Compound microbial flora for preventing and controlling tomato bacterial wilt as well as preparation method and application of compound microbial flora

Abstract

The invention discloses a method for carrying out various machine learning modeling by utilizing global public data to construct a synthetic flora with broad-spectrum adaptability, and the constructed and simplified synthetic flora is composed of three strains of bacteria, namely, Pseudomonas putida YDTA3, Pseudomonas putida PPF1 and Snotrophomonas sp. Syn-C. The invention also discloses a method for constructing the synthetic flora with broad-spectrum adaptability, and the constructed and simplified synthetic flora is composed of three strains of bacteria, namely Pseudomonas putida YDTA3, Pseudomonas putida PPF1 and Snotrophomonas sp. Syn-C. Compared with a single bacterium, the synthetic flora constructed by the invention has higher environmental adaptability, and the strain for constructing the synthetic flora is irreplaceable and unique. After the synthetic flora is added to the rhizosphere of the continuous cropping tomato, the pathogenicity of pathogenic bacteria is inhibited, so that the morbidity of the bacterial wilt of the continuous cropping tomato is remarkably reduced.

Description

Technical Field

[0001] The invention belongs to the biotechnology field of constructing composite functional flora by microbial strains, and relates to a composite microbial flora for preventing and controlling tomato bacterial wilt, and a preparation method and application thereof. Background Art

[0002] Soil-borne diseases seriously plague my country's agricultural production, especially cash crops under intensive agriculture. Year-round planting of a single crop often leads to the deterioration of soil microbial communities and ultimately to the outbreak of soil-borne diseases. For a long time, chemical pesticides have been widely used to prevent and control soil-borne diseases, such as using soil fumigants to treat the soil. However, fumigation can cause adverse effects such as soil compaction, pesticide residues, and rhizosphere ecological damage, which is not conducive to sustainable development and environmental protection. Therefore, how to regulate microbial communities on the basis of achieving soil ecological balance to achieve crop protection has become an urgent problem to be solved.

[0003] The rhizosphere is a highly dynamic interface between roots and soil, rich in a variety of potentially beneficial microorganisms that exhibit a variety of biological activities that are essential for plant growth and health. In the past few decades, many beneficial microorganisms from the rhizosphere have been used for biological control in agricultural production. However, the control efficiency of a single microbial strain is often limited by its poor colonization in plants and the rhizosphere, so its biocontrol effect in the field is often unstable. The synthetic bacterial community constructed by utilizing the synergistic effect between strains enhances the robustness and stability of the community, can better adapt to environmental changes, and thus further enhances the rhizosphere immunity.

[0004] With the rapid development of high-throughput sequencing technology and bioinformatics, researchers can use sequencing data combined with phenotypic results to simulate and predict interactions and regulatory mechanisms between microorganisms, and design synthetic microbiota with specific functions based on this. This method is called a "bottom-up" construction method. It is still difficult to simplify and standardize the huge data of the rhizosphere microbiome and select suitable strains for the design of synthetic microbiota. The current common separation and screening criteria are to select related microorganisms based on differential analysis or microbial network analysis. However, different experimental conditions between studies often blur the true characteristics of microbial communities, sometimes leading to unreliable or inconsistent conclusions. Therefore, it is necessary to mine the characteristics of microbial communities and guide the design of synthetic microbiota through data integration, so as to more accurately reflect the natural microbial communities and their ecological evolutionary dynamics. Summary of the invention

[0005] The purpose of the present invention is to propose a design method for synthetic bacterial flora, and based on this design principle, a synthetic bacterial flora for efficient and stable prevention and control of tomato bacterial wilt is constructed. After adding the synthetic bacterial flora, its colonization performance in the rhizosphere is excellent, the pathogenicity of soil bacterial wilt is weakened, the activity of plant defense enzymes is increased, and the incidence of plants is significantly reduced.

[0006] To achieve the above object, the technical solution provided by the present invention is:

[0007] The first object of the present invention is to provide a composite microbial flora for preventing and controlling tomato bacterial wilt, wherein the composite microbial flora is composed of Pseudomonas putidaYDTA3, Pseudomonas putidaPPF1, and Stenotrophomonas sp.Syn-C;

[0008] The Pseudomonas putida YDTA3 is deposited in China Center for Type Culture Collection on July 3, 2023, with a deposit number of CCTCC NO: M 20231178.

[0009] The Pseudomonas putida PPF1 is deposited in China Center for Type Culture Collection on January 17, 2024, with a deposit number of CCTCC NO: M 2024130;

[0010] The Stenotrophomonas sp.Syn-C is deposited in the China Center for Type Culture Collection on March 28, 2023, and the deposit number is CCTCC NO: M 2023426.

[0011] Furthermore, in the composite microbial flora, Pseudomonas putidaYDTA3, Pseudomonas putidaPPF1, and Stenotrophomonas sp.Syn-C are mixed in equal proportions.

[0012] Furthermore, the tomato bacterial wilt is caused by the pathogenic bacteria Ralstonia solanacearum.

[0013] The second object of the present invention is to provide a method for constructing the aforementioned composite microbial flora, the method comprising the following steps:

[0014] (1) Download soil sequencing data related to bacterial wilt from the public data platform, build multiple machine learning classification models for healthy and diseased samples, and screen key microorganisms by integrating the important features of multiple machine learning models;

[0015] (2) Based on the abundance differences of characteristic microorganisms in the classification model between diseased and healthy samples, the classification information of microorganisms that are significantly enriched in healthy samples is obtained;

[0016] (3) screening bacteria in the bacterial library that match the bacterial sequences that are significantly enriched in the healthy sample, and mixing them in equal amounts to obtain synthetic bacterial population S1;

[0017] (4) inoculating the synthetic bacterial community S1 into the root system of the plant, further screening the successfully colonized strains, and mixing the single bacteria to construct the synthetic bacterial community S2;

[0018] (5) The control effect of synthetic bacterial flora S2 on tomato bacterial wilt was determined to obtain the aforementioned composite microbial flora.

[0019] Furthermore, (1) the machine learning model is Random Forest, Xgboost, SVM, DecisionTree, GLM, Naive Bayes, Logistic Regression, or KNN.

[0020] The third object of the present invention is to provide the use of the aforementioned composite microbial flora in preventing and controlling tomato bacterial wilt.

[0021] Compared with the prior art, the present invention has the following beneficial effects:

[0022] This study collected global soil sequencing data related to bacterial wilt, integrated the common features and difference analysis results of eight machine learning models, including Random Forest, Xgboost, SVM, Decision Tree, GLM, Naive Bayes, Logistic Regression, and KNN, and obtained 10 strains of bacteria with high abundance in healthy samples after pure bacterial isolation and matching with existing strains. The bacterial solution concentration was adjusted to 1×10 7 CFU / mL were mixed in equal proportions to obtain synthetic bacterial flora S1.

[0023] The synthetic bacterial community S1 has a good disease resistance effect. The 16S rRNA full-length sequence was sequenced by the third-generation sequencing technology, and it was found that there were three strains in the S1 synthetic bacterial community that eventually colonized the tomato root system efficiently, namely Pseudomonas putida YDTA3, Pseudomonas putida PPF1, and Stenotrophomonas sp. Syn-C. These three strains were mixed in equal amounts to obtain the final synthetic bacterial community S2.

[0024] In order to further verify the irreplaceability of the bacterial community combination, a random removal experiment was set up to construct the bacterial communities S2-Y, S2-P, and S2-S after random removal of a single bacteria. In order to further verify the uniqueness of the bacterial community combination, strains with the same classification information as S2 were randomly selected to construct synthetic bacterial communities S3, S4, and S5.

[0025] Compared with the three single bacteria, the synthetic bacterial community S2 of the present invention is more effective in colonizing the plant root system. After the single bacteria are randomly removed, the total colonization amount of the bacterial community S2-Y, S2-P, and S2-S is reduced, and the biofilm production is reduced. The randomly constructed synthetic bacterial communities S3, S4, and S5 do not have obvious synergistic effects compared with the single bacteria.

[0026] Compared with the three single bacteria and the bacterial communities S2-Y, S2-P and S2-S after random removal of single bacteria, the synthetic bacterial community S2 of the present invention is more effective in reducing the pathogenicity of soil solanacearum. The randomly constructed synthetic bacterial communities S3, S4 and S5 cannot effectively inhibit the pathogenicity of soil solanacearum.

[0027] Compared with the three single bacteria and the bacteria S2-Y, S2-P and S2-S after random removal of single bacteria, the synthetic bacteria S2 of the present invention is more effective in activating the plant defense system. The randomly constructed synthetic bacteria S3, S4 and S5 did not activate the plant defense system.

[0028] Compared with the three single bacteria and the bacterial communities S2-Y, S2-P and S2-S after random removal of single bacteria, the synthetic bacterial community S2 of the present invention is more effective in preventing and controlling the occurrence of tomato bacterial wilt. The incidence of tomato bacterial wilt after adding the randomly constructed synthetic bacterial communities S3, S4 and S5 is significantly higher than that of S2.

[0029] The construction method provided by the present invention is universal and can effectively improve the screening accuracy of synthetic bacterial communities. It can also provide a theoretical basis for the construction of synthetic bacterial communities in the fields of environmental management, human health, etc. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Machine learning classification model accuracy;

[0031] Figure 2Ranking of the importance of characteristic microorganisms in machine learning models; relative abundance of characteristic microorganisms in healthy and diseased samples; construction of 10 strains of synthetic bacterial community S1;

[0032] Figure 3 The control effect of synthetic bacterial consortium S1 on tomato bacterial wilt;

[0033] Figure 4 The strains from synthetic bacterial colony S1 successfully colonized the tomato rhizosphere;

[0034] Figure 5 The number of single strains, synthetic bacterial colonies S2, S2-Y, S2-P, S2-S, random synthetic bacterial colonies S3, S4, and S5 colonized in the tomato rhizosphere;

[0035] Figure 6 The biofilm yields of single strains, synthetic bacterial colonies S2, S2-Y, S2-P, S2-S, and random synthetic bacterial colonies S3, S4, and S5;

[0036] Figure 7 The single strain, synthetic bacterial colonies S2, S2-Y, S2-P, S2-S, and random synthetic bacterial colonies S3, S4, and S5 had a weakening effect on Ralstonia solanacearum;

[0037] Figure 8 The effects of single strain, synthetic bacterial consortium S2, S2-Y, S2-P, S2-S, random synthetic bacterial consortium S3, S4, and S5 on inducing reactive oxygen species in tomato roots;

[0038] Fig. 9 The incidence of bacterial wilt after a single strain, synthetic bacterial flora S2, S2-Y, S2-P, S2-S, and random synthetic bacterial flora S3, S4, and S5 were applied to the roots of continuously cropped tomatoes for one month.

[0039] Description of biological deposit:

[0040] Biomaterials:

[0041] Pseudomonas putida YDTA3; classification name: Pseudomonas putida (Latin name: Pseudomonas putida); deposited in China Center for Type Culture Collection, the deposit date is July 3, 2023, the deposit address is: Wuhan University, Wuhan, China, China Center for Type Culture Collection, the deposit number is CCTCC NO: M20231178;

[0042] Pseudomonas putida PPF1; classification name: Pseudomonas putida (Latin name: Pseudomonas putida); deposited in China Center for Type Culture Collection, the deposit date is January 17, 2024, the deposit address is: Wuhan University, Wuhan, China, China Center for Type Culture Collection, the deposit number is CCTCC NO: M 2024130;

[0043] Stenotrophomonas sp.Syn-C; classification name: Stenotrophomonas (Latin name: Stenotrophomonas sp.); deposited in China Center for Type Culture Collection, the preservation date is March 28, 2023, the preservation address: Wuhan University, China Center for Type Culture Collection, the preservation number is CCTCC NO: M2023426. DETAILED DESCRIPTION

[0044] The present invention is further explained below with reference to the examples, but the examples do not limit the present invention in any form.

[0045] The culture medium and chemical reagents involved in the embodiments:

[0046] MM inorganic salt culture medium: sodium chloride 0.15g, magnesium sulfate heptahydrate 0.5g, calcium chloride hexahydrate 0.1g, ferrous sulfate heptahydrate 0.0025g, ammonium sulfate 0.5g.

[0047] Inorganic salt solution: sodium chloride 0.015g, magnesium sulfate heptahydrate 0.05g, calcium chloride hexahydrate 0.01g, ferrous sulfate heptahydrate 0.00025g, ammonium sulfate 0.05g.

[0048] Nutrient agar medium (NB): 3.0 g beef extract, 5.0 g peptone, 2.5 g glucose, pH 7.0.

[0049] SMSA medium: agar 16 g, peptone 10 g, acid hydrolyzed casein 1 g, glycerol 5 mL, 1% polymyxin 10 mL, 1% crystal violet 0.5 mL, 1% TTC 5 mL, 1% bacitracin 2.5 mL, penicillin 0.5 mL, 1% chloramphenicol 10 mL, 1% cycloheximide 10 mL.

[0050] Example 1. Analysis of global soil microbial communities associated with bacterial wilt

[0051] Through the Google Scholar search keywords (Bacterial wilt community, Ralstoniasolanacearum community), 1700 data were downloaded, including 1130 healthy samples and 570 diseased samples. In the R (v 4.1) environment, classification models were constructed for healthy and diseased samples and differential analysis was performed.

[0052] The results are as follows Figure 1 As shown in the figure, eight common machine learning methods were tested, including Random Forest, Xgboost, SVM, Decision Tree, GLM, NaiveBayes, Logistic Regression, and KNN. A total of 49 characteristic OTUs were identified by combining the common features of multiple models. Combined with differential analysis, it was found that 15 OTUs had a higher relative abundance in healthy samples, such as Pseudomonas, Sphingomonas, Nocardioides, Stenotrophomonas, and Devosia e ( Figure 2 ).

[0053] Example 2. Method for constructing a synthetic bacterial community

[0054] 1 Preliminary construction of synthetic bacterial flora

[0055] 1.1 Strain screening

[0056] Mix the healthy tomato rhizosphere soil samples collected in the laboratory in the previous period with sterile water in a ratio of 1:9 and vortex thoroughly. Take 1 mL of liquid and spread it evenly on the nutrient agar solid medium using the gradient dilution plate method, and invert and culture for 12-24 hours. Pick a single colony and inoculate it again in the nutrient agar liquid medium, purify it and store it using the glycerol method.

[0057] The strains that grew well during the purification process were selected as the research objects, and the full-length sequences of the strains were determined based on first-generation sequencing. The strains with the highest similarity were selected as the target strains by comparing the sequences with the aforementioned characteristic microorganisms. A total of 10 strains enriched in healthy samples were screened, namely Nocardioides lentus, Nocardioides deserti, Nocardioides sp., Klebsiella sp., Chryseobacterium sp., Devosiariboflavina, Enterobacter sp., Lysobacter niastensis, Aeromicrobium sp., and Stenotrophomonas sp. The concentration of the obtained 10 bacterial suspensions was adjusted to 1×10 7 CFU / mL, and the synthetic bacterial colony S1 was obtained by mixing equal volumes.

[0058] 1.2 Biocontrol effect of synthetic bacterial consortium S1 on tomato bacterial wilt

[0059] Tomato seeds were soaked in 100 mL NaClO solution (0.75%) for 30 minutes, washed 5 times with sterile water, and cultured in a sterile culture dish at 25°C for 3 days. 150 grams of soil was placed in a tissue culture container and sterilized three times with high pressure to inactivate all soil microorganisms. After sterilization, the colony growth was tested on NA medium to determine the sterilization effect. Five-day-old tomato seedlings grown under sterile conditions were transferred to sterile soil in a tissue culture container. When the plant height reached about 4 cm, 20 ml of synthetic bacterial flora (S1) solution was added, and an equal amount of deionized water was added to the control treatment, and the pathogen Ralstonia solanacearum was inoculated in all bags. The plant incidence was counted one month after inoculation.

[0060] The results are as follows Figure 3 As shown, the disease incidence of the control tomatoes was 72%, and the disease incidence of the plants inoculated with S1 was 20%, which was significantly reduced after inoculation with S1 compared with the control.

[0061] 2. Optimization of synthetic bacterial flora

[0062] The synthetic bacterial consortium S1 has good disease resistance. In order to determine which bacteria successfully colonize in the tomato rhizosphere and exert their functions through plant-microbe interactions under greenhouse conditions, S1 was inoculated into sterile tomato seedlings. Plant samples were collected 30 days later, DNA was extracted, and full-length sequencing of 16S rRNA amplicons was completed on the PacBio platform.

[0063] Full-length sequencing results ( Figure 4) showed that among the 10 strains of the S1 synthetic flora, only 3 strains, namely YDTA3, PPF1, and Syn-C, successfully colonized the tomato rhizosphere. These 3 strains were mixed in equal proportions to obtain the final synthetic flora S2.

[0064] Example 3. Synergistic effect between synthetic bacterial strains S2 and efficacy verification

[0065] The strains named YDTA3, PPF1, and Syn-C screened in Example 2, as well as the strains Pseudomonas1 (Pseudomonas fluorescens), Pseudomonas2 (Pseudomonas stutzeri), Pseudomonas3 (Pseudomonas paucimobilis), Pseudomonas4 (Pseudomonas fluorescens), Pseudomonas5 (Pseudomonas putida), Pseudomonas6 (Pseudomonas putida), Stenotrophomonas1 (Stenotrophomonas maltophilia), Stenotrophomonas2 (Stenotrophomonas rhizophila), and Stenotrophomonas3 (Stenotrophomonas maltophilia) with the same classification information stored in the laboratory in the early stage were taken out from the -80°C refrigerator, and after they were slightly thawed, a disposable inoculation loop was used to dip the bacterial liquid and streaked on NB solid culture medium, which was inverted and cultured overnight at 28°C. Select a plate without contamination and with uniform appearance of colonies and select a single colony to inoculate in NB liquid culture medium and place it in a constant temperature shaker at 180rpm at 28℃ for enrichment culture until the absorbance OD600 of the bacterial solution is 1.0. Centrifuge the bacterial solution at 10000g for 5 minutes, discard the supernatant, rinse the bacteria three times with sterile inorganic salt solution, and then concentrate or dilute the bacterial solution to 1×10 7 CFU / mL.

[0066] The synthetic bacterial community S2 was constructed by mixing three biocontrol bacteria (YDTA3, PPF1, and Syn-C) in equal proportions.

[0067] S2-Y is composed of PPF1 and Syn-C mixed in equal proportions.

[0068] S2-P is composed of YDTA3 and Syn-C mixed in equal proportions.

[0069] S2-S is composed of a mixture of YDTA3 and PPF1 in equal proportions.

[0070] The random synthetic bacterial community S3 was composed of a mixture of Pseudomonas1, Pseudomonas2, and Stenotrophomonas1 in equal proportions.

[0071] The random synthetic bacterial community S4 was composed of a mixture of Pseudomonas3, Pseudomonas4, and Stenotrophomonas2 in equal proportions.

[0072] The random synthetic bacterial community S4 is composed of Pseudomonas5, Pseudomonas6, and Stenotrophomonas3 in equal proportions. 1 Experiments on rhizosphere colonization of single strains and synthetic bacterial communities and biofilm production:

[0073] The cultivation of sterile tomato seedlings is as described in 1.2 of Example 2. When the plant height reaches about 4 cm, 20 ml of single strain, synthetic bacterial flora S2, S2-Y, S2-P, S2-S, random synthetic bacterial flora S3, S4, S5 bacterial solution 20 ml were added respectively, and an equal amount of deionized water was added to the control. The rhizosphere soil of sterile tomatoes was collected 4 weeks after inoculation, and the number of rhizosphere soil bacteria was counted by dilution coating and plate counting method. All plates were cultured in a constant temperature incubator at 28°C for 48 h (Shanghai Ximo Medical Device Manufacturing Co., Ltd., China).

[0074] The results show that ( Figure 5 ), when the four strains were inoculated individually, the rhizosphere colonization numbers of YDTA3, PPF1, and Syn-C were 5.4×10 4 , 5.7×10 5 and 5.8×10 5 CFU / g. The colonization number of synthetic bacterial flora S2 was 6.4×10 6 CFU / g, and the rhizosphere colonization numbers of S2-Y, S2-P, and S2-S were 6.4×10 6 , 7.4×10 5 and 6.0×10 6 CFU / g. Compared with a single strain, the total number of rhizosphere bacteria colonized significantly increased. The number of rhizosphere bacteria colonized by random synthetic bacterial communities S3, S4, and S5 was 3.5×10 4 4.0×10 4 , 4.5×10 3 CFU / g, which was significantly lower than the colonization number of synthetic flora S2 and a single strain.

[0075] The biofilm yield was measured after the strain was shaken at 28°C and 180rpm for 24 hours. The results showed that the biofilm yield of YDTA3 and PPF1 was less than 0.02g, and the biofilm yield of Syn-C was 0.04g. The biofilm yields of the synthetic bacterial communities S2-Y, S2-P, and S2-S without a single strain were 0.02, 0.04, and 0.01, respectively. The synthetic bacterial community S2 constructed by 3 strains had the highest biofilm yield of 0.06g. The biofilm yields of the randomly constructed synthetic bacterial communities S3, S4, and S5 were all less than 0.02g ( Figure 6 ).

[0076] 2 Weakening effects of single strains and synthetic bacterial communities on Ralstonia solanacearum:

[0077] Individual colonies were cultured on NB medium for 24 h and resuspended in sterile water after centrifugation (15 min, 5000 g). One milliliter of bacterial suspension from a single strain or synthetic flora was mixed with an equal amount of bacterial suspension from R. solanacearum, with sterile water as a control. Each treatment was repeated three times and cultured at 28 °C for 48 h in a constant temperature incubator (CIMO Medical Device Manufacturing Co., Ltd., Shanghai, China), followed by colony counts using SMSA plates. Ten colonies were selected from each sample to calculate their attenuation index and the average value was determined.

[0078] Weakening index = red spot diameter of a single colony of Ralstonia solanacearum / total diameter of a single colony of Ralstonia solanacearum

[0079] Strains with a weakening index of <0.60 are highly pathogenic strains, strains with a weakening index of >0.80 are non-pathogenic strains, and the pathogenicity of strains with a weakening index between 0.60 and 0.80 is uncertain.

[0080] The results are as follows Figure 7 As shown in the figure, YDTA3, PPF1, Syn-C, S2-Y, S2-P, and S2-S all have a certain weakening effect on Ralstonia solanacearum. The weakening index of Ralstonia solanacearum co-cultured with the synthetic bacterial community S2 reached 0.85, which is a weak pathogenic strain. Compared with YDTA3, PPF1, Syn-C, S2-Y, S2-P, and S2-S, the weakening index of Ralstonia solanacearum has obvious advantages. Moreover, the weakening effect caused by the addition of a single strain varies greatly, and the effect is unstable, while the weakening effect of the synthetic bacterial community S2 on Ralstonia solanacearum is very stable. The randomly constructed synthetic bacterial communities S3, S4, and S5 have no obvious weakening effect on Ralstonia solanacearum.

[0081] 3Single strain and synthetic flora induce plant resistance:

[0082] 1.2 in Example 2 of Cultivation of Sterile Tomato Seedlings. When the plant height reaches about 4 cm, add 20 ml of single strain and synthetic bacterial flora respectively, and add an equal amount of deionized water to the Control. Collect plant root samples 0, 1, 3, 6, 9 and 22 hours after inoculation of the bacterial flora, weigh 50 mg after washing with PBS, add 500 μL of homogenization buffer, and homogenize thoroughly and quickly with a homogenizer. Centrifuge at 100×g, 4°C for 5 minutes, and take the supernatant for later use. Add 190 μL of homogenate supernatant and 10 μL of O11 probe to a 96-well plate, and blow with a pipette to mix it thoroughly. Incubate at 37°C in the dark for 20 to 30 minutes. Place in an enzyme reader, and then detect the fluorescence intensity at an excitation wavelength of 488 nm and an emission wavelength of 530 nm.

[0083] The results are as follows Figure 8 As shown in the figure, YDTA3, S2-Y, S3, and S4 had no induction effect on the active oxygen production in tomato roots, while the synthetic bacterial consortia S2, S5, S2-P, S2-S, PPF1, and Syn-C could induce an increase in the active oxygen content in tomato roots. The active oxygen production reached a peak at 3h, then gradually decreased, and returned to the initial level at 22h. The amount of active oxygen induced by the synthetic bacterial consortium S2 was significantly higher than that of PPF1, S5, S2-P, S2-S, and Syn-C, reaching a maximum of 5e+07.

[0084] 4 Application of single strain and synthetic bacterial flora in the prevention and control of continuous cropping tomatoes

[0085] The cultivation of tomato plants and the preparation of YDTA3, PPF1, Syn-C, synthetic flora S2, S2-Y, S2-P, S2-S, S3, S4, and S5 are as described above. The prepared biocontrol bacteria solution is poured into the roots of the crop at a volume of 10 mL per seedling, once a week, and watered and sprayed with pesticides normally at other times. After one month, the disease situation is observed and the incidence rate is counted. Thirty tomato seedlings are set for each treatment, with a total of 4 replicates. Incidence rate (%) = number of diseased plants / total number of plants × 100%.

[0086] Tomato disease incidence Fig. 9 As shown in the figure, the incidence of tomato in continuous cropping was greatly improved, indicating that the biocontrol bacteria have a significant effect in preventing and controlling the bacterial wilt of continuous cropped tomatoes. YDTA3, PPF1, Syn-C, S2-Y, S2-P, and S2-S all reduced the incidence to a certain extent. S3, S4, and S5 had no obvious control effect. The synthetic bacterial community S2 has a significant advantage over the control effect of a single strain and other combinations of the same genus, indicating that the combination of S2 can exert a synergistic effect and more effectively control the outbreak of the disease, reducing the incidence by 40% compared with the control.

[0087] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A composite microbial flora for preventing and controlling tomato bacterial wilt, characterized in that: The composite microbial flora consists of Pseudomonas putidaYDTA3, Pseudomonas putidaPPF1, and Stenotrophomonas sp.Syn-C; The Pseudomonas putidaYDTA3 is deposited in China Center for Type Culture Collection on July 3, 2023, with a deposit number of CCTCC NO: M 20231178. The Pseudomonas putidaPPF1 is deposited in China Center for Type Culture Collection on January 17, 2024, with a deposit number of CCTCC NO: M 2024130; The Stenotrophomonas sp.Syn-C is deposited in the China Center for Type Culture Collection on March 28, 2023, and the deposit number is CCTCC NO: M 2023426.

2. The composite microbial flora according to claim 1, characterized in that The composite microbial flora includes Pseudomonas putidaYDTA3, Pseudomonas putidaPPF1, and Stenotrophomonas sp.Syn-C mixed in equal proportions.

3. The composite microbial flora according to claim 1, characterized in that The tomato bacterial wilt is caused by the pathogenic bacterium Ralstonia solanacearum.

4. The method for constructing a composite microbial flora according to claim 1, characterized in that: The method comprises the following steps: (1) Download soil sequencing data related to bacterial wilt from the public data platform, build multiple machine learning classification models for healthy and diseased samples, and screen key microorganisms by integrating the important features of multiple machine learning models; (2) Based on the abundance differences of characteristic microorganisms in the classification model between diseased and healthy samples, the classification information of microorganisms that are significantly enriched in healthy samples is obtained; (3) screening bacteria in the bacterial library that match the bacterial sequences that are significantly enriched in the healthy sample, and mixing them in equal amounts to obtain synthetic bacterial population S1; (4) inoculating the synthetic bacterial community S1 into the root system of the plant, further screening the successfully colonized strains, and mixing the single bacteria to construct the synthetic bacterial community S2; (5) Determine the control effect of the synthetic bacterial flora S2 on tomato bacterial wilt, and obtain the composite microbial flora according to claim 1.

5. The method for constructing a composite microbial flora according to claim 4, characterized in that: (1) The machine learning models are Random Forest, Xgboost, SVM, Decision Tree, GLM, Naive Bayes, LogisticRegression, and KNN.

6. Use of the composite microbial flora according to claim 1 in preventing and controlling tomato bacterial wilt.

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