Bidirectional correlation analysis method for plant and pathogenic bacterium gene interaction and application

Abstract

The invention discloses a bidirectional correlation analysis method for gene interaction of plants and pathogenic bacteria and application, and belongs to the technical field of biology. Comprising the following steps: (1) obtaining genetic variation information of a plant, genetic variation information of pathogenic bacteria and an anti-infection phenotype of the plant to the pathogenic bacteria; (2) carrying out forward whole genome correlation analysis, and locating pathogenic bacteria effect factors from plant disease-resistant genes; (3) carrying out reverse whole genome correlation analysis, and locating a plant disease-resistant gene from a pathogenic bacterium effect factor; and (4) disease-resistant genes obtained by forward whole genome correlation analysis and reverse whole genome correlation analysis are the same as the effect factors, and the disease-resistant genes and the effect factors are gene pairs interacting in plants and pathogenic bacteria. The method provided by the invention can efficiently, accurately and simultaneously screen out the interaction gene pair of the plant disease-resistant gene and the pathogenic bacterium effect factor, provides powerful support for analyzing a resistance mechanism exerted by the plant disease-resistant gene, and lays a foundation for cultivating a new variety of durable and broad-spectrum disease-resistant plants.

Description

Technical Field

[0001] The invention belongs to the field of biotechnology, and specifically relates to a bidirectional correlation analysis method for plant and pathogen gene interaction and its application. Background Art

[0002] Blumeria graminis Blumeria graminis f. sp. tritici (Bgt) is one of the major fungal diseases that harm wheat production. Large-scale outbreaks of wheat powdery mildew can lead to serious wheat yield losses. Breeding and promoting disease-resistant wheat varieties is considered to be the most economical and effective means of controlling powdery mildew. However, the genetic basis of disease-resistant genes in the main wheat varieties in my country is narrow, resulting in the risk of losing resistance to disease-resistant genes that once performed well in the process of rapid evolution of pathogens (e.g. Pm8 In addition, the disease resistance of newly bred varieties is also on the decline. Therefore, in the current wheat breeding practice, it is urgent to discover new powdery mildew resistance gene resources and analyze the mechanism of powdery mildew resistance genes to lay the foundation for breeding new varieties with long-lasting and broad-spectrum disease resistance.

[0003] Wheat Nucleotide-binding Leucine-rich Repeat (NLR) disease resistance proteins play a key role in disease resistance, and the significant diversity of NLRs provides a broad space for the exploration of disease resistance gene resources. Studies have shown that there are large differences in the number of NLR genes in different wheat varieties. For example, in the common wheat variety Chinese Spring, about 3,400 NLR genes have been identified, of which about half are functional genes that can be expressed and have complete open reading frames (Steuernagelet al., 2020). However, the high-quality reference genome sequences of 10 core wheat varieties assembled by the 10+ Genome Project show that only about 1 / 3 of the NLR genes are shared between varieties, and the rest are specific NLR genes. Based on this calculation, the total number of NLR genes in wheat populations may be as high as about 7,000, while less than 10% of wheat disease resistance genes have been identified so far.

[0004] Although some progress has been made in the study of NLR disease resistance genes, there are still two major bottlenecks in the interaction mechanism between wheat and powdery mildew: first, the specific recognition rules between disease resistance genes and effector factors have not been fully understood; second, the study of effector factor cloning is limited by traditional strategies, that is, the corresponding disease resistance genes must be obtained before reverse screening of their corresponding non-toxic factors. The powdery mildew effector factors that have been reported so far mainly include AvrPm1a (Hewitt et al., 2021), AvrPm2 (Praz et al., 2017), AvrPm3a (Bourras et al., 2015), AvrPm3b2 / c2 and AvrPm3d3 (Bourras et al., 2019) and AvrPm17 (Mueller et al., 2021). These effectors are Pm1a , Pm2 , Pm3a / 3f , Pm3b / c , Pm3d3 and Pm17 The specific identification of disease-resistance genes such as β-lactamase and β-lactamase fully confirms the strict applicability of the “gene-for-gene” hypothesis in the wheat-powdery mildew interaction system.

[0005] Although the disease resistance of wheat NLR genes depends on the specific recognition of pathogen effectors, a large number of potential disease resistance genes and effectors have not yet been discovered, and their interaction mechanisms are far from being fully resolved. According to the "gene-to-gene" hypothesis proposed by Flor in 1956, wheat disease resistance genes trigger immune responses by specifically recognizing effectors secreted by powdery mildew fungi, leading to cell necrosis at the infection site to prevent the spread of the pathogen. However, powdery mildew fungi evade recognition by rapidly mutating to produce new effectors, while wheat regains recognition capabilities by evolving new disease resistance genes. The two sides are engaged in a long-term "arms race" and continue to co-evolve. The diversity and regulatory mechanisms of this recognition relationship remain unsolved mysteries, and there is an urgent need to develop high-throughput and rapid technical methods for mining disease resistance genes and their corresponding effectors.

[0006] Pm3 It is the first cloned wheat powdery mildew resistance gene, and its resistance mechanism has been studied most intensively. Currently, 17 genes have been identified. Pm3 Alleles ( Pm3a to Pm3g and Pm3k to Pm3t ) and two orthologous genes ( Pm8 and Pm17 ), their sequence similarity exceeds 97%, and both show resistance to specific powdery mildew strains (Bhullar et al., 2010; Stirnweis et al., 2014). Studies have shown that the strain-specific resistance of these alleles is genetically regulated by a combination of multiple effector factors and their inhibitors (Bourras et al., 2015). Further studies have found that haplotype variation in effector proteins in natural populations significantly affects recognition ability. For example, changes in a single amino acid in the AVRPM3A2 / F2 protein may destroy specific recognition, and only changes in a few amino acids can enhance the immune response (McNally et al., 2018). Some studies have pointed out that Pm3The strain-specific resistance of alleles depends on the specific recognition of the structure of the effector protein rather than the sequence. Even if the sequence differences are large, the similarity of the protein structure may still support functional recognition (Bourras et al., 2019). This shows that the changes in protein structure caused by sequence variation in different haplotypes are important determinants of specific recognition and immune response, thus emphasizing the key role of natural variation in genetic resistance mechanisms. Therefore, different haplotypes of the same gene play an important role in strain-specific resistance, and the variation between haplotypes largely affects the specific recognition of the effector by the disease resistance gene. Summary of the invention

[0007] In view of the above-mentioned prior art, the present invention provides a bidirectional correlation analysis method and application of the interaction between plant and pathogen genes, which solves the problem of difficulty in screening plant disease resistance genes and pathogen effector factors in the prior art.

[0008] In order to achieve the above object, the technical solution adopted by the present invention is: to provide a bidirectional association analysis method for the interaction between plant and pathogen genes, comprising the following steps: (1) Obtaining genetic variation information of plants, genetic variation information of pathogens, and the resistance and susceptibility phenotypes of plants to pathogens; (2) Forward genome-wide association analysis to locate pathogen effector factors based on plant disease resistance genes: ① Perform genome-wide association analysis on the genetic variation information of plants and the resistance and susceptibility phenotypes of plants to pathogens, obtain the association results between the variation sites on the plant genome and the resistance and susceptibility phenotypes of plants to pathogens, draw a Manhattan plot and identify candidate peaks; ② Identify candidate disease resistance genes in plants from candidate peaks; ③ Combined with the resistance phenotype of the plant to pathogens, the disease resistance haplotype of the candidate disease resistance gene was identified; ④ The infection phenotype of the pathogen on the plant with the disease-resistant haplotype containing the candidate disease-resistant gene is used as a trait, and the trait is subjected to genome-wide association analysis with the genetic variation information of the pathogen to locate the candidate effector factors on the pathogen genome; (3) Reverse whole genome association analysis, locating plant disease resistance based on pathogen effector factors: ① Perform genome-wide association analysis on the genetic variation information of pathogens and the resistance and susceptibility phenotype of plants to pathogens, obtain the association results of the variation sites on the pathogen genome and the resistance and susceptibility phenotype of plants to pathogens, draw a Manhattan plot and identify candidate peaks; ② Identify candidate effector factors in pathogens from candidate peaks; ③ Combined with the resistance and sensitivity phenotype of the plant to the pathogen, identify the non-toxic haplotype of the candidate effector; ④ Taking the plant's resistance phenotype to the avirulent haplotype strain containing the candidate effector as a trait, the trait is analyzed with the genetic variation information of the plant for genome-wide association analysis to locate the candidate disease resistance gene on the plant genome; (4) The candidate disease resistance gene obtained in step (2) and step (3) is the same as the candidate effector factor, and the two are the gene pair that interacts between the plant and the pathogen.

[0009] Based on the above technical solution, the present invention can also be improved as follows.

[0010] Furthermore, the genetic variation information of plants is single nucleotide polymorphism, and the genetic variation information of pathogens is single nucleotide polymorphism.

[0011] Furthermore, in step ① of the forward whole-genome association analysis and step ① of the reverse whole-genome association analysis, whole-genome association analysis was performed using association models MLM (mixed linear model), FarmCPU (fixed and random model improvement), MLMM (multivariate linear model), and BLINK (Bayesian information and linkage unbalanced iterative nested keying), and at least three association models located the same peak region in the Manhattan plot as candidate peaks.

[0012] Furthermore, the specific operation of step ② of the forward genome-wide association analysis is: check the functional annotations of genes near the significant peak in the Manhattan plot, and search for candidate disease resistance genes within 1 Mb upstream and downstream of the significant peak; The candidate peaks meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-8; (b) the distance between two adjacent SNPs that meet the previous condition must be less than 1Mb; (c) the number of SNPs that meet the first two conditions in the candidate peak must be greater than 5; (d) considering linkage disequilibrium, the r² value is greater than 0.8; Candidate disease resistance genes meet the following conditions simultaneously: (a) the gene function annotation is related to immune response, pathogen recognition or defense mechanism; (b) the gene has homologous genes in the known disease resistance gene database; (c) the gene expression level in disease-resistant varieties is higher than that in susceptible varieties.

[0013] Furthermore, the specific operation of step ② of the reverse genome-wide association analysis is: check the functional annotations of genes near the significant peak in the Manhattan plot, and search for candidate effectors within 100 kb upstream and downstream of the significant peak; The candidate peaks meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-5; (b) the distance between two adjacent SNPs that meet the first condition must be less than 10kb; (c) the number of SNPs that meet the first two conditions in the candidate peak must be greater than 10; (d) considering linkage disequilibrium, the r² value is greater than 0.8; The candidate effectors meet the following conditions at the same time: (a) have a signal peptide; (b) have no transmembrane domain; (c) have a molecular weight less than 35 kDa; and (d) contain a specific YxC motif.

[0014] Furthermore, the specific operation of step ③ of the forward genome-wide association analysis is as follows: adjacent SNP sites on the candidate disease resistance gene are strung together to form the haplotype of the candidate disease resistance gene, and different haplotypes of the gene are associated with the resistance phenotype of the plant to the pathogen using chi-square test, logistic regression, variance analysis and / or Kruskal-Wallis test to evaluate the significance of the association between the haplotype and the phenotype, and select the disease resistance haplotype of the candidate disease resistance gene; The specific operation of step ③ of the reverse whole genome association analysis is as follows: adjacent SNP sites on the candidate effector are strung together to form the haplotype of the candidate effector, and the different haplotypes of the effector are associated with the resistance phenotype of the pathogen-infected plant using chi-square test, logistic regression, analysis of variance and / or Kruskal-Wallis test to evaluate the significance of the association between the haplotype and the phenotype, and select the non-toxic haplotype of the candidate effector.

[0015] Furthermore, in step ③ of the forward genome-wide association analysis, logistic regression was used to evaluate the disease resistance haplotypes that were significantly associated with the resistance traits of the candidate disease resistance genes: the plant resistance phenotype was regarded as a binary classification, with phenotypic values ​​<= 2.3 as resistance and phenotypic values ​​> 2.3 as non-resistance, with resistance and non-resistance as dependent variables, resistance as 1, non-resistance as 0, and haplotype as the independent variable; if the regression coefficient of the haplotype was positive and p<0.05, the haplotype was a disease resistance haplotype; In step ③ of the reverse genome-wide association analysis, logistic regression was used to evaluate the avirulent haplotypes that were significantly associated with the haplotypes of the candidate effector and the toxicity trait: the pathogen toxicity phenotype was regarded as a binary classification, with a phenotypic value <= 2.3 as avirulent and a phenotypic value > 2.3 as toxic. Toxicity and non-toxicity were used as dependent variables, with toxicity being 1 and non-toxicity being 0. The haplotype was used as the independent variable. If the regression coefficient of the haplotype was negative and p < 0.05, the haplotype was avirulent.

[0016] Furthermore, in step ④ of the forward whole-genome association analysis and step ④ of the reverse whole-genome association analysis, whole-genome association analysis was performed using the association model MLM.

[0017] Furthermore, the above-mentioned bidirectional association analysis method for plant-pathogen gene interaction is applied in screening interacting gene pairs.

[0018] Furthermore, the bidirectional association analysis method of plant-pathogen gene interaction was applied in screening wheat disease resistance genes and corresponding effector factors in powdery mildew.

[0019] The beneficial effect of the present invention is that the method provided by the present invention for screening interactive gene pairs in plants and pathogens based on bidirectional genome association analysis can efficiently and accurately screen out disease-resistant genes in plants and corresponding effector factors in pathogens at the same time. The screening of interactive gene pairs in plants and pathogens can provide strong support for analyzing the resistance mechanism of plant disease-resistant genes and lay the foundation for cultivating new varieties of persistent and broad-spectrum disease-resistant plants. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 This is a schematic diagram of forward GWAS, which locates the interacting pathogen Avr based on the plant disease resistance R gene; Figure 2 This is a schematic diagram of reverse GWAS, which locates the interacting plant R genes starting from the pathogen effector Avr; Figure 3 Schematic diagram for determining gene haplotype (resistance haplotype of R or avirulence haplotype of Avr); Plant resistance gene PxX (or pathogen effector factor AvrP ) has 3 SNP sites, and these three SNPs form 9 haplotypes. a is the relationship between the haplotype heat map of the gene and the phenotype. Each row is a plant variety in the plant population. Each column on the left is a SNP that constitutes the haplotype. The green color in the right column represents that the plant is a disease-resistant phenotype, and the red color represents a disease-susceptible phenotype. From the schematic diagram, we can see that the phenotypes of samples with haplotypes "222" or "202" are mostly resistant, and b also shows that the phenotypes of samples with these two haplotypes are resistant phenotypes. Therefore, "222" and "202" are the disease-resistant (or avirulent) haplotypes of this R gene (or effector Avr). Figure 4 For a forward GWAS example, from the disease resistance gene Pm1a Effector factors were located in the disease-resistant haplotype of wheat AvrPm1a ; Figure 5 For the reverse GWAS example, from the effect factor AvrPm1a Disease resistance genes were located in the avirulent haplotype strain Pm1a ; Figure 6 Validation of transient expression in tobacco Pm1a and AvrPm1a interaction; EV represents empty load, BAX represents positive control, Scal bar: 1cm. DETAILED DESCRIPTION

[0021] Data basis for bidirectional GWAS: It requires ① the variation information file (SNPs) generated by the whole genome sequencing data of the plant population; ② the whole genome sequencing data of the pathogen population and its variation information file (SNPs); ③ the resistance phenotype data of the plant population to the pathogen strain population.

[0022] The core idea of ​​bidirectional genome-wide association analysis (bidirectional GWAS): The so-called bidirectional genome-wide association study (bidirectional GWAS) refers to the process of locating the pathogen effector (Avr gene) recognized by the resistance gene (R gene) in the plant in the forward direction, and locating the R gene in the reverse direction from the Avr gene. Using genotype and phenotype data, genome-wide association studies (GWAS) are used to locate and identify plant disease resistance genes (such as specific pathogen strains) in plant populations. Figure 1 a), identification of candidate resistance genes PxX Afterwards, for PxX Different haplotypes and corresponding phenotypic relationships (such as Figure 3 a and Figure 3 b), the plant population is divided into two groups: one is genetic PxX The disease resistance haplotype group; the other group is gene PxX The susceptible haplotype group is lacking in plants that can identify AvrP The functional disease resistance gene haplotype of PxX Different plant individuals with the disease resistance haplotype group of the gene were used to locate the candidate disease resistance gene of this plant in the pathogen population. PxX Pathogen effector factor AvrP When the plants of the disease-resistant haplotype group can always interact with the effector factor in the pathogen population AvrP Establish significant associations (such as Figure 1 b), it indicates PxX and AvrP There is a specific recognition relationship between them, which triggers the immune response and thus resists the infection of pathogens.

[0023] Similarly, the process is reversed, starting from the avirulent effector Avr to locate the plant R gene that interacts with it. First locate the candidate effector in the pathogen population AvrP (like Figure 2 a), and then the pathogen strains were divided according to the different haplotypes and phenotypic relationships of this effector factor (such as Figure 3 a and Figure 3 b), the pathogen strains were divided into avirulent haplotype group and toxic haplotype group. When the avirulent haplotype group strains could locate the same PxX When (such as Figure 2 b), indicating AvrP yes PxX Specific recognition of effector factors.

[0024] Analyze from two directions (from resistance gene to avirulent effector and from avirulent effector to resistance gene) PxX and AvrP There is a bidirectional strict specific recognition relationship. This method can accelerate the identification of plant disease resistance genes and analyze their interaction rules with effector factors. This not only helps to fill the gaps in current research, but also provides an important theoretical basis and technical support for revealing the molecular mechanism of plant disease resistance, improving disease resistance breeding strategies, and cultivating plant varieties with broad-spectrum and durable disease resistance.

[0025] The specific implementation modes of the present invention are described in detail below with reference to the embodiments.

[0026] Example 1 Detailed operation of the bidirectional GWAS analysis method for wheat and powdery mildew gene interaction: 1. Data: ①Genetic variation information of 581 wheat populations (usually single nucleotide polymorphisms, SNPs), data source: https: / / ngdc.cncb.ac.cn / gvm / getProjectFile?t=ada7a2d8; ②Genotypic variation information (SNPs) of 120 powdery mildew strains, data source: https: / / ngdc.cncb.ac.cn / gvm / getProjectFile?t=2848772b; ③Resistance phenotypes of 581 wheat samples to 120 powdery mildew strains, as shown in Tables 1 and 2.

[0027] Table 1 Data on the phenotypic grade of resistance of wheat populations (first column) to powdery mildew strains (first row)

[0028]

[0029]

[0030]

[0031]

[0032]

[0033]

[0034] Note: In the table, level 0 means super resistance, level 1 means medium resistance, level 2 means weak resistance, level 3 means medium sensitivity, and level 4 means super sensitivity.

[0035] Table 2 Phenotypic grade data of wheat population (first row) resistance to powdery mildew strains (first column)

[0036]

[0037] Note: In the table, level 0 means super resistance, level 1 means medium resistance, level 2 means weak resistance, level 3 means medium sensitivity, and level 4 means super sensitivity.

[0038] 2. Forward GWAS: Locating the powdery mildew effector Avr from the wheat R gene (1) Batch identification of candidate powdery mildew resistance genes in candidate wheat: A genome-wide association study (GWAS) was performed on the genotype variation files (SNPs) of the wheat population and the phenotypes of 120 strains of the wheat population. The models used in the GWAS included MLM (mixed linear model), FarmCPU (fixed and random model improvement), MLMM (multivariate linear model), and BLINK (Bayesian information and linkage unbalanced iterative nested key-link). The parameters were the default parameters. The association results between the variation sites on the genome of the wheat population and the phenotype of each strain were obtained (the phenotype of each strain in the wheat population was taken as a trait and an association result was obtained, for a total of 120 association results). A Manhattan plot was drawn to display the association on the genome. The same peak area located by at least three association models was regarded as a candidate peak.

[0039] The peak on the Manhattan plot represents a statistically significant association between a specific chromosome region of wheat and the powdery mildew resistance phenotype. The higher the peak (the smaller the p-value or the larger the -log10(p-value)), the stronger the significance of the association. At the same time, the region where the peak is located often contains candidate genes related to the phenotype.

[0040] (2) Select candidate disease resistance genes that are significantly associated with the trait and pointed to by the candidate peak of interest: The significant region of the peak often contains candidate disease resistance genes, which may be related to disease resistance mechanisms, such as genes encoding disease resistance proteins (such as the NLR gene family), transcription factors that regulate gene expression, or genes involved in signal transduction and defense response. By checking the functional annotations of genes near the significant peaks, candidate disease resistance genes of interest related to disease resistance can be identified.

[0041] The candidate peaks in the wheat population are considered significant peaks if they meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-8; (b) the distance between two adjacent SNPs that meet the first condition must be less than 1Mb; (c) the number of SNPs in the candidate peak that meet the first two conditions must be greater than 5; (d) considering linkage disequilibrium, the r² value is greater than 0.8; search for candidate disease resistance genes within 1Mb upstream and downstream of the significant peak, and select genes with disease resistance annotations from the 10 genes inside and upstream and downstream of the significant peak as candidate disease resistance genes; Candidate disease resistance genes meet the following conditions simultaneously: (a) the gene function annotation is related to immune response, pathogen recognition or defense mechanism; (b) the gene has homologous genes in the known disease resistance gene database; (c) the gene expression level in disease-resistant varieties is significantly higher than that in susceptible varieties.

[0042] In this example, GWAS association models MLM, MLMM, FarmCPU and BLINK (corresponding to Figure 4 (a) Model 1-4) The resistance phenotype of wheat population to powdery mildew strain B029-A1 was used as a trait to associate with the genetic variation of wheat population. The four association models simultaneously obtained the powdery mildew resistance gene corresponding to the significant peak at the end of chromosome 7A. Pm1a (like Figure 4 a), Pm1a It is a cloned powdery mildew resistance gene.

[0043] (3) Identify haplotypes that are significantly associated with disease resistance traits, i.e., disease resistance haplotypes: candidate disease resistance genes Pm1a The SNPs above will have multiple combinations in the population, and each combination of SNPs is a haplotype. Combined with the resistance phenotype of the wheat population to the B029A1 strain, the haplotype with a high correlation with the disease resistance phenotype was identified; The specific operation is: Pm1a The association analysis between different haplotypes and the resistance-susceptibility phenotype of wheat population to powdery mildew strain B029-A1 was performed, and the statistical method logistic regression was used to evaluate Pm1a Disease-resistant haplotypes that are significantly associated with the resistance phenotype among the haplotypes: The phenotypic data are regarded as binary categories (phenotypic value <= 2.3 is resistance, phenotypic value > 2.3 is non-resistance), haplotype is used as the independent variable, resistance as the dependent variable, resistance (1) and non-resistance (0), and binary logistic regression analysis is performed. The resistance haplotype is determined by the regression coefficient and its significance level. If the regression coefficient of the haplotype is significant (p-value < 0.05) and the regression coefficient is positive, then the haplotype is positively correlated with the resistance phenotype, that is, the disease-resistant haplotype.

[0044] These disease-resistant haplotypes may be Pm1aThe coding region or regulatory region of the disease-resistance gene carries key functional mutations (such as non-synonymous mutations, splicing site mutations or regulatory enhancer mutations), which confer disease resistance to wheat against the powdery mildew strain B029A1.

[0045] (4) Contains Pm1a Disease-resistant haplotypes of wheat can be used to locate candidate effector factors AvrPm1a :contain Pm1a The wheat varieties with disease-resistant haplotypes include Huzhuhong (B049), CItr14087 (B103), PI350766 (B104), PI525317 (B145), etc. The powdery mildew populations were isolated from these varieties. Pm1a The infection phenotype of disease-resistant haplotype wheat was used as a trait, and a mixed linear model (MLM) analysis was performed to conduct a genome-wide association study (GWAS) between the trait and the genetic variation of the powdery mildew strain, effectively identifying the genetic variation related to the disease resistance phenotype on the powdery mildew genome and locating the candidate effector factors on the powdery mildew genome.

[0046] The results showed that these Pm1a The infection phenotypes of wheat resistant haplotypes all mapped common candidate effector factors on the powdery mildew genome AvrPm1a (like Figure 4 b). This means that Pm1a The resistance phenotypes of different wheat varieties with disease-resistant haplotypes to powdery mildew are all affected by the same effector AvrPm1a regulation of disease resistance genes Pm1a Effector AvrPm1a The interaction between them is a key factor in wheat disease resistance.

[0047] 3. Reverse GWAS: Localizing wheat R genes based on the powdery mildew effector Avr (1) Batch identification of candidate effector factors in powdery mildew: Similar to the forward GWAS method, we first performed a genome-wide association study (GWAS) on the genetic variation file (SNPs) of the powdery mildew population and the infection phenotype of 581 wheat varieties in the powdery mildew population. The models used in the GWAS were MLM, FarmCPU, MLMM, and BLINK, with default parameters. The association results between the SNPs in the powdery mildew population and the resistance-susceptibility phenotype of each wheat variety were obtained. Here, the phenotype of each wheat variety in the powdery mildew population was used as a trait to obtain an association result, for a total of 581 association results. A Manhattan plot was drawn for each association result to show the degree of association between the SNPs on the powdery mildew genome and the phenotype. The same peak area located by at least three association models was regarded as a candidate peak.

[0048] (2) Identify candidate Avr genes associated with the phenotype in the region where the peak is located: Effector genes are genes in the pathogen genome that encode small molecule proteins that can be secreted into host cells and interfere with the host's immune response or metabolic process. In powdery mildew, these genes are generally considered to be key factors in interacting with host resistance genes, regulating the host's resistance phenotype directly or indirectly.

[0049] The candidate peaks in the pathogen powdery mildew population are considered significant peaks if they meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-5; (b) the distance between two adjacent SNPs that meet the first condition must be less than 10kb; (c) the number of SNPs that meet the first two conditions in the candidate peak must be greater than 10; (d) considering linkage disequilibrium, the r² value is greater than 0.8; search for candidate effect factors within 100kb upstream and downstream of the significant peak, and the genes that meet the characteristics of effect factors among the 10 genes inside and upstream and downstream of the significant peak are candidate effect factors; Candidate effectors meet the following conditions at the same time: (a) have a signal peptide to ensure that the protein can be secreted; (b) have no transmembrane domain, excluding membrane-localized proteins; (c) have a molecular weight of less than 300 amino acids (approximately 30-35 kDa), which meets the typical characteristics of effectors; and (d) contain a specific YxC motif, which is a conservative feature of effectors.

[0050] In this example, the resistance phenotype of wheat variety PI350766 (code B104) to powdery mildew was used as the trait, and the GWAS association models MLM, MLMM, FarmCPU, and BLINK (corresponding to Figure 5 Model 1-4 in a) was significantly associated with the effect factor AvrPm1a ( Figure 5 a), AvrPm1a It is an important cloned powdery mildew effector factor located on chromosome 6 of the powdery mildew genome. Its function and variation directly affect wheat's resistance to powdery mildew.

[0051] (3) Identify avirulent haplotypes of effectors that are significantly associated with traits: The avirulent haplotype (Avr) of pathogen effectors enables the pathogen's proteins or metabolites to be sensed by the host's disease resistance genes, thereby stimulating the host's immune response; in contrast, the toxic haplotype (Vir) changes the protein structure of the effector through mutations at key sites, allowing the pathogen to evade immune recognition and successfully infect the host. The avirulent haplotype is the key to the pathogen effector being recognized by the host's disease resistance genes and inducing a resistance response.

[0052] The specific operation is as follows: Use logistic regression to evaluate the non-toxic haplotypes with significant associations between the haplotypes of candidate effectors and toxic traits: treat the pathogen toxicity phenotype as a binary category (phenotype value <= 2.3 is non-toxic, phenotype value > 2.3 is toxic), use haplotype as the independent variable, toxicity (1) and non-toxicity (0) as the dependent variables, and perform binary logistic regression analysis. If the regression coefficient of the haplotype is negative and p < 0.05, then the haplotype is a non-toxic haplotype.

[0053] Combined with successful positioning AvrPm1a The resistance-susceptibility phenotype of wheat material PI350766 to powdery mildew populations was investigated, and 120 powdery mildew strains were classified into effector factors. AvrPm1a Avirulent haplotype group and toxic haplotype group. The phenotypes of the avirulent haplotype group (powdery mildew strains include B016A3, B096A1, B029A1, B073A1, etc.) are all unable to infect PI350766 (i.e. wheat is resistant to these strains); there is no significant correlation between the phenotype and genotype of the toxic haplotype group to PI350766.

[0054] (4) Contains AvrPm1a Strains with avirulent haplotypes can be located to the same AvrPm1a :contain AvrPm1a The non-toxic haplotypes of powdery mildew strains include B016A3, B096A1, B029A1, B073A1, etc. The resistance phenotypes of wheat populations to these strains were used as traits and successfully identified through GWAS. AvrPm1a Disease resistance genes Pm1a ( Figure 5 b) Pathogen effector factors were further clarified AvrPm1a Host resistance genes Pm1a Specific recognition between.

[0055] Example 2 Tobacco transient expression experiment verifies disease resistance gene Pm1a and effector AvrPm1a Interaction Verification of wheat disease resistance genes using tobacco transient expression system Pm1a Powdery mildew effector AvrPm1a In the experiment, we used Pm1a and AvrPm1a The Agrobacterium resuspension was used to prepare different Agrobacterium combinations, and the different combinations of Agrobacterium were injected into the leaf pulp of Nicotiana tabacum (leaves grown for one month). Pm1a or AvrPm1a When the leaves were annotated with empty vector (EV), there was no spontaneous hypersensitive response (HR), and empty vector itself had no HR response, but when Pm1a and AvrPm1aWhen injected into tobacco leaves simultaneously, a significant HR response occurred in the region expressing the two genes ( Figure 6 ), which is manifested by rapid cell death or browning in some areas, while BAX is used as a positive control. Pm1a Able to specifically recognize effector factors AvrPm1a , triggering the plant's immune response.

[0056] In summary, the present invention provides a bidirectional association analysis method for plant-pathogen gene interaction, which analyzes the existence of a bidirectional strict specific recognition relationship between resistance genes and effect factors from two directions (from resistance genes to avirulent effect factors and from avirulent effect factors to resistance genes), and can be used to screen for interactive gene pairs between plants and pathogens. This method can accelerate the identification of plant disease resistance genes and analyze their interaction patterns with effect factors. This not only helps to fill the gaps in current research, but also provides an important theoretical basis and technical support for revealing the molecular mechanism of plant disease resistance, improving disease resistance breeding strategies, and cultivating plant varieties with broad-spectrum and durable disease resistance.

[0057] Although the specific implementation of the present invention is described in detail in conjunction with the embodiments, it should not be understood as limiting the scope of protection of this patent. Within the scope described in the claims, various modifications and variations that can be made by those skilled in the art without creative work still fall within the scope of protection of this patent.

Claims

1. A bidirectional association analysis method for plant and pathogen gene interaction, characterized in that: The following steps are involved: (1) Obtaining genetic variation information of plants, genetic variation information of pathogens, and the resistance and susceptibility phenotypes of plants to pathogens; (2) Forward genome-wide association analysis to locate pathogen effector factors based on plant disease resistance genes: ① Performing genome-wide association analysis on the genetic variation information of the plant and the resistance and susceptibility phenotype of the plant to pathogens, obtaining the association results between the variation sites on the plant genome and the resistance and susceptibility phenotype of the plant to pathogens, drawing a Manhattan plot and identifying candidate peaks; ② Identify candidate disease resistance genes in plants from candidate peaks; ③ Combined with the resistance phenotype of the plant to pathogens, the disease resistance haplotype of the candidate disease resistance gene was identified; ④ Taking the infection phenotype of the pathogen on the plant with the disease-resistant haplotype containing the candidate disease-resistant gene as a trait, performing genome-wide association analysis on the trait and the genetic variation information of the pathogen to locate the candidate effector factor on the pathogen genome; (3) Reverse whole genome association analysis, locating plant disease resistance based on pathogen effector factors: ① Performing genome-wide association analysis on the genetic variation information of the pathogen and the resistance and susceptibility phenotype of the plant to the pathogen, obtaining the association results between the variation sites on the pathogen genome and the resistance and susceptibility phenotype of the plant to the pathogen, drawing a Manhattan plot and identifying candidate peaks; ② Identify candidate effector factors in pathogens from candidate peaks; ③ Combined with the resistance and sensitivity phenotype of the plant to the pathogen, identify the non-toxic haplotype of the candidate effector; ④ Taking the plant's resistance phenotype to a non-toxic haplotype strain containing a candidate effector factor as a trait, performing genome-wide association analysis on the trait and the genetic variation information of the plant to locate the candidate disease resistance gene on the plant genome; (4) The candidate disease resistance gene obtained in step (2) and step (3) is the same as the candidate effector factor, and the two are the gene pair that interacts between the plant and the pathogen.

2. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 1, characterized in that: The genetic variation information of the plant is single nucleotide polymorphism, and the genetic variation information of the pathogen is single nucleotide polymorphism.

3. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 2, characterized in that: In step ① of the forward whole-genome association analysis and step ① of the reverse whole-genome association analysis, whole-genome association analysis was performed using association models MLM, FarmCPU, MLMM and BLINK, and at least three association models located the same peak region in the Manhattan plot as candidate peaks.

4. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 3, characterized in that: The specific operation of step ② of the forward genome-wide association analysis is: check the functional annotations of genes near the significant peak in the Manhattan plot, and search for candidate disease resistance genes within 1 Mb upstream and downstream of the significant peak; The candidate peaks meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-8; (b) the distance between two adjacent SNPs that meet the first condition must be less than 1Mb; (c) the number of SNPs that meet the first two conditions in the candidate peak must be greater than 5; (d) considering linkage disequilibrium, the r² value is greater than 0.8; Candidate disease resistance genes meet the following conditions simultaneously: (a) the gene function annotation is related to immune response, pathogen recognition or defense mechanism; (b) the gene has homologous genes in the known disease resistance gene database; (c) the gene expression level in disease-resistant varieties is higher than that in susceptible varieties.

5. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 3, characterized in that: The specific operation of step ② of the reverse genome-wide association analysis is: check the functional annotations of genes near the significant peak in the Manhattan plot, and search for candidate effector factors within 100 kb upstream and downstream of the significant peak; The candidate peaks meet the following conditions at the same time: (a) the p-value of the SNP in the candidate peak is less than the significance threshold of 1e-5; (b) the distance between two adjacent SNPs that meet the first condition must be less than 10kb; (c) the number of SNPs that meet the first two conditions in the candidate peak must be greater than 10; (d) considering linkage disequilibrium, the r² value is greater than 0.8; The candidate effectors meet the following conditions at the same time: (a) have a signal peptide; (b) have no transmembrane domain; (c) have a molecular weight less than 35 kDa; and (d) contain a specific YxC motif.

6. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 1, characterized in that: The specific operation of step ③ of the forward genome-wide association analysis is as follows: adjacent SNP sites on the candidate disease resistance gene are strung together to form the haplotype of the candidate disease resistance gene, and different haplotypes of the gene are associated with the resistance phenotype of the plant to pathogens using chi-square test, logistic regression, variance analysis and / or Kruskal-Wallis test to evaluate the significance of the association between the haplotype and the phenotype, and select the disease resistance haplotype of the candidate disease resistance gene; The specific operation of step ③ of the reverse whole genome association analysis is as follows: adjacent SNP sites on the candidate effector are strung together to form the haplotype of the candidate effector, and the different haplotypes of the effector are associated with the resistance phenotype of the pathogen-infected plant using chi-square test, logistic regression, analysis of variance and / or Kruskal-Wallis test to evaluate the significance of the association between the haplotype and the phenotype, and select the non-toxic haplotype of the candidate effector.

7. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 6, characterized in that: In step ③ of the forward genome-wide association analysis, logistic regression is used to evaluate the disease resistance haplotypes that are significantly associated with the resistance traits of the candidate disease resistance gene haplotypes: the plant resistance phenotype is regarded as a binary classification, the phenotypic value <= 2.3 is resistance, the phenotypic value > 2.3 is non-resistance, resistance and non-resistance are used as dependent variables, resistance is recorded as 1, non-resistance is recorded as 0, and haplotype is the independent variable; if the regression coefficient of the haplotype is positive and p<0.05, the haplotype is a disease resistance haplotype; In step ③ of the reverse genome-wide association analysis, logistic regression is used to evaluate the non-toxic haplotypes that are significantly associated with the haplotypes of the candidate effector factors and the toxicity traits: the pathogen toxicity phenotype is regarded as a binary classification, the phenotypic value <= 2.3 is non-toxic, the phenotypic value > 2.3 is toxic, toxicity and non-toxicity are used as dependent variables, toxicity is recorded as 1, non-toxicity is recorded as 0, haplotype is used as the independent variable, the regression coefficient of the haplotype is negative and p<0.05, then the haplotype is a non-toxic haplotype.

8. The method for bidirectional association analysis of plant-pathogen gene interactions according to claim 1, characterized in that: In step ④ of the forward whole-genome association analysis and step ④ of the reverse whole-genome association analysis, the association model MLM is used to perform whole-genome association analysis.

9. Use of the bidirectional association analysis method for plant-pathogen gene interaction according to any one of claims 1 to 8 in screening interacting gene pairs.

10. The use according to claim 9, characterized in that: The bidirectional association analysis method for the interaction between plant and pathogen genes is applied in screening wheat disease resistance genes and corresponding effector factors in powdery mildew.

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