Method and system for evaluating the effect of soil microbial community on corn growth

Abstract

The application discloses a kind of soil microbial community to corn growth influence evaluation method and system, it is related to soil microbial technology field, the present application is by introducing functional gene expression potential index to carry out quantification to strain function, and it is combined with the optimization algorithm with constraint to carry out community design, it is ensured that the maximization of target function, while meeting the development diversity and functional redundancy requirement, improve the stable colonization potential of community;Combination multidimensional physiology and root system configuration data analysis, through standardization formula calculation comprehensive efficacy index, make the evaluation of community growth-promoting effect more comprehensive, objective and comparable;Formed with self-evolution ability closed-loop optimization system, through algorithm active avoidance similar function spectrum design, so that the system can learn from historical failure, continuously improve the success rate of subsequent design, greatly improve the intelligent level and iteration efficiency of research and development, help soil microbial community to corn growth influence evaluation.

Description

Technical Field

[0001] This application relates to the field of soil microbial technology, specifically to a method and system for assessing the impact of soil microbial communities on maize growth. Background Technology

[0002] The development of traditional agricultural microbial agents often relies on empirical screening and simple compounding, which suffers from problems such as uncontrollable function and poor stability. The functional orientation of the constructed communities is unclear, the growth-promoting effect is difficult to predict and guarantee, and the design and subsequent validation stages are disconnected, failing to provide effective feedback to guide the design.

[0003] Existing technologies, such as the invention patent application with publication number CN118303167A, disclose a method for constructing and regulating soil microbial communities to promote the germination and growth of garden plants. This invention, through a microbial resource screening module employing multiple screening methods and techniques, can rapidly and effectively screen target strains from a large number of microorganisms. Combining microscopic observation and microbial metagenomic sequencing methods, it can ensure that the screened microorganisms possess specific functions and characteristics. The screened microbial resources can be used in multiple fields such as agriculture, environmental protection, and medicine, promoting the development and innovation of related industries. Simultaneously, through a growth monitoring and feedback module, automated and intelligent monitoring methods are employed, reducing manual intervention and improving monitoring accuracy and efficiency. Based on real-time monitoring data and feedback results, management measures can be adjusted promptly to promote healthy plant growth.

[0004] Regarding the above-mentioned solutions, the inventors of this application have discovered that the above-mentioned technologies have at least the following technical problems: 1. Currently, there is a lack of design functional gene expression potential index models, which cannot break through the limitations of the traditional binary judgment of "gene presence or absence". It does not integrate multi-dimensional genomic information such as gene copy number, promoter strength and cofactor integrity, and cannot achieve accurate quantitative assessment of strain functional potential; the absence of a "strain-FEPI matrix" means that a high-dimensional functional quantitative map cannot be constructed, which cannot drive strain screening to shift from empirical selection to rational design based on multi-functional quantitative data, and cannot improve the scientific nature and goal orientation of screening.

[0005] 2. Currently, there is a lack of constrained simulation optimization algorithms for community design. The goal is not to maximize the total functional potential of the community, and phylogenetic diversity and functional redundancy are not used as hard constraints for synergistic optimization. As a result, the optimal balance between "improving function" and "ensuring community stability and niche complementarity" cannot be achieved. This makes it impossible to avoid designing high-energy but fragile ineffective combinations, and it fails to improve the efficiency and scientific nature of the design. Not only does it fail to output the optimal strain combination, but it also fails to provide the inoculation ratio based on the functional contribution of each strain. Summary of the Invention

[0006] To address the aforementioned technical shortcomings, the purpose of this application is to provide a method and system for assessing the impact of soil microbial communities on maize growth.

[0007] To solve the above-mentioned technical problems, this application adopts the following technical solution: In the first aspect, this application provides a method for assessing the impact of soil microbial communities on maize growth. The method includes the following steps: S1, calculating the functional gene expression potential index and constructing a core strain library to obtain a strain-FEPI matrix.

[0008] S2. Based on the strain-FEPI matrix and functional gene expression potential index, the optimal combination of candidate strains is obtained.

[0009] S3. Based on the optimal combination of candidate strains, establish a positive benchmark aseptic cultivation verification experiment.

[0010] S4. Based on the aseptic cultivation verification experiment, collect multi-dimensional phenotypic and root architecture data;

[0011] S5. Based on multi-dimensional phenotypic and root system architecture data, perform standardized efficacy calculations, grade assessments, and closed-loop feedback optimization.

[0012] Preferably, the construction of the core strain library and the calculation of the functional gene expression potential index include: constructing a core strain library based on a pure bacterial culture pre-isolated and purified in the target soil to generate a core strain library containing several strains; and performing whole-genome sequencing on each strain in the core strain library to obtain the genome sequence data of each strain.

[0013] Based on the genomic sequence data, the functional gene expression potential index calculation model is used to calculate the functional gene expression potential index of each strain for each preset growth-promoting function, so as to obtain the functional gene expression potential index of each strain corresponding to each growth-promoting function, and then construct a matrix to obtain the strain-FEPI matrix.

[0014] Preferably, the step of calculating the functional gene expression potential index of each strain of bacteria for each preset growth-promoting function based on the genomic sequence data using a preset functional gene expression potential index calculation model, to obtain the functional gene expression potential index of each strain of bacteria corresponding to each growth-promoting function, includes: using the calculation formula of the functional gene expression potential index calculation model. The first strain in the core strain library was obtained strain of bacteria in the first Functional gene expression potential index for promoting reproductive function ,in This is represented by the corresponding number for each strain of bacteria. , This represents the total number of bacterial strains. This is represented by the number corresponding to each reproductive function. , This represents the total number of reproductive-promoting functions; Indicates the first strain of bacteria in the first Copy number of key functional genes involved in growth promotion. Indicates the first strain of bacteria in the first Intensity prediction score of promoter regions of key functional genes in the gene-promoting function. Indicates the first strain of bacteria in the first The sum of the integrity scores of the set of cofactor genes essential for reproductive function.

[0015] Preferably, the constraint-based functional community optimization design includes: setting the target community species number, minimum developmental distance threshold, and maximum functional redundancy based on the strain-FEPI matrix; then, based on a preset constrained simulation optimization algorithm, while satisfying the developmental diversity constraint and functional redundancy constraint, and aiming to maximize the total functional potential of the community, iteratively searching for each strain combination within the target community species number to obtain the optimal candidate strain combination; and calculating the inoculation ratio of each strain based on its functional gene expression potential index in the optimal candidate strain combination to complete the synthetic community formulation design and generate the optimal synthetic community.

[0016] Preferably, the total functional potential of the community is calculated as follows: The calculation formula for the optimization objective is: ,in Represented as the total functional potential of the community. Represented as the first The weighting coefficients corresponding to the gene-promoting functions.

[0017] Preferably, the aseptic cultivation verification experiment for establishing a positive benchmark includes: cultivating pre-acquired aseptic corn seedlings under strict aseptic conditions to obtain initial cultivated plants.

[0018] Based on the optimal synthetic community and the initial cultivated plants, experimental groups were established and inoculated to obtain the experimental group cultivation system.

[0019] A negative control group was established and inoculated based on sterile buffer and the initial cultivated plants to obtain a negative control group cultivation system.

[0020] Based on the pre-acquired compound microbial inoculant and the initial cultivated plants, a positive reference control group was established and inoculated to obtain a positive control group cultivation system.

[0021] Based on preset environmental conditions, the experimental group cultivation system, the negative group cultivation system, and the positive control group cultivation system were simultaneously cultured for a preset period to complete the aseptic cultivation verification experiment.

[0022] Preferably, the multidimensional phenotypic and root architecture data include the aboveground dry weight, underground dry weight, total nitrogen content and total phosphorus content of the plant, total root length and root surface area to root volume ratio of the experimental group cultivation system, the negative group cultivation system and the positive control group cultivation system, respectively.

[0023] Preferably, the multi-dimensional phenotypic and root architecture data acquisition involves harvesting plants from the experimental, negative, and positive control groups after cultivation to obtain independently grouped plant samples. Based on these independently grouped plant samples, the aboveground and underground parts are separated and their dry weight is measured to obtain the aboveground and underground dry weights of each group of plants.

[0024] Based on the plant samples from the independent groups, the total nitrogen and total phosphorus contents of the plants were measured to obtain the total nitrogen and total phosphorus contents of each group of plants.

[0025] Based on the root system of the independently grouped plant samples, the total root length is measured to obtain the total root length of each group of plants.

[0026] Root images were acquired from the root systems of plant samples from the experimental and negative control groups to obtain root image data. Then, the root surface area and root volume ratio were extracted to obtain the root surface area and root volume ratio.

[0027] Preferably, the standardized efficacy calculation, grade assessment and closed-loop feedback optimization include: based on multi-dimensional phenotypic and root architecture data, calculating the standardized relative improvement rates of aboveground dry weight, underground dry weight, total nitrogen content and total phosphorus content of plants, total root length and root surface area to root volume ratio relative to the positive control group.

[0028] Based on the ratio of root surface area to root volume between the experimental group cultivation system and the negative control group cultivation system, the root configuration correction factor was calculated.

[0029] Based on the standardized relative improvement rate and root configuration correction factor, the growth-promoting efficacy index of the synthetic community in the experimental group was calculated.

[0030] Based on the value of the growth-promoting efficacy index of the synthetic community, the efficacy level of the optimal synthetic community is evaluated; and a negative sample feedback data packet is generated and transmitted to the optimization algorithm database of S2.

[0031] In a second aspect, this application provides a system for assessing the impact of soil microbial communities on maize growth, comprising: preferably, a strain-FEPI matrix generation module for calculating functional gene expression potential index and constructing a core strain library to obtain a strain-FEPI matrix.

[0032] The optimal candidate strain combination generation module analyzes and obtains the optimal candidate strain combination based on the strain-FEPI matrix and functional gene expression potential index.

[0033] The aseptic cultivation validation experiment module establishes a positive benchmark aseptic cultivation validation experiment based on the optimal combination of candidate strains.

[0034] The data acquisition module collects multi-dimensional phenotypic and root architecture data based on aseptic cultivation verification experiments.

[0035] The rating module performs standardized efficacy calculations, ratings, and closed-loop feedback optimization based on multi-dimensional phenotypic and root architecture data.

[0036] The beneficial effects of this application are as follows: 1. The method and system for assessing the impact of soil microbial communities on maize growth provided by this application quantifies the function of strains by introducing a functional gene expression potential index and designing communities using a constrained optimization algorithm, ensuring the maximization of target functions while meeting the requirements of developmental diversity and functional redundancy, thus improving the stable colonization potential of the community; combined with multi-dimensional physiological and root architecture data analysis, a comprehensive efficacy index is calculated using a standardized formula, making the assessment of the community's growth-promoting effect more comprehensive, objective, and comparable; a closed-loop optimization system with self-evolutionary capabilities is formed, which actively avoids the design of similar functional spectra through the algorithm, enabling the system to learn from historical failures and continuously improve the success rate of subsequent designs, greatly improving the intelligence level and iteration efficiency of research and development, and contributing to the assessment of the impact of soil microbial communities on maize growth.

[0037] 2. The functional gene expression potential index model designed in this application breaks through the limitations of the traditional binary judgment of "gene presence or absence". By integrating multidimensional genomic information such as gene copy number, promoter strength and cofactor integrity, it achieves accurate quantitative assessment of strain functional potential. The generated "strain-FEPI matrix" constitutes a high-dimensional functional quantification map, driving strain screening from empirical selection to rational design based on multifunctional quantitative data, which greatly improves the scientific nature and goal orientation of screening.

[0038] 3. This application employs a constrained simulation optimization algorithm for community design, aiming to maximize the total functional potential of the community. Simultaneously, it uses phylogenetic diversity and functional redundancy as hard constraints for synergistic optimization, thereby achieving the best balance between "enhancing function" and "ensuring community stability and niche complementarity". This avoids designing high-energy but fragile ineffective combinations, greatly improving the efficiency and scientific nature of the design. It not only outputs the optimal strain combination but also provides the inoculation ratio based on the functional contribution of each strain. Attached Figure Description

[0039] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a flowchart illustrating the implementation steps of the method described in this application.

[0041] Figure 2 This is a schematic diagram of the system structure connection of this application. Detailed Implementation

[0042] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0043] Please see Figure 1 As shown, this application provides a method for assessing the impact of soil microbial communities on maize growth in the first aspect, including: S1, calculating the functional gene expression potential index and constructing a core strain library to obtain a strain-FEPI matrix.

[0044] In a specific example, the construction of the core strain library and the calculation of the functional gene expression potential index include: constructing a core strain library based on a pure bacterial culture that has been isolated and purified in the target soil in advance, generating a core strain library containing several strains; and performing whole-genome sequencing on each strain in the core strain library based on the core strain library to obtain the genome sequence data of each strain.

[0045] Based on the genomic sequence data, the functional gene expression potential index calculation model is used to calculate the functional gene expression potential index of each strain for each preset growth-promoting function, so as to obtain the functional gene expression potential index of each strain corresponding to each growth-promoting function, and then construct a matrix to obtain the strain-FEPI matrix.

[0046] It should be noted that the process of constructing a core strain library involves expanding the culture of pre-acquired single colonies obtained through isolation and purification, and standardizing the preservation of each strain to form a standardized strain set that can be used for subsequent unified analysis. (This establishes a resource set representing culturable microorganisms in the target soil, providing standardized data for subsequent quantitative functional analysis and community design.)

[0047] It should be noted that performing whole-genome sequencing on each strain in the library refers to using a high-throughput sequencing platform to sequence the complete genetic material of each strain to obtain its complete gene sequence information. This is the foundation for obtaining genomic information of the strains, providing a data source for subsequent quantification of their functional potential at the gene level.

[0048] It should be noted that the genome sequence data of each strain refers to the digital representation of all the genetic information of the strain (the functional information of all potential genes of the strain). Its purpose is to serve as input data for subsequent gene mining and functional potential calculation.

[0049] It should be noted that the calculation using the preset functional gene expression potential index calculation model is implemented as follows: First, based on the preset growth-promoting function type (such as nitrogen fixation, phosphorus solubilization, and hormone production), key functional gene clusters related to it are identified and located in the whole genome sequence; then, for each functional gene cluster, parameters of three dimensions are extracted: gene copy number, promoter prediction strength, and cofactor gene integrity; finally, these three parameters are substituted into the preset model for calculation.

[0050] It should be noted that the strain-FEPI matrix is ​​a global view of the functional potential of all strains in the core strain library in various preset growth-promoting functions. The action of constructing the strain-FEPI matrix means that, with the strains in the core strain library as row indexes and the preset growth-promoting functions as column indexes, the calculated expression potential indexes of each functional gene are filled into the corresponding row and column intersections, thereby forming a two-dimensional numerical table.

[0051] In a specific example, based on the genomic sequence data, the calculation of each strain's functional gene expression potential index for each preset growth-promoting function using a pre-defined functional gene expression potential index calculation model includes: using the calculation formula of the functional gene expression potential index calculation model. The first strain in the core strain library was obtained strain of bacteria in the first Functional gene expression potential index for promoting reproductive function ,in This is represented by the corresponding number for each strain of bacteria. , This represents the total number of bacterial strains. This is represented by the number corresponding to each reproductive function. , This represents the total number of reproductive-promoting functions; Indicates the first strain of bacteria in the first Copy number of key functional genes involved in growth promotion. Indicates the first strain of bacteria in the first Intensity prediction score of promoter regions of key functional genes in the gene-promoting function. Indicates the first strain of bacteria in the first The sum of the integrity scores of the set of cofactor genes essential for reproductive function.

[0052] It should be noted that gene copy number (the number of repetitions of key genes encoding functions in the strain genome) is usually determined by whole-genome sequencing assembly and gene cluster identification software. A higher copy number means that the functional gene is more redundant in the genome, theoretically enhancing its robustness of expression. Promoter strength score (a quantitative assessment of the strength of the DNA regulatory sequence (promoter) driving the expression of functional genes); a specific length (e.g., 300 bp) of sequence upstream of the start codon of the functional gene is extracted and scored by comparison with a pre-established database of high-expression promoter characteristics; a higher promoter strength score indicates that the gene has higher efficiency in the transcription initiation stage. Cofactor integrity score sum (assessing whether the auxiliary genes (cofactors) necessary for the function are fully present in the strain genome); a set of necessary cofactor genes is pre-defined for each function, and a fixed score (e.g., 0.05) is added for each gene detected that is complete and has an intact functional domain. A higher cofactor integrity score means that the complete biochemical pathway required for the function is more likely to be supported, thus increasing the probability of function realization.

[0053] It should be noted that the functional gene expression potential index corresponding to each growth-promoting function of each strain is a quantitative vector (characterizing the potential ability of a strain to perform various preset growth-promoting functions under specific environmental conditions). The functional gene expression potential index serves as an objective numerical indicator for comparing the functional strength of different strains and provides core data for subsequent synthetic community optimization design.

[0054] This application's functional gene expression potential index model breaks through the limitations of the traditional binary judgment of "gene presence or absence". By integrating multidimensional genomic information such as gene copy number, promoter strength, and cofactor integrity, it achieves accurate quantitative assessment of strain functional potential. The generated "strain-FEPI matrix" constitutes a high-dimensional functional quantification map, driving strain screening from empirical selection to rational design based on multifunctional quantitative data, greatly improving the scientific nature and goal orientation of screening.

[0055] S2. Based on the strain-FEPI matrix and functional gene expression potential index, the optimal combination of candidate strains is obtained.

[0056] In a specific example, the constraint-based functional community optimization design includes: setting the target community species number, minimum developmental distance threshold, and maximum functional redundancy based on the strain-FEPI matrix; then, based on a preset constrained simulation optimization algorithm, while satisfying the developmental diversity constraint and functional redundancy constraint, and aiming to maximize the total functional potential of the community, iteratively searching for each strain combination within the target community species number to obtain the optimal candidate strain combination; and calculating the inoculation ratio of each strain based on its functional gene expression potential index in the optimal candidate strain combination to complete the synthetic community formulation design and generate the optimal synthetic community.

[0057] It should be noted that the target community species number defines the number of strains ultimately included in the synthetic community; the minimum developmental distance threshold is used to ensure evolutionary diversity among the selected strains; and the maximum functional redundancy is used to control the number of strains with high potential for the same function within the community, avoiding excessive functional concentration.

[0058] It should be noted that the specific formula for implementing developmental diversity constraints is as follows: ,in and This indicates any two selected strains; Represented as strain With strains The developmental distance between them was calculated based on the 16S rRNA gene sequence; This represents the set minimum developmental distance threshold; This represents any combination of candidate strains found during the search. Furthermore, the purpose of setting developmental diversity constraints is to force the optimization algorithm to select strains that are evolutionarily distant from each other, in order to construct a synthetic community with higher developmental diversity, which is related to the stability and functionality of the community.

[0059] It should be noted that, for each growth-promoting function, the functional redundancy constraint counts the number of strains in the candidate strain combination whose functional gene expression potential index for that growth-promoting function is greater than the functional gene expression potential index threshold, and this number is required to not exceed the maximum functional redundancy; further, the specific formula for implementing the functional redundancy constraint is as follows: ,in This represents the preset threshold for the functional gene expression potential index. This is represented as a counting operation. This represents the maximum functional redundancy set. This is represented by the number corresponding to each candidate strain combination. Furthermore, functional redundancy constraints prevent a single function in a synthetic community from becoming overly dependent on a few high-potential strains. By limiting the number of high-potential strains, functional redundancy is managed, aiming to balance the functional strength and stability of the community and prevent a significant decline in function due to the failure of individual strains.

[0060] It should be noted that constrained simulation optimization algorithms (heuristic global optimization algorithms; mimicking environmental changes by introducing environmental parameters to control the search process). For example, when the environmental parameter is temperature, at high temperatures, the algorithm is more likely to accept solutions that worsen the objective function value, thus escaping local optima; as the temperature gradually decreases, the algorithm tends to accept better solutions, eventually converging to a high-quality approximate optimal solution. Constrained simulation optimization algorithms take maximizing the total functional potential of the community as the optimization objective, and take developmental diversity constraints and functional redundancy constraints as conditions that must be satisfied during the search. They perform an iterative search in a high-dimensional solution space defined by the "strain-FEPI matrix" to retrieve several strain combinations that satisfy all constraints and maximize the total functional potential of the community, and these are denoted as the optimal candidate strain combinations.

[0061] It should be noted that the process of calculating the inoculation ratio of each strain based on its functional contribution is as follows: First, based on the optimal candidate strain combination, the expression potential index of all functional genes corresponding to each strain in the optimal candidate strain combination is extracted from the "strain-FEPI matrix"; then, for each strain in the optimal candidate strain combination, its relative contribution to the total functional potential of the entire community is calculated: the weighted functional potential of each strain ( The contribution integral of a strain to the overall function of the community is used as the inoculation ratio, and the ratio of this integral to the overall functional potential of the community is the inoculation ratio of that strain. This allows us to obtain the inoculation ratio of each strain. This ensures that strains with greater functional potential are obtained in a higher proportion in the synthetic formulation, thereby driving the community to achieve optimal overall functional output.

[0062] It should be noted that the synthetic community formulation design is a structured output, containing two core pieces of information: first, the identification of each strain (from the core strain library); and second, the inoculation ratio of each strain; and then preparing the optimal synthetic community.

[0063] In a specific example, the total functional potential of the community is calculated as follows: The formula for calculating the optimization objective is: ,in Represented as the total functional potential of the community. Represented as the first The weighting coefficients corresponding to the gene-promoting functions.

[0064] It should be noted that in the constraint-based functional community optimization design, the weight coefficients corresponding to each growth-promoting function are objectively determined using multivariate statistical analysis methods based on a pre-acquired historical efficacy dataset of the synthetic community. Specifically, this is based on a dataset containing historical synthetic community functional spectra (FEPI) and their corresponding growth-promoting function evaluation results. Principal component analysis is performed on this dataset to calculate the loadings of each growth-promoting function on the principal components, and the loadings are normalized according to the variance explained rate, thereby obtaining the weight coefficients reflecting the contribution of each growth-promoting function to the overall efficacy.

[0065] This application employs a constrained simulation optimization algorithm for community design, aiming to maximize the total functional potential of the community. Simultaneously, it uses phylogenetic diversity and functional redundancy as hard constraints for synergistic optimization, thereby achieving the best balance between "enhancing function" and "ensuring community stability and niche complementarity." This avoids designing high-energy but fragile ineffective combinations, greatly improving the efficiency and scientific rigor of the design. It not only outputs the optimal strain combination but also provides the inoculation ratio based on the functional contribution of each strain.

[0066] S3. Based on the optimal combination of candidate strains, establish a positive benchmark aseptic cultivation verification experiment.

[0067] In one specific instance, the aseptic cultivation validation experiment for establishing a positive benchmark includes: cultivating pre-acquired aseptic corn seedlings under strictly aseptic conditions to obtain initial cultivated plants.

[0068] Based on the optimal synthetic community and the initial cultivated plants, experimental groups were established and inoculated to obtain the experimental group cultivation system.

[0069] A negative control group was established and inoculated based on sterile buffer and the initial cultivated plants to obtain a negative control group cultivation system.

[0070] Based on the pre-acquired compound microbial inoculant and the initial cultivated plants, a positive reference control group was established and inoculated to obtain a positive control group cultivation system.

[0071] Based on preset environmental conditions, the experimental group cultivation system, the negative group cultivation system, and the positive control group cultivation system were simultaneously cultured for a preset period to complete the aseptic cultivation verification experiment.

[0072] It should be noted that cultivation under strictly aseptic conditions refers to transplanting and fixing sterile corn seedlings in a sterile solid or liquid culture medium within a sterile operating environment, such as a biosafety cabinet or laminar flow hood, using a culture container that has undergone high-temperature and high-pressure sterilization (e.g., tissue culture flasks). This completely eliminates interference from environmental microorganisms in the experiment, ensuring that any subsequent observed growth effects can be clearly attributed to the specific microbial treatment used for inoculation.

[0073] It should be noted that sterile maize seedlings are maize plants obtained by sterilizing the seed surface, germinating and cultivating them on a sterile culture medium, and carrying no detectable microorganisms both inside and outside the plant. Obtaining sterile maize seedlings is fundamental to establishing standardized, reproducible microorganism-plant interaction research models.

[0074] It should be noted that the experimental group cultivation system refers to maize cultivation units inoculated with the optimal synthetic community (the core test object for evaluating the actual growth-promoting effect of the optimal synthetic community); the negative control group cultivation system refers to maize cultivation units inoculated with an equal volume of sterile buffer solution. The purpose of establishing the negative control group is to provide a baseline completely free of exogenous microorganisms, used to quantify the basic biomass brought about by the culture medium and the plant's own growth potential, thereby excluding the influence of non-microbial factors in subsequent calculations. The positive reference control group cultivation system refers to maize cultivation units inoculated with a compound microbial agent with known efficacy (commercially available compound microbial agent); the purpose of establishing the positive reference control group is to provide a clearly defined positive baseline for the entire validation experiment; the phenotypic data of this control group will serve as standard reference values, thereby achieving objective quantification and cross-comparison of the evaluation results.

[0075] It should be noted that the preset environmental conditions include, but are not limited to, light intensity, photoperiod, temperature, and humidity, and these parameters are kept consistent across all cultivation systems. Maintaining consistent environmental conditions helps control abiotic variables, ensuring that phenotypic differences observed between different treatment groups primarily stem from variations in the inoculated microorganisms, rather than fluctuations in the growth environment.

[0076] It should be noted that the preset period is a culture duration pre-set based on the typical growth rate of corn seedlings in this aseptic culture system and the time required for stable establishment of the microbial community, such as 21 days or 28 days. The purpose of setting the preset period is to ensure that the microorganisms and plants have sufficient interaction time so that the growth-promoting or inhibitory effects can be fully manifested in the plant phenotype.

[0077] S4. Based on the aseptic cultivation verification experiment, multi-dimensional phenotypic and root architecture data were collected.

[0078] In a specific example, the multidimensional phenotypic and root architecture data include the experimental group cultivation system, the negative group cultivation system, and the positive control group cultivation system, respectively, the aboveground dry weight, underground dry weight, total nitrogen content and total phosphorus content of the plant, total root length, and root surface area to root volume ratio of each group of plants.

[0079] In a specific example, the multidimensional phenotypic and root architecture data collection is as follows: based on the plants in the experimental group cultivation system, negative group cultivation system, and positive control group cultivation system after the end of cultivation, harvesting operations are performed to obtain independently grouped plant samples; based on the independently grouped plant samples, the aboveground and underground parts are separated and dry weight is measured to obtain the aboveground dry weight and underground dry weight of each group of plants.

[0080] Based on the plant samples from the independent groups, the total nitrogen and total phosphorus contents of the plants were measured to obtain the total nitrogen and total phosphorus contents of each group of plants.

[0081] Based on the root system of the independently grouped plant samples, the total root length is measured to obtain the total root length of each group of plants.

[0082] Root images were acquired from the root systems of plant samples from the experimental and negative control groups to obtain root image data. Then, the root surface area and root volume ratio were extracted to obtain the root surface area and root volume ratio.

[0083] It should be noted that the harvesting operation refers to the complete removal of the corn plants from their respective cultivation containers or substrates after the pre-set culture period, including the experimental group, the negative control group, and the positive reference control group, to obtain plant samples for each independent group.

[0084] It should be noted that the separation of the aboveground and underground parts and the measurement of dry weight refer to the process of using tools (such as scissors) to cut the corn plant at the root-stem junction, dividing it into aboveground (stem and leaves) and underground (root) parts. The two parts are then placed separately in an oven and dried at a constant temperature (e.g., 65°C or 105°C) until constant weight, and finally weighed using a precision balance. Furthermore, the aboveground dry weight is the mass of the corn plant's stems and leaves in a completely dehydrated state (the accumulation of aboveground biomass, one of the core growth indicators for evaluating the effect of microbial growth promotion). The underground dry weight is the mass of the corn plant's roots in a completely dehydrated state (the accumulation of root biomass, a key indicator for assessing the impact of microorganisms on root growth).

[0085] It should be noted that the determination of total nitrogen and total phosphorus content in plants refers to the process of pulverizing and digesting dried plant samples (usually the aboveground parts or the whole plant), and then determining the total nitrogen and phosphorus content in the sample using specific chemical analysis methods (such as the Kjeldahl method for total nitrogen determination and the molybdenum-antimony colorimetric method for total phosphorus determination). Furthermore, total nitrogen content in plants is the sum of all forms of nitrogen (organic and inorganic nitrogen) within the maize plant tissues (a key parameter for plant nitrogen nutrition status and microbial nitrogen fixation and absorption). Total phosphorus content in plants is the sum of all forms of phosphorus within the maize plant tissues (a key parameter for plant phosphorus nutrition status and microbial phosphorus solubilization and absorption).

[0086] It should be noted that the total root length measurement operation refers to the process of acquiring images of a clean, intact root system using a root scanner, and automatically identifying and calculating the total length of all root branches through image analysis (existing technology). Furthermore, the total root length is the sum of the lengths of all root segments (including the taproot and lateral roots at all levels) of a single maize plant (a direct morphological indicator that quantifies the spatial expansion range and absorption surface area potential of the root system).

[0087] It should be noted that root image acquisition refers to using a root scanner to capture high-resolution images of clean, spread-out plant root samples laid flat on a transparent scanning plate. Furthermore, root image data are digital image files containing complete two-dimensional morphological information of the root system, serving as the primary data source for subsequent quantitative analysis of root architecture.

[0088] It should be noted that the root surface area to root volume ratio extraction operation refers to inputting the root system image data into the image analysis module, where the built-in algorithm automatically identifies the root system outline and calculates the total surface area and total volume of the root system based on pixel calibration and 3D model assumptions, and finally calculates the root surface area to root volume ratio. Furthermore, the root surface area to root volume ratio is a key indicator characterizing root system architecture (quantifying the impact of microbial inoculation on root morphogenesis), reflecting the density of the root structure; a higher root surface area to root volume ratio means that the root system tends to produce a large number of fine roots, has a larger specific surface area, and is beneficial for water and nutrient absorption.

[0089] S5. Based on multi-dimensional phenotypic and root system architecture data, perform standardized efficacy calculations, grade assessments, and closed-loop feedback optimization.

[0090] In a specific example, the standardized efficacy calculation, grading and closed-loop feedback optimization include: based on multi-dimensional phenotypic and root architecture data, calculating the standardized relative improvement rates of aboveground dry weight, underground dry weight, total nitrogen content and total phosphorus content of plants, total root length and root surface area to root volume ratio relative to the positive control group.

[0091] Based on the ratio of root surface area to root volume between the experimental group cultivation system and the negative control group cultivation system, the root configuration correction factor was calculated.

[0092] Based on the standardized relative improvement rate and root configuration correction factor, the growth-promoting efficacy index of the synthetic community in the experimental group was calculated.

[0093] Based on the value of the growth-promoting efficacy index of the synthetic community, the efficacy level of the optimal synthetic community is evaluated; and a negative sample feedback data packet is generated and transmitted to the optimization algorithm database of S2.

[0094] It should be noted that the standardized relative improvement rate of each phenotypic index relative to the positive control group is calculated as follows: First, the measured values ​​of the experimental group and the positive control group on the same phenotypic index are obtained. Then, the ratio of the absolute improvement value of the experimental group relative to the positive control group to the measured value of the same index on the negative control group is calculated to eliminate the interference of the baseline growth level. Furthermore, the standardized relative improvement rate is a dimensionless relative value used to measure the extent to which the experimental group surpasses the positive control group (which serves as the gold standard) on a specific growth index. A value greater than 0 indicates superiority over the positive control, equal to 0 indicates parity with the positive control, and less than 0 indicates inferiority over the positive control.

[0095] It should be noted that the root configuration correction factor is calculated by dividing the root surface area to root volume ratio of the experimental group by the root surface area to root volume ratio of the negative control group. Furthermore, the root configuration correction factor is used to quantify the specific effect of microbial inoculation on the root configuration of the host plant. When the root configuration correction factor is greater than 1, it indicates that inoculation promotes the development of a finer root configuration with a larger specific surface area, theoretically enhancing the root's absorption potential. The root configuration correction factor is then used as a multiplier to positively correct the total efficacy index. When the root configuration correction factor is less than or equal to 1, it indicates that inoculation has not improved or even deteriorated the root configuration, and the root configuration correction factor will not produce a positive gain.

[0096] It should be noted that the calculation formula for the synthetic community growth-promoting efficacy index... The synthetic community growth-promoting efficacy index was obtained. , This represents the root configuration correction factor. These are represented as weighting factors corresponding to each data point in the multidimensional phenotypic and root system architecture data. This is represented by the corresponding ID of each data point in the multidimensional phenotypic and root system architecture data. , This represents the total number of data points in the multidimensional phenotypic and root system architecture data. Indicates the first The standardized relative improvement rate of each phenotypic indicator; the multidimensional phenotypic and root architecture data include aboveground dry weight, underground dry weight, total nitrogen content and total phosphorus content of the plant, total root length, root surface area and root volume. Furthermore, the synthetic community growth-promoting efficacy index is a single quantitative value that integrates multidimensional phenotypic improvement, the objective importance of each indicator, and the specific influence of root architecture. Its value directly and comprehensively reflects the overall growth-promoting advantage of the evaluated synthetic community compared to known effective positive controls.

[0097] It should be noted that, based on the pre-acquired historical efficacy dataset of synthetic communities, principal component analysis and regression analysis are used to determine the weighting factors corresponding to each data point in the multi-dimensional phenotypic and root architecture data. The specific process is as follows: First, principal component analysis is performed on a dataset containing the historical functional spectrum of synthetic communities and their corresponding multi-dimensional phenotypic improvement rates to extract principal components that can explain most of the phenotypic variation. Then, the factor loadings of each phenotypic index on the principal components are calculated; these loadings reflect the contribution intensity of each index to the overall growth efficacy pattern. Finally, based on the absolute values ​​of the factor loadings or the standardized regression coefficients obtained through regression analysis, normalization is performed to obtain the weighting factors reflecting the differences in the importance of each phenotypic index. Furthermore, these weighting factors are based on statistical results driven by historical data, rather than being artificially preset (objectively quantifying the relative importance of different phenotypic indicators in the comprehensive evaluation system, ensuring the objectivity and scientific nature of the efficacy assessment).

[0098] It should be noted that the evaluation of the efficacy level of the optimal synthetic community is based on a preset efficacy level threshold range, and the calculated synthetic community growth-promoting efficacy index is mapped to the corresponding level label; for example, when the synthetic community growth-promoting efficacy index is greater than or equal to 1.2, it is rated as "highly effective", when the synthetic community growth-promoting efficacy index is greater than or equal to 0.8 and less than or equal to 1.2, it is rated as "effective", and when the synthetic community growth-promoting efficacy index is less than 0.8, it is rated as "inefficient".

[0099] It should be noted that the operation of generating negative sample feedback data packets involves identifying synthetic community designs with low synthetic community growth-promoting efficacy index, extracting the optimal candidate strain combination and its functional gene expression potential index corresponding to the synthetic community design, associating these data with the corresponding low efficacy results, and storing them in a structured manner.

[0100] It should be noted that the negative sample feedback data packet contains information on the community composition and functional characteristics that lead to low efficiency. After transmitting the negative sample feedback data packet to the optimization algorithm database of S2, in subsequent functional community optimization designs based on constraints, the optimization algorithm will actively avoid design schemes similar to historical negative samples (for example, the penalty term can be implemented by adding a negative term proportional to the average cosine similarity between the functional spectrum vectors of the current candidate community and all historical negative sample communities to the objective function of the optimization algorithm). This closed-loop feedback enhances the ability to learn from failed designs, dynamically updates design rules, and continuously improves the success rate and efficiency of synthetic community designs in iterations, achieving an adaptive cycle of evaluation-optimization-redesign.

[0101] Please see Figure 2 As shown, in a second aspect, this application provides a system for assessing the impact of soil microbial communities on maize growth.

[0102] The system 100 of the method for assessing the impact of soil microbial communities on maize growth described in this invention can be installed in an electronic device. Depending on the functions implemented, the system 100 may include a strain-FEPI matrix generation module 101, an optimal candidate strain combination generation module 102, an aseptic cultivation verification experiment establishment module 103, a data acquisition module 104, and a rating module 105. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and perform a fixed function, stored in the memory of the electronic device.

[0103] In this embodiment, the functions of each module / unit are as follows: The strain-FEPI matrix generation module is used to calculate the functional gene expression potential index and construct the core strain library to obtain the strain-FEPI matrix.

[0104] The optimal candidate strain combination generation module analyzes and obtains the optimal candidate strain combination based on the strain-FEPI matrix and functional gene expression potential index.

[0105] The aseptic cultivation validation experiment module establishes a positive benchmark aseptic cultivation validation experiment based on the optimal combination of candidate strains.

[0106] The data acquisition module collects multi-dimensional phenotypic and root architecture data based on aseptic cultivation verification experiments.

[0107] The rating module performs standardized efficacy calculations, ratings, and closed-loop feedback optimization based on multi-dimensional phenotypic and root architecture data.

[0108] This application provides a method and system for assessing the impact of soil microbial communities on maize growth. It quantifies strain function by introducing a functional gene expression potential index and combines it with a constrained optimization algorithm for community design, ensuring the maximization of target functions while meeting developmental diversity and functional redundancy requirements, thus enhancing the community's stable colonization potential. By combining multi-dimensional physiological and root architecture data analysis and calculating a comprehensive efficacy index using a standardized formula, the assessment of the community's growth-promoting effects becomes more comprehensive, objective, and comparable. A closed-loop optimization system with self-evolutionary capabilities is formed. Through the algorithm's proactive avoidance of designs with similar functional spectra, the system can learn from past failures, continuously improving the success rate of subsequent designs, greatly enhancing the intelligence level and iteration efficiency of research and development, and contributing to the assessment of the impact of soil microbial communities on maize growth.

[0109] In the several embodiments provided by this invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0110] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0111] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0112] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0113] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence is the theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for assessing the impact of soil microbial communities on maize growth, characterized in that, include: S1. Perform functional gene expression potential index calculation and core strain library construction to obtain strain-FEPI matrix; The construction of the core strain library and the calculation of the functional gene expression potential index include: Based on the pure bacterial culture isolated and purified in the target soil in advance, a core strain library is constructed to generate a core strain library containing several strains. Based on the core strain library, whole genome sequencing was performed on each strain in the core strain library to obtain the genome sequence data of each strain. Based on the genomic sequence data, the functional gene expression potential index calculation model is used to calculate the functional gene expression potential index of each strain for each preset growth-promoting function, so as to obtain the functional gene expression potential index of each strain corresponding to each growth-promoting function, and then construct a matrix to obtain the strain-FEPI matrix. S2. Based on the strain-FEPI matrix and functional gene expression potential index, the optimal combination of candidate strains is obtained. S3. Based on the optimal combination of candidate strains, establish a positive benchmark aseptic cultivation verification experiment; S4. Based on the aseptic cultivation verification experiment, collect multi-dimensional phenotypic and root architecture data; S5. Based on multi-dimensional phenotypic and root system architecture data, perform standardized efficacy calculations, grade assessments, and closed-loop feedback optimization.

2. The method for assessing the impact of soil microbial communities on maize growth according to claim 1, characterized in that, Based on the genomic sequence data, a preset functional gene expression potential index calculation model is used to calculate the functional gene expression potential index of each strain for each preset growth-promoting function, including: The calculation formula of the functional gene expression potential index calculation model The first strain in the core strain library was obtained strain of bacteria in the Functional gene expression potential index for promoting reproductive function ,in This is represented by the corresponding number for each strain of bacteria. , This represents the total number of strains. This is represented by the number corresponding to each reproductive function. , This represents the total number of reproductive-promoting functions; Indicates the first strain of bacteria in the Copy number of key functional genes involved in growth promotion. Indicates the first strain of bacteria in the Intensity prediction score of promoter regions of key functional genes in the gene-promoting function. Indicates the first strain of bacteria in the The sum of the integrity scores of the set of cofactor genes essential for reproductive function.

3. The method for assessing the impact of soil microbial communities on maize growth according to claim 2, characterized in that, Constraint-based functional community optimization design includes: Based on the strain-FEPI matrix, the target community species number, minimum developmental distance threshold, and maximum functional redundancy are set. Then, based on a pre-defined constrained simulation optimization algorithm, while satisfying the developmental diversity and functional redundancy constraints, the algorithm aims to maximize the total functional potential of the community. Iterative search is performed on each strain combination within the target community species number to obtain the optimal candidate strain combination. Based on the functional gene expression potential index of each strain in the optimal candidate strain combination, the inoculation ratio of each strain based on its functional contribution is calculated to complete the synthetic community formulation design and generate the optimal synthetic community.

4. The method for assessing the impact of soil microbial communities on maize growth according to claim 3, characterized in that, The total functional potential of the community is calculated in the following process: The formula for calculating the optimization objective is: ,in Represented as the total functional potential of the community. Represented as the first The weighting coefficients corresponding to the gene-promoting functions.

5. The method for assessing the impact of soil microbial communities on maize growth according to claim 3, characterized in that, The aseptic cultivation validation experiment that establishes a positive benchmark includes: Based on pre-acquired sterile maize seedlings, they were cultivated under strict aseptic conditions to obtain initial cultivated plants; Based on the optimal synthetic community and the initial cultivated plants, experimental groups were established and inoculated to obtain the experimental group cultivation system. Based on sterile buffer and the initial cultivated plants, a negative control group was established and inoculated to obtain a negative control group cultivation system. Based on the pre-acquired compound microbial inoculant and the initial cultivated plants, a positive reference control group was established and inoculated to obtain a positive control group cultivation system. Based on preset environmental conditions, the experimental group cultivation system, the negative group cultivation system, and the positive control group cultivation system were simultaneously cultured for a preset period to complete the aseptic cultivation verification experiment.

6. The method for assessing the impact of soil microbial communities on maize growth according to claim 3, characterized in that, The multidimensional phenotypic and root architecture data include the experimental group cultivation system, the negative group cultivation system, and the positive control group cultivation system, corresponding to the aboveground dry weight, underground dry weight, total nitrogen content, total phosphorus content, total root length, and root surface area to root volume ratio of each group of plants.

7. The method for assessing the impact of soil microbial communities on maize growth according to claim 1, characterized in that, The multidimensional phenotypic and root architecture data collection: Based on the plants in the experimental group cultivation system, negative group cultivation system and positive control group cultivation system after the end of cultivation, the plants were harvested separately to obtain plant samples in independent groups. Based on the plant samples in the independent groups, the aboveground and underground parts were separated and their dry weights were measured to obtain the aboveground and underground dry weights of each group of plants. Based on the plant samples from the independent groups, the total nitrogen and total phosphorus contents of the plants were measured to obtain the total nitrogen and total phosphorus contents of each group of plants. Based on the root system of the plant samples in the independent groups, the total root length is measured to obtain the total root length of each group of plants. Root images were acquired from the root systems of plant samples from the experimental and negative control groups to obtain root image data. Then, the root surface area and root volume ratio were extracted to obtain the root surface area and root volume ratio.

8. The method for assessing the impact of soil microbial communities on maize growth according to claim 6, characterized in that, The standardized efficacy calculation, rating, and closed-loop feedback optimization include: Based on multidimensional phenotypic and root architecture data, the standardized relative improvement rates of aboveground dry weight, underground dry weight, total nitrogen content of plants, total phosphorus content of plants, total root length, and root surface area to root volume ratio relative to the positive control group were calculated. Based on the root surface area to root volume ratio of the experimental group cultivation system and the negative control group cultivation system, the root configuration correction factor was calculated. Based on the standardized relative improvement rate and root configuration correction factor, the growth-promoting efficacy index of the synthetic community in the experimental group was calculated. Based on the value of the growth-promoting efficacy index of the synthetic community, the efficacy level of the optimal synthetic community is evaluated; and a negative sample feedback data packet is generated and transmitted to the optimization algorithm database of S2.

9. A system for implementing the method for assessing the impact of soil microbial communities on maize growth according to any one of claims 1-8, characterized in that, include: The strain-FEPI matrix generation module is used to calculate the functional gene expression potential index and construct the core strain library to obtain the strain-FEPI matrix. The optimal candidate strain combination generation module analyzes and obtains the optimal candidate strain combination based on the strain-FEPI matrix and functional gene expression potential index. The aseptic cultivation validation experiment module establishes a positive benchmark aseptic cultivation validation experiment based on the optimal candidate strain combination. The data acquisition module collects multi-dimensional phenotypic and root architecture data based on aseptic cultivation verification experiments. The rating module performs standardized efficacy calculations, ratings, and closed-loop feedback optimization based on multi-dimensional phenotypic and root architecture data.

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