Precise identification method for corn pythium stem rot resistance phenotype through adaptive gene mining

Abstract

The invention discloses a corn pythium stem rot resistance phenotype accurate identification method based on adaptive gene mining, and particularly relates to the field of corn disease resistance identification and gene mining, and the method comprises the following steps: S1, selecting a corn material to be identified, planting the selected corn material in a greenhouse, and adopting a unified seedling culture substrate and cultivation management measures to ensure that the growth state of corn seedlings is consistent; step S2, inoculating a strong pathogenicity pythium miscanthus or fusarium graminearum strain on a PDA plate culture medium with the diameter of 9 mm and containing 20 mL of sterilized bacteria, carrying out dark culture at 25 DEG C for 5-7 days, and preparing to inoculate when hyphae are fully distributed on the surface of PDA; s3, phenotype data collection is conducted on the 3th day, the 7th day, the 10th day and the 14th day after inoculation. According to the method, strict requirements of accurate gene mining on phenotypic data stability are met, disease-resistant gene omission caused by index one-sidedness is avoided, and comprehensiveness and accuracy of adaptive gene mining are remarkably improved.

Description

Technical Field

[0001] This invention relates to the field of maize disease resistance identification and gene mining, and more specifically, to a method for accurate identification of maize phenotypes for resistance to Phytophthora stalk rot based on adaptive gene mining. Background Technology

[0002] Pythium stalk rot in maize is an important soil-borne disease caused by various Pythium fungi, severely impacting maize yield and quality. Accurate identification of Pythium stalk rot resistance phenotypes in maize materials is fundamental for disease resistance breeding and resistance gene discovery. Existing methods for identifying Pythium stalk rot resistance phenotypes in maize mainly include two categories: field-based natural disease identification and artificial inoculation identification.

[0003] Field-based natural disease identification is significantly influenced by environmental factors, resulting in poor stability of identification results. Data from different years and plots are not comparable, making it difficult to meet the stringent phenotypic data requirements of precise gene mining. Specifically, in the field environment, sudden temperature fluctuations significantly affect the activity of *Pythium* fungi. When temperatures are below 15°C or above 35°C, the pathogen's infectivity decreases drastically, leading to significant differences in disease severity for the same maize material under different temperature conditions. Uneven humidity distribution, such as the high-humidity environment created by water accumulation in low-lying areas versus the dry environment in higher areas, causes regional differences in pathogen spread and infection in the field, further exacerbating the instability of identification results. This instability makes it difficult for researchers to establish reliable phenotypic and genotypic associations based on field-based natural disease data, greatly hindering the progress of disease resistance gene mining.

[0004] While artificial inoculation can control disease-causing conditions to some extent, traditional inoculation methods suffer from problems such as uneven pathogen infection efficiency, difficulty in quantifying disease severity, and limited phenotypic identification indicators. Soil inoculation involves mixing the pathogen into the soil, allowing infection through absorption by the corn roots. However, due to factors such as soil texture and organic matter content, the distribution of the pathogen in the soil is uneven, leading to significant differences in infection levels among different corn plants. Some plants may exhibit false resistance due to insufficient exposure to the pathogen. Root immersion inoculation involves soaking the corn roots in a pathogen suspension. While this ensures contact between the roots and the pathogen, the degree of mechanical damage to the roots varies. Severely damaged plants may exhibit excessive disease due to impaired physiological function, failing to accurately reflect their actual disease resistance level.

[0005] In terms of phenotypic identification indicators, traditional methods only classify plants based on the degree of wilting or the length of stem decay, which cannot comprehensively reflect the disease resistance mechanisms of maize materials. Some maize materials may resist pathogen invasion by forming a physical barrier through rapid lignification of stem tissue, resulting in a shorter length of stem decay, but traditional indicators cannot reflect this disease resistance mechanism. Other materials may inhibit pathogen growth by synthesizing large amounts of antimicrobial substances, exhibiting more significant physiological metabolic changes in their leaves, but relying solely on external phenotypic grading will overlook these important disease resistance characteristics. This one-sidedness of phenotypic identification leads to discrepancies between the identification results and actual disease resistance, thus affecting the accuracy and efficiency of subsequent disease resistance gene discovery, causing some potentially valuable disease resistance genes to go undiscovered due to oversights in phenotypic identification.

[0006] To address the aforementioned problems, a technical solution is provided. Summary of the Invention

[0007] To overcome the aforementioned deficiencies of the prior art, embodiments of the present invention provide a method for accurate identification of maize stalk rot resistance phenotypes based on adaptor gene mining, in order to solve the problems mentioned in the background art.

[0008] To achieve the above objectives, the present invention provides the following technical solution:

[0009] A precise identification method for maize stalk rot resistance phenotypes based on adaptor gene mining includes the following steps:

[0010] Step S1: Select the corn material to be identified and plant it in a greenhouse. Use a uniform seedling substrate and cultivation management measures to ensure that the corn seedlings grow in a consistent manner.

[0011] Step S2: Inoculate the highly pathogenic Pythium spp. or Fusarium graminearum strains onto a 9 mm diameter PDA (200 g potato, 20 g glucose, 20 g agar, 1000 mL water) plate and incubate in the dark at 25 °C for 5–7 days. When the mycelium covers the surface of the PDA plate, it is ready for inoculation.

[0012] Step S3: Phenotypic data were collected on days 3, 7, 10 and 14 after inoculation. The collected indicators included disease severity indicators, physiological resistance indicators and pathogen spread indicators.

[0013] Step S4: Standardize the collected multi-dimensional phenotypic data to eliminate differences in the units of different indicators;

[0014] Step S5: Based on the standardized multi-dimensional phenotypic data, construct a comprehensive evaluation model for maize resistance to Phytophthora stalk rot.

[0015] Step S6: Correlation analysis is performed between the comprehensive disease resistance score of the maize material and the standardized values ​​of each phenotypic index and the genome sequencing data of the maize material.

[0016] In a preferred embodiment, in step S1, sowing is carried out according to a uniform sowing density and sowing depth to ensure that each corn seedling has equal growth space and soil resources.

[0017] Use the same watering frequency, amount and method to keep the soil moisture uniform and avoid some seedlings having too much or too little water.

[0018] Maintain uniformity in the type, amount, timing, and method of fertilization to ensure that corn seedlings receive an equal amount of nutrients to meet their growth and development needs.

[0019] Adopt consistent pest control measures to prevent and control potential pests in a timely manner, while avoiding the application of fungicides to prevent the pesticides from having different effects on seedling growth.

[0020] In a preferred embodiment, in step S2, the culture is homogenized into a paste in a regular food homogenizer at a ratio of 700 mL of water per 10 culture dishes for injection inoculation. The inoculation is carried out 5 to 10 days after pollination of the corn plants, using the stem injection inoculation method.

[0021] The inoculation site is the first normally extending stem node above the ground. Use an electric drill to drill a hole at an angle downwards from 1 / 4 to 1 / 3 of the height of the stem node to the center of the stem. Then, use a large syringe without a needle to inject about 0.5 mL of homogenized inoculum into the inoculation hole and seal the hole with petroleum jelly.

[0022] In a preferred embodiment, in step S3, when collecting disease severity indicators, the length of stem decay and the proportion of decay area to the cross-sectional area of ​​the stem are measured.

[0023] When collecting physiological resistance indicators, the relative chlorophyll content and proline content of leaves were measured.

[0024] When collecting pathogen expansion indicators, real-time quantitative PCR was used to detect the relative content of pathogens in the stem base tissue.

[0025] In a preferred embodiment, in step S4, the Z-score standardization method is used to convert the original data of each indicator into a standardized value. The calculation formula is: Z = (X - μ) / σ, where X is the original data, μ is the mean of all samples of the indicator, and σ is the standard deviation of all samples of the indicator.

[0026] In a preferred embodiment, in step S5, principal component analysis is used to extract the main phenotypic components, calculate the weight of each principal component, and then obtain the overall disease resistance of each maize material.

[0027] In a preferred embodiment, the disease resistance of maize materials is classified into four levels based on the overall disease resistance: highly resistant, moderately resistant, susceptible, and highly susceptible.

[0028] In a preferred embodiment, in step S6, genome-wide association analysis is used to screen gene loci that are significantly associated with the phenotype of resistance to Pythium stem rot, thereby achieving precise discovery of adaptor genes.

[0029] The technical effects and advantages of this invention's method for precise identification of maize stalk rot resistance phenotypes adapted to gene mining are as follows:

[0030] 1. By using greenhouse controlled environment planting and unified cultivation management measures, the influence of environmental factors such as temperature and humidity fluctuations on plant growth and pathogen infection in natural disease identification in the field is eliminated, ensuring that the growth status of maize seedlings is consistent. At the same time, the standardized stem injection inoculation method accurately controls the pathogen concentration, inoculation site and inoculation amount, avoiding the problems of uneven pathogen distribution and large differences in infection efficiency in traditional artificial inoculation methods. This makes the disease phenotype more stably reflect the true disease resistance of maize materials, significantly improves the comparability of identification results of different batches and different times, and meets the strict requirements of precision gene mining for the stability of phenotypic data.

[0031] 2. Phenotypic data were collected from three dimensions: disease severity, physiological resistance, and pathogen spread. This comprehensively covered the physical defense, physiological metabolic regulation, and pathogen inhibition mechanisms of maize resistance to Pythium stalk rot. Through Z-score standardization and principal component analysis, the multi-dimensional data were integrated into a comprehensive disease resistance score. This not only more accurately distinguishes between high resistance, moderate resistance, susceptible, and highly susceptible levels, but also provides rich phenotypic clues for genome-wide association analysis, avoiding the omission of disease resistance genes due to the one-sidedness of indicators, and significantly improving the comprehensiveness and accuracy of adaptor gene mining. Attached Figure Description

[0032] Figure 1 This is a flowchart illustrating the method for precise identification of maize stalk rot resistance phenotypes based on gene mining, which is adapted to this invention. Detailed Implementation

[0033] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0034] Example 1

[0035] Figure 1 This invention presents a method for precise identification of maize stalk rot resistance phenotypes based on adaptor gene mining, specifically including the following steps:

[0036] Step S1: Select the corn material to be identified and plant it in a greenhouse. Use a uniform seedling substrate and cultivation management measures to ensure that the corn seedlings grow in a consistent manner.

[0037] Step S2: Inoculate the highly pathogenic Pythium spp. or Fusarium graminearum strains onto a 9 mm diameter PDA plate containing 20 mL of sterile medium and incubate in the dark at 25°C for 5–7 days. When the mycelium covers the surface of the PDA plate, it is ready for inoculation.

[0038] Step S3: Phenotypic data were collected on days 3, 7, 10 and 14 after inoculation. The collected indicators included disease severity indicators, physiological resistance indicators and pathogen spread indicators.

[0039] Step S4: Standardize the collected multi-dimensional phenotypic data to eliminate differences in the units of different indicators;

[0040] Step S5: Based on the standardized multi-dimensional phenotypic data, construct a comprehensive evaluation model for maize resistance to Phytophthora stalk rot.

[0041] Step S6: Correlation analysis is performed between the comprehensive disease resistance score of the maize material and the standardized values ​​of each phenotypic index and the genome sequencing data of the maize material.

[0042] In step S1, sowing is carried out according to a uniform sowing density and sowing depth to ensure that each corn seedling has equal growth space and soil resources.

[0043] Use the same watering frequency, amount and method to keep the soil moisture uniform and avoid some seedlings having too much or too little water.

[0044] Maintain uniformity in the type, amount, timing, and method of fertilization to ensure that corn seedlings receive an equal amount of nutrients to meet their growth and development needs.

[0045] Adopt consistent pest control measures to prevent and control potential pests in a timely manner, while avoiding the application of fungicides to prevent the pesticides from having different effects on seedling growth.

[0046] Maize seedlings under unified management exhibit consistent growth, resulting in more uniform physiological states during stem base injection inoculation. This ensures consistent pathogen infection efficiency and reduces uneven disease severity caused by variations in plant condition. Consequently, subsequent phenotypic data, such as lesion length, relative pathogen content, and physiological resistance indicators, are more comparable and accurately reflect differences in disease resistance among different maize materials. Based on this uniform and reliable phenotypic data, association analysis with genomic data allows for more precise screening of gene loci associated with resistance to Pythium stalk rot, improving the efficiency and accuracy of resistance gene discovery and shortening the research cycle from phenotypic identification to gene localization.

[0047] In step S2, the culture is homogenized into a paste in a regular food homogenizer at a ratio of 700 mL of water per 10 dishes. This paste is then used for injection inoculation. Inoculation is performed 5–10 days after pollination of the corn plants, using the stem injection method.

[0048] The inoculation site is the first normally extending stem node above the ground. Use an electric drill to drill a hole at an angle downwards from 1 / 4 to 1 / 3 of the height of the stem node to the center of the stem. Then, use a large syringe without a needle to inject about 0.5 mL of homogenized inoculum into the inoculation hole and seal the hole with petroleum jelly.

[0049] Homogenizing the culture by adding 700 mL of water to every 10 culture dishes allows for precise control of the pathogen suspension concentration, preventing variations in infection pressure among different corn plants due to excessively high or low concentrations. Using a standard food homogenizer to homogenize the culture into a paste ensures even distribution of the pathogen in the suspension, reducing localized concentration inconsistencies. This standardized pathogen preparation method ensures that each corn plant is exposed to an equal amount of pathogen with consistent activity, guaranteeing that subsequent differences in disease phenotypes stem primarily from the plant's own disease resistance, rather than fluctuations in pathogen concentration. This lays the foundation for accurately distinguishing the disease resistance levels of different plants.

[0050] Inoculation was chosen at a specific time, 5–10 days after pollination of the maize plants. At this stage, the plants are in a vigorous growth phase, with stable stem tissue structure and physiological state, resulting in a consistent response to pathogens and avoiding differences in disease resistance due to different plant growth stages. The inoculation site was the first normally extending stem node above the ground, with a hole drilled obliquely downwards to the center of the stem at the lower 1 / 4–1 / 3 of the node. This location represents the stem structure, ensuring a consistent infection path and expansion environment for the pathogen after entering the plant. Drilling with an electric drill and injecting with a needleless syringe allowed for precise control of the inoculum injection volume (approximately 0.5 mL) and injection location, reducing human error. Sealing the hole with petroleum jelly prevented leakage of inoculum and contamination by other microorganisms, ensuring stable pathogen infection at the inoculation site. This made the disease progression more comparable across different plants and significantly improved the reproducibility of disease phenotypes.

[0051] Stem injection directly introduces the pathogen into the center of the stem, bypassing barrier tissues such as roots or epidermis. This allows the pathogen to quickly reach susceptible tissues, increasing infection efficiency and shortening the disease cycle. Compared to soil inoculation or root dipping, this method reduces pathogen loss during transmission and infection, ensuring sufficient pathogen interaction with plant tissues and more easily inducing obvious resistance or susceptibility phenotypes.

[0052] In step S3, when collecting disease severity indicators, the length of stem decay and the proportion of decay area to the cross-sectional area of ​​the stem are measured.

[0053] When collecting physiological resistance indicators, the relative chlorophyll content and proline content of leaves were measured.

[0054] When collecting pathogen expansion indicators, real-time quantitative PCR was used to detect the relative content of pathogens in the stem base tissue.

[0055] The stem rot length in the disease severity indicators directly reflects the longitudinal expansion range of the pathogen within the stem, while the proportion of rotten area to the stem cross-sectional area reflects the lateral degree of infection. The combination of these two indicators allows for precise quantification of the physical extent of plant damage. Among the physiological resistance indicators, the relative chlorophyll content in leaves reflects the degree to which the plant's photosynthetic function is affected by the disease, while changes in leaf proline content reflect the plant's osmotic regulation ability under stress. These two indicators reveal the response mechanism of maize to the disease from a physiological metabolic perspective. The pathogen expansion indicator, detected by real-time quantitative PCR, quantifies the dynamics of pathogen reproduction and spread within the plant at the molecular level. These three types of indicators construct a complete disease resistance evaluation system from three dimensions: physical damage, physiological response, and pathogen activity. Compared to single indicators, this system more comprehensively captures the differences in disease resistance among maize materials, avoiding misjudgments of disease resistance due to the limitations of individual indicators.

[0056] Quantitative data on disease severity indicators can be directly used as phenotypic traits for QTL mapping of disease resistance, and their precise numerical recording can improve the significance of the association between gene loci and phenotype. The detection results of physiological resistance indicators can reflect the physiological regulatory mechanisms of maize materials during disease resistance, providing clues for screening disease resistance genes related to metabolic regulation. Pathogen extension indicators, through quantitative data at the molecular level, can precisely link the functions of genes that can inhibit pathogen proliferation, helping to discover candidate genes with direct antibacterial effects. Standardized data from multidimensional indicators can be deeply correlated with genome sequencing data, and through methods such as genome-wide association analysis, gene loci associated with disease resistance phenotypes can be more accurately identified, reducing gene localization bias caused by phenotypic data errors and significantly improving the efficiency and accuracy of disease resistance gene discovery.

[0057] In step S4, the Z-score standardization method is used to convert the original data of each indicator into a standardized value. The calculation formula is: Z = (X - μ) / σ, where X is the original data, μ is the mean of all samples of the indicator, and σ is the standard deviation of all samples of the indicator.

[0058] Using the Z-score standardization method to process data can eliminate the influence of differences in units and orders of magnitude among different indicators. For example, the unit for stem rot length is cm, while the unit for leaf proline content may be μg / g. The numerical ranges of the two differ significantly, and direct comparison or comprehensive analysis may lead to results biased towards the indicator with the larger value. However, after conversion using this formula, all indicator data are transformed into standardized values ​​with a mean of 0 and a standard deviation of 1, placing them on the same order of magnitude. This allows for fair comparison and comprehensive calculation on the same dimension, providing a reliable data foundation for subsequent multi-dimensional data integration and analysis, ensuring that the analysis results truly reflect the comprehensive impact of each indicator on the phenotype of maize resistance to Pythium stalk rot.

[0059] In step S5, principal component analysis is used to extract the main phenotypic components, calculate the weight of each principal component, and then obtain the overall disease resistance of each maize material.

[0060] Based on the overall disease resistance, the disease resistance of maize materials is divided into four levels: highly resistant, moderately resistant, susceptible, and highly susceptible.

[0061] Principal component analysis (PCA) can extract several independent principal components from multiple dimensions of indicators such as disease severity, physiological resistance, and pathogen spread. These principal components collectively reflect most of the information in the original data. By calculating the weights of each principal component and synthesizing the overall disease resistance, complex multi-indicator data can be transformed into a single comprehensive quantitative value. This preserves the contribution of each indicator to maize disease resistance while avoiding information overlap and mutual interference between indicators. Based on this, the four levels of highly resistant, moderately resistant, susceptible, and highly susceptible can clearly distinguish the differences in disease resistance among different maize materials. This elevates disease resistance evaluation from scattered indicator data to a systematic comprehensive judgment, providing a concise and clear phenotypic classification basis for subsequent disease resistance gene mining. It facilitates the rapid identification of the association between target genes and specific disease resistance levels, improving the targeting and efficiency of gene mining.

[0062] In step S6, genome-wide association analysis is used to screen gene loci that are significantly associated with the phenotype of resistance to Pythium stem rot, thereby achieving precise discovery of adaptor genes.

[0063] Genome-wide association analysis (GWAS) utilizes high-density molecular markers to perform genome-wide association scans between genomic variations in maize materials and comprehensive disease resistance phenotypes and standardized phenotypic data obtained through principal component analysis. Since reliable comprehensive disease resistance evaluation results have already been obtained through multi-dimensional phenotypic identification and standardization, there is no need to pre-define candidate genes. It can unbiasedly mine potential disease resistance genes at the whole genome level, and is particularly suitable for gene localization of complex disease resistance traits. Ultimately, it achieves precise discovery of adaptable genes, providing direct gene resources for subsequent functional verification of disease resistance genes and maize disease resistance breeding.

[0064] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0065] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for precise identification of maize stalk rot resistance phenotypes based on adaptive gene mining, characterized in that, Includes the following steps: Step S1: Select the corn material to be identified and plant it in a greenhouse. Use a uniform seedling substrate and cultivation management measures to ensure that the corn seedlings grow in a consistent manner. Step S2: Inoculate the highly pathogenic Pythium spp. or Fusarium graminearum strains onto a 9 mm diameter PDA plate containing 20 mL of sterile medium and incubate in the dark at 25°C for 5–7 days. When the mycelium covers the surface of the PDA plate, it is ready for inoculation. Step S3: Phenotypic data were collected on days 3, 7, 10 and 14 after inoculation. The collected indicators included disease severity indicators, physiological resistance indicators and pathogen spread indicators. Step S4: Standardize the collected multi-dimensional phenotypic data to eliminate differences in the units of different indicators; Step S5: Based on the standardized multi-dimensional phenotypic data, construct a comprehensive evaluation model for maize resistance to Phytophthora stalk rot. Step S6: Correlation analysis is performed between the comprehensive disease resistance score of the maize material and the standardized values ​​of each phenotypic index and the genome sequencing data of the maize material.

2. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 1, characterized in that: In step S1, sowing is carried out according to a uniform sowing density and sowing depth to ensure that each corn seedling has equal growth space and soil resources. Use the same watering frequency, amount and method to keep the soil moisture uniform and avoid some seedlings having too much or too little water. Maintain uniformity in the type, amount, timing, and method of fertilization to ensure that corn seedlings receive an equal amount of nutrients to meet their growth and development needs. Adopt consistent pest control measures to prevent and control potential pests in a timely manner, while avoiding the application of fungicides to prevent the pesticides from having different effects on seedling growth.

3. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 2, characterized in that: In step S2, the culture is homogenized into a paste in a regular food homogenizer at a ratio of 700 mL of water per 10 dishes. This paste is then used for injection inoculation. Inoculation is performed 5–10 days after pollination of the corn plants, using the stem injection method. The inoculation site is the first normally extending stem node above the ground. Use an electric drill to drill a hole at an angle downwards from 1 / 4 to 1 / 3 of the height of the stem node to the center of the stem. Then, use a large syringe without a needle to inject about 0.5 mL of homogenized inoculum into the inoculation hole and seal the hole with petroleum jelly.

4. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 3, characterized in that: In step S3, when collecting disease severity indicators, the length of stem decay and the proportion of decay area to the cross-sectional area of ​​the stem are measured. When collecting physiological resistance indicators, the relative chlorophyll content and proline content of leaves were measured. When collecting pathogen expansion indicators, real-time quantitative PCR was used to detect the relative content of pathogens in the stem base tissue.

5. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 4, characterized in that: In step S4, the Z-score standardization method is used to convert the original data of each indicator into a standardized value. The calculation formula is: Z = (X - μ) / σ, where X is the original data, μ is the mean of all samples of the indicator, and σ is the standard deviation of all samples of the indicator.

6. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 5, characterized in that: In step S5, principal component analysis is used to extract the main phenotypic components, calculate the weight of each principal component, and then obtain the overall disease resistance of each maize material.

7. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 6, characterized in that: Based on the overall disease resistance, the disease resistance of maize materials is divided into four levels: highly resistant, moderately resistant, susceptible, and highly susceptible.

8. The method for precise identification of maize stalk rot resistance phenotype based on adaptor gene mining according to claim 7, characterized in that: In step S6, genome-wide association analysis is used to screen gene loci that are significantly associated with the phenotype of resistance to Pythium stem rot, thereby achieving precise discovery of adaptor genes.

Images