Fv / Fm ratio, and significantly increased malondialdehyde content, indicating that in vivo expression inhibition of LHCP1, LHCP2, LHCP3, LHCP4, LHCP5, LHCP6, LHCP7, LHCP8, and LHCP9 genes can significantly reduce the drought tolerance of tea plants.

[0268] ROC validation of gene markers for 8 key drought memory genes in the breeding of drought-resistant tea varieties

[0269] Tea leaf samples were collected from 100 tea varieties at the Gaoqiao Tea Experimental Base in Hunan Province (113°08′E, 28°20′N) during the summer drought period (June-August). The samples included: drought-resistant varieties (50 samples): screened by drought resistance phenotypic identification for three consecutive years (ratio of drought-damaged leaves ≤30%), and non-drought-resistant varieties (50 samples): confirmed by the same identification (ratio of drought-damaged leaves ≥85%). Target genes: LHCP1 (TEA009770, SEQ ID NO.1), LHCP2 (TEA019232, SEQ ID NO.2), LHCP3 (TEA001864, LHCP4 (TEA008963, SEQ ID NO.4), LHCP5 (TEA000535, SEQ ID NO.5), LHCP6 (TEA015295, SEQ ID NO.6), LHCP7 (TEA016942, SEQ ID NO.7), LHCP8 (TEA023017, SEQ ID NO.8), LHCP9 (TEA026680, SEQ ID NO.9) (i.e., the 9 key drought memory genes screened in 4.3 of Example 1).

[0270] First, the expression levels of 9 genes in different samples were analyzed by RT-qPCR. Total RNA was extracted from tea leaves using the FastPure Universal Plant Total RNA Isolation Kit (Vazyme, China). The first-strand cDNA was synthesized using Evo M-MLV Reverse Transcription Premix (Agbio, China). RT-qPCR was performed on a QuantStudio 3 system (Thermo Fisher Scientific, USA) using a 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 30s, 95°C 5s, 60°C 30s, and 40 cycles. The primers for the 9 genes were the same as in step 4.5. Gene expression was normalized to the internal reference β-actin, and 2 -ΔΔCT Relative expression levels were calculated using the ROC method. ROC analysis was performed using the R language (v4.3.1) and the pROC package (v1.18.4). Based on 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. A significant criterion: AUC > 0.85 indicates that the gene is a marker for tea trees under drought and can be used to screen drought-resistant tea varieties.

[0271] The results are as follows Figure 19As shown, the areas under the curve (AUC) for the nine key drought memory genes were all greater than 0.85, indicating that each gene had high predictive ability for drought resistance classification and met the excellent diagnostic criteria. The results included LHCP1: AUC = 0.896; LHCP2: AUC = 0.89; LHCP3: AUC = 0.895; LHCP4: AUC = 0.9; LHCP5: AUC = 0.878; LHCP6: AUC = 0.898; LHCP7: AUC = 0.899; LHCP8: AUC = 0.896; and LHCP9: AUC = 0.89. Analysis: LHCP4 had the highest AUC value (0.9), indicating that it performed best in distinguishing drought sensitivity. LHCP7 followed closely behind with an AUC value of 0.899, also demonstrating good discriminatory ability. The AUC values ​​for the six genes, LHCP1, LHCP6, LHCP8, LHCP2, LHCP3, and LHCP9, ranged from 0.89 to 0.896, demonstrating good performance. LHCP5, with an AUC value of 0.878, performed relatively well in distinguishing drought sensitivity. Conclusion: Based on AUC values, LHCP4 and LHCP7 performed best in predicting drought sensitivity, particularly when combined, achieving an even higher AUC. The other seven genes performed relatively well in predicting drought sensitivity. These genes play an important role in the biological mechanisms of drought sensitivity. In summary, these results can be applied in practical breeding to screen drought-resistant tea varieties using these nine key drought memory gene markers.

[0272] Verification of the accuracy of genetic markers for 6 key drought memory genes in the breeding of drought-resistant tea varieties

[0273] Tea leaf samples were collected from 100 tea varieties at the Gaoqiao Tea Experimental Base in Hunan Province (113°08′E, 28°20′N) during the summer drought period (June-August). The samples included: drought-resistant varieties (50 samples): screened by drought resistance phenotypic identification for three consecutive years (ratio of drought-damaged leaves ≤30%), and non-drought-resistant varieties (50 samples): confirmed by the same identification (ratio of drought-damaged leaves ≥85%). Target genes: LHCP1 (TEA009770, SEQ ID NO. 1), LHCP2 (TEA019232, SEQ ID NO. 2), LHCP3 (TEA001864, SEQ ID NO. 3), LHCa6 (TEA008963, SEQ ID NO. 4), LHCa3 (TEA000535, SEQ ID NO. 5), LHCb7 (TEA015295, SEQ ID NO. 6), LHCP4 (TEA016942, SEQ ID NO. 7), LHCb5 (TEA023017, SEQ ID NO. 8), LHCb4 (TEA026680, SEQ ID NO. 9) (i.e., the 9 key drought memory genes screened in 4.3 of Example 1). At the same time, the present invention selected a gene TCP8 (TEA003322) that responded to the first drought but not the second and third droughts as a control.

[0274] The sequence for TCP8 (TEA003322) is as follows:

[0275] ATGGAACTTACAGATATGCAAGGCAATAAACACAGCACCACCACCACACCC

[0276] ACCACTTCTCAATCCCACCACCACCTCCACCATCACCTTCACTCTGGTTTTTGATGG

[0277] CCGATCTCATCATCAACCTCCATTCATTGGCTCCATCTCCATCCAAGCTGGCCTCA

[0278] CAAGCACCGCCGGCGGCGGAGGTGGAGGAAGTGGATCTTCCTCTGTTTCACCATC

[0279] TACCTCCTCTACTAACACTTCAACCACCTCCACCACCACCACACAACTTCCGCCGC

[0280] TGCAGCTCGTCGACGCCTCGCTAGCCATCGCCACCAGATCCGAGGGTCTGGCCGC

[0281] CGTAGATCCCTCCCAAAAAAGCCAATAACCAGCTGCAATCGCCACCGCAGCAACT

[0282] AACAATCGCACCGTGCCTCCAAGAGGTCACCAAGGACCGCCACACCAAAGT

[0283] CGACGGAAGAGGGCGGGCGCATCCGAATGCCGGCGACCTGCGCGGCTAGGGTTTT

[0284] TCAATTGACGAAAGAATTGGGGAATAAATCTGATGGGGGAGACAATTGAGTGGCT

[0285] GTTACAGCAGGCGGAGCCGGCGATTATCGCCGCCACGGGCACCGGCACAATTCC

[0286] GGCGAACTTCTCGACCCTCAACGTGTCGCTACGGAGCAGCGGATCGACGATTTCA

[0287] GCGCCGCCGTCGAAGTCAGCGCCTCACTCGTTTCACGGCGCCTTAGCCCTAGCCC

[0288] ACCACCACTACGAGAAGGGTTCTCTCACATGTTAGGGTTTCACCACCACCAGCCA

[0289] ACAACAACAACAACAGCAGCAGCCTCATCTTTTGACCGCCGATCAAATTGCCGAA

[0290] GCCATGCCTGGCGGTGGCGGCGATGGAAGTAATTCGGCGGAGAATTATTTGAGA

[0291] AAGAGATATAGAGAAGATTTGTTCAAAGAAGAATCGCAGCAGCAACCTCAAGGG

[0292] GAAGTATCGGAGTCTACTTCGCCTTCGAACAAGCAATTCAAAGGCGGTAATATGC

[0293] AATTGCCGAAGCAACAACAAGAAGCCGGACCATCATCCAGTATGCTTCGGCATA

[0294] CTAACATGATGCCCGCCACTGCCATGTGGGCAGTGGCTCCGGCTCCCAGCAGTGC

[0295] AGCCGGCAGCACATTCTGGATGCTGCCCGTCACTGCTGGTGGTGCAGGTGGCAGT

[0296] GCCCAATCTCTGGCCGCTGCCACCGGCACATCTCCATCTGAACCTCAGATGTGGC

[0297] CCTTTGCCACTGCGCCAACAACGAGTGGAAACACGCTGCAAGCACCGCTGCATTT

[0298] TATGCCGAGGTTTAACATCCCGGGGGCATCACTTGAATTTCAAGGGGGAAGAGCC

[0299] AGTCCATTGCAATTGGGTTCCATGTTAATGCAACAACAACAACCACCACCTTCTC

[0300] AACATCTTGGGTTGGGAATGGCTGAGACTAATTTGGGCATGTTGGCTGCTCTCAA

[0301] TGCTTACTCGAGAGGTGGTTTGAATATGAATTCTGAGCAAAATAATCCATTGGAG

[0302] CATCACCATCATCATCAACATCAACATCAACAACAACACCAGTCTCAAGCTACTG

[0303] ATAGTGGAGAGGATGACCCAAACAGTTCTCACTGA(SEQ ID NO.28)。

[0304] First, the expression levels of 10 genes in different samples were blindly tested for RT-qPCR analysis. The detection process and retrieval reagents used were the same as above. Among them, the newly added primers for TCP8 were: F-terminal primer: GTTTGATGGCCGATCTCAT (SEQ ID NO.29), R-terminal primer: GGTGGAGGTGGTTGAAGTGT (SEQ ID NO.30). Gene expression was normalized to the internal reference β-actin, and 2 -ΔΔCT Relative expression levels were calculated using the RT-qPCR analysis method. Drought-resistant plants were determined based on the RT-qPCR analysis results, and then compared with the actual results to obtain accuracy and recall data. Accuracy is defined as: among all samples predicted to be "drought-resistant," the higher the proportion of truly drought-resistant samples, the higher the proportion of truly drought-resistant samples. For example, the accuracy of LHCP1 is 0.94, indicating that when the gene expression exceeds the threshold, it is indeed drought-resistant in 94% of cases, which can reduce false positives. Recall is defined as: the proportion of samples that are correctly identified among all truly drought-resistant samples. For example, the recall rate of LHCb4 is 0.952, indicating that when the gene expression exceeds the threshold, 95.2% of truly drought-resistant samples can be detected. The optimal threshold, accuracy, and recall data for the 10 genes are shown in Table 4.

[0305] Table 4 Precision and recall data for 10 genes

[0306] Gene Optimal threshold Accuracy Recall LHCP1 13.01 0.94 0.734 LHCP2 15.14 0.76 0.950 LHCP3 14.18 0.82 0.891 LHCP4 13.65 0.86 0.843 LHCP5 14.05 0.82 0.854 LHCP6 13.54 0.88 0.733 LHCP7 14.40 0.80 0.870 LHCP8 15.63 0.80 0.952 LHCP9 14.36 0.84 0.913 TCP8 / 0.50 0.500

[0307] Note: Significant standard: AUC greater than 0.85 indicates that the model has good prediction effect.

[0308] The higher the accuracy, the more reliable the prediction result; the higher the recall rate, the higher the proportion of correct identification in real drought-resistant samples, indicating that the gene is a marker of tea trees under drought and can be used to screen drought-resistant tea varieties. As shown in Table 4, each gene has a high predictive ability for drought resistance classification and meets the excellent diagnostic standard. LHCP1 feature: when the expression level is greater than 13.01, the accuracy can reach 94% and the recall rate can reach 73.4%; LHCP2 feature: when the expression level is greater than 15.14, the accuracy can reach 76% and the recall rate can reach 95%; LHCP3 feature: when the expression level is greater than 14.18, the accuracy can reach 82% and the recall rate can reach 89.1%; LHCP4 feature: when the expression level is greater than 13.65, the accuracy can reach 86% and the recall rate can reach 84.3%; LHCP5 feature: when the expression level is greater than 14.05 , with an accuracy of 82% and a recall rate of 85.4%; LHCP6 feature: when the expression level is greater than 13.54, the accuracy can reach 88% and the recall rate can reach 73.3%; LHCP7 feature: when the expression level is greater than 14.40, the accuracy can reach 80% and the recall rate can reach 87%; LHCP8 feature: when the expression level is greater than 15.63, the accuracy can reach 80% and the recall rate can reach 95.2%; LHCP9 feature: when the expression level is greater than 14.36, the accuracy can reach 84% and the recall rate can reach 91.3%. At the same time, the control gene is a gene that responds to the first drought but does not respond to multiple droughts. It does not belong to the key memory gene. Its prediction effect is extremely poor, with an accuracy of 50% and a recall rate of 50%, indicating that its drought sensitivity prediction ability is extremely low. This indirectly illustrates the accuracy and necessity of the nine key memory genes screened by the present invention through repeated droughts. This indicates that the results can be applied in actual breeding process, using these nine key drought memory gene markers to screen drought-resistant tea varieties.

[0309] Therefore, the present invention takes the intrinsic genetic genes of tea trees as the starting point, analyzes the metabolites and genes of tea trees in response to drought stress based on the metabolome and transcriptome, screens out key genes of tea tree drought memory, and applies them in the screening of drought-resistant tea trees, providing support for further exploring the drought-resistant germplasm resources of tea trees and cultivating new drought-resistant varieties.

[0310] It should be noted that the above embodiments are merely examples for the purpose of clearly illustrating the present invention and are not intended to limit the embodiments of the present invention. Those skilled in the art will appreciate that other variations or modifications may be made based on the above description. It is not possible to enumerate all embodiments here. Any obvious variations or modifications arising from the technical solution of the present invention remain within the scope of protection of the present invention.

Claims

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

6.

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

3. A method for screening key memory genes that respond to periodic drought stress, characterized in that: The following steps are involved: Step 1: After the tea trees are repeatedly subjected to drought treatment, new shoot leaves are taken as samples; Determine the phenotype, metabolome, and transcriptome data of the samples; Step 2: Screen the metabolomics data for differentially expressed metabolites that showed no response or no significant response to the first drought exposure, but showed significant response after repeated droughts, as "memory" metabolites. Step 3: Screening and analyzing differentially expressed genes in tea plants subjected to repeated drought treatments: Screening out key memory genes from the differentially expressed genes; The key memory gene is a photosynthesis-related gene, and the photosynthesis-related gene is selected from any one or more of the sequences SEQ ID NO.1-SEQ ID NO.4 and SEQ ID NO.

6.

4. The application of photosynthesis-related genes in screening drought-resistant tea trees is characterized in that: The photosynthesis-related gene is represented by any one or more of the sequences SEQ ID NO.1-SEQ ID NO.4 and SEQ ID NO.

6.

5. Application of a kit for screening drought-resistant tea trees, characterized in that: The kit comprises a reagent for detecting the transcription level of photosynthesis-related genes; the photosynthesis-related genes are represented by any one or more of the sequences SEQ ID NO.1 to SEQ ID NO.4 and SEQ ID NO.

6.

6. The use according to claim 5, characterized in that The reagents for detecting the transcription level of photosynthesis-related genes include primers, TaqMan probes or gene chips for detecting photosynthesis-related genes.

7. The use according to claim 6, characterized in that The primers for detecting photosynthesis-related genes include the following primer pairs: Primer pair for detecting the gene shown in SEQ ID NO.1: SEQ ID NO.10: AGGCTTTTGCAGAGCTCAAG; SEQ ID NO.11: CATAAGCCCATGCATTGTTG; Primer pair for detecting the gene shown in SEQ ID NO.2: SEQ ID NO.12: TTGTATTTGGGCCCACTCTC; SEQ ID NO.13: CACCGAACTTGACACCATTG; Primer pair for detecting the gene shown in SEQ ID NO.3: SEQ ID NO.14: GTTCGGTGAGGCTGTATGGT; SEQ ID NO.15: TAGAGTGGGTCGGTCACCTC; Primer pair for detecting the gene shown in SEQ ID NO.4: SEQ ID NO.16: TTGAATTCCTTGCCATCTCC; SEQ ID NO.17: AACACAGATCCCCCACGAAAG; The primer pair for detecting the gene shown in SEQ ID NO. 6 is: SEQ ID NO. 20: ATATTCTCGGCGGTTCCTTT; SEQ ID NO. 21: AGCCGTAGTCCCCTGGTAGT.

8. A method for identifying drought resistance of plant germplasm, characterized in that: The transcription level of any one or more genes represented by SEQ ID NO.1 to SEQ ID NO.4 and SEQ ID NO.6 in the plant is detected using a kit, and the plant germplasm with a high transcription level is a drought-resistant plant; when the transcription level of the gene represented by SEQ ID NO.1 is greater than 13.01, the tea plant is determined to be a drought-resistant tea plant; when the transcription level of the gene represented by SEQ ID NO.2 is greater than 15.14, the tea plant is determined to be a drought-resistant tea plant; when the transcription level of the gene represented by SEQ ID NO.3 is greater than 14.18, the tea plant is determined to be a drought-resistant tea plant; when the transcription level of the gene represented by SEQ ID NO.4 is greater than 13.65, the tea plant is determined to be a drought-resistant tea plant; when the transcription level of the gene represented by SEQ ID NO.6 is greater than 13.54, the tea plant is determined to be a drought-resistant tea plant.

9. A method for improving drought resistance of plants, characterized in that: The preparation is used to increase the level and / or activity of any one or more endogenous genes in the plant, such as SEQ ID NO.1 to SEQ ID NO.4, or SEQ ID NO.6; The preparation comprises at least one of the following i)-v): i) a nucleic acid molecule as shown in any one or more of SEQ ID NO.1 to SEQ ID NO.4, or SEQ ID NO.6; ii) an expression vector comprising any one or more of the nucleic acid molecules shown in SEQ ID NO.1 to SEQ ID NO.4, or SEQ ID NO.6; iii), a recombinant host containing ii); iv) a promoter or enhancer that enhances the expression of any one or more of the genes represented by SEQ ID NO.1 to SEQ ID NO.4, or SEQ ID NO.6; v) an inducer that promotes the expression of any one or more genes represented by SEQ ID NO. 1 to SEQ ID NO. 4, or SEQ ID NO. 6.

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