Construction method and application of rhizosphere synthetic flora based on carbon source utilization and abundance-occupancy model

Abstract

The invention relates to a rhizosphere synthetic flora construction method based on carbon source utilization and an abundance-occupancy model. The method comprises the following steps: collecting a rhizosphere soil sample of a target plant, carrying out non-targeted metabonomics analysis, and identifying a sample metabolite; screening key metabolites on the basis of the standard that the metabolites are consistently detected in at least 50% of variety samples and the relative abundance of the metabolites ranks top 10%; the method comprises the following steps of: obtaining an ASV sequence of rhizosphere microorganisms through sequencing, constructing an ASSVs table, determining a core flora and an auxiliary flora, and identifying positive correlation metabolite-microorganism pairs through correlation network analysis of key metabolites and core ASV so as to obtain a core ASV library and an auxiliary ASV library. According to the rational synthetic flora construction method, a rhizosphere key carbon source is used as a design blueprint, an abundance-occupancy model is used as a precise screening tool, and the utilization capacity of the carbon source is used as a key verification link, so that an efficient, stable and powerful synthetic flora is actively selected and molded by controlling the most fundamental carbon source resource of the rhizosphere.

Description

Technical Field

[0001] This invention relates to a method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models, belonging to the field of root microbial technology in plant cultivation, and is particularly applicable to the functional regulation of rhizosphere microorganisms and the design of growth-promoting microbial communities for crops such as soybeans. Background Technology

[0002] The rhizosphere microbial community plays a crucial role in promoting plant growth and enhancing stress resistance. Currently, research on rhizosphere microbial communities largely relies on high-throughput sequencing and metagenomics techniques. However, rationally screening core strains with synergistic growth-promoting functions from complex microbial communities remains a challenge in current research.

[0003] In existing technologies, the construction of synthetic microbial communities is mostly based on the simple superposition of microbial abundance or functional genes, lacking a deep understanding of the interactions between rhizosphere metabolites and microorganisms. For example, patent CN 115161406 A, "A Method for Constructing Synthetic Microbial Communities and Its Application in High-Quality Cultivation of Medicinal Plants," discloses the construction of an association network between microbial species based on their relative abundance. According to the association network analysis diagram, microbial communities with strong positive interactions between strains are selected as synthetic microbial communities. This method mainly relies on relative abundance and positive network interactions, with relatively singular standards, and the microbial species have poor stability under different application environments.

[0004] Current research has found that simply relying on the accumulation of microbial abundance or functional genes results in poor stability and insignificant effects in practical applications of the constructed microbial communities. There is a lack of systematic analysis of carbon source utilization patterns and their relationship with selective enrichment of microorganisms. Therefore, there is an urgent need for a more microscopic, rational, and predictive rational design method that combines "carbon source-driven" and "model screening" to achieve efficient construction and precise application of rhizosphere synthetic microbial communities. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models, which overcomes the shortcomings of the existing technology in that the construction standards for synthetic microbial communities are relatively simple and the microbial strains have poor stability under different application environments, and greatly improves the success rate and stability of synthetic microbial community colonization.

[0006] The technical solution of this invention to solve the above-mentioned technical problems is as follows: A method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models includes the following steps: (1) Collect rhizosphere soil samples from multiple varieties and / or sources of the target plant, perform non-targeted metabolomics analysis, and identify sample metabolites; (2) Key metabolites were screened based on the criteria that the metabolites were consistently detected in at least 50% of the variety samples and that their relative abundance ranked in the top 10%. (3) The ASV of rhizosphere microorganisms was obtained by sequencing the V4 region of the 16S rRNA gene. (Amplicon Sequence Variant) sequences were used to construct an ASVs table, and core and auxiliary microbial communities were built. By performing correlation network analysis between key metabolites and core ASVs, positively correlated metabolite-microbe pairs were identified to obtain a core ASV library and an auxiliary ASV library.

[0007] The beneficial effects of this invention are as follows: This invention proposes a rational method for constructing synthetic microbial communities, using key rhizosphere carbon sources as a design blueprint, an abundance-occupancy model as a precise screening tool, and carbon source utilization capacity as a key verification step. It no longer relies on uncertain inter-microbial interactions in nature, but actively selects and shapes a highly efficient, stable, and powerful synthetic microbial community by manipulating the most fundamental carbon source resources in the rhizosphere. This invention represents a more advanced and predictive direction in the field of rhizosphere microbiome engineering.

[0008] Based on the above technical solution, the present invention can be further improved as follows.

[0009] Furthermore, the rhizosphere synthetic microbial community construction method based on carbon source utilization and abundance-occupancy model described in this invention establishes the following criteria for constructing core and auxiliary microbial communities: the core microbial community consists of ASVs that are prevalent in more than 80% of the samples and ASVs whose relative abundance is in the top 10% of all samples, while the remainder consists of auxiliary microbial communities (ASVs).

[0010] The core ASVs of this invention are ubiquitous: they can be detected in more than 80% of the samples; they have high relative abundance: they rank in the top 10% of relative abundance in all samples; only strains that meet both of the above criteria are defined as "core strains". This ensures that the screened bacterial communities are not only "dominant species" (high abundance) in this ecological niche, but also "loyal members" (high occupancy). They have extremely strong adaptability to the environment. The core bacterial communities selected by this screening method of the present invention have theoretically far greater functionality and stability than bacterial communities screened by a single indicator.

[0011] Furthermore, the correlation network analysis between the key metabolites and the core ASVs is as follows: Spearman correlation analysis is used to pairwise calculate the abundance of each metabolite with each ASV to obtain a correlation coefficient, with a value ranging from -1 to 1. When the correlation coefficient is greater than 0 and passes the statistical test with a p-value < 0.05, it is determined to be a "positive correlation pair", further confirming the core microbial community.

[0012] Furthermore, it also includes the validation of the core microbiota and auxiliary microbiota: Assessment of the utilization capacity of key metabolites: Based on the constructed core microbial community and auxiliary microbial community, with the auxiliary microbial community as a control, the utilization capacity of the core microbial community and auxiliary microbial community for key metabolites was assessed, and the maximum OD value was determined by growth curves. Strains were isolated from rhizosphere soils of different varieties and geographical origins. The sequences of the isolated strains were compared with the core ASV library and the auxiliary ASV library by BLAST. Strains were identified as the same strains if the sequence homology was 100% and the comparison length was ≥253bp. Based on the results of strain homology comparison, metabolite utilization capacity and functional trait assessment of core strains and helper strains, core single strains and / or core synthetic microbial communities were further identified. The core single strains and core synthetic microbial communities identified by this method are stable and have significant promoting effects.

[0013] Furthermore, in step (1), the number of rhizosphere soil samples collected from multiple varieties and / or sources of the target plant is ≥30.

[0014] Furthermore, the target plant was soybean. The utilization capacity of the core and auxiliary microbial communities for key metabolites was evaluated, and the maximum OD was determined by measuring the growth curve. 600 value.

[0015] The key metabolites of soybean rhizosphere soil of the present invention include at least 4-(2-methylbutyryl)sucrose, acetamide, 1,2,2-dimethylcyclotetraene, hydroxyurea, n-hexadecanoic acid, octadecanoic acid, malonic acid, sorbitol, bis(carbamoyl)imide, oleamide, sulfonium, (cyanoamino)diphenyl-hydroxyl, inner salt, palmitic acid glyceride, monostearate glyceride, glycerol, D-phthalic acid, and ergosterol.

[0016] This invention relates to the determination of the utilization capacity of key metabolites in soybean rhizosphere soil: The growth curves of the strain under 13 key metabolites were determined using sterile 96-well plates. M9 liquid medium was used, and the carbon source concentration was required to reach a final carbon atom concentration of 100 mmol / L. OD values ​​were measured from 12 h to 7 days. 600 , with OD 600 The maximum value is used as a growth indicator to judge the ability to utilize metabolites.

[0017] Functional trait assessment: Nine functional traits of the core and auxiliary bacterial communities were measured to comprehensively characterize the plant growth promotion potential of the strains. These functional traits included nitrogen fixation, inorganic phosphorus solubility, organic phosphorus solubility, potassium solubility, indoleacetic acid (IAA) production, 1-aminocyclopropane-1-carboxylic acid (ACC) deaminase activity, siderophore production, biofilm formation, and extracellular polysaccharide production.

[0018] The core and auxiliary bacterial communities were inoculated into soybean roots and cultured for 30 days, with biomass measured. The soybean variety could be Zhonghuang 13. Inoculation was performed when the soybean developed its first trifoliate leaf, and the inoculation density was OD0.05. 600 =0.5, 5 mL inoculated at the root of each soybean plant, and the biomass refers to the fresh weight and dry weight of roots, stems, and leaves.

[0019] Furthermore, the core microbial species of the soybean rhizosphere microorganisms include at least the following: Pseudarthrobacter , Paenarthrobacter , Pedobacter , Pseudomonas , Neorhizobium , Phyllobacterium , Bradyrhizobium , Variovorax , Devosia , Rhizobium , Rhodococcus (Rhodococcus) Sphingobacterium , Ensifer (Genus *Cymbidium*) Paeniglutamicibacter (Glutamicin-like bacteria) and Mycobacterium One or more of the genus Mycobacterium.

[0020] Furthermore, the non-targeted metabolomics analysis in step (1) uses gas chromatography-mass spectrometry, with methanol:isopropanol:water = 3:3:2 (V / V / V) as the extraction solvent, and derivatization treatment including methoxyamine hydrochloride and BSTFA, containing 1% TMCS, and the reaction is performed to identify the measurement results.

[0021] Furthermore, the 16S rRNA gene sequencing described in step (2) was performed using primer pair 515F. And 806R targeted amplification of the V4 region fragment of the bacterial 16S rRNA gene; the construction flow of the ASV table Cheng Wei used the DADA2 function of RStudio software (version 4.3.2) to perform quality control, filtering, and paired-end sequence assembly on the FASTA sequence files obtained from sequencing. He also used the SILVA database (v.138) to annotate the bacterial ASV sequences, obtaining the ASV sequences, annotation information, and the number of sample reads.

[0022] Primer pair 515F is (5'-GTGCCAGCMGCCGCGGTAA-3'); The primer pair 806R is (5'-GGACTACHVGGGTWTCTAAT-3').

[0023] This invention also provides a method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models, and the use of core bacteria / microbial communities and auxiliary bacteria / microbial communities in improving soil and / or promoting plant growth and high-quality cultivation.

[0024] The rhizosphere synthetic microbial community construction method of this invention represents a significant methodological upgrade. It moves from a relatively macroscopic "association network" construction method in existing technologies to a more microscopic, rational, and predictive design process that combines "carbon source-driven" and "model screening." Based on the key carbon sources in the rhizosphere and the ability of microorganisms to utilize these carbon sources, i.e., whoever can efficiently utilize specific "food" (carbon sources) secreted by plant rhizosphere has a greater competitive advantage in rhizosphere colonization, this invention's method is a unique approach based on rational design of "resource competition."

[0025] This invention is the first to deeply couple "rhizosphere metabolomics" with the construction of synthetic microbial communities. Instead of focusing solely on the natural relationships between strains, it creates "artificial selection pressure" by artificially providing the most critical and ubiquitous carbon source in the rhizosphere, thereby screening out strains that are best adapted to this specific rhizosphere environment. This is closer to "rhizosphere customization" and greatly improves the success rate and stability of synthetic microbial community colonization.

[0026] This invention introduces the "abundance-occupancy model," a rigorous screening model with two indicators, which greatly improves the success rate and stability of synthetic microbial colonization. Attached Figure Description

[0027] Figure 1 This is a bar chart showing the abundance and quantity of metabolites in the first classification level of this invention. Figure 2 This is a restricted principal coordinate analysis diagram based on Bray-Curtis distance in this invention; Figure 3 This is a relative abundance distribution diagram of the top 16 most abundant monomeric metabolites (relative abundance > 1%) among the 30 soybean varieties of this invention; Figure 4 This is a diagram showing the ratio and relative abundance distribution of core and auxiliary bacteria ASV across soybean varieties in this invention. Figure 5 This is a bar chart showing the species composition (phylum level) of the core and auxiliary microorganisms of this invention; Figure 6 This is a bar chart showing the species composition (genus level) of the top 15 most abundant core and auxiliary microorganisms in this invention. Figure 7 This is an analysis diagram of the random forest model based on output in this invention; Figure 8 This image shows the isolation and identification of rhizosphere bacterial strains from different soybean varieties and geographical origins according to the present invention. Figure 9 Phylogenetic distribution and frequency statistics of the 25 core and 25 helper bacterial strains of this invention; Figure 10Morphological diagrams of the 25 core and 25 helper bacterial strains of this invention; Figure 11 This is a graph showing the differences in growth changes and available metabolite quantities of 25 core strains and 25 helper strains mediated by metabolites of the present invention. Figure 12 This diagram illustrates the association between key metabolites and core strains of the present invention, as well as the differences in preferences among strains. Figure 13 This diagram illustrates the differences in plant growth-promoting characteristics between the core bacterial strain and the auxiliary bacterial strain of this invention. Figure 14 This is a diagram showing the effect of the core and auxiliary synthetic bacterial communities of this invention on the dry weight of soybeans. Figure 15 This is a comparison diagram of the overall plant growth-promoting abilities of the core strain and the auxiliary strains of this invention; Figure 16 This diagram illustrates the plant growth-promoting effects of the core and auxiliary bacterial strains of this invention on soybean roots, stems, and leaves. Detailed Implementation

[0028] The principles and features of the present invention are described below with reference to the accompanying drawings. The examples given are only for explaining the present invention and are not intended to limit the scope of the present invention.

[0029] This invention, using soybean as an example, describes a method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models. The method includes the following steps: collecting rhizosphere soil samples from multiple varieties and / or sources of the target plant, performing non-targeted metabolomics analysis, and identifying sample metabolites; screening key metabolites based on the criterion that the metabolites are consistently detected in at least 50% of the variety samples and rank in the top 10% in relative abundance; and using 16S... Rhizosphere microorganism ASV sequences were obtained through rRNA gene V4 region sequencing. An ASV table was constructed, along with core and auxiliary microbial communities. Correlation network analysis was performed between key metabolites and core ASVs to identify positively correlated metabolite-microbe pairs. (Specifically, statistical methods (Spearman correlation analysis in this invention) were used to pairwise calculate the abundance of each metabolite with each ASV, obtaining a correlation coefficient ranging from -1 to 1. A positive correlation pair was defined as one with a correlation coefficient greater than 0 and passing a statistical test (P < 0.05). This means that in a sample, whenever the abundance of a particular ASV increases, the concentration of its paired metabolite tends to increase synchronously, and vice versa.) This yielded a core ASV library and an auxiliary ASV library. The criteria for defining the core microbial community were ASVs prevalent in over 80% of the samples and the top 10% of ASVs in terms of relative abundance across all samples; the remainder were considered auxiliary microbial communities.

[0030] Example 1 Analysis of soybean rhizosphere soil metabolite composition: (1) Soil sample collection In August 2022, 30 soybean varieties were planted in the soybean experimental field (33°63'N, 117°08'E) of the Suzhou Academy of Agricultural Sciences in Anhui Province. Stubble was removed and the land was plowed before sowing, and no fertilizer was applied during the planting period. The sowing plot area was 18 m². 2 The rows were 6m long and 0.5m apart, with 6 rows per plot. At harvest, 5 plants of each variety were randomly collected, and morphological indicators such as plant height (PH), bottom pod height (FPH) and yield indicators such as purple spot rate (PSR), brown spot rate (BSR), insect damage rate (ISR), 100-seed weight (100-SW), plot yield (Yield), and control yield increase (YIR) were recorded for 30 soybean varieties. Root samples were also collected and labeled with variety information and numbers. For each variety, rhizosphere soil was collected and mixed into one sample. Each sample was stored at -80℃.

[0031] (2) Metabolite composition analysis Non-targeted gas chromatography-tandem mass spectrometry (GC-MS) was used to detect and identify substances in rhizosphere soil samples from 30 soybean varieties. Soil metabolites were detected based on the GC-MS detection platform and a self-built database. Take 0.5g of lyophilized and ground sample, add 1mL of methanol:isopropanol:water (3:3:2, V / V / V) for extraction, vortex for 3min, sonicate for 20min, centrifuge, collect the supernatant, add internal standard solution, lyophilize under nitrogen, and then derivatize. The derivatization conditions are: 0.1mL methoxyamine hydrochloride (0.015g / mL pyridine) at 37℃ for 2h, then add BSTFA (containing 1% TMCS) and react at 37℃ for 30min. The analysis is performed using an Agilent 8890 GC-MS system, DB-5MS column, helium carrier gas, injection volume 1μL, split ratio 5:1, temperature program is 40℃ for 1min, increase to 100℃ at 20℃ / min, then increase to 300℃ at 15℃ / min and hold for 5min. The sample to be tested was sent to Wuhan Maiwei Metabolic Biotechnology Co., Ltd. for analysis.

[0032] According to the test results, a total of 156 compounds were detected in the rhizosphere soil samples of 30 soybean varieties, belonging to 15 major categories, including 29 acids, 27 lipids, 25 alcohols, 22 carbohydrates, 12 other compounds, 9 heterocyclic compounds, 7 amines, 7 esters, 4 aldehydes, 4 aromatics, 4 hydrocarbons, 2 nitrogen compounds, 2 phenols, 1 ketone, and 1 terpenes.

[0033] To clarify the variations at the metabolomics level and the impact of phenotypes on rhizosphere metabolites, such as Figure 1 As shown in the bar chart, the abundance and quantity of metabolites in the first category are as follows: carbohydrates have the highest abundance, followed by lipids, acids, amines, nitrogenous compounds, alcohols, other metabolites, heterocyclic compounds, esters, and phenols; acids have the highest quantity, followed by alcohols, lipids, and carbohydrates.

[0034] like Figure 2 As shown in the restricted principal coordinate analysis plot based on Bray-Curtis distance, phenotypic indices are represented in bold, significantly correlated phenotypic indices are represented in bold black, and varieties are represented by colored dots. Among all phenotypic indices, YIR, Yield, ISR, FPH, PSR, and 100-SW significantly affect rhizosphere metabolite composition, with YIR being the phenotypic indice contributing the most. The results show that soybean plot yield varies with rhizosphere metabolite abundance, exhibiting a significant linear correlation.

[0035] like Figure 3As shown, this is a relative abundance distribution chart of the top 16 most abundant monomeric metabolites (relative abundance > 1%) in 30 soybean varieties. The colors of each bar are labeled according to the corresponding metabolite category indicated in the legend. Key metabolites were selected based on the criteria that they were consistently detected in at least 50% of the varieties and ranked in the top 10% in relative abundance. Specifically, metabolites with a relative abundance greater than 0 were assigned a value of "1", and the sum of these metabolites in the 30 samples was calculated. Metabolites with a value greater than or equal to 15 were retained. Metabolites were analyzed, and their average relative abundance was calculated and ranked. The top 10% of metabolites were selected as key metabolites, including 4-(2-methylbutyryl)sucrose, acetamide, 1,2,2-dimethylcyclotetraene, hydroxyurea, n-hexadecanoic acid, octadecanoic acid, malonic acid, sorbitol, bis(carbamoyl)imide, oleamide, sulfonium, (cyanoamino)diphenyl-hydroxyl, inner salt, glyceryl palmitate, glyceryl monostearate, glycerol, D-phthalic acid, and ergosterol. Considering the availability and low cost of metabolites, 13 metabolites were purchased for subsequent carbon source utilization determination. Among them, 4-(2-methylbutyryl)sucrose had an abundance exceeding half in the detection, but was unavailable. Therefore, its precursors 2-methylbutyric acid and sucrose were selected as substitutes.

[0036] The results showed that key metabolites in soybean rhizosphere soil included at least 4-(2-methylbutyryl)sucrose, acetamide, 1,2,2-dimethylcyclotetraene, hydroxyurea, n-hexadecanoic acid, octadecanoic acid, malonic acid, sorbitol, bis(carbamoyl)imide, oleamide, sulfonium, (cyanoamino)diphenyl-hydroxyl, inner salt, palmitic acid glyceride, monostearate glyceride, glycerol, D-phthalic acid, and ergosterol.

[0037] Example 2 Identification of core rhizosphere microorganisms in soybeans: (1) Extraction of rhizosphere soil DNA: Weigh 1.0 g of rhizosphere soil and extract total DNA from the rhizosphere soil using a soil DNA extraction kit (MPBiomedicals, Solon, USA). The extraction method was provided by the manufacturer. A total of 30 rhizosphere soil DNA samples were obtained.

[0038] (2) Sequencing of 16S rRNA gene amplicon 16S rRNA amplicon sequencing was performed on rhizosphere soil DNA samples. Primer pairs 515F (5'-GTGCCAGCMGCCGCGGTAA-3') and 806R (5'-GGACTACHVGGGTWTCTAAT-3') were used to target and amplify the V4 region of the bacterial 16S rRNA gene. All samples were mixed at equimolar concentrations and then subjected to paired-end sequencing on a HiSeq 2500 platform (Illumina). The samples were then sent to Guangdong Megvii Gene Technology Co., Ltd. for analysis. The obtained FASTA sequence files were quality controlled, filtered, and paired-end sequenced using DADA2 software (RStudio version 4.3.2). The bacterial ASV sequences were annotated using the SILVA database (v.138).

[0039] Thirty rhizosphere soil samples were sequenced using high-throughput sequencing, yielding a total of 6,448,684 high-quality sequences. After filtering and assembly, 8,434 bacterial amplicon sequence variants (ASVs) were obtained. Rare bacteria (<2 reads) and non-bacterial ASVs (chloroplasts and mitochondria) were removed and homogenized, resulting in 28,546 reads per sample and 7,315 effective ASVs. Species annotation of the ASV sequences was performed, identifying 29 phyla, 82 classes, 148 orders, 234 families, and 460 genera.

[0040] (3) Identification of core microorganisms The core microbiota is defined based on two criteria: ASVs that are prevalent in more than 80% of the samples and ASVs whose relative abundance is in the top 10% of all samples. The remaining ASVs are defined as the accessory microbiota. Specifically, ASVs with a relative abundance greater than 0 are assigned a value of "1". The sum of the ASVs in 30 samples is calculated. ASVs with a value greater than or equal to 24 are retained. The average relative abundance of these ASVs is calculated and sorted. The top 10% of ASVs are selected and identified as key ASVs.

[0041] Based on this, 223 ASVs were defined as core ASVs from 7,315 ASVs, collectively referred to as the core ASV pool. These core microorganisms occupy a very large proportion (83.25%~96.16%) of the total abundance of rhizosphere bacterial communities in different soybean varieties, with a very small number of taxa (3.05%). Meanwhile, 7,092 ASVs were defined as auxiliary ASVs, collectively referred to as the auxiliary ASV pool.

[0042] like Figure 4As shown, the distribution of the proportion and relative abundance of core and helper bacterial ASVs across soybean varieties is illustrated in the following charts: the top left pie chart shows the percentage of core and helper bacterial ASVs in the total number of ASVs; the top right pie chart shows the relative proportion of core and helper bacterial ASVs in the total abundance of the community; and the bottom stacked bar chart shows the relative abundance distribution of core (red) and helper (gray) bacterial ASVs in different soybean varieties (labeled on the X-axis), with the Y-axis representing the percentage relative abundance.

[0043] like Figure 5 As shown in the bar chart, the species composition (phylum level) of core and auxiliary microorganisms is analyzed. By calculating the species composition and abundance percentage of core and auxiliary microorganisms, it was found that auxiliary microorganisms have more taxa than core microorganisms, but their abundance is lower. The core microorganisms consist of 10 phyla: Actinobacteria, Proteobacteria, Bacteroidetes, Firmicutes, Acidobacteria, Chloroflexi, Nitrospirae, Verrucomicrobia, Gemmatimonadetes, and Planctomycetes. Their combined relative abundance accounts for 89.89% of the total rhizosphere bacterial community, and represents 48.46%, 39.13%, 9.79%, 2.04%, 0.27%, 0.10%, 0.07%, 0.07%, 0.04%, and 0.02% of the total abundance of core microorganisms, respectively. The percentages were 54, 121, 22, 14, 4, 3, 2, 1, 1, and 1, respectively; the auxiliary microorganisms included 29 phyla, accounting for 10.11% of the total rhizosphere bacterial community, among which the top 10 most abundant groups included Proteobacteria. 、 Actinobacteria, Bacteroidetes, Acidobacteria, Chloroflexi, Firmicutes, Planctomycetes, Verrucomicrobia, Gemmatimonadetes, and Entotheonellaeota accounted for 37.90%, 19.80%, 16.59%, 7.81%, 4.53%, 3.94%, 3.10%, 2.57%, 1.70%, and 0.60% of the total abundance of helper microorganisms, respectively, with quantities of 2188, 931, 707, 436, 728, 333, 861, 254, 167, and 37, respectively.

[0044] like Figure 6The bar chart shows the species composition (genus level) of the top 15 most abundant core and accessory microorganisms. At the genus level, the core microorganisms comprise 92 genera, with the top 15 groups including... Pseudarthrobacter, Paenarthrobacter, Pedobacter, Pseudomonas, Neorhizobium, Phyllobacterium, (Rhodococcus) Bradyrhizobium, Variovorax, Devosia, Rhizobium, Rhodococcus (Genus *Cymbidium*) Sphingobacterium, Ensifer (Glutamicin-like bacteria) and Paeniglutamicibacter (Mycobacteria genus), accounting for 33.41%, 14.41%, 8.47%, 6.19%, 5.81%, 4.17%, 3.16%, 3.08%, 2.54%, 1.86%, 1.54%, 1.33%, 0.75%, 0.74%, and 0.50% respectively, with quantities of 1, 1, 7, 9, 1, 2, 2, 3, 4, 7, 4, 2, 3, 1, and 5 respectively; accessory microorganisms included 438 genera, with the top 15 most abundant groups including... Mycobacterium (Aureobacterium genus) Pedobacter, Sphingomonas, Nocardioides, Streptomyces, Chryseobacterium (Flavobacterium) Flavobacterium Paenibacillus, Devosia, Pseudomonas, RB41, (Lysobacterium) Bryobacter (Rhodopseudomonas) Rubrobacter (Nitrifying Spirulina) and Novosphingobium, Nitrosospira They accounted for 9.36%, 3.24%, 3.16%, 2.66%, 2.47%, 2.39%, 2.35%, 2.26%, 2.08%, 1.94%, 1.77%, 1.72%, 1.66%, 1.62%, and 1.44% respectively, with quantities of 26, 25, 41, 14, 14, 52, 70, 8, 11, 23, 48, 7, 10, 3, and 12 respectively.

[0045] like Sphingobacterium The figure shows the analysis using a random forest model based on yield. To explore the importance of core and helper microorganisms to yield, the random forest model was used to assess the importance of core and helper microorganisms at the ASVs level. It was found that core microorganisms are more important for yield; a total of 74 ASVs were important for yield, of which 16 belonged to core microorganisms, accounting for 7.17% of core microorganisms; and 58 belonged to helper microorganisms, accounting for 0.82% of helper microorganisms. Important ASVs among core microorganisms include 6 phyla: Figure 7 Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, They accounted for 44%, 25%, 13%, 6%, 6%, and 6% respectively; 13 genera, including Acidobacteria and Nitrospirae Azospirillum, Cryobacterium, Devosia, Ensifer, Mycobacterium, Nitrospira, (Pseudomonas spp.), RB41, Olivibacter, Phyllobacterium, Pseudarthrobacter, Pseudoxanthomonas (Geobacterium); Important ASVs among helper microorganisms include 9 phyla, Sphingobacterium and TerrabacterAcidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Gemmatimonadetes, Planctomycetes, Proteobacteria and Verrucomicrobia; 31 genera, such as Acidibacter (Acidobacterium spp.) Bryobacter, Burkholderia-Caballeronia-Paraburkholderia, Devosia, Methylobacillus (Methylobacterium) , Microbacterium, Mycobacterium, Nocardioides, Paenibacillus and Stenotrophobacter (Oligotrophomonas spp.). The results showed that soybean yield in the plots varied with the abundance of core ASVs in the rhizosphere, indicating a significant linear correlation.

[0046] Example 3 Microbial isolation and ASV comparison: (1) Isolation of rhizosphere bacterial strains Bacterial strains were isolated from rhizosphere soils of different soybean varieties and from different geographical sources. Rhizosphere soils of different soybean varieties were collected from pot experiments of different soybean varieties in 2022; rhizosphere soils from different geographical sources were collected from soybean fields in Suzhou, Anhui Province, Yunnan Province, and Caoxinzhuang, Yangling County, Shaanxi Province. 1 g of soil was mixed with 9 mL of sterile water, serially diluted, and spread onto R2A, LB, and G1 media, and incubated at 28°C for 3–7 days. Morphologically different colonies were picked, purified, and DNA was extracted. The 16S rRNA gene was amplified using primers 27F / 1492R, and sequenced, then compared with the NCBI database. A total of 2850 bacterial strains were obtained and stored in glycerol tubes at -80°C.

[0047] like Figure 8 The following data are presented: isolation and identification of rhizosphere bacteria strains from different soybean varieties and geographical origins; (a) morphology of strains isolated in LB, R2A, and G1 media; (b) phylogenetic tree of the strains; BLAST alignment of the isolated strain sequences with the ASV core and auxiliary libraries was performed, with 100% sequence homology and an alignment length ≥253 bp, resulting in 68 core strains (corresponding to 25 ASVs) and 196 auxiliary strains (corresponding to 76 ASVs). Based on minimizing redundancy at the genus level, one or two strains were randomly selected at each genus level, ultimately selecting 25 core strains and 25 auxiliary strains for subsequent experiments. The results show that, based on the principles of minimizing redundancy and randomness at the genus level, 25 core strains and 25 auxiliary strains were finally matched.

[0048] like Figure 9 As shown, the phylogenetic distribution and frequency statistics of 25 core and 25 helper bacterial strains are presented. Figure 9 (a) A circular phylogenetic diagram shows the phylogenetic relationships of 50 selected bacterial strains (25 core strains and 25 helper strains); the outermost ring indicates the phylum to which each strain belongs, with Actinobacteria (blue), Bacteroidetes (yellow), Firmicutes (green) and Proteobacteria (red) distinguished by different colors, and the internal nodes represent different taxonomic levels; core strains are marked with red circles on the outer ring, and helper strains are marked with gray triangles; Figure 9(b) The bar chart shows the distribution frequency of core strains in the four dominant phyla at different order levels; the horizontal axis represents the bacterial order classification, and the vertical axis represents the number of core strains contained in the corresponding order; each bar is colored according to its phylum.

[0049] Given the above description of "minimizing redundancy based on genus-level classification", Figure 10 The morphological images of the 25 core and 25 helper bacterial strains shown further demonstrate their consistency with the screening criteria. Specifically, the morphology visually confirms the high diversity of the identified 50 strains, with a very low genus-level repetition rate, indicating representativeness. Since "morphology" is equivalent to the appearance of a strain, and different morphologies essentially indicate different genes, this invention covers a large number of different genera, ultimately classifying the 50 bacterial strains into 38 genera.

[0050] Example 4 Metabolite utilization capacity assay Prepare less than 1 L of M9 basic salt medium (Na₂HPO₄ 6.78 g / L, KH₂PO₄ 3.0 g / L, NaCl 0.5 g / L, NH₄Cl 1.0 g / L), autoclave, and then add 2 mL of sterile MgSO₄ solution (1M), 100 µL of CaCl₂ solution (1M), and 20 mL of filtered (0.22 µm filter membrane) carbon source solution sequentially, bringing the volume to 1000 mL to ensure a carbon concentration of 100 mmol / L. This yields a liquid M9 medium containing a carbon source. For example, using acetamide (C₂H₅NO), dissolve 2.95 g of solid acetamide in distilled water and bring the volume to 1000 mL.

[0051] A single colony was picked from a solid agar plate and inoculated into liquid R2A medium (0.25 g / L tryptone, 0.5 g / L acid-hydrolyzed casein, 0.5 g / L yeast extract, 0.5 g / L soluble starch, 0.3 g / L K₂HPO₄, 0.1 g / L MgSO₄, 0.3 g / L sodium pyruvate, 0.25 g / L peptone, 0.5 g / L glucose). The culture was incubated overnight at 28°C with a shaker. The cells were collected by centrifugation at 8000 rpm for 5 min. After discarding the supernatant, carbon-free M9 solution was added to adjust the OD. 600 Reserved at 0.2.

[0052] Add 198 μL of M9 medium containing a carbon source and 2 μL of bacterial suspension to each well of a 96-well plate, using M9 medium and M9 medium containing 0.5% DMSO as controls. Incubate at 28°C for 7 days, and measure OD every 12–72 hours. 600 Value. With maximum OD 600The values ​​were growth indicators. The core strain could utilize an average of 5.52 ± 0.557 metabolites, while the auxiliary strain could utilize an average of 3.8 ± 0.451 metabolites (p = 0.0203).

[0053] like Figure 11 As shown in the figure, the differences in growth changes and available metabolite quantities of 25 core strains and 25 helper strains mediated by metabolites are plotted. The p-values ​​obtained from the Wilcoxon rank-sum test for comparing the two groups are labeled above the box plot, indicating that the core strains have a wider range of carbon source utilization. Figure 12 As shown, the association between key metabolites and core strains and the differences in preferences among strains are evident, with core strains exhibiting a clear preference for different key metabolites.

[0054] Example 5 Functional trait assessment: Nine functional traits were evaluated in 50 strains: nitrogen fixation, inorganic phosphorus solubility, organic phosphorus solubility, potassium solubility, indoleacetic acid (IAA) production, ACC deaminase activity, siderophore production, biofilm formation, and extracellular polysaccharide production.

[0055] (1) Determination of nitrogen fixation capacity Single colonies were picked and inoculated into R2A liquid medium for activation. The strains were cultured in a shaker at 28 °C and 180 rpm until the logarithmic growth phase. The bacterial suspension was collected by centrifugation at 8000 rpm for 10 min and the cells were resuspended in sterile water to OD200. 600 =0.8 (OD 600 After mixing single-strain suspensions with a concentration of 0.8 in equal proportions, the OD was adjusted. 600 =0.8, which is the bacterial suspension); inoculate 10 μL of bacterial suspension onto Ashby nitrogen-free medium, with 3 replicates per strain (n=3); incubate in a constant temperature incubator at 28 ℃ for 7 days, observe its growth status and record whether a clear zone is formed around it; judge its ability based on the clear zone and the diameter of the colony growth (clear zone diameter D / strain diameter d).

[0056] (2) Determination of inorganic phosphorus solubility Prepare the bacterial suspension as described above. Inoculate 10 μL of the prepared bacterial suspension onto inorganic phosphorus solid medium, with 3 replicates per strain (n=3). Subsequent procedures are the same as above (1).

[0057] (3) Determination of organophosphorus solubility Prepare the bacterial suspension as described above. Inoculate 10 μL of the prepared bacterial suspension onto an organophosphate solid medium, with three replicates per strain (n=3). Subsequent procedures are the same as above (1).

[0058] (4) Determination of potassium solubilization capacity Prepare the bacterial suspension as described above. Inoculate 10 μL of the prepared bacterial suspension onto potassium-solubilizing solid medium, with 3 replicates per strain (n=3). Subsequent procedures are the same as above (1).

[0059] (5) IAA production capacity determination Prepare the bacterial culture to be used using the same method as above; take 10 μL of bacterial culture into Kings medium, with 3 replicates for each strain (n=3); after culturing in a shaker at 28 ℃ and 180 rpm for 3 days, centrifuge at 12000 r / min for 10 min, take 200 μL of supernatant into a 96-well plate, add an equal volume of Salkowski reagent colorimetric solution, and react in the dark for 30 min.

[0060] Results observation: A positive result (pink) indicates that the strain has the ability to secrete IAA; the darker the color, the stronger the secretion ability. A negative result (same color as the control medium) indicates that the strain does not have this ability. OD was measured for strains with positive results. 535 Finally, the ability of the strain to secrete IAA was calculated and characterized based on the standard curve.

[0061] Preparation of standard curves: Using IAA standards, standard solutions with concentrations of 10, 20, 30, 40, and 50 mg / L were prepared. 200 μL of each standard solution and Salkowski reagent were taken and incubated in the dark for 30 min, then the OD was immediately measured using a microplate reader. 535 The value was zeroed with culture medium containing the colorimetric solution, and the experiment was repeated three times to plot a standard curve.

[0062] (6) Assay of ACC deaminase activity Prepare the bacterial culture for use using the same method as described above. Determine the ACC deaminase activity of the strain using an ACC deaminase enzyme-linked immunosorbent assay kit (FANKEWEI Biotechnology, Shanghai, China). Follow the kit instructions for the assay procedure.

[0063] (7) Determination of iron production capacity Prepare the bacterial suspension as described above. Inoculate 10 μL of the bacterial suspension onto the ferrophilic detection solid medium, with 3 replicates per strain (n=3); incubate at 28 ℃ for 7 days, observe its growth and record whether an orange-yellow halo forms around it; determine its ability based on the orange-yellow halo and the diameter of the colony growth (diameter of orange-yellow halo D / diameter of strain d).

[0064] (8) Determination of biofilm formation capacity Prepare the bacterial culture as described above. Add 100 μL of R2A and 10 μL of bacterial culture to a 96-well plate and incubate at 37 °C for 72 h. After incubation, aspirate the bacterial culture and wash each well three times with 200 μL of phosphate buffer. Then, fix with 100 μL of methanol for 15 min and aspirate. Next, stain with 100 μL of 1% crystal violet solution for 5 min. After staining, wash the wells. After drying, add 100 μL of 33% glacial acetic acid solution and incubate at 37 °C for 30 min before measuring the OD. 590 Six replicates were made for each strain, and the average value (D value) was used as the test value; the uninoculated culture medium was used as a negative control, and twice the negative value was used as the cutoff value (Dc value).

[0065] Classification of biofilm formation capacity: 1) Strong ability (D>2×Dc); 2) Weak ability (Dc) <D≤2×Dc); 3) No ability (D≤Dc).

[0066] (9) Determination of extracellular polysaccharide production capacity Prepare the bacterial culture for use using the same method as described above. Determine the strain's ability to produce extracellular polysaccharides using a microbial extracellular polysaccharide (EPS) ELISA kit (Solarbio Biotechnology, Beijing, China). Follow the kit's instructions for the assay procedure.

[0067] like Figure 13The figure shows the differences in plant growth-promoting characteristics between core bacterial strains and helper bacterial strains. For nitrogen, 15 and 19 strains (60% and 76% of their respective strains) exhibited nitrogen-fixing activity, with D / d values ​​of 1.22–3.38 and 1.19–3.75, respectively. 4 and 8 strains (16% and 32% of their respective strains) showed strong nitrogen-fixing abilities. For phosphorus, 12 and 8 strains (48% and 32% of their respective strains) exhibited organic phosphorus-solubilizing activity, with D / d values ​​of 1.13–2.65 and 1.05–2.27, respectively. 3 and 4 strains (12% and 16% of their respective strains) showed strong organic phosphorus-solubilizing abilities. 7 and 7 strains (both 28% of their respective strains) exhibited inorganic phosphorus-solubilizing activity, with D / d values ​​of 1.12–1.48 and 1.12–1.85, respectively. 2 and 4 strains (8% and 9% of their respective strains) showed strong inorganic phosphorus-solubilizing abilities. The bacteria (7 and 5 strains, accounting for 28% and 20% of their respective strains) showed strong ability to solubilize inorganic phosphorus. For potassium, 7 and 5 strains (accounting for 28% and 20% of their respective strains) showed potassium-soothing activity, with D / d values ​​of 1.12~2.11 and 0.59~2.79, respectively. 3 and 3 strains (accounting for 12% and 12% of their respective strains) showed strong potassium-soothing ability. Twenty and twenty-two strains (80% and 88% of their respective strains) were capable of producing IAA, with yields of 0.331–95.107 and 0.360–56.260 mg / L, respectively. Eight and five strains (32% and 20% of their respective strains) showed relatively strong production capabilities. 100% of the strains were capable of producing ACC deaminase, with yields of 54.196–145.176 and 54.980–144.000 ng / L, respectively. Thirteen and twelve strains (52% and 48% of their respective strains) showed relatively strong production capabilities. Thirteen and nineteen strains (52% and 76% of their respective strains) were capable of producing siderophores, with yields of 1.057–1.477 and 1.097–4.870, respectively. One and six strains (4% and 24% of their respective strains) showed relatively strong production capabilities. Regarding biofilm formation, 17 strains (68% of their respective strains) were capable of forming biofilms, with yields ranging from 0.120 to 3.106 and 0.125 to 1.516, respectively. Seven and six strains (28% and 24% of their respective strains) showed stronger biofilm formation capabilities. For EPS production, 100% of the strains possessed this capability, with yields ranging from 13.626 to 28.610 and 5.831 to 29.633 ng / mL, respectively. Nine strains (36% of their respective strains) showed stronger capabilities in both categories.

[0068] The results showed that, despite exhibiting similar characteristics in individual traits, the core strains showed greater potential for overall plant growth-promoting properties.

[0069] Example 6 Application validation of synthetic microbial communities: Vermiculite and perlite were mixed at a volume ratio of 2:1, and an equal volume of nutrient solution was added and mixed thoroughly to prepare a mixed culture medium. 350 g of the medium was placed into planting bags and sterilized by autoclaving at 121 ℃ for 1 h. The widely cultivated soybean variety, Zhonghuang 13, was selected as the planting target. After surface sterilization with 75% alcohol and 50% sodium hypochlorite, the soybeans were planted in the sterile medium. Once the soybeans sprouted, the bags were opened and thinned. The bags were immediately sealed after thinning to ensure a sterile environment around the roots. At the single-leaf stage of the soybeans, a single bacterial strain or a synthetic bacterial flora was inoculated along the soybean roots. Two weeks after inoculation, the phenotypic indicators of the plants—fresh weight and dry weight of the roots, stems, and leaves—were measured.

[0070] Inoculation treatment: Core single strains (CR1~CR25), auxiliary single strains (NR1~NR25), and two synthetic microbial groups (Core SynComs and Accessory SynComs) were inoculated separately. A control group (inoculated with inactivated strain: Heat-killed SynComs) and an experimental group (inoculated with live strain: Live SynComs) were set up, with a total of 104 treatments: (25 core strains + 25 auxiliary strains + 2 synthetic microbial groups) × 2 treatments, with 20 replicates for each treatment (2 plants per bag, 10 bags in total), for a total of 1040 bags.

[0071] Preparation of bacterial culture: Single colonies were picked and inoculated into R2A liquid medium for activation. After culturing to the logarithmic growth phase in a shaker at 28 ℃ and 180 rpm, the culture was further expanded. The cells were collected by centrifugation at 8000 r / min for 5 min and resuspended in sterile water to OD200. 600 =0.5 (synthetic microbial community is composed of OD) 600 Mix single bacterial cultures with an OD ratio of 0.8 in equal proportions, and then adjust to the appropriate OD value. 600 =0.5); half of the prepared bacterial suspension was inactivated at 121 ℃ for 30 min to prepare an inactivated bacterial solution as a control (Heat-killed SynComs), and the other half of the bacterial suspension was used as a treatment (core strain, auxiliary strain, core live SynComs and accessory live SynComs); 5 mL of bacterial solution or inactivated bacterial solution was injected into each plant.

[0072] like Figure 14 The effects of core and auxiliary synthetic bacterial communities on soybean dry weight are shown in the figure. The bar charts illustrate the plant dry weights compared to the control group (containing a core community of 25 core strains, an auxiliary community of 25 auxiliary strains, and a heat-inactivated synthetic bacterial community). Error bars represent the standard deviation of biological replicates. An asterisk indicates statistically significant differences between core and auxiliary synthetic bacterial community treatments at all tissue sites (Wilcoxon rank-sum test: *p<0.05, **p<0.01, ***p<0.001).

[0073] For inactivated bacterial strains, neither the core nor the auxiliary bacterial communities had a significant effect on roots, stems, and leaves. However, for live bacterial strains, the growth-promoting effects of the two synthetic bacterial communities on soybean roots, stems, and leaves were: core synthetic community > auxiliary synthetic community. Specifically, the core synthetic community significantly promoted the growth of soybean roots, stems, and leaves in terms of dry and fresh weight, with growth-promoting efficiencies of 98.34% and 138.25%, 43.3% and 66.4%, and 73.45% and 76.82%, respectively. Furthermore, the dry weight of roots, stems, and leaves in the core synthetic community treatment group was significantly higher than that in the auxiliary synthetic community group.

[0074] like Figure 15 As shown, the overall plant growth-promoting capacity of the core strain and the helper strain is compared, quantified by the growth-promoting rate of soybean dry weight under controlled conditions. Figure 16 As shown, the ability of core and helper bacterial strains to promote plant growth in soybean roots, stems, and leaves was calculated by measuring the dry weight of the plants relative to the sterile control group.

[0075] In the single-strain addition experiment, the growth-promoting efficiency of the core single-strain treatment group was significantly higher than that of the non-core single-strain control group (p = 0.018). Compared with the control group, the treatment group significantly promoted the dry weight of soybean roots (p = 0.0014), while there was no significant difference in the dry weight of stems and leaves. Among them, 22 core strains had a growth-promoting effect on soybean roots (accounting for 88% of core strains), while only 12 non-core strains (accounting for 48% of non-core strains) had such an effect; 18 and 13 strains, respectively, had a growth-promoting effect on soybean stems (accounting for 72% and 52% of their respective strains); and 17 and 13 strains, respectively, promoted soybean leaf biomass (accounting for 68% and 52% of their respective strains).

[0076] The results showed that the core microbial community constructed by the present invention had the best growth-promoting effect on soybeans, which was significantly stronger than that of the auxiliary microbial community. Furthermore, the growth-promoting effect of the core microbial community members on soybeans was also significantly stronger than that of the auxiliary microbial community members.

[0077] The verification system of this invention is more systematic and in-depth. Carbon source utilization ability serves as a bridge, directly demonstrating through experiments that the "core strain" has a broader and more efficient capacity for utilizing key carbon sources than the "auxiliary strain." This establishes a direct and measurable causal link between the identity of the "core strain" and its "colonization advantage." Up to nine plant growth-promoting functional traits were evaluated, and it was found that the core strain has a greater advantage in overall functional potential. Large-scale inoculation verification was conducted, including an unprecedented number of potted plant validations (50 single strains + 2 synthetic microbial communities × live / inactivated controls, totaling 1040 bags), with highly convincing results. The data clearly show that the growth-promoting effect of the core synthetic microbial community is significantly better than that of the auxiliary synthetic microbial community, and the core strain, as an individual, also has a significantly better growth-promoting effect than the auxiliary strain. This last point is also crucial, demonstrating that the core strains selected through the "abundance-occupancy model" and "carbon source utilization" not only have strong individual capabilities but also produce synergistic effects when used in combination, thus achieving a "1+1>2" effect.

[0078] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a rhizospheric synthetic consortium based on carbon source utilization and abundance-occupancy models, characterized by, Includes the following steps: (1) Collect rhizosphere soil samples from multiple varieties and / or sources of the target plant for non-targeted generation. omics analysis was used to identify metabolites in the samples; (2) Based on the fact that metabolites were consistently detected in at least 50% of the samples and their relative abundance ranking The top 10% criteria are used to screen key metabolites; (3) The ASV sequence of rhizosphere microorganisms was obtained by sequencing the V4 region of the 16S rRNA gene. An ASV table was constructed, along with core and auxiliary microbial communities. By performing correlation network analysis between key metabolites and core ASVs, positively correlated metabolite-microbe pairs were identified to obtain a core ASV library and an auxiliary ASV library.

2. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, The criteria for constructing the core and auxiliary microbial communities are as follows: the core microbial community consists of ASVs that are prevalent in more than 80% of the samples and ASVs whose relative abundance is in the top 10% of all samples, and the rest are auxiliary microbial communities.

3. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, The correlation network analysis between the key metabolites and the core ASVs is as follows: Spearman correlation analysis is used to pairwise calculate the abundance of each metabolite with each ASV to obtain a correlation coefficient, with a value ranging from -1 to 1. When the correlation coefficient is greater than 0 and passes the statistical test, with a p-value < 0.05, it is determined to be a "positive correlation pair", further confirming the core microbial community.

4. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, This also includes the validation of the core microbiota and auxiliary microbiota: Assessment of the utilization capacity of key metabolites: Based on the constructed core microbial community and auxiliary microbial community, with the auxiliary microbial community as a control, the utilization capacity of the core microbial community and auxiliary microbial community for key metabolites was assessed, and the maximum OD value was determined by growth curves. Strains were isolated from rhizosphere soils of different varieties and geographical origins. The sequences of the isolated strains were compared with the core ASV library and the auxiliary ASV library by BLAST. Strains were identified as the same strains if the sequence homology was 100% and the comparison length was ≥253bp. Based on the results of strain homology comparison, metabolite utilization capacity and functional trait assessment of core strains and helper strains, core single strains and / or core synthetic microbial communities were further obtained.

5. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, In step (1), the number of rhizosphere soil samples collected from multiple varieties and / or sources of the target plant is ≥30.

6. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, The target plant is soybean.

7. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 6, characterized in that, The core microbial species in the rhizosphere microorganisms of the soybean plant include at least the following: Pseudarthrobacter , Paenarthrobacter , Pedobacter , Pseudomonas , Neorhizobium , Phyllobacterium , Bradyrhizobium , Variovorax , Devosia , Rhizobium , Rhodococcus , Sphingobacterium , Ensifer , Paeniglutamicibacter and Mycobacterium One or more of them.

8. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, The non-targeted metabolomics analysis in step (1) uses gas chromatography-mass spectrometry (GC-MS), with the extraction solvent being methanol:isopropanol:water in a volume ratio of 3:3:

2. The derivatization process includes methoxyamine hydrochloride and BSTFA, containing 1% TMCS. The reaction is then performed to identify the measurement results.

9. The method for constructing rhizosphere synthetic microbial communities based on carbon source utilization and abundance-occupancy models according to claim 1, characterized in that, In step (2), the 16S rRNA gene sequencing is performed by using primer pairs 515F and 806R to target and amplify the V4 region fragment of the bacterial 16S rRNA gene. The ASV table is constructed by using DADA2 software in RStudio to perform quality control, filtering, and paired-end sequence splicing on the FASTA sequence file obtained from sequencing, and using the SILVA database to annotate the bacterial ASV sequences to obtain the ASV sequences, annotation information, and sample read lengths.

10. The use of the rhizosphere synthetic microbial community construction method based on carbon source utilization and abundance-occupancy model as described in any one of claims 1 to 9 to obtain core strains, core microbial communities and / or auxiliary strains and auxiliary microbial communities in improving soil and / or promoting plant growth and high-quality cultivation.

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