Method for identifying aspergillus flavus-resistant peanut variety based on phenolic acid metabolism biomarkers

Abstract

The embodiment of the invention discloses a method for identifying an anti-aspergillus flavus peanut variety based on a phenolic acid metabolism biomarker, relates to the technical field of biological variety identification, and aims to solve the technical problem of relatively low screening efficiency of the anti-aspergillus flavus peanut variety in the prior art. The method comprises the following steps: measuring the total content value of a target phenolic acid biomarker in a target peanut seed; the target phenolic acid biomarker comprises isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid and 2, 4-dihydroxybenzoic acid; comparing the total phenolic acid content value of the target peanut seeds with a preset content value to obtain a comparison result; if the total phenolic acid content value of the target peanut seed is greater than or equal to the preset content value, determining that the target peanut seed is an aspergillus flavus-resistant peanut variety; if the total phenolic acid content value of the target peanut seeds is smaller than the preset content value, the target peanut seeds are not the aspergillus flavus-resistant peanut variety.

Description

Technical Field

[0001] The present application relates to the technical field of biological variety identification, and specifically to a method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers. Background Art

[0002] Peanut (Arachis hypogaea L.), also known as groundnut, is classified as an annual herbaceous plant belonging to the Leguminosae family, Papilionoideae subfamily, and Arachis genus. Peanut is one of the world's important oil and cash crops, and is widely planted in more than 100 countries. China's total peanut production and consumption have ranked first in the world for many years. The total planting area of ​​peanuts is about 70 million mu, and the total output exceeds 14 million tons. If 52% of the total peanut production is used for oil extraction (the rest is used for food, export, seed preservation, etc.), the annual output of peanut oil is about 3 million tons, second only to rapeseed oil. It is the second largest source of domestic vegetable oil, accounting for about a quarter of domestic vegetable oil production, and plays an important role in ensuring the supply of edible oil. At this stage, further increasing the supply of peanuts, improving product quality, and enhancing food safety are major needs for the development of domestic and foreign industries.

[0003] Aspergillus flavus is a facultative saprophytic fungus of the genus Aspergillus, with a yellow-green surface and colorless or brown on the back. Aspergillus flavus is a morphologically complex fungus that exists in different forms (hyphae and conidia or sclerotia) in different environments to adapt to environmental changes. It usually exists in the form of sclerotia in extreme environments. According to the size of the sclerotia, Aspergillus flavus can be divided into L-type strains and S-type strains. L-type strains produce aflatoxins B1 and B2, and S strains mainly produce aflatoxins B1, B2, G1, G2, etc. Aflatoxin pollution is an important factor affecting the safety of peanuts, restricting import and export trade, and threatening consumer health. Therefore, strengthening the prevention and control of toxin pollution is one of the difficulties and hotspots in promoting the sustainable development of the peanut industry. Practice has proved that cultivating and planting aflatoxin-resistant peanut varieties is the most economical and effective measure to prevent and control toxin pollution. However, the screening efficiency of aflatoxin-resistant peanut varieties in existing technologies is low. Summary of the invention

[0004] The main purpose of this application is to provide a method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers, aiming to solve the technical problem of low screening efficiency of aflatoxin-resistant peanut varieties in the prior art.

[0005] To achieve the above objectives, the technical solutions adopted in the embodiments of the present application are as follows:

[0006] In a first aspect, the present invention provides a method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers, comprising the following steps:

[0007] Determining the total content value of target phenolic acid biomarkers in target peanut seeds; the target phenolic acid biomarkers include isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid and 2,4-dihydroxybenzoic acid;

[0008] Comparing the total phenolic acid content value of the target peanut seeds with a preset content value to obtain a comparison result;

[0009] If the total phenolic acid content of the target peanut seeds is greater than or equal to the preset content value, the target peanut seeds are aflatoxin-resistant peanut variety;

[0010] If the total phenolic acid content of the target peanut seeds is less than the preset content value, the target peanut seeds are not aflatoxin-resistant peanut variety.

[0011] As some optional implementation modes of the present application, the preset content value is ≥4 mg / g.

[0012] As some optional embodiments of the present application, the target phenolic acid biomarker is obtained by the following steps:

[0013] Using broad targeted metabolomics technology, basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties are obtained, and the basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties are used as basic data sets; metabolites of peanut seeds of aflatoxin-resistant peanut varieties include amino acids and their derivatives, alkaloids, flavonoids, lignin coumarins, lipids, nucleotides and their derivatives, organic acids, phenolic acids, terpenoids, tannins and quinones;

[0014] After the peanut seeds of the aflatoxin-resistant peanut variety are inoculated with Aspergillus flavus, the differential metabolite content values ​​of the peanut seeds of the aflatoxin-resistant peanut variety after being infected with Aspergillus flavus are obtained, and the differential metabolite content values ​​are compared with the basic values ​​of the metabolites of the peanut seeds of the aflatoxin-resistant peanut variety to obtain the phenolic acid metabolites with increased specific expression levels after the peanut seeds of the aflatoxin-resistant peanut variety are infected with Aspergillus flavus;

[0015] The phenolic acid metabolites whose specific expression levels increase after the peanut seeds of the aflatoxin-resistant peanut variety are infected by Aspergillus flavus are subjected to an in vitro phenolic acid inhibition of Aspergillus flavus growth experiment to obtain phenolic acid biomarkers.

[0016] As some optional embodiments of the present application, the step of obtaining basic values ​​of peanut seed metabolites by using broadly targeted metabolomics technology and using the basic values ​​of peanut seed metabolites as a basic data set includes:

[0017] The dried peanut seeds are subjected to surface pretreatment and then ground to obtain dried seed powder;

[0018] Based on the dry seed powder, obtaining a UPLC-MS / MS analysis sample;

[0019] Perform UPLC-MS / MS analysis and substance qualitative and quantitative processing based on the UPLC-MS / MS analysis sample to obtain metabolite mass spectrometry analysis data;

[0020] The metabolite mass spectrometry analysis data are processed to obtain basic values ​​of peanut seed metabolites, and the basic values ​​of the peanut seed metabolites are used as a basic data set.

[0021] As some optional embodiments of the present application, the step of grinding the dried peanut seeds after surface pretreatment to obtain dried seed powder; and obtaining a UPLC-MS / MS analysis sample based on the dried seed powder comprises:

[0022] The dried seeds of the peanut variety "Yueyou 1823" were obtained, and the surface was disinfected with 75% alcohol for 5 minutes, rinsed with sterile water three times, and liquid nitrogen was added. The seeds were ground into powder using a grinder; 50 mg of sample powder was weighed using an electronic balance, and 1200 μL of 70% methanol water internal standard extract precooled at -20°C was added; the seeds were vortexed once every 30 minutes, each time for 30 seconds, and vortexed 6 times in total; after centrifugation, the supernatant was aspirated, the sample was filtered with a microporous filter membrane, and stored in an injection bottle to obtain a UPLC-MS / MS analysis sample for UPLC-MS / MS analysis;

[0023] The grinding process is performed at 30 Hz for 1.5 minutes.

[0024] The centrifugal treatment conditions are: a rotation speed of 12000 rpm for 3 minutes;

[0025] The microporous filter membrane has a pore size of 0.22 μm.

[0026] As some optional embodiments of the present application, the step of processing the metabolite mass spectrometry analysis data to obtain basic values ​​of peanut seed metabolites, and using the basic values ​​of peanut seed metabolites as a basic data set includes:

[0027] Based on ALL_sample_data_raw.xlsx, 1 / 5 of the minimum value of each row of metabolites was used to fill the missing values ​​in the data; the CV value of the quality control sample was calculated, and the metabolites with a CV value less than 0.5 were screened out to form the final data file ALL_sample_data.xlsx;

[0028] Analyst 1.6.3 software was used to process the metabolite mass spectrometry data. The characteristic ions of each metabolite were screened out by a triple quadrupole mass spectrometer, and the signal intensity of the characteristic ions was recorded in the detector. MultiQuant software was used to integrate and correct the chromatographic peaks of the sample's mass spectrometry files. The peak area of ​​each chromatographic peak reflects the relative content of the corresponding metabolite.

[0029] Export the area integrated data of all chromatographic peaks as the basic data set.

[0030] As some optional embodiments of the present application, after inoculating the peanut seeds with Aspergillus flavus, obtaining the differential metabolite content value of the peanut seeds after being infected with Aspergillus flavus, comparing it with the basic value of the peanut seed metabolites, and obtaining the phenolic acid metabolites with increased specific expression levels after the peanut seeds are infected with Aspergillus flavus, comprises:

[0031] After activating the Aspergillus flavus spores, an Aspergillus flavus spore suspension is prepared;

[0032] After inoculating the Aspergillus flavus spore suspension into peanut seeds, the seeds were quickly frozen with liquid nitrogen and ground into powder, and a QC sample was prepared;

[0033] Based on the QC sample, UPLC-HRMS detection was performed to obtain the differential metabolite content values ​​of the peanut seeds after being infected by Aspergillus flavus, which were compared with the basic values ​​of the peanut seed metabolites to obtain the phenolic acid metabolites with specifically increased expression levels after the peanut seeds were infected by Aspergillus flavus.

[0034] As some optional embodiments of the present application, the step of preparing a suspension of Aspergillus flavus spores after activating the Aspergillus flavus spores comprises:

[0035] Prepare PDA culture medium, pour the prepared culture solution into a 90 mm sterile culture dish and wait for solidification; take the Aspergillus flavus strain out of the -80°C refrigerator and thaw at room temperature; use a pipette to draw 10 μl of spore droplets onto a PDA plate, seal the plate with a sealing film and place it in an artificial climate box at 30°C for 7 days to obtain activated Aspergillus flavus spores;

[0036] Place the PDA plate full of spores in a biosafety cabinet, wash off the Aspergillus flavus conidia from the culture medium with 0.1% Tween 80 solution, collect them in a sterile 100 ml Erlenmeyer flask, place the Erlenmeyer flask on a vortex oscillator and fully shake it to make the Aspergillus flavus spores evenly distributed in the Tween solution, and obtain an Aspergillus flavus spore suspension;

[0037] Add 10 μl of Aspergillus flavus spore suspension to the hemocytometer, observe the concentration of conidia under a microscope, dilute the concentration of the spore suspension to 2×106 CFU / ml, and the prepared spore suspension will be used for artificial inoculation.

[0038] As some optional embodiments of the present application, the step of inoculating the Aspergillus flavus spore suspension into peanut seeds, quick-freezing with liquid nitrogen and grinding into powder, and preparing QC samples comprises:

[0039] Weigh 50 mg (± 5 mg) of sample, add 500 μL 80% ice methanol solution into a 2.0 mL EP tube, put a small amount of steel beads and crush with a grinder, put it in a -20 ° C refrigerator for 30 min to precipitate the protein in the sample, centrifuge at 20000g for 15 min, transfer 400 μL of supernatant to another EP tube, freeze and dry the supernatant, add 100 μL 50% ice methanol solution to dissolve it, centrifuge at 20000g for 15 min, transfer the supernatant to an injection bottle for UPLC-HRMS detection, take 10-20 μL of extract from each sample and mix it into QC samples for UPLC-HRMS detection.

[0040] As some optional embodiments of the present application, the conditions for UPLC-HRMS detection are:

[0041] The liquid phase system is the ACQUITY UPLC System Ⅰ Class of Waters Company;

[0042] The chromatographic analysis column used was ACQUITY UPLC T3, 100 mm*2.1 mm, 1.8 μm, Waters, UK;

[0043] During collection, the column temperature was set to 40 degrees and the flow rate was 0.3 ml / min;

[0044] The mobile phase used was phase A: water + 5mmol / L ammonium acetate + 5mmol / L acetic acid; phase B: LC-MS acetonitrile. 5) The liquid phase gradient was set as:

[0045]

[0046] The mass spectrometry conditions were:

[0047] The high-resolution mass spectrometer used was a TripleTOF 6600, SCIEX, Framingham, MA, USA time-of-flight mass spectrometer;

[0048] Each sample was collected once in positive ion mode and once in negative ion mode; the shielding gas pressure of the ion source was 30 PSI, and the pressures of gas 1-auxiliary gas and gas 2-sheath gas were both set to 60 PSI; the source temperature was 500°C; the voltage was positive 5000 V in positive ion mode and -4500 V in negative ion mode;

[0049] The data acquisition mode was IDA mode; in one acquisition cycle, the primary acquisition range was 60-1200 Daltons, the primary acquisition time was 150 ms, and then the first 12 signal ions with signal accumulation intensity exceeding 100 were selected from the primary spectrum for secondary fragmentation scanning; the dynamic exclusion was set for 4 seconds;

[0050] During the acquisition process, the instrument accuracy was calibrated every 20 samples. At the same time, the QC sample was scanned every 10 samples. The quality difference of the QC samples was used to correct the systematic error of the entire batch of experiments.

[0051] Compared with the prior art, the beneficial effects of this application are:

[0052] Application of comprehensive targeted metabolomics technology: Based on the extensive targeted metabolomics technology, this application systematically identified the basic data of endogenous metabolites in peanut seeds (1,462 metabolites) and the differentially expressed metabolites (425 metabolites) after infection with Aspergillus flavus for the first time. Compared with the traditional technical means that rely on limited metabolite research, this solution has significantly improved the comprehensiveness and scientificity of the data.

[0053] Discovery and application of phenolic acid biomarkers: This application screened out five phenolic acid metabolites for the first time. These substances can significantly inhibit the growth of Aspergillus flavus in vitro and are clearly defined as metabolic biomarkers for resistance to Aspergillus flavus. Compared with the existing traditional methods that only rely on resistance evaluation or large-scale inoculation, this application directly provides efficient and accurate biomarkers, providing clear indicators for the screening of peanut varieties resistant to Aspergillus flavus.

[0054] Efficient and environmentally friendly detection method: This application does not require large-scale aflatoxin inoculation experiments. By detecting the content of phenolic acid metabolites, resistant peanut varieties can be accurately screened. This method avoids complex biological inoculation tests, significantly reduces the risks to the environment and experimenters, and realizes a green assessment of aflatoxin infection resistance.

[0055] Technical basis for screening and breeding of aflatoxin-resistant germplasm: This application not only identified metabolic biomarkers, but also clarified their use as important indicators for screening highly resistant peanut varieties. This method provides an innovative technical path for the subsequent development, identification and breeding of aflatoxin-resistant peanut varieties, and has significantly improved breeding efficiency and scientific guidance compared to existing technologies.

[0056] Systematic integration and differential analysis of multidimensional metabolites: Through the systematic integration of basic metabolome data and differential expression data after aflatoxin infection, this application achieves accurate metabolite classification and targeted analysis, especially in the functional research of phenolic acid metabolites, filling the gap in the existing technology and reflecting a higher research depth.

[0057] In summary, this application demonstrates higher scientificity, environmental protection and practicality from metabolomics analysis to biomarker screening, functional verification and practical application, laying an important foundation for the breeding and promotion of aflatoxin-resistant peanut varieties. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 The distribution ratio and expression level distribution diagram of 1462 metabolites of the target peanut seeds involved in the embodiments of the present application;

[0059] Figure 2 This is a differential metabolite identification diagram of the target peanut seeds involved in the embodiment of the present application after inoculation with Aspergillus flavus;

[0060] Figure 3 A schematic diagram of identifying 10 highly expressed phenolic acid substances from 425 differential metabolites of the target peanut seeds involved in the embodiment of the present application after being infected by Aspergillus flavus;

[0061] Figure 4 This is a graph showing that five phenolic acid substances in target peanut seeds involved in the examples of the present application inhibit the growth of Aspergillus flavus in vitro;

[0062] Figure 5 This is a graph showing the correlation between the total phenolic acid content of peanuts and the resistance to aflatoxin and the difference between the materials resistant to five phenolic acid substances involved in the embodiments of the present application;

[0063] Figure 6 It is a schematic diagram of the technical solution involved in the embodiment of the present application. DETAILED DESCRIPTION

[0064] It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0065] Research has found that after peanuts are shelled, the seed coat becomes a natural barrier against aflatoxin infection. The integrity of the seed coat, the tight arrangement of palisade cells, the wax content, the thickness of the cuticle, the presence of cracks and cavities, and the composition of the chemical composition of the seed coat jointly determine its ability to resist aflatoxin infection. The biochemical substances contained in the seed coat, such as tannic acid and phenolic compounds, have a significant resistance to the infection of aflatoxin. Studies have shown that tannic acid in the seed coat can effectively inhibit the germination of aflatoxin spores. There are differences in the tannic acid content in the seed coat of different varieties of peanuts, which leads to different manifestations of their resistance to aflatoxin. Methylcatechin (a phenolic compound) extracted from peanut seed coats has shown a strong ability to inhibit the growth and toxin production of aflatoxin. At present, the method of inoculating Aspergillus flavus and evaluating the surface area of ​​peanut seeds infected with Aspergillus flavus is used to identify resistant varieties or germplasm resources, such as the highly resistant peanut varieties Zhonghua No. 6, Yueyou No. 9, Yueyou No. 20, Xinhui Xiaoli, Zhanqiu 48, Meixian Hongyi, etc. However, artificial inoculation of Aspergillus flavus poses risks to the environment and the health of operators. Therefore, in the current breeding process of peanut varieties resistant to Aspergillus flavus, it is particularly necessary to adopt an environmentally friendly identification method.

[0066] In order to overcome the shortcomings of the prior art, the present application provides a technical method for identifying peanut varieties resistant to aflatoxin based on phenolic acid metabolic biomarkers, based on the detection of phenolic acid metabolic marker content, and then directly determining the phenolic acid metabolic marker content in peanut seeds to identify, screen, and cultivate peanut materials, germplasm resources, and varieties resistant to aflatoxin infection. That is:

[0067] like Figure 6 As shown, the method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers comprises the following steps:

[0068] Step S11, determining the total content value of target phenolic acid biomarkers in target peanut seeds; the target phenolic acid biomarkers include isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid and 2,4-dihydroxybenzoic acid;

[0069] Step S12, comparing the total phenolic acid content value of the target peanut seeds with a preset content value to obtain a comparison result; the preset content value is ≥ 4 mg / g; if the total phenolic acid content value of the target peanut seeds is greater than or equal to the preset content value, the target peanut seeds are aflatoxin-resistant peanut variety; if the total phenolic acid content value of the target peanut seeds is less than the preset content value, the target peanut seeds are not aflatoxin-resistant peanut variety.

[0070] It should be noted that the target phenolic acid biomarker is obtained by the following steps:

[0071] Step S21, using broad targeted metabolomics technology to obtain basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties, and using the basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties as basic data sets; the metabolites of peanut seeds of aflatoxin-resistant peanut varieties include amino acids and their derivatives, alkaloids, flavonoids, lignin coumarins, lipids, nucleotides and their derivatives, organic acids, phenolic acids, terpenoids, tannins and quinones;

[0072] Step S22, after inoculating the peanut seeds of the aflatoxin-resistant peanut variety with Aspergillus flavus, obtain the differential metabolite content value of the peanut seeds of the aflatoxin-resistant peanut variety after being infected with Aspergillus flavus, compare the differential metabolite content value with the basic value of the metabolites of the peanut seeds of the aflatoxin-resistant peanut variety, and obtain the phenolic acid metabolites with increased specific expression level after the peanut seeds of the aflatoxin-resistant peanut variety are infected with Aspergillus flavus;

[0073] Step S23, subjecting the peanut seeds of the aflatoxin-resistant peanut variety to an in vitro phenolic acid inhibition of the growth of Aspergillus flavus experiment, wherein the phenolic acid metabolites whose specific expression levels increase after being infected by Aspergillus flavus are subjected to a phenolic acid biomarker in order to obtain the phenolic acid biomarkers.

[0074] In a further embodiment, the step of obtaining the basic values ​​of peanut seed metabolites by using the broad targeted metabolomics technology in step S21, and using the basic values ​​of peanut seed metabolites as the basic data set, comprises:

[0075] Step S211, grinding the dried peanut seeds after surface pretreatment to obtain dry seed powder; that is, obtaining dry seeds of the peanut variety "Yueyou 1823", disinfecting the surface with 75% alcohol for 5 minutes, rinsing with sterile water three times, adding liquid nitrogen, and grinding them into powder using a grinder; weighing 50 mg of sample powder using an electronic balance, adding 1200 μL of -20°C precooled 70% methanol water internal standard extract; vortexing once every 30 minutes, each time for 30 seconds, and vortexing 6 times in total; after centrifugation, absorbing the supernatant, filtering the sample with a microporous filter membrane, and storing it in a sample injection bottle to obtain a UPLC-MS / MS analysis sample for UPLC-MS / MS analysis; the grinding treatment conditions are 30 Hz, 1.5 minutes; the centrifugation treatment conditions are a rotation speed of 12000 rpm, 3 minutes; the microporous filter membrane is 0.22 μm pore size.

[0076] Step S212, obtaining a UPLC-MS / MS analysis sample based on the dry seed powder;

[0077] Step S213, performing UPLC-MS / MS analysis and substance qualitative and quantitative processing based on the UPLC-MS / MS analysis sample to obtain metabolite mass spectrometry analysis data;

[0078] Step S214, processing the metabolite mass spectrometry analysis data to obtain basic values ​​of peanut seed metabolites, and using the basic values ​​of peanut seed metabolites as the basic data set; that is, on the basis of ALL_sample_data_raw.xlsx, using 1 / 5 of the minimum value of each row of metabolites to fill the missing values ​​in the data; and calculating the CV value of the quality control sample, screening out metabolites with a CV value less than 0.5 to form the final data file ALL_sample_data.xlsx; using Analyst1.6.3 software to process the metabolite mass spectrometry analysis data, screening out the characteristic ions of each metabolite through a triple quadrupole mass spectrometer, and recording the signal intensity of the characteristic ions in the detector; using MultiQuant software to integrate and correct the chromatographic peaks of the sample's offline mass spectrometry file, the peak area of ​​each chromatographic peak reflects the relative content of the corresponding metabolite; exporting the area integration data of all chromatographic peaks as the basic data set.

[0079] In a further embodiment, the step S22 of inoculating the peanut seeds with Aspergillus flavus, obtaining the content value of differential metabolites of the peanut seeds after being infected with Aspergillus flavus, comparing the content value with the basic value of the metabolites of the peanut seeds, and obtaining the phenolic acid metabolites with specifically increased expression levels after the peanut seeds are infected with Aspergillus flavus, comprises: activating the Aspergillus flavus spores and preparing an Aspergillus flavus spore suspension; inoculating the Aspergillus flavus spore suspension into peanut seeds, quick-freezing with liquid nitrogen and grinding it into powder, and preparing a QC sample; performing UPLC-HRMS detection based on the QC sample to obtain the content value of differential metabolites of the peanut seeds after being infected with Aspergillus flavus, comparing the content value with the basic value of the metabolites of the peanut seeds, and obtaining the phenolic acid metabolites with specifically increased expression levels after the peanut seeds are infected with Aspergillus flavus.

[0080] Furthermore, after the Aspergillus flavus spores are activated, the step of preparing an Aspergillus flavus spore suspension comprises: preparing a PDA culture medium, pouring the prepared culture solution into a 90 mm sterile culture dish and waiting for solidification; taking the Aspergillus flavus strain out of a -80°C refrigerator and thawing it at room temperature; using a pipette to draw 10 μl of spore droplets onto a PDA plate, sealing the plate with a sealing film and placing it in an artificial climate box at a constant temperature of 30°C for 7 days to obtain activated Aspergillus flavus spores; placing the PDA plate full of spores in a biological safety In the whole cabinet, use 0.1% Tween 80 solution to wash off the Aspergillus flavus conidia from the culture medium, and collect them into a sterile 100ml Erlenmeyer flask, place the Erlenmeyer flask on a vortex oscillator and fully shake it to make the Aspergillus flavus spores evenly distributed in the Tween solution to obtain an Aspergillus flavus spore suspension; add 10μl of the Aspergillus flavus spore suspension to the hemocytometer, observe the concentration of conidia under a microscope, dilute the concentration of the spore suspension to 2×106CFU / ml, and the prepared spore suspension will be used for artificial inoculation.

[0081] Furthermore, after the Aspergillus flavus spore suspension is inoculated into peanut seeds, it is quickly frozen with liquid nitrogen and ground into powder to prepare a QC sample, which includes: weighing 50 mg (±5 mg) of the sample, adding 500 μL of 80% ice methanol solution into a 2.0 mL EP tube, adding a small amount of steel beads and crushing it with a grinder, placing it in a -20°C refrigerator for 30 minutes to precipitate protein in the sample, centrifuging it at 20,000 g for 15 minutes, transferring 400 μL of the supernatant to another EP tube, freezing and draining the supernatant, adding 100 μL of 50% ice methanol solution to dissolve it, centrifuging it at 20,000 g for 15 minutes, transferring the supernatant to an injection bottle for UPLC-HRMS detection, and taking 10 to 20 μL of the extract from each sample to mix into a QC sample for UPLC-HRMS detection.

[0082] The conditions for the UPLC-HRMS detection are:

[0083] The liquid phase system is the ACQUITY UPLC System Ⅰ Class of Waters Company;

[0084] The chromatographic analysis column used was ACQUITY UPLC T3, 100 mm*2.1 mm, 1.8 μm, Waters, UK;

[0085] During collection, the column temperature was set to 40 degrees and the flow rate was 0.3 ml / min;

[0086] The mobile phase used was phase A: water + 5mmol / L ammonium acetate + 5mmol / L acetic acid; phase B: LC-MS acetonitrile;

[0087] The liquid phase gradient was set as:

[0088]

[0089] The mass spectrometry conditions were:

[0090] The high-resolution mass spectrometer used was a TripleTOF 6600, SCIEX, Framingham, MA, USA time-of-flight mass spectrometer;

[0091] Each sample was collected once in positive ion mode and once in negative ion mode; the shielding gas pressure of the ion source was 30 PSI, and the pressures of gas 1-auxiliary gas and gas 2-sheath gas were both set to 60 PSI; the source temperature was 500°C; the voltage was positive 5000 V in positive ion mode and -4500 V in negative ion mode;

[0092] The data acquisition mode was IDA mode; in one acquisition cycle, the primary acquisition range was 60-1200 Daltons, the primary acquisition time was 150 ms, and then the first 12 signal ions with signal accumulation intensity exceeding 100 were selected from the primary spectrum for secondary fragmentation scanning; the dynamic exclusion was set for 4 seconds;

[0093] During the acquisition process, the instrument accuracy was calibrated every 20 samples. At the same time, the QC sample was scanned every 10 samples. The quality difference of the QC samples was used to correct the systematic error of the entire batch of experiments.

[0094] Below, in conjunction with specific embodiments, the technical solution described in the present application is described in detail to facilitate those skilled in the art to understand the technical solution of the present application:

[0095] Example 1 Identification of metabolites in peanut seeds

[0096] The dry seeds of the peanut variety "Yueyou 1823" were surface disinfected with 75% ethanol for 5 minutes, then rinsed three times with sterile water, and then liquid nitrogen was added. The samples were ground at a frequency of 30 Hz for 1.5 minutes using a grinder (MM 400, Retsch) until the samples were powdered. 50 mg of sample powder was accurately weighed using an electronic balance (MS105DM), and 1200 μL of 70% methanol water internal standard extract precooled at -20 ° C was added (if the sample was less than 50 mg, 1200 μL of extractant was added per 50 mg sample). The samples were placed on a vortex oscillator and vortexed once every 30 minutes for 30 seconds each time, for a total of 6 vortexes. Afterwards, the supernatant was separated by a centrifuge (speed 12000 rpm, 3 minutes), and the samples were filtered using a microporous filter membrane (pore size 0.22 μm), and the filtered samples were stored in a sample injection bottle for subsequent UPLC-MS / MS analysis.

[0097] Chromatography mass spectrometry acquisition conditions: The data acquisition instrument system mainly includes Ultra Performance Liquid Chromatography (UPLC) (ExionLC TM AD, https: / / sciex.com.cn / ) and tandem mass spectrometry (MS / MS).

[0098] The liquid phase conditions mainly include: 1) Chromatographic column: Agilent SB-C181.8μm, 2.1mm*100mm; 2) Mobile phase: Phase A is ultrapure water (added with 0.1% formic acid), Phase B is acetonitrile (added with 0.1% formic acid); 3) Elution gradient: 0.00min B phase ratio is 5%, within 9.00min, the B phase ratio linearly increases to 95%, and maintains at 95% for 1min, 10.00-11.10min, the B phase ratio is reduced to 5%, and balanced at 5% for 14min; 4) Flow rate 0.35mL / min; column temperature 40℃; injection volume 2μL. The mass spectrometry conditions mainly include: electrospray ionization (ESI) temperature 550℃; ion spray voltage (IS) 5500V (positive ion mode) / -4500V (negative ion mode); ion source gas I (GSI), gas II (GSII) and curtain gas (CUR) were set to 50, 60 and 25psi respectively, and the collision induced ionization parameter was set to high. The QQQ scan used the MRM mode and the collision gas (nitrogen) was set to medium. Through further optimization of declustering potential (DP) and collision energy (CE), the DP and CE of each MRM ion pair were completed. According to the metabolites eluted in each period, a specific set of MRM ion pairs was monitored in each period.

[0099] Theoretical basis for qualitative and quantitative analysis of metabolites: This application uses secondary spectrum information based on MWDB (metware database) for qualitative analysis of substances, excluding isotope signals, repeated signals containing K+, Na+, and NH4+ ions, and repeated signals of fragment ions from other larger molecular weight substances in the process. The quantitative analysis of metabolites is achieved through the multiple reaction monitoring (MRM) mode of the triple quadrupole mass spectrometer. In the MRM mode, the quadrupole first screens the precursor ions (parent ions) of the target substance to exclude the interference of other molecular weight substances; then, the precursor ions are induced to ionize and break into multiple fragment ions in the collision chamber, and specific characteristic fragment ions are further screened out by the triple quadrupole, thereby excluding the interference of non-target ions and ensuring the accuracy and repeatability of the quantitative analysis. After obtaining the metabolite mass spectrometry analysis data of different samples, the peak area of ​​the chromatographic peaks of all substances is integrated, and the mass spectrometry peaks of the same metabolite in different samples are integrated and corrected.

[0100] Data processing: Based on the original data set ALL_sample_data_raw.xlsx, this application first fills the missing values ​​with 1 / 5 of the minimum value of each row (metabolite), then calculates the coefficient of variation (CV) of the quality control (QC) sample, and screens out metabolites with CV values ​​less than 0.5, thereby obtaining the final data file ALL_sample_data.xlsx. The processing of mass spectrometry data is completed with the aid of Analyst 1.6.3 software. The characteristic ions of each metabolite are screened out by a triple quadrupole mass spectrometer, and the signal intensity (CPS) of these characteristic ions is obtained in the detector. The off-machine mass spectrometry file of the sample is opened using MultiQuant software to perform integration and correction of the chromatographic peaks. The peak area (Area) of each chromatographic peak reflects the relative content of the corresponding metabolite. In order to compare the differences in the material content of all detected metabolites in different samples, this application calibrates the chromatographic peaks detected in different samples based on the retention time and peak type information of the metabolites to ensure the accuracy of qualitative and quantitative analysis.

[0101] analyze Figure 1The data shown show that the present application successfully identified 1462 metabolites in peanut seeds. These metabolites can be mainly classified into 11 major categories, including amino acids and their derivatives (accounting for 20.93%), alkaloids (accounting for 8.89%), flavonoids (accounting for 11.83%), lignin and coumarin (accounting for 2.26%), lipids (accounting for 14.36%), nucleotides and their derivatives (accounting for 5.34%), organic acids (accounting for 4.65%), phenolic acids (accounting for 10.67%), terpenoids (accounting for 4.99%), tannins (accounting for 0.89%) and quinones (accounting for 0.96%);

[0102] Example 2 Peanut seeds inoculated with Aspergillus flavus

[0103] Activation process of Aspergillus flavus spores: First, prepare PDA culture medium, pour the prepared culture solution into a 90mm diameter sterile culture dish, and wait for it to solidify. Then, take out the Aspergillus flavus strain from the -80℃ refrigerator and thaw it at room temperature. Use a pipette to draw 10μl of spore solution, drop it onto the PDA plate, seal the plate with sealing film, and place it in an artificial climate box and culture it at a constant temperature of 30℃ for 7 days.

[0104] Preparation of Aspergillus flavus spore suspension: After the activation of Aspergillus flavus spores is completed, place the PDA plate full of spores in a biosafety cabinet, use 0.1% Tween 80 solution to elute the Aspergillus flavus conidia from the surface of the culture medium, and collect them in a sterile 100ml Erlenmeyer flask. Place the Erlenmeyer flask on a vortex oscillator and shake it thoroughly to ensure that the Aspergillus flavus spores are evenly dispersed in the Tween solution. Use a hemocytometer to add 10μl of Aspergillus flavus spore suspension, observe the concentration of conidia under a microscope, and adjust the concentration of the spore suspension to 2×10^6CFU / ml for subsequent artificial inoculation.

[0105] Artificial inoculation experiment of peanut seeds: 20 peanut seeds were selected, and the surface of the seeds was disinfected with 75% ethanol solution for 5 minutes, and then rinsed with sterile water 3 times. The rinsed seeds were drained and placed in a 90mm diameter culture dish, 1ml of Aspergillus flavus spore suspension was added, and the culture dish was gently shaken to ensure that the spore suspension was in full contact with the seeds. The culture dish was placed in an incubator and cultured at 30°C in the dark for 7 days.

[0106] Grading standards for aflatoxin kernel contamination: Based on the coverage of aflatoxin spores on the seed kernel surface, the infection degree is divided into 5 levels: Level 0, no hyphae or aflatoxin spores on the seed kernel; Level 1, a small amount of aflatoxin hyphae attached to the seed kernel; Level 2, a large amount of hyphae or aflatoxin spores attached to the seed kernel with a coverage rate of 0% to 20%; Level 3, the coverage rate of aflatoxin spores on the seed kernel is greater than 20% and less than 50%; Level 4, the coverage rate of aflatoxin spores on the seed kernel is greater than 50%.

[0107] Further analysis showed that according to Figure 2 As shown in the figure, the seed contamination level reached level 2 3 days after aflatoxin inoculation, while the aflatoxin resistance of peanut seeds on the 6th day after inoculation was level 4. A total of 895 differential metabolites were identified between early infection and uninfected peanut seeds, of which 403 were upregulated and 492 were downregulated. Compared with the early infection, a total of 671 differential metabolites were identified in the late infection, of which 521 were upregulated and 150 were downregulated.

[0108] Example 3 Identification of differential metabolites in peanut seeds infected with Aspergillus flavus

[0109] After infection by Aspergillus flavus, the processing procedures for peanut seeds are as follows:

[0110] 1) Accurately weigh 50 mg (±5 mg) of sample;

[0111] 2) Add 500 μL of 80% ice methanol solution to a 2.0 mL EP tube, add a small amount of steel beads, and grind using a grinder;

[0112] 3) Place the sample in a -20°C refrigerator for 30 minutes to precipitate the protein;

[0113] 4) Centrifuge at 20,000 g for 15 min, and then transfer 400 μL of the supernatant to another EP tube;

[0114] 5) After the supernatant is frozen and dried, 100 μL of 50% ice methanol solution is added for reconstitution;

[0115] 6) Centrifuge again at 20,000 g for 15 min, and transfer the supernatant to a sample injection bottle for UPLC-HRMS detection;

[0116] 7) Take 10-20 μL of the extract from each sample and mix them into QC samples for UPLC-HRMS detection.

[0117] The ultra-high performance liquid chromatography (UPLC) analysis conditions used in this application are as follows: 1) The liquid phase system used for data acquisition is the ACQUITY UPLC System I Class ultra-high pressure liquid phase system of Waters; 2) The chromatographic analysis column uses the ACQUITY UPLC T3 model (specifications are 100 mm×2.1 mm, 1.8 μm, Waters, UK); 3) During the chromatographic analysis, the column temperature is set to 40°C and the flow rate is 0.3 mL / min; 4) The mobile phase A is an aqueous solution containing 5 mmol / L ammonium acetate and 5 mmol / L acetic acid, and the mobile phase B is LC-MS grade acetonitrile; 5) The liquid phase gradient is set as follows:

[0118]

[0119] The mass spectrometry analysis conditions are as follows: 1) The high-resolution mass spectrometer used is the TripleTOF 6600 time-of-flight mass spectrometer produced by SCIEX. Each sample was collected in positive ion mode and negative ion mode respectively; 2) The shielding gas pressure of the ion source was set to 30PSI, the pressures of gas 1 (auxiliary gas) and gas 2 (sheath gas) were both 60PSI, and the source temperature was set to 500°C. In positive ion mode, the voltage was set to +5000V, and in negative ion mode, the voltage was set to -4500V; 3) The data acquisition mode was IDA (information-dependent acquisition) mode. The primary mass spectrometry acquisition range was 60-1200Da, and the acquisition time was 150ms. Subsequently, the first 12 signal ions with signal intensities exceeding 100 were selected from the primary mass spectrum for secondary mass spectrometry fragmentation scanning. The dynamic exclusion time was set to 4 seconds; 4) During the data acquisition process, the instrument accuracy was calibrated every 20 samples, and the quality control (QC) samples were scanned every 10 samples to correct the systematic error of the whole batch experiment with the mass difference between QC samples. The data processing process was the same as the raw data processing of normal peanut seed metabolites. The subsequent metabolite content data was processed using the UV (unit variance scaling) method. The heat map was drawn using the ComplexHeatmap package of R software, and a hierarchical cluster analysis (HCA) of the accumulation pattern of metabolites among different samples was performed.

[0120] Metabolomics data presents high-dimensional and large-scale characteristics. Therefore, it is necessary to use univariate statistical analysis and multivariate statistical analysis methods in combination, and conduct in-depth analysis from multiple dimensions based on data characteristics in order to accurately identify differential metabolites. Univariate statistical analysis methods include hypothesis testing and fold change (FC) analysis. Multivariate statistical analysis methods include principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA), etc. Based on the variable importance projection (VIP) obtained by the OPLS-DA model (biological replicates ≥ 3), metabolites that differ between different varieties or tissues can be preliminarily screened out. At the same time, combined with the P-value / FDR (biological replicates ≥ 2) or FC value of the univariate analysis, differential metabolites can be further screened out. The screening criteria for differential metabolites are as follows: 1) Select metabolites with VIP>1. The VIP value reflects the influence of the corresponding metabolite in the classification and discrimination of each group of samples in the model. Metabolites with VIP>1 are generally considered to have significant inter-group differences. 2) Select metabolites with fold change ≥ 2 and fold change ≤ 0.5. If the difference between the control group and the experimental group is more than 2 times or less than 0.5, the metabolite is considered to have a significant difference. The original relative content of the differential metabolites identified by the screening criteria was processed by UV (Unit Variance Scaling) row by row, and the differential metabolite expression heat map was drawn using the R software package.

[0121] Further analysis Figure 3 The results show that a total of 425 differential metabolites appeared in both the early and late stages of aflatoxin inoculation, of which 239 metabolites showed a continuous increase in expression from the early to the late stages of inoculation, while 88 metabolites showed a continuous decrease in expression. In addition, 10 phenolic acid metabolites with continuous high expression levels were identified, and their contents continued to increase during seed infection.

[0122] Example 4 Identification of the inhibitory effect of phenolic acid metabolites from peanut seeds on the growth of Aspergillus flavus

[0123] This application targets five phenolic acid compounds in peanut seeds - isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid, and 2,4-dihydroxybenzoic acid, which are dissolved in 10 ml of dimethyl sulfoxide (DMSO) to prepare a solution with a final concentration of 0.1 mol / L. Subsequently, 1 ml of the phenolic acid solution was mixed with 10 ml of potato agarose solid culture medium, and 10 microliters of Aspergillus flavus strain AF2022 was inoculated in the center of the culture medium. A culture medium with DMSO and sterile water as controls was set up and cultured at 30°C for 7 days. During this period, the growth area and number of spores of Aspergillus flavus were measured on the 3rd and 6th days, respectively.

[0124] Further analysis Figure 4 The data shown in the figure show the inhibitory effects of five phenolic acid compounds - isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid, and 2,4-dihydroxybenzoic acid - on the growth of Aspergillus flavus in vitro. The experimental results show that these five phenolic acid substances can effectively inhibit the growth of Aspergillus flavus, among which 2,4-dihydroxybenzoic acid has a particularly significant inhibitory effect on the number of Aspergillus flavus spores.

[0125] Example 5 Detection of total phenolic acid content in seeds of different varieties of peanuts

[0126] This application adopts the determination of total phenol content in peanut seeds by spectrophotometry as specified in the Anhui Food Industry Association Group Standard T_AHFIA 005-2018. The reagents used in the experiment include 60% ethanol solution, Folin phenol reagent, 15% sodium carbonate solution and 1000ug / ml gallic acid standard solution, the latter of which was diluted into a standard series of solutions of 0, 40, 80, 120, 160, and 200ug / ml before the experiment. The instruments required for the experiment include a one-tenth analytical balance, a water bath, an ultraviolet spectrophotometer, and a high-throughput grinder. The experimental steps are as follows: take an appropriate amount of peanut seed sample and place it in a 10ml centrifuge tube, add 5mL60% ethanol, use a high-throughput grinder for grinding and homogenization, and then perform ultrasonic extraction for 30 minutes. After extraction, take out the sample, cool it, fix the volume, filter it, and set the filtrate aside. Take 0.1mL of filtrate (add 60% ethanol to 1ml) or 1ml of gallic acid standard solution, add 2.5ml of forlin phenol and 2.5ml of 15% sodium carbonate solution respectively, and then dilute to 10mL with distilled water. After mixing evenly, heat in a 40℃ water bath for 60 minutes, let it stand and cool for 20 minutes, and then measure the absorbance at a wavelength of 778nm. According to the content obtained from the standard curve, calculate the total phenol content in the sample. The calculation formula is: total phenol content (mg / g) = C×N×0.001 / m, where C is the total phenol content (μg) obtained from the standard curve, N is the fractionation multiple, and m is the sample weight (g).

[0127] Further analysis Figure 5 The data shown show that among the 20 peanut germplasm resources, 10 showed resistance, with a resistance index of 0.072 and a total phenolic acid content of 3.77 mg / g for the resistant materials; the other 10 were susceptible materials, with an average resistance index of 0.865 and an average total phenolic acid content of 3.09 mg / g for the susceptible materials. It can be seen that peanut varieties with higher total phenolic acid content have stronger resistance to aflatoxin. In addition, the detection results of 5 phenolic acid metabolites between resistant and susceptible materials showed that the contents of sinapinic acid, 2-hydroxyphenylacetic acid and 2,4-dihydroxybenzoic acid in the seeds of resistant materials were significantly higher than those in the peanut seeds of susceptible materials.

[0128] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers, characterized in that: The following steps are involved: Determining the total content value of target phenolic acid biomarkers in target peanut seeds; the target phenolic acid biomarkers include isoferulic acid, sinapic acid, p-coumaric acid, 2-hydroxyphenylacetic acid and 2,4-dihydroxybenzoic acid; Comparing the total phenolic acid content value of the target peanut seeds with a preset content value to obtain a comparison result; If the total phenolic acid content of the target peanut seeds is greater than or equal to the preset content value, the target peanut seeds are aflatoxin-resistant peanut variety; If the total phenolic acid content of the target peanut seeds is less than the preset content value, the target peanut seeds are not aflatoxin-resistant peanut variety.

2. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 1, characterized in that: The preset content value is ≥4 mg / g.

3. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 1, characterized in that: The target phenolic acid biomarker is obtained by the following steps: Using broad targeted metabolomics technology, basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties are obtained, and the basic values ​​of metabolites of peanut seeds of aflatoxin-resistant peanut varieties are used as basic data sets; metabolites of peanut seeds of aflatoxin-resistant peanut varieties include amino acids and their derivatives, alkaloids, flavonoids, lignin coumarins, lipids, nucleotides and their derivatives, organic acids, phenolic acids, terpenoids, tannins and quinones; After the peanut seeds of the aflatoxin-resistant peanut variety are inoculated with Aspergillus flavus, the differential metabolite content values ​​of the peanut seeds of the aflatoxin-resistant peanut variety after being infected with Aspergillus flavus are obtained, and the differential metabolite content values ​​are compared with the basic values ​​of the metabolites of the peanut seeds of the aflatoxin-resistant peanut variety to obtain the phenolic acid metabolites with increased specific expression levels after the peanut seeds of the aflatoxin-resistant peanut variety are infected with Aspergillus flavus; The phenolic acid metabolites whose specific expression levels increase after the peanut seeds of the aflatoxin-resistant peanut variety are infected by Aspergillus flavus are subjected to an in vitro phenolic acid inhibition of Aspergillus flavus growth experiment to obtain phenolic acid biomarkers.

4. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 3, characterized in that: The step of using a broadly targeted metabolomics technology to obtain basic values ​​of peanut seed metabolites and using the basic values ​​of the peanut seed metabolites as a basic data set includes: The dried peanut seeds are subjected to surface pretreatment and then ground to obtain dried seed powder; Based on the dry seed powder, obtaining a UPLC-MS / MS analysis sample; Perform UPLC-MS / MS analysis and substance qualitative and quantitative processing based on the UPLC-MS / MS analysis sample to obtain metabolite mass spectrometry analysis data; The metabolite mass spectrometry analysis data are processed to obtain basic values ​​of peanut seed metabolites, and the basic values ​​of the peanut seed metabolites are used as a basic data set.

5. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 4, characterized in that: The step of grinding the dried peanut seeds after surface pretreatment to obtain dried seed powder; and obtaining a UPLC-MS / MS analysis sample based on the dried seed powder comprises: The dried seeds of the peanut variety "Yueyou 1823" were obtained, and the surface was disinfected with 75% alcohol for 5 minutes, rinsed with sterile water three times, and liquid nitrogen was added. The seeds were ground into powder using a grinder; 50 mg of sample powder was weighed using an electronic balance, and 1200 μL of 70% methanol water internal standard extract precooled at -20°C was added; the seeds were vortexed once every 30 minutes, each time for 30 seconds, and vortexed 6 times in total; after centrifugation, the supernatant was aspirated, the sample was filtered with a microporous filter membrane, and stored in an injection bottle to obtain a UPLC-MS / MS analysis sample for UPLC-MS / MS analysis; The grinding process is performed at 30 Hz for 1.5 minutes. The centrifugal treatment conditions are: a rotation speed of 12000 rpm for 3 minutes; The microporous filter membrane has a pore size of 0.22 μm.

6. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 4, characterized in that: The step of processing the metabolite mass spectrometry analysis data to obtain basic values ​​of peanut seed metabolites and using the basic values ​​of peanut seed metabolites as a basic data set includes: Based on ALL_sample_data_raw.xlsx, 1 / 5 of the minimum value of each row of metabolites was used to fill the missing values ​​in the data; the CV value of the quality control sample was calculated, and the metabolites with a CV value less than 0.5 were screened out to form the final data file ALL_sample_data.xlsx; Analyst 1.6.3 software was used to process the metabolite mass spectrometry data. The characteristic ions of each metabolite were screened out by a triple quadrupole mass spectrometer, and the signal intensity of the characteristic ions was recorded in the detector. MultiQuant software was used to integrate and correct the chromatographic peaks of the sample's mass spectrometry files. The peak area of ​​each chromatographic peak reflects the relative content of the corresponding metabolite. Export the area integrated data of all chromatographic peaks as the basic data set.

7. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 3, characterized in that: The step of obtaining the differential metabolite content value of the peanut seeds after being infected by Aspergillus flavus after inoculating the peanut seeds with Aspergillus flavus, and comparing the differential metabolite content value with the basic value of the peanut seed metabolites to obtain the phenolic acid metabolites with increased specific expression level after the peanut seeds are infected by Aspergillus flavus comprises: After activating the Aspergillus flavus spores, an Aspergillus flavus spore suspension is prepared; After inoculating the Aspergillus flavus spore suspension into peanut seeds, the seeds were quickly frozen with liquid nitrogen and ground into powder, and a QC sample was prepared; Based on the QC sample, UPLC-HRMS detection was performed to obtain the differential metabolite content values ​​of the peanut seeds after being infected by Aspergillus flavus, which were compared with the basic values ​​of the peanut seed metabolites to obtain the phenolic acid metabolites with specifically increased expression levels after the peanut seeds were infected by Aspergillus flavus.

8. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 7, characterized in that: The step of preparing a suspension of Aspergillus flavus spores after activating the Aspergillus flavus spores comprises: Prepare PDA culture medium, pour the prepared culture solution into a 90 mm sterile culture dish and wait for solidification; take the Aspergillus flavus strain out of the -80°C refrigerator and thaw at room temperature; use a pipette to draw 10 μl of spore droplets onto a PDA plate, seal the plate with a sealing film and place it in an artificial climate box at 30°C for 7 days to obtain activated Aspergillus flavus spores; Place the PDA plate full of spores in a biosafety cabinet, wash off the Aspergillus flavus conidia from the culture medium with 0.1% Tween 80 solution, collect them in a sterile 100 ml Erlenmeyer flask, place the Erlenmeyer flask on a vortex oscillator and fully shake it to make the Aspergillus flavus spores evenly distributed in the Tween solution, and obtain an Aspergillus flavus spore suspension; Add 10 μl of Aspergillus flavus spore suspension to the hemocytometer, observe the concentration of conidia under a microscope, dilute the concentration of the spore suspension to 2×106 CFU / ml, and the prepared spore suspension will be used for artificial inoculation.

9. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 7, characterized in that: The step of inoculating the Aspergillus flavus spore suspension into peanut seeds, quick-freezing with liquid nitrogen, grinding into powder, and preparing QC samples comprises: Weigh 50 mg (± 5 mg) of sample, add 500 μL 80% ice methanol solution into a 2.0 mL EP tube, put a small amount of steel beads and crush with a grinder, put it in a -20 ° C refrigerator for 30 min to precipitate the protein in the sample, centrifuge at 20000g for 15 min, transfer 400 μL of supernatant to another EP tube, freeze and dry the supernatant, add 100 μL 50% ice methanol solution to dissolve it, centrifuge at 20000g for 15 min, transfer the supernatant to an injection bottle for UPLC-HRMS detection, take 10-20 μL of extract from each sample and mix it into QC samples for UPLC-HRMS detection.

10. The method for identifying aflatoxin-resistant peanut varieties based on phenolic acid metabolism biomarkers according to claim 9, characterized in that: The conditions for the UPLC-HRMS detection are: The liquid phase system is the ACQUITY UPLC System Ⅰ Class of Waters Company; The chromatographic analysis column used was ACQUITY UPLC T3, 100 mm*2.1 mm, 1.8 μm, Waters, UK; During collection, the column temperature was set to 40 degrees and the flow rate was 0.3 ml / min; The mobile phase used was phase A: water + 5mmol / L ammonium acetate + 5mmol / L acetic acid; phase B: LC-MS acetonitrile; The liquid phase gradient was set as: The mass spectrometry conditions were: The high-resolution mass spectrometer used was a TripleTOF 6600, SCIEX, Framingham, MA, USA time-of-flight mass spectrometer; Each sample was collected once in positive ion mode and once in negative ion mode; the shielding gas pressure of the ion source was 30 PSI, and the pressures of gas 1-auxiliary gas and gas 2-sheath gas were both set to 60 PSI; the source temperature was 500°C; the voltage was positive 5000 V in positive ion mode and -4500 V in negative ion mode; The data acquisition mode was IDA mode; in one acquisition cycle, the primary acquisition range was 60-1200 Daltons, the primary acquisition time was 150 ms, and then the first 12 signal ions with signal accumulation intensity exceeding 100 were selected from the primary spectrum for secondary fragmentation scanning; the dynamic exclusion was set for 4 seconds; During the acquisition process, the instrument accuracy was calibrated every 20 samples. At the same time, the QC sample was scanned every 10 samples. The quality difference of the QC samples was used to correct the systematic error of the entire batch of experiments.

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