A method for rational design of rice growth-promoting synthetic microbial community based on synergistic optimization of directed evolution and metabolic model

Abstract

This invention relates to the field of agricultural biotechnology, specifically to a method for rationally designing rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models. The method involves: continuously subculturing multi-source soil microbial communities until stable, then co-culturing them with rice seeds; selecting the optimal community based on growth phenotypes; applying a bottleneck to the optimal community and adding exogenous microbial disturbances to construct progeny communities; repeating the "screening-disturbance-subculturing" process five times to obtain a directed-evolutionary synthetic microbial community with enhanced growth-promoting function, dividing it into multiple modules; and constructing a genome-scale metabolic model to simulate the effects of interactions between different modules on rice growth, obtaining the module combinations with better predicted growth-promoting effects. Based on this, the directed-evolutionary synthetic microbial communities are paired in different proportions to obtain rationally designed synthetic microbial communities with even better growth-promoting effects. This invention solves the problem of prediction bias caused by the lack of host-microbe interaction mechanisms in existing methods.

Description

Technical Field

[0001] This invention relates to the field of agricultural biotechnology, specifically to a method for the rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models. Background Technology

[0002] Synthetic microbial communities (SynCom), as an important agricultural microbial engineering technology, have received widespread attention in recent years, especially in applications related to crop growth, soil health, and environmental remediation. SynCom designs are based on the complementary metabolic functions of microorganisms in specific environments, optimizing community structure and function to improve crop growth, nutrient use efficiency, stress resistance, and promote environmental remediation. Traditional microbial community design methods mainly rely on the following techniques:

[0003] Genome-constrained metabolic model (GEM) driven community assembly: Metabolic network models are constructed based on single-strain genome sequencing data. Algorithms such as metabolic flow analysis (FBA) are combined to simulate metabolic synergy and resource competition among strains in microbial communities, and strain combinations with metabolic synergy potential are screened to optimize community function.

[0004] Modular strain pairing strategy: By analyzing the metabolic functional profiles of different microorganisms (such as nitrogen fixation, phosphorus solubilization, plant hormone synthesis, etc.), strains with complementary metabolic pathways are selected for modular combination, and the overall effectiveness of the community is enhanced by resource sharing and functional superposition mechanisms.

[0005] Directed evolution strategy: By simulating environmental conditions (such as high salt, low temperature, nutrient deficiency, etc.), the microbial community is subjected to directed evolution to screen out strains with higher adaptability and stability, so as to enhance the growth performance and environmental adaptability of the community in practical applications.

[0006] While the above methods provide a theoretical framework for SynCom design, they still face the following technical limitations in practical applications for staple crops such as rice:

[0007] 1) Lack of host-microbe interaction mechanisms leads to model prediction bias: Most existing metabolic modeling focuses on static simulations of metabolic flows and resource allocation among microorganisms, failing to incorporate host regulatory mechanisms such as root exudates, nutrient absorption feedback, and changes in plant hormones into the model constraints. This lack of bidirectional host-microbe interaction information results in significant differences between theoretically optimized community composition and actual functional expression after field colonization, reducing the targeting effectiveness of SynCom designs.

[0008] 2) Insufficient stability and resilience of microbial communities: While existing microbial community design technologies can optimize strain performance through metabolic models and directed evolution strategies, they often fail to effectively ensure the stability and resilience of communities in real-world environments. Environmental changes, external disturbances, and intra-community competition can lead to the loss or degradation of community functions, thereby affecting the long-term stability and application effectiveness of the community.

[0009] Therefore, developing a method for promoting rice growth and synthesizing microbial communities that can simulate host-microbe interaction mechanisms and enhance the stability and resistance to disturbance of microbial communities is of great practical significance for increasing rice yield, improving soil health, and promoting environmental remediation. Summary of the Invention

[0010] In view of this, the purpose of this invention is to provide a method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models, so as to solve the problem of model prediction bias caused by the lack of host-microbe interaction mechanism in the existing SynCom design method, and also to solve the problem of insufficient stability and disturbance resistance of existing synthetic microbial communities.

[0011] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0012] A method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models includes the following steps:

[0013] S1. Select rice soil microbial communities from different sources and conduct continuous subculture to obtain multiple subcultured and stable initial community libraries;

[0014] S2. Multiple stable initial community libraries were co-cultured with rice seeds to screen for the starting communities for directed evolution.

[0015] S3. The starting community of directed evolution is continuously passaged using a perturbation strategy that combines bottleneck perturbation with random addition of exogenous microbial communities to obtain multiple stable microbial communities.

[0016] S4. Multiple groups of stable microbial communities were co-cultured with rice seeds to screen for the microbial community with the best phenotype.

[0017] S5. Repeat operations S3 and S4 to obtain a directed evolutionary synthetic microbial community;

[0018] S6. Based on co-occurrence network analysis, the directed evolution synthetic microbial community is divided into a predetermined number of modules. A genome-scale metabolic model containing rice and microorganisms is constructed based on the rice genome and the directed evolution synthetic microbial community genome to simulate the effect of interactions between different modules on rice growth and obtain the module combination with better predicted growth-promoting effect.

[0019] S7. Adjust the directed evolution synthetic microbial community accordingly based on the predicted combination of modules with better growth-promoting effects to obtain a rationally designed synthetic microbial community with better growth-promoting effects.

[0020] Based on the aforementioned technical methods, firstly, during directed evolution, community function is optimized by repeatedly integrating bottleneck and migration effects. This ensures that the community not only maintains efficient biological functions during long-term environmental adaptation but also possesses stronger resistance to disturbances. Furthermore, through continuous screening under environmental pressures, directed evolution enables the community to maintain good stability and functionality when facing external disturbances such as temperature changes and salinity fluctuations, effectively solving the problem of insufficient stability and resistance to disturbances in existing synthetic biological communities. Secondly, by introducing a host-microbe interaction metabolic model, the feedback from the host plant is incorporated into the community design, thereby enabling more accurate prediction of the synergistic effect between the microbial community and the host. This ensures that the community can grow stably and perform its expected functions in the rhizosphere environment of the host plant, effectively solving the problem of model prediction bias caused by the lack of host-microbe interaction mechanisms in existing SynCom design methods. Furthermore, community stability is enhanced through modular metabolic model design. By decomposing microbial functions into multiple independent modules, the metabolic model makes community design more flexible and controllable. Modular design not only optimizes metabolic flow, enabling efficient operation in dynamic environments, but also enhances the community's resilience to disturbances, significantly improving its adaptability and long-term stability in variable environments. This effectively addresses the shortcomings in stability and resilience of existing synthetic biological communities.

[0021] The present invention provides a method for rationally designing rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models. This method includes the following aspects: 1) Generating simplified microbial communities through directed evolution. During the experiment, multiple proximal variant libraries are generated by combining two perturbation methods: bottleneck effect and migration effect. Community function is then optimized through repeated screening based on rice phenotypes. Through this strategy, the microorganisms in the community, after undergoing selection under environmental pressure, can gradually form simplified communities with higher stability and resistance to perturbation. 2) Integrating the host plant's feedback mechanism through the host plant's metabolic flow and the microbial community's GEM metabolic flow model. Factors such as root exudates, changes in plant hormones, and nutrient absorption of the host plant significantly affect the functional performance of the microbial community. Through the host-microbe co-metabolic model, the synergistic metabolism of the host and microorganisms can be simulated and optimized, ensuring that the function of the microbial community matches the needs of the host plant, thereby improving plant growth and nutrient utilization efficiency. 3) Optimizing the microbial community using modular metabolic model design. Metabolic functions within the community are divided into multiple functional modules, each representing a specific metabolic process or function (such as nitrogen cycling, carbon fixation, etc.). Metabolic flux analysis (FBA) and other tools are used to optimize the metabolic flux of each module, enabling them to work efficiently and collaboratively within the community. In this process, this invention specifically considers the optimization of species abundance within each module. Module species abundance affects the metabolic flux and overall function of the community. By regulating the abundance of species in different modules, the metabolic efficiency, stability, and functional redundancy of the community can be optimized, enhancing its resilience to disturbances. 4) This invention combines directed evolution with the synergistic optimization of metabolic models. Communities selected through directed evolution are further optimized using metabolic models to ensure that the microbial community can function stably and sustainably under specific environmental conditions. Metabolic models provide theoretical support for adjusting species abundance within the community, while directed evolution verifies its practical effects through experiments. The combination of both allows for the maintenance of efficient synergy and stability of the community under environmental changes. 5) This invention provides an efficient method for designing and optimizing microbial communities, capable of rapidly generating highly functional and stable microbial communities. By generating simplified communities through directed evolution and combining them with modular metabolic design and host feedback mechanism optimization, the design and optimization of microbial communities can be completed in a short period of time. In particular, by optimizing the species abundance of modules, the efficient operation and long-term stable function of microbial communities in specific environments have been achieved.

[0022] The directed evolution method includes: (1) creating an initial community pool by inoculating different paddy soil microbial communities into the same habitat, and continuously subculturing them without (community-level) artificial selection until all communities reach a stable state; (2) screening these subcultured stable communities by inoculating them into the nutrient solution of rice seedlings and comparing the seedling growth to select the best-performing community; (3) applying a bottleneck (within 10) to the selected community. 7 (4) After dilution, the progeny communities were inoculated into fresh LB liquid medium and subjected to interference from a combination of exogenous random addition of microbial communities to form a new round of culture and selection of new parents; (5) The progeny communities were allowed to reach a stable equilibrium state of their own generation; (6) These now stable progeny communities were screened by comparing seedling growth. This process was repeated 5 times, and finally 200 stable synthetic microbial communities were produced, which had differentiated plant growth-promoting phenotypes (non-plant rhizosphere growth-promoting bacteria, root growth-promoting bacteria, aboveground growth-promoting bacteria, and dual growth-promoting bacteria).

[0023] Preferably, the method for rationally designing rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models includes the following steps:

[0024] S1. Rice soil microbial communities from different sources were selected and inoculated into LB medium for continuous non-selective subculturing to obtain multiple subcultured and stable initial community libraries.

[0025] S2. The multiple groups of stable initial community libraries were inoculated into Hoagland nutrient solution to obtain multiple groups of first mixed nutrient solutions. Then, the multiple groups of first mixed nutrient solutions were co-cultured with rice seeds. Based on the morphological indicators of rice seedlings, the microbial communities with the top 50% growth-promoting function were screened as the starting communities for directed evolution.

[0026] S3. The top 50% of the microbial communities with growth-promoting functions were inoculated into fresh LB medium using a perturbation strategy that combined bottleneck perturbation with exogenous random addition of microbial communities to form the parent samples for the next iteration and were continuously passaged without selection to obtain multiple groups of passaged stable microbial communities.

[0027] S4. The multiple groups of stable microbial communities were inoculated into Hoagland nutrient solution to obtain multiple groups of second mixed nutrient solutions. Then, the multiple groups of second mixed nutrient solutions were co-cultured with rice seeds. Based on the morphological indicators of rice seedlings, the microbial community with the best phenotype was screened.

[0028] S5. Repeat operations S3 and S4 to obtain a directed evolutionary synthetic microbial community;

[0029] S6. Based on the co-occurrence network analysis of the directed evolution synthetic microbial community, the directed evolution synthetic microbial community is divided into a predetermined number of modules. Analysis reveals that the abundance and integrity of the modules are closely related to the rice seedling phenotype. Then, based on the rice genome and the directed evolution synthetic microbial community genome, a genome-scale metabolic model containing rice and microorganisms is constructed. The genome-scale metabolic model containing rice and microorganisms is then used to simulate the effect of interactions between different modules on rice growth. Through flux balance analysis, the module combination is optimized to obtain a module combination with better predicted growth-promoting effect.

[0030] S7. Adjust the directed evolution synthetic microbial community accordingly based on the predicted combination of modules with better growth-promoting effects to obtain a rationally designed synthetic microbial community with better growth-promoting effects.

[0031] Preferably, in step S1, the initial community bank is obtained from paddy soils from different sources, including the following steps:

[0032] Rice soils from different sources were placed in a phosphate buffer solution, vortexed, and allowed to stand to obtain the supernatant.

[0033] Preferably, in step S1, the supernatant is inoculated into fresh LB medium and subcultured 15 times without selection to obtain 96 subcultured stable initial community libraries.

[0034] By conducting 15 consecutive rounds of non-selective subculturing, the microbial community can reach a stable state, which is one of the key conditions for the successful directed evolution of the microbiome.

[0035] Preferably, in step S1, culturing for 8 hours on a shaker at a temperature of 30°C and a rotation speed of 180 rpm constitutes one non-selective subculture.

[0036] Preferably, in S1, the LB medium is LB liquid medium. The preparation method of LB liquid medium includes the following steps: adding tryptone, yeast extract and sodium chloride (NaCl) to deionized water and stirring until completely dissolved; adjusting the pH value to 7.0 ± 0.2 (25℃) using 5 M NaOH aqueous solution or 1 M HCl solution; and then autoclaving at 121℃ for 15-20 minutes to obtain LB liquid medium.

[0037] Preferably, in step S1, the physiological indicators of the stable microbial community were also measured, including indoleacetic acid content, gibberellin content, and siderophore production capacity.

[0038] Indoleacetic acid (IAA) content, gibberellin content, and siderophore production capacity are all indicators of a microorganism's growth-promoting ability. The core purpose of measuring these physiological indicators is to establish a causal bridge between microbial community function and plant phenotype, and to reveal their growth-promoting mechanisms. Specifically:

[0039] Interpreting phenotypes and revealing mechanisms: Directly linking plant measurement "results" (such as increased root and leaf length) with microbial "functions" (such as producing IAA to promote root growth and producing GAs to promote seedling growth) to explain the mechanisms by which microbial communities promote plant growth.

[0040] Differentiating functions and defining types: This provides an intrinsic functional basis for the different growth-promoting types (such as Root-PGPR and Shoot-PGPR). For example, Root-PGPR communities are usually accompanied by high IAA production, while Shoot-PGPR communities may have high GAs production.

[0041] Assisted screening and evolution tracking: The above physiological indicators can be used as high-throughput auxiliary screening criteria to help select communities with well-defined functions in addition to plant phenotypic results, and to track the strengthening trajectory of their beneficial functions in the process of directed evolution.

[0042] In summary, measuring the above physiological indicators is to move from a "correlation" relationship (community A promotes growth) to a "causal" mechanism (community A promotes growth by secreting IAA), thereby gaining a deeper understanding of the results of directed evolution.

[0043] Preferably, step S2 includes: inoculating 96 groups of the passaged and stable initial community library into Hoagland nutrient solution to prepare 96 groups of first mixed nutrient solutions, which are then used for co-nutritional culture with rice seeds for 8 days. Subsequently, based on the morphological indicators of rice seedlings, the top 50% of microbial communities with growth-promoting functions are screened out as the starting communities for directed evolution;

[0044] The volume ratio of the stable initial community in the first mixed nutrient solution of each group to the Hoagland nutrient solution is 1:10.

[0045] Preferably, the ingredients of the Hogland nutrient solution are selected from the Hogland & Arnon 1950 standard formula.

[0046] Preferably, in S2, the conditions for co-culturing rice seeds with the first mixed nutrient solution in each group are: day / night temperature of 25℃ / 20℃, humidity of 70%, and day / night duration of 18 hours / 8 hours.

[0047] Preferably, in step S2, the morphological indicators include the seedling height, root length, dry / wet biomass of the seedling, and dry / wet biomass of the root.

[0048] Preferably, in step S3, the top 50% of the microbial communities with growth-promoting functions are inoculated into fresh LB medium using a perturbation strategy that combines bottleneck perturbation with exogenous random addition of microbial communities to form 200 parent samples, which are then continuously passaged without selection for 15 times to obtain 200 groups of passaged stable microbial communities.

[0049] By perturbing the bottleneck (in 10) 7 The method of combining exogenous randomly added bacterial communities with diluted (after dilution) in fresh LB liquid medium as an artificial perturbation to generate a library of proximal variants is another key condition for the successful directed evolution of microorganisms.

[0050] The bottleneck perturbation method is as follows: For example, take 1 mL of bacterial culture and add it to 100 mL of fresh LB liquid medium. This step dilutes the medium 100 times. Then, take another 1 mL of the 100-fold diluted bacterial culture and add it to 100 mL of fresh LB medium. This step dilutes the medium 100 times. Continue this process to obtain a dilution of 100 times. 7 A multiple of mixed bacterial solutions.

[0051] Preferably, step S4 includes: inoculating 200 groups of the passaged stable microbial communities into Hoagland nutrient solution to obtain 200 groups of second mixed nutrient solution, then co-culturing the 200 groups of second mixed nutrient solution with rice seeds for 8 days, and then screening out the top 50% of microbial communities with growth-promoting function based on the morphological indicators of rice seedlings.

[0052] In each group, the volume ratio of the stable microbial community in the second mixed nutrient solution to the Hogland nutrient solution is 1:10.

[0053] Preferably, in S4, the conditions for co-culturing the second mixed nutrient solution with rice seeds in each group are: day / night temperature of 25℃ / 20℃, humidity of 70%, and day / night duration of 18 hours / 8 hours.

[0054] Preferably, in step S4, the morphological indicators include the seedling height, root length, dry / wet biomass of the seedling, and dry / wet biomass of the root.

[0055] Preferably, step S5 includes repeating steps S3 and S4 to obtain 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects.

[0056] Preferably, in step S6, the directed evolution synthetic microbial community is divided into a predetermined number of modules based on co-occurrence network analysis, including:

[0057] Metagenomic sequencing was performed on the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects. Then, co-occurrence network analysis was performed based on the sequencing results. Subsequently, the directed evolution synthetic microbial communities were divided into 30 independent and functionally defined modules according to the symbiotic relationships between microbial species. Analysis revealed that the abundance and integrity of the modules were closely related to the growth of rice seedlings.

[0058] Preferably, in step S6, a genome-scale metabolic model incorporating rice and microorganisms is used to simulate the impact of interactions between different modules on rice growth. The module combination is optimized through flux balance analysis to obtain a module combination with a better predicted growth-promoting effect, including:

[0059] A genome-scale metabolic model incorporating rice and microorganisms was used to simulate the effects of interactions between different modules on rice growth. By optimizing module combinations through flux balance analysis, 40 module combinations with better growth-promoting effects were predicted.

[0060] Preferably, S7 includes:

[0061] Based on the predicted 40 module combinations with better growth-promoting effects, the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects were adjusted accordingly to obtain the corresponding 40 rationally designed synthetic microbial communities with better growth-promoting effects.

[0062] Specifically, with the goal of predicting 40 module combinations with better growth-promoting effects, a genome-scale metabolic model including rice and microorganisms was used to simulate 200 directed evolution synthetic microbial communities, randomly pairing them and matching their mixing ratios. The 40 mixing schemes that were closest to the 40 module combinations with better growth-promoting effects were selected, which are the corresponding 40 rationally designed synthetic microbial communities with better growth-promoting effects. Each scheme clearly defines the two specific communities involved in the mixing and their mixing ratio.

[0063] Among them, the rice-microbe genome-scale metabolic model is a coupled model that integrates the metabolic networks of rice plants and microbial communities. This model simulates the effects of each functional module on rice seedling growth and the synergistic or antagonistic interactions between modules by defining metabolite exchange reactions. Based on the simulation results, flux balance analysis is used to quantitatively evaluate the promoting effect of different module combinations on rice seedling growth, and then the module assembly strategy with the best growth-promoting effect is screened out.

[0064] Preferably, metagenomic sequencing is performed on the 200 growth-enhancing directed evolution synthetic microbial communities to analyze the structure and functional characteristics of the growth-enhancing microbial communities. Then, through co-occurrence network analysis, the directed evolution synthetic microbial communities are divided into 30 mutually independent and functionally defined modules based on the symbiotic relationships between microbial species.

[0065] Preferably, metagenomic sequencing was performed on the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects. Functional enrichment revealed that the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects were significantly enriched in the following pathways: starch and sucrose metabolism, phosphotransferase system (PTS), fructose and mannose metabolism, glycolysis, gluconeogenesis, quorum sensing, amino sugar and nucleotide sugar metabolism, galactose metabolism, homologous recombination, pyruvate metabolism, ribosomes, aminoacyl-tRNA biosynthesis, fatty acid biosynthesis, oxidative phosphorylation, protein export, pantothenic acid and coenzyme A biosynthesis, peptidoglycan biosynthesis, terpene skeleton biosynthesis, butyrate metabolism, ubiquinone and other terpene-quinone biosynthesis, siderophore nonribosomal peptide biosynthesis, lipoic acid metabolism, nitrogen metabolism, interconversion of pentoses and glucuronic acid, teichoic acid biosynthesis, extracellular polysaccharide biosynthesis, and aminobenzoic acid degradation.

[0066] In summary, the method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models of this invention selects rice soil microbial communities from 12 different sources, inoculates them into LB medium to create an initial community library, and performs 15 consecutive subcultures without (community-level) artificial selection to bring all communities to a stable state. These subcultured stable communities are then screened by inoculating them into Hoagland nutrient solution and introducing them into 96-well plates planted with rice seeds for co-culture and comparing rice growth to select the community with the best growth-promoting effect. The selected community undergoes two treatments: one part is preserved, and the other part is subjected to a bottleneck (with a 10... 7The diluted microbial community was inoculated into fresh LB liquid medium and subjected to interference from exogenous randomly added microbial communities to form new parents for a new round of culture and selection. The progeny communities were passaged 15 times to reach a stable equilibrium. These now stable progeny communities were then screened by comparing seedling growth. This process was repeated 5 times, ultimately producing 200 directed evolutionary synthetic microbial communities with enhanced growth-promoting effects. Subsequently, co-occurrence network analysis of these 200 communities divided them into 30 modules, and a host-microbe genome-scale metabolic model was constructed. By simulating the potential impact of interactions between different modules on rice growth, the module combinations were optimized, predicting 40 module combinations with better growth-promoting effects. To obtain these module combinations in practice, computer simulations were used to randomly pair 200 directed evolution synthetic microbial communities and determine their mixing ratios. Forty mixing schemes were then selected that most closely resembled the 40 predicted growth-promoting module combinations. Each scheme clearly defined the two specific communities involved in the mixing and their mixing ratio, thereby deriving and constructing 40 rationally designed synthetic microbial communities that most closely approximate the model predictions. This invention, through a strategy of "model prediction guiding entity construction," effectively overcomes the problem of model prediction bias caused by the lack of host-microbe interaction mechanisms in existing technologies.

[0067] The beneficial effects of this invention are:

[0068] This invention presents a method for the rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models. First, by generating simplified communities through directed evolution, this invention can screen for microbial communities with high biological function and stronger resistance to disturbance under environmental pressure. Directed evolution, combined with bottleneck and migration effects, optimizes the community's function, making it more stable during long-term environmental adaptation and effectively resisting external environmental disturbances (such as temperature fluctuations and salinity changes). This method significantly improves the stability of microbial communities in practical applications, avoiding the problem of poor community stability in existing technologies. Second, a modular metabolic model design is adopted, decomposing the community's metabolic function into multiple independent and functionally defined modules. Each module represents a specific metabolic pathway or function in the community (such as nitrogen cycling and carbon fixation). Metabolic flow analysis optimizes the metabolic flow of each module, ensuring that the community can work efficiently and collaboratively. This modular design makes the community more flexible and able to adjust its metabolic pathways flexibly in the face of different environmental conditions, significantly improving the community's adaptability and stability. Third, by combining the GEM metabolic flow analysis of the host and the microbial community, this invention effectively integrates the feedback mechanism of the host plant. The root exudates, hormone regulation, and nutrient absorption of the host plant are incorporated into the metabolic model to optimize the function of the microbial community, enabling it to work synergistically with the host plant in the rhizosphere system. This innovation significantly improves the promoting effect of the microbial community on the growth and nutrient utilization of the host plant, enhancing the crop's growth rate and nutrient absorption capacity. Fourth, by generating simplified communities through directed evolution, the screening and optimization process of complex communities in traditional methods is avoided. By preferentially screening a small number of strains with high biological functions in directed evolution, the community structure is simplified, reducing the consumption of experimental resources. Compared with existing technologies, the simplified community generation process of this invention is more efficient, enabling the design of efficient communities in a short time and effectively reducing the cost and cycle of experiments. Fifth, by adjusting the abundance of species in each module, the metabolic flow and overall function of the community are optimized. Through precise control of the species abundance within the module, the metabolic efficiency of the community can be effectively improved, ensuring the efficient operation and stability of the community in long-term applications. This method ensures that the community can maintain functional durability and stability when facing complex environments. Finally, through the synergistic optimization of directed evolution and metabolic models, this invention significantly improves the efficiency of community design and optimization. The communities selected through directed evolution are further optimized using metabolic models to ensure that the communities maintain efficient synergistic effects and long-term stable functions under environmental changes. Compared with the low efficiency and long cycle of community design in existing technologies, this invention can complete the construction of efficient, stable, and disturbance-resistant microbial communities in a short time, offering significant time and cost advantages and possessing widespread application value in the field of agricultural biotechnology. Attached Figure Description

[0069] Figure 1 A schematic diagram of the experimental design and overall scheme for constructing synthetic microbial communities;

[0070] Figure 2 A bar packing diagram showing the growth-promoting effect of five rounds of iterative microbial communities and the species composition of 200 directed evolution synthetic microbial communities;

[0071] Figure 3 A schematic diagram of 30 modules (M1-M30) obtained from co-occurrence network analysis of species composition of 200 directed evolution synthetic microbial communities;

[0072] Figure 4 A group diagram for each module;

[0073] Figure 5 Functional diagrams for each module;

[0074] Figure 6 The module abundance diagram shows the phenotypes of the four plants.

[0075] Figure 7 Module completeness diagram for four plant phenotypes;

[0076] Figure 8 Metabolic flux changes for 30 modules in a GEM simulation;

[0077] Figure 9 The image shows all microbial communities simulated using GP-CAD (gray) and 40 microbial communities with enhanced growth-promoting effects obtained from AI-driven simulation using GP-CAD (red).

[0078] Figure 10 A comparison of rice growth-promoting effects between 200 directed evolution synthetic communities (gray) and 40 rationally designed synthetic communities (red). Detailed Implementation

[0079] The following description, with reference to preferred embodiments, illustrates the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be understood that the preferred embodiments are merely illustrative of the present invention and not intended to limit the scope of protection of the present invention.

[0080] Example 1

[0081] like Figure 1 As shown, a method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models includes the following steps:

[0082] Directed evolution to build simplified communities:

[0083] S1. Rice soil microbial communities from different sources were selected and inoculated into LB medium for continuous, non-selective subculturing to obtain multiple stable initial communities, including:

[0084] Twelve paddy soil samples were collected from different regions (Fujian Province, Tibet Autonomous Region, Chongqing Municipality, Inner Mongolia Autonomous Region, Xinjiang Uygur Autonomous Region, Guangxi Zhuang Autonomous Region, and Heilongjiang Province). Five grams of soil from each sample were placed in a 50 mL sterile tube, then resuspended in 50 mL of phosphate buffer solution. The mixture was then vortexed and allowed to stand at room temperature for 24 hours. The microbial community in the supernatant was used as the seed culture. Two μL of the supernatant was placed in 200 μL of LB liquid medium in a 96-well plate. Eight portions of the supernatant were placed in eight wells of each plate. The plate was then covered with a breathable sealing film and cultured on a shaker at 30°C and 180 rpm for 8 hours. The plates were subcultured 15 times (before each subculture, the communities were homogenized by pipetting) to stabilize all communities, resulting in 96 stable initial community libraries.

[0085] The 96 stable initial community banks were used for two purposes: (1) 50 μL of culture medium was centrifuged at 3000 rpm for 15 minutes, and the supernatant was collected for analysis of physiological indicators (including the production capacity of indoleacetic acid, gibberellin and siderophores); (2) the culture medium was inoculated into rice seedlings for analysis of their growth-promoting ability.

[0086] The preparation method of LB liquid culture medium includes the following steps: adding tryptone, yeast extract and sodium chloride (NaCl) to deionized water and stirring until completely dissolved; adjusting the pH value to 7.0 ± 0.2 (25℃) using 5 M NaOH aqueous solution or 1 M HCl solution; and then autoclaving at 121℃ for 15-20 minutes to obtain LB liquid culture medium.

[0087] S2. Multiple stable initial community libraries were inoculated into Hoagland nutrient solution to obtain multiple sets of first mixed nutrient solutions. These first mixed nutrient solutions were then co-cultured with rice seeds. Based on rice seedling morphological indicators, the top 50% of microbial communities in terms of growth-promoting function were screened as starting communities for directed evolution. Specifically, these included:

[0088] Rice seeds (Oryza sativa ssp. indica) were surface-sterilized and germinated. Uniformly germinated rice seedlings were then transferred to 96-well plates containing 1.5 mL of 25% Hoagland nutrient solution. The 96 subcultured stable initial rice microbial communities obtained in S1 were added to the 96-well plates at a volume ratio of bacterial solution to Hoagland nutrient solution of 1:10. The plates were then placed in a climate incubator with a day / night temperature ratio of 25℃ / 20℃, a relative humidity of 70%, and a day / night time ratio of 18 hours / 8 hours for co-nutritional culture. Rice growth was observed and water was replenished every two days. On day 8, morphological indicators (seedling height / root length / dry biomass / wet biomass) of each rice seedling were measured, and the top 50% of the microbial communities in terms of growth-promoting function were selected.

[0089] The top 50% of the selected microbial communities with growth-promoting functions were treated in two parts: one part was preserved and the other part was used for S3 operation.

[0090] S3. The top 50% of the microbial communities in terms of growth-promoting function were inoculated into fresh LB medium using a perturbation strategy combining bottleneck perturbation and exogenous random addition of microbial communities (Migration) to form the parent samples for the next iteration. These samples were then subjected to continuous, non-selective subculturing to obtain multiple stable subcultures, including:

[0091] The top 50% of the growth-promoting microbial communities in S2 were cultured in LB liquid medium at 10... 7 After dilution, the culture was inoculated into LB liquid medium, and exogenous bacteria were randomly added to form a new round of culture and selection of new parents (generating 200 new parents). The progeny communities were then passaged 15 times without selection (before each passage, each community was homogenized by pipetting) to reach a stable equilibrium state for passage, resulting in 200 stable microbial communities.

[0092] S4. Multiple groups of stable subcultured microbial communities were inoculated into Hoagland nutrient solution to obtain multiple groups of second mixed nutrient solutions. These second mixed nutrient solutions were then co-cultured with rice seeds. Based on rice seedling morphological indicators, the microbial communities with the best phenotypes were screened, specifically including:

[0093] Rice seeds (Oryza sativa ssp. indica) were surface-sterilized and germinated. Uniformly germinated rice seedlings were then transferred to 96-well plates containing 1.5 mL of 25% Hoagland nutrient solution. Two hundred stable subculture communities obtained from S3 were added to each 96-well plate at a bacterial culture to Hoagland nutrient solution volume ratio of 1:10. The plates were then placed in a climate incubator with a day / night temperature ratio of 25℃ / 20℃, a relative humidity of 70%, and a day / night time ratio of 18 hours / 8 hours for co-nutritional culture. Rice growth was observed and water was replenished every two days. On day 8, morphological indicators (seedling height / root length / dry biomass / wet biomass) of each rice seedling were measured.

[0094] S5. Repeat steps S3 and S4 five times to obtain 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects (e.g., ...). Figure 2 As shown); where, Figure 2 In the first image at the top left, the lowercase letters a, b, c, and d above the horizontal bar indicate that there are significant differences in seedling height or root length between each round, as determined by one-way ANOVA combined with Fisher's minimum significance test. Figure 2 In the species below, Pseudomonas_Emosselii represents Pseudomonas mossini group E, Acinetobacter baumannii represents Acinetobacter baumannii, Lactococcus garvieae represents Lactococcus garvieae, Klebsiella oxytoca represents Klebsiella acidogenetic, Klebsiella quasipneumoniae represents Klebsiella pneumoniae, Pseudomonas_E sp001642705 represents Pseudomonas group E sp001642705, others represent others, Enterobacter hormaechei_C represents Enterobacter hormaechei type C, Leclercia_A pneumoniae represents Leclercia pneumoniae type A, JANEWG01 sp016811995 represents JANEWG01 sp016811995 (strain number), Enterobacterasburiae_B represents Enterobacter aspergillus type B, Pseudomonas_E alkylphenolica represents group E of Pseudomonas alkylphenolica, and Enterobacter hormaechei_B represents type B of Enterobacter hormaechei.

[0095] Through the above-mentioned directed evolution method, the resulting synthetic microbial community not only maintains the synergistic growth-promoting effect of leaves and roots of rice seedlings in a specific environment (day / night temperature ratio: 25℃ / 20℃, humidity: 70%, day / night time ratio: 18 hours / 8 hours), but is also more stable.

[0096] Model building and community optimization:

[0097] S6. Based on the co-occurrence network analysis of the directed evolution synthetic microbial community, the directed evolution synthetic microbial community is divided into a predetermined number of modules. Analysis reveals that the abundance and integrity of these modules are closely related to the rice seedling phenotype. Therefore, a genome-scale metabolic model incorporating rice and microorganisms is constructed based on the rice genome and the directed evolution synthetic microbial community genome. This model is then used to simulate the impact of interactions between different modules on rice growth. Through flux balance analysis, the module combination is optimized to obtain a module combination with better predicted growth-promoting effects, including:

[0098] S61. Metagenomic sequencing was performed on the 200 growth-enhancing directed-evolutionary synthetic microbial communities obtained in S5. Based on the sequencing results, co-occurrence network analysis was conducted, and the 200 growth-enhancing directed-evolutionary synthetic microbial communities were divided into 30 modules according to the symbiotic relationships among microbial species. Specifically, these include:

[0099] like Figure 3 As shown, based on microbial co-occurrence network analysis, 200 directed evolutionary synthetic microbial communities with enhanced growth-promoting effects were divided into 30 independent and functionally defined modules. Figure 4 and Figure 5 This indicates that different modules are dominated by different groups, and that different functional modules have different functions. Figure 5The text effectively reveals the specialized division of labor among different functional modules.It is mainly enriched in the following functional pathways: Starch and sucrose metabolism, Phosphotransferase system (PTS), Fructose and mannose metabolism, Glycolysis, Gluconeogenesis, Quorum sensing, Amino sugar and nucleotide sugar metabolism, Galactose metabolism, Homologous recombination, Pyruvate metabolism, Ribosome, Aminoacyl-tRNA biosynthesis, Fatty acid biosynthesis, Oxidative phosphorylation, Protein export, Pantothenate and CoA biosynthesis, Peptidoglycan biosynthesis, and Terpenoid backbone. Biosynthesis of terpenoids, Butanoate metabolism, Ubiquinone and other terpenoid-quinone biosynthesis, Biosynthesis of siderophore group nonribosomal peptides, Lipoic acid metabolism, Nitrogen metabolism, Pentose and glucuronate interconversions, Teichoic acid biosynthesis, Exopolysaccharide biosynthesis, and Aminobenzoate degradation.The high enrichment of these pathways indicates that the microbial community is in a state of vigorous metabolism.

[0100] Correlation analysis between functional modules and host phenotypes revealed that the abundance of modules (such as...) Figure 6 (as shown) and completeness (such as) Figure 7 (As shown) It is closely related to the host's phenotype.

[0101] S62. Construction and Optimization of Genome-Scale Metabolic Models: Based on the rice genome and the directed evolution synthetic microbial community genome, a genome-scale metabolic model incorporating rice and microorganisms was constructed. This model was then used to simulate the impact of interactions between different modules on rice growth. Through flux balance analysis, the module assembly method was optimized, and 40 module combinations with superior growth-promoting effects were predicted. Specifically, this includes:

[0102] S621. Constructing host-microbe genome-scale metabolic models: Based on the rice genome and the genomes of 200 growth-enhancing directed evolutionary synthetic microbial communities, high-quality metagenomic assembled genomes (MAGs; integrity >90%, contamination rate <5%) were reconstructed through metagenomic assembly and grouping. Genome-scale metabolic models (GEMs) were then reconstructed using the CarveMe pipeline (v1.5.1) under the default general model database and the `--init atp biomass` parameter. All models were manually calibrated to ensure their feasibility under minimal culture medium and Hoagland nutrient solution conditions, providing environmental boundary conditions for subsequent gapfilling.

[0103] S622. Based on the taxonomic classification of metagenomic assembled genomes and their abundance distribution in 200 growth-enhancing directed evolution synthetic microbial communities, the 200 growth-enhancing directed evolution synthetic microbial communities were divided into 30 co-occurrence network modules.

[0104] For each co-occurrence network module, the genome-scale metabolic models corresponding to each metagenomic assembly were first integrated within the module. Redundant reactions were removed, and key metabolic pathways were retained to establish the functional framework of the module-level genome-scale metabolic model. Subsequently, a module-level community metabolic model (community GEM) was constructed using MICOM (v0.33). Under the condition of Hoagland nutrient solution as the substrate medium, a metabolite sharing parameter (compartment exchange = 0.05) was defined among module members, and the solver was set to CPLEX to optimize the distribution of metabolic fluxes and material interactions within the module. Through this process, a total of 30 module-level genome-scale metabolic models were constructed, forming the overall GP-GEMs framework.

[0105] S623. In the community-level simulation phase, the 30 module-level genome-scale metabolic models from S62 are integrated into a regionalized community metabolic model to simulate the microbial-plant metabolic dynamics in the plant rhizosphere environment. The model allows for bidirectional metabolite exchange between modules and between the community and the plant metabolic model (centered on the rice central metabolic network), including key metabolites such as organic acids, amino acids, sugars, and inorganic nutrients. Environmental constraints are set based on experimentally measured nutrient concentrations (N, P, K, C sources) and solution ion balance parameters, with the objective function being to maximize the total biomass production of the community and the plant.

[0106] S624. Subsequently, flux balance analysis (FBA) was used to systematically solve the integrated model to obtain the metabolic flux distribution of each module and its combination in the plant-microbe system. This was achieved by analyzing the carbon and nitrogen exchange networks between modules, energy coupling efficiency, and flux changes in key metabolic reactions (such as the TCA cycle, ammonia assimilation, and phosphate metabolism). Figure 8 As shown in the figure, the contributions of different co-occurring network modules to plant biomass growth and their metabolic complementarity in the symbiotic system are quantitatively predicted. The results of this analysis provide a theoretical basis for assessing the growth-promoting potential of each co-occurring network module and its combinations, and provide model support for subsequent synthetic community optimization design.

[0107] Modular design not only improves the metabolic efficiency of the community but also makes it more stable. Through this design approach, the microbial community can flexibly adjust its metabolic pathways in the face of environmental changes or external disturbances, maintaining the long-term stability of community function.

[0108] S625 utilizes an AI-driven community design framework (GP-CAD) that integrates network module analysis, community metabolism modeling, and multi-objective evolutionary optimization algorithms to achieve intelligent design of plant-promoted synthetic communities.

[0109] In this embodiment, firstly, complete co-occurrence network modules (such as modules 7, 16, 23, and 30 related to root growth) are used as basic design units to ensure functional integrity. Then, modules are combined based on metabolic interactions, prioritizing the integration of modules with synergistic metabolic effects (such as cross-nutrition). After determining the optimal module combination, the initial inoculation ratio of different modules is further adjusted to optimize their relative abundance and overall functional contribution. Next, a community-scale metabolic model is constructed using MICOM (v0.33) to simulate metabolic flux exchange between modules; and the metabolites and plant biomass production of candidate synthetic communities are predicted using the GP-GEMs simulator. Finally, a multi-objective genetic optimization algorithm based on DEAP (v1.3.3) implemented in Python (population size 200, crossover rate 0.7, mutation rate 0.1, 10,000 generations or until convergence) is used to iteratively optimize the module combination and abundance configuration, using the predicted plant biomass gain as the fitness index. This process predicted and screened 40 synthetic community designs with the highest growth-promoting potential (such as...). Figure 9 As shown in the figure, 40 module combinations with enhanced growth-promoting microbial communities were predicted for subsequent experimental verification.

[0110] S7. Based on the predicted enhanced growth-promoting module combinations, the directed-evolutionary rice microbial synthetic community is adjusted accordingly to obtain a rice microbial synthetic community with enhanced growth-promoting effects, specifically including:

[0111] Based on the predicted 40 module combinations with better growth-promoting effects, a genome-scale metabolic model involving rice and microorganisms was used to simulate the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects obtained in S5. Random pairwise combinations and mixing ratios were then used to screen out the 40 mixing schemes that were closest to the 40 module combinations with better growth-promoting effects. These were the corresponding 40 target rice microbial synthetic communities, i.e., 40 rationally designed synthetic microbial communities with better growth-promoting effects.

[0112] The 40 rationally designed synthetic microbial communities with better growth-promoting effects were used in a hydroponic experiment on rice seedlings.

[0113] The hydroponic experiment with rice seedlings includes:

[0114] Rice seeds (Oryza sativa ssp. indica) were surface-sterilized and germinated. Uniformly germinated rice seedlings were then transferred to 96-well plates containing 1.5 mL of 25% Hoagland's solution. Forty rationally designed synthetic microbial communities with superior growth-promoting effects were added to each plate at a 1:10 volume ratio of bacterial culture to Hoagland's solution. The plates were then co-cultured in a climate incubator with a day / night temperature ratio of 25℃ / 20℃, a relative humidity of 70%, and a day / night time ratio of 18 hours / 8 hours. Rice growth was observed every two days, and water was replenished accordingly. On day 8, morphological indicators (seedling height / root length / dry biomass / wet biomass) were measured for each group. Results are as follows: Figure 10 As shown.

[0115] Figure 10 In the diagram, the red communities represent 40 synthetic microbial communities with superior growth-promoting effects, while the gray communities represent 200 directed-evolutionary synthetic microbial communities with enhanced growth-promoting effects.

[0116] from Figure 10 Comparative analysis shows that the 40 rationally designed synthetic microbial communities with better growth-promoting effects are indeed more effective than the original 200 directed evolution synthetic microbial communities, fully demonstrating the effectiveness and reliability of the strategy of combining directed evolution with metabolic model prediction.

[0117] In summary, the method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models, as presented in this invention, successfully constructs synthetic microbial communities with high efficiency, stability, and resistance to disturbances through the synergistic optimization of directed evolution and metabolic models. Compared with existing technologies, this invention has the following advantages:

[0118] 1) Enhancing Community Functional Stability and Disturbance Resistance: By generating simplified communities through directed evolution, this invention can screen for microbial communities with high biological function and stronger resistance to disturbance under environmental pressure. Directed evolution, combined with bottleneck and migration effects, optimizes community function, making it more stable during long-term environmental adaptation and effectively resisting external environmental disturbances (such as temperature fluctuations and salinity changes). This method significantly improves the stability of microbial communities in practical applications, avoiding the problem of poor community stability in existing technologies.

[0119] 2) Optimizing the systematic nature and flexibility of community design: This invention employs a modular metabolic model design, decomposing the metabolic functions of the community into multiple independent and functionally defined modules. Each module represents a specific metabolic pathway or function within the community (such as nitrogen cycling, carbon fixation, etc.). Metabolic flow analysis optimizes the metabolic flow of each module, ensuring the community can work efficiently and collaboratively. This modular design makes the community more flexible and allows it to adjust its metabolic pathways in response to different environmental conditions, significantly improving the community's adaptability and stability.

[0120] 3) Enhancing the synergistic effect between the microbial community and the host plant: By combining the GEM metabolic flux analysis of the host and the microbial community, this invention effectively integrates the feedback mechanism of the host plant. Factors such as root exudates, hormone regulation, and nutrient absorption of the host plant are incorporated into the metabolic model, thereby optimizing the function of the microbial community and enabling it to work synergistically with the host plant in the rhizosphere system. This innovation significantly improves the promoting effect of the microbial community on the growth and nutrient utilization of the host plant, enhancing the crop's growth rate and nutrient uptake capacity.

[0121] 4) Simplified community generation process, reducing experimental costs and time: This invention generates simplified communities through directed evolution, avoiding the screening and optimization process of complex communities in traditional methods. By preferentially screening a small number of strains with high biological functions in directed evolution, the community structure is simplified, reducing the consumption of experimental resources. Compared with existing technologies, the simplified community generation process of this invention is more efficient, enabling the design of efficient communities in a short time and effectively reducing experimental costs and time.

[0122] 5) Optimizing community species abundance to improve metabolic efficiency and stability: This invention specifically considers the optimization of modular species abundance. By adjusting the species abundance in each module, the metabolic flow and overall function of the community are optimized. Precise control of species abundance within modules effectively improves the metabolic efficiency of the community, ensuring efficient operation and stability in long-term applications. This method ensures the community maintains functional durability and stability when facing complex environments.

[0123] 6) High Efficiency of Community Design and Optimization: Through the synergistic optimization of directed evolution and metabolic models, this invention significantly improves the efficiency of community design and optimization. The communities selected through directed evolution are further optimized using metabolic models to ensure that the communities maintain efficient synergistic effects and long-term stable functions under environmental changes. Compared with the low efficiency and long cycle of community design in existing technologies, this invention can complete the construction of efficient, stable, and disturbance-resistant microbial communities in a short time, demonstrating significant time and cost advantages.

[0124] The above embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention.

Claims

1. A method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models, characterized in that, Includes the following steps: S1. Select rice soil microbial communities from different sources and conduct continuous subculture to obtain multiple subcultured and stable initial community libraries; S2. Multiple stable initial community libraries were co-cultured with rice seeds to screen for the starting communities for directed evolution. S3. The starting community of directed evolution is continuously passaged using a perturbation strategy that combines bottleneck perturbation with random addition of exogenous microbial communities to obtain multiple stable microbial communities. S4. Multiple groups of stable microbial communities were co-cultured with rice seeds to screen for the microbial community with the best phenotype. S5. Repeat operations S3 and S4 to obtain a directed evolutionary synthetic microbial community; S6. Based on co-occurrence network analysis, the directed evolution synthetic microbial community is divided into a predetermined number of modules. A genome-scale metabolic model containing rice and microorganisms is constructed based on the rice genome and the directed evolution synthetic microbial community genome to simulate the effect of interactions between different modules on rice growth and obtain the module combination with better predicted growth-promoting effect. S7. Adjust the directed evolution synthetic microbial community accordingly based on the predicted combination of modules with better growth-promoting effects to obtain a rationally designed synthetic microbial community with better growth-promoting effects.

2. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 1, characterized in that, In step S1, the initial community bank is obtained from rice soils of different sources, including the following steps: Rice soils from different sources were placed in a phosphate buffer solution, vortexed, and allowed to stand to obtain the supernatant. The supernatant was inoculated into fresh LB medium and subcultured without selection 15 times to obtain 96 stable initial community libraries. And / or, in S1, culturing for 8 hours on a shaker at a temperature of 30°C and a rotation speed of 180 rpm constitutes one non-selective subculture.

3. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 1, characterized in that, S2 includes: inoculating 96 groups of the passaged and stable initial community library into Hoagland nutrient solution to prepare 96 groups of first mixed nutrient solution, which are used for co-nutrition with rice seeds for 8 days; then, based on the morphological indicators of rice seedlings, the top 50% of microbial communities with growth-promoting functions are screened out as the starting communities for directed evolution. The volume ratio of the stable initial community in the first mixed nutrient solution of each group to the Hoagland nutrient solution is 1:

10. And / or, in S2, the conditions for co-culturing the first mixed nutrient solution of each group with rice seeds are: day / night temperature of 25℃ / 20℃, humidity of 70%, and day / night time of 18 hours / 8 hours. And / or, in S2, the morphological indicators include the seedling height, root length, dry / wet biomass of the seedling, and dry / wet biomass of the root.

4. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 1, characterized in that, S3 includes inoculating the top 50% of the microbial communities in terms of growth-promoting function into fresh LB medium using a perturbation strategy that combines bottleneck perturbation with random addition of exogenous microbial communities, forming 200 parent samples, and performing continuous non-selective subculturing 15 times to obtain 200 groups of stable subculture microbial communities.

5. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 1, characterized in that, S4 includes: inoculating 200 groups of the passaged stable microbial communities into Hoagland nutrient solution to obtain 200 groups of second mixed nutrient solution, and then co-culturing the 200 groups of second mixed nutrient solution with rice seeds for 8 days; subsequently, based on the morphological indicators of rice seedlings, screening out the top 50% of microbial communities in terms of growth-promoting function. The volume ratio of the stable microbial community in the second mixed nutrient solution of each group to the Hoagland nutrient solution is 1:

10. And / or, in S4, the conditions for co-culturing the second mixed nutrient solution with rice seedlings in each group are: day / night temperature of 25℃ / 20℃, humidity of 70%, and day / night duration of 18 hours / 8 hours. And / or, in S4, the morphological indicators include the seedling height, root length, dry / wet biomass of the seedling, and dry / wet biomass of the root.

6. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 1, characterized in that, S5 includes repeating operations S3 and S4 to obtain 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects.

7. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 6, characterized in that, In step S6, the directed evolution synthetic microbial community is divided into a predetermined number of modules based on the co-occurrence network analysis, including: Metagenomic sequencing was performed on the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects. Then, co-occurrence network analysis was performed based on the sequencing results. Subsequently, the directed evolution synthetic microbial communities were divided into 30 independent and functionally defined modules according to the symbiotic relationships between microbial species. Analysis revealed that the abundance and integrity of the modules were closely related to the growth of rice seedlings.

8. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 7, characterized in that, In step S6, a genome-scale metabolic model incorporating rice and microorganisms is used to simulate the impact of interactions between different modules on rice growth. Through flux balance analysis, the module combination is optimized to obtain a module combination with better predicted growth-promoting effects, including: A genome-scale metabolic model incorporating rice and microorganisms was used to simulate the effects of interactions between different modules on rice growth. By optimizing module combinations through flux balance analysis, 40 module combinations with better growth-promoting effects were predicted.

9. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 8, characterized in that, S7 includes: Based on the predicted 40 module combinations with better growth-promoting effects, the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects were adjusted accordingly to obtain the corresponding 40 rationally designed synthetic microbial communities with better growth-promoting effects.

10. The method for rational design of rice growth-promoting synthetic microbial communities based on the synergistic optimization of directed evolution and metabolic models according to claim 7, characterized in that, Metagenomic sequencing was performed on the 200 directed evolution synthetic microbial communities with enhanced growth-promoting effects to analyze the structure and functional characteristics of the microbial communities with enhanced growth-promoting effects. Then, through co-occurrence network analysis, the directed evolution synthetic microbial communities were divided into 30 mutually independent and functionally defined modules based on the symbiotic relationships between microbial species. And / or, metagenomic sequencing was performed on the 200 growth-enhancing directed evolution synthetic microbial communities, and functional enrichment revealed that the 200 growth-enhancing directed evolution synthetic microbial communities were significantly enriched in the following pathways: starch and sucrose metabolism, phosphotransferase system, fructose and mannose metabolism, glycolysis, gluconeogenesis, quorum sensing, amino sugar and nucleotide sugar metabolism, galactose metabolism, homologous recombination, pyruvate metabolism, ribosomes, aminoacyl-tRNA biosynthesis, fatty acid biosynthesis, oxidative phosphorylation, protein export, pantothenic acid and coenzyme A biosynthesis, peptidoglycan biosynthesis, terpene backbone biosynthesis, butyrate metabolism, ubiquinone and other terpene-quinone biosynthesis, siderophoretic nonribosomal peptide biosynthesis, lipoic acid metabolism, nitrogen metabolism, interconversion of pentoses and glucuronic acid, teichoic acid biosynthesis, extracellular polysaccharide biosynthesis, and aminobenzoic acid degradation.

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