Soil flora analysis method, related soil flora and application thereof

Abstract

The invention provides a soil flora analysis method, a related soil flora and application thereof. Based on a soil long-term positioning test, an absolute quantitative method is used for the first time to explore a dynamic assembly rule of rhizosphere and in-root microbiome in a plant full life cycle, and the response characteristics of the rhizosphere microbiome to different fertilization treatments are studied. By combining methods of metagenome, microorganism isolated culture, community synthesis and the like, functional adaptation of rhizosphere microorganisms under different fertilization conditions and a mechanism of influencing the growth of leguminous plants are disclosed. The invention also provides a method for improving the chemical properties and soil flora of soil; the invention also provides an artificially synthesized flora which has a promoting effect on the growth and biomass of leguminous plants.

Description

Technical Field

[0001] The present invention belongs to the field of botany. More specifically, the present invention relates to a method for analyzing soil flora, related soil flora and their applications. Background Art

[0002] Soil microbial communities are interacting communities living in soil, consisting of bacteria, archaea, fungi, viruses, and protists, etc. The taxonomic diversity of these microbial communities and the abundance of their individual members are called community structure. The soil microbiome is a key component of ecosystems and the most diverse community in the biosphere, possessing more than a quarter of the total global biodiversity.

[0003] Tens of millions of bacteria, archaea, fungi, viruses, and protists coexist underground, and hundreds of thousands have been described. Although with the development of technologies, techniques such as marker genes, genomes, metabolomes, metagenomes, and metatranscriptomes have improved people's understanding of the soil microbiome. However, most soil microorganisms remain undescribed.

[0004] Soil microbial community structures are diverse in different ecosystems and microhabitats. Multiple soil factors can shape the composition of microbial communities, including pH value, nutrient levels, the content and quality of organic matter, available moisture, and oxygen levels.

[0005] Soil microorganisms are participants in forming soil structure and important indicators of soil quality. Soil structure is not a simple combination of soil particles and chemical fertilizers, but a three-dimensional arrangement of organic complexes (aggregates) and pore spaces. As an important part of the soil, during their life processes, soil microorganisms contribute to the formation of aggregate structures of soil particles through oxygen and carbon dioxide exchange caused by metabolic activities and secreted organic acids, etc. Arbuscular Mycorrhizal Fungi (AMF) play an important role in this process. The importance of soil microorganisms has been widely recognized, and they play important roles in nutrient cycling, carbon fixation, environmental remediation, soil fertility, and plant health and productivity, etc.

[0006] Living soil microorganisms are the main engines of terrestrial biogeochemical cycles, driving the turnover of soil organic matter through their major metabolic activities. The cell debris after microbial death can serve as fuel for the biogeochemical engine because its chemical composition accumulates as soil organic matter in the soil, constituting about 50% of the soil organic matter pool. Soil microorganisms are mainly responsible for the cycling of soil organic carbon (SOC) and other nutrients and play a key role in climate feedbacks, including the production and consumption of greenhouse gases such as CO2, CH4, and N2O. Soil microorganisms are involved in the mineralization and stabilization of organic carbon, and the balance between these two processes controls the net flux of greenhouse gases into the atmosphere, affecting the global climate. Soil microorganisms are game-changers in the restoration of degraded lands. In arid soils, some algae can enhance the water-holding and water-conducting capacities of the soil by breaking down hydrophobicity and improving water retention, while fungal hyphae can penetrate air-filled soil pores to obtain water and nutrients. Different bacteria can also produce hydrophilic or hydrophobic substances as part of extracellular polymeric substances (EPSs), thus affecting soil moisture. For example, Bacillus sphaericus in the phylum Firmicutes is hydrophilic; some bacteria in the phyla Actinobacteria and Proteobacteria can degrade waxes, thereby reducing hydrophobicity. In saline-alkali soils, salt-tolerant or halophilic plant growth-promoting rhizobacteria (PGPR) and AMF have been used for the bioremediation of saline-alkali soils, which is also a sustainable alternative to conventional physical and chemical remediation. Halophilic PGPR can directly remediate saline soils by improving soil nutrient conditions, soil structure, organic matter, pH, electrical conductivity, and ion salt deposition. In contaminated soils, some indigenous microorganisms can degrade pollutants through metabolic activities, playing a role in bioremediation. Some studies have reported that PGPR also has an indirect positive effect on the removal of pollutants, and these microorganisms can improve the ability of plants to remediate contaminated soils by stimulating plant growth and increasing the bioavailability of pollutants. Microorganisms can also assist in the remediation of soils contaminated with heavy metals, pesticides, or hydrocarbon compounds, and this ability further promotes the restoration of degraded ecosystems. For example, when reintroducing vegetation into contaminated soils, inoculating the soil with fungi can not only absorb heavy metals from the contaminated soil but also enable plants to successfully colonize in the degraded soil, thus improving the quality and health of the soil.

[0007] Rhizosphere microorganisms have a close relationship with host plants. Plants, due to their unique genetic, physiological, and metabolic characteristics, affect the composition and structure of the rhizosphere microbial community. Conversely, the rhizosphere microbial community also plays a crucial role in plant growth, development, health, and yield due to its complex interactions and functions.

[0008] To increase the yield of food crops, large amounts of nitrogen, phosphorus, and potassium fertilizers have been input into agricultural production, which not only causes water eutrophication and air pollution but also seriously damages the structure and function of soil microorganisms. Plant rhizosphere microorganisms play important functions in soil nutrient cycling, crop nutrition, and health. However, the dynamic response characteristics of the rhizosphere microbial community under long-term different fertilization conditions and its interaction relationship with crops still lack systematic research.

[0009] Soil microorganisms are crucial for nutrient cycling and crop health, and play roles in regulating plant growth and development, promoting plant nutrient uptake, and enhancing plant resistance to abiotic and biotic stresses. During the plant life cycle, there is still a lack of comprehensive understanding of the response of the root-associated microbial community structure and its function under nutrient deficiency conditions. The interaction between rhizosphere microorganisms and plants is relatively complex. Although many current studies have revealed some of the underlying mechanisms, there are still many unclear issues. Therefore, it is necessary in this field to further develop effective analysis methods and explore more technical solutions for improving soil, optimizing soil flora, and increasing yields. Revealing the interaction mechanism between plants and rhizosphere microorganisms under different nutrient conditions is beneficial to achieving healthy growth and increased yields of crops by improving the rhizosphere microbial community and other means. Summary of the Invention

[0010] The purpose of the present invention is to provide a method for analyzing soil flora, related soil flora, and their applications.

[0011] In the first aspect of the present invention, a method for improving the soil properties of fertilized soil and soil flora, or promoting symbiotic nitrogen fixation between rhizobia and leguminous plants is provided, including: adjusting the fertilization methods of nitrogen, phosphorus, and potassium fertilizers and reducing (including reducing to 0) the nitrogen fertilizer application rate.

[0012] In one or more embodiments, the improvement of soil properties includes: reducing soil acidification (increasing the soil pH value, such as increasing by 0.5 units or 1 unit or more), increasing the number and diameter of root nodules, and increasing the abundance of rhizobia (the amount of symbiotic rhizobia in the rhizosphere); preferably, in leguminous plants, the development pattern of the flora changes, its α-diversity increases in the early stage of development, the nitrogen metabolism function of the flora is enhanced, and the nitrogen mineralization ability of the flora is enhanced; preferably, in leguminous plants, the complexity and stability of the rhizosphere flora co-occurrence network are improved.

[0013] In one or more embodiments, the soil includes: black soil.

[0014] In one or more embodiments, the black soil is black soil for cultivating or growing plants.

[0015] In one or more embodiments, phosphate fertilizer and potassium fertilizer are applied at normal application rates.

[0016] In one or more embodiments, the method maintains (without reducing or affecting) or increases the yield of plants while improving the soil properties and soil flora of the fertilized soil, or promoting the symbiotic nitrogen fixation of rhizobia and leguminous plants.

[0017] In one or more embodiments, the method causes changes in the assembly of rhizosphere and endophytic flora, and changes dynamically with the development of plants.

[0018] In one or more embodiments, leguminous plants are planted in the soil, and the fertilization method is: 75 kg N ha -1 , 150 kg P2O5 ha -1 , 75 kg K2O ha -1 , where the application rates of N, P2O5, or K2O can fluctuate by 50% (preferably 40%, 30%, 20%, 10%, or 5%).

[0019] In one or more embodiments, the nitrogen fertilizer includes urea (containing active ingredient N, or with N as the main active ingredient), the phosphate fertilizer includes triple superphosphate (containing active ingredient P2O5, or with P2O5 as the main active ingredient) and diammonium phosphate (containing active ingredient P2O5, or with P2O5 as the main active ingredient), and the potassium fertilizer includes potassium sulfate (containing active ingredient K2O, or with K2O as the main active ingredient).

[0020] In one or more embodiments, the cultivation or planting is the cultivation or planting of soybean - corn - wheat rotation.

[0021] In one or more embodiments, the fertilization method in the wheat season and the corn season is: 150 kg N ha -1 , 75 kg P2O5 ha -1 , 75 kg K2O ha -1 . Preferably, the application rates of each component can fluctuate by 50% (preferably 40%, 30%, 20%, 10%, or 5%).

[0022] In one or more embodiments, nitrogen, phosphorus, and potassium fertilizers are all applied after the harvest of autumn crops; preferably, 50% of the nitrogen fertilizer in the corn season is applied at the jointing stage.

[0023] In one or more embodiments, the method further includes: preparing synthetic community SynCom5 or SynCom7, applying (such as inoculating) it to leguminous plants to promote the growth of leguminous plants, increase biomass, increase the level of growth hormone, and improve ACC deaminase activity.

[0024] In one or more embodiments, SynCom5 includes the following strains: Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., Phyllobacterium sp.

[0025] In one or more embodiments, SynCom7 includes: the strains of synthetic community SynCom5, as well as Bosea sp. and Aeromicrobium sp.

[0026] In one or more embodiments, the increase in biomass includes: increasing the plant height of leguminous plants and increasing the plant weight of leguminous plants; or, the growth hormone includes: IAA.

[0027] In another aspect of the present invention, there is provided the application of the described method for improving the soil properties and soil flora of fertilized soil, or promoting the symbiotic nitrogen fixation of rhizobia and leguminous plants.

[0028] In another aspect of the present invention, there is provided the application of the described method for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and improving ACC deaminase activity; preferably, the increase in biomass includes: increasing plant height and increasing plant weight; preferably, the growth hormone includes: IAA.

[0029] In another aspect of the present invention, there is provided a synthetic community, selected from synthetic community SynCom5 or synthetic community SynCom7; SynCom5 includes the following strains: Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., Phyllobacterium sp.; SynCom7 includes: the strains of synthetic community SynCom5, as well as Bosea sp. and Aeromicrobium sp.; preferably, the synthetic community is contained in a composition; more preferably, the composition is a solution, suspension medium.

[0030] In one or more embodiments, the preservation number of Rhodococcus sp. at the China Center for Type Culture Collection is CCTCC M 2024006, the preservation number of Lysobacter sp. at the China Center for Type Culture Collection is CCTCC M2024011, the preservation number of Terrabacter sp. at the China Center for Type Culture Collection is CCTCC M2024007, the preservation number of Arthrobacter sp. at the China Center for Type Culture Collection is CCTCC M2024009, the preservation number of Phyllobacterium sp. at the China Center for Type Culture Collection is CCTCC M2024010, the preservation number of Bosea sp. at the China Center for Type Culture Collection is CCTCC M 2024008, and the preservation number of Aeromicrobium sp. at the China Center for Type Culture Collection is CCTCC M 2024005.

[0031] In another aspect of the present invention, there is provided a composition containing the above-mentioned synthetic microbial community or a culture of the synthetic microbial community; preferably, it further includes an agriculturally acceptable carrier or culture medium components (nutritional components suitable for strain culture).

[0032] In one or more embodiments, the composition includes: a pesticide composition; preferably, the dosage form of the composition includes (but is not limited to): a solution (such as a sprayable solution), a suspension, an emulsion, an oily or aqueous dispersion system, a powder (such as a freeze-dried powder), a granule, or a microcapsule; preferably, the composition is a culture medium containing the synthetic microbial community.

[0033] In another aspect of the present invention, there is provided the use of the above-mentioned synthetic microbial community or the composition for promoting the growth of leguminous plants, increasing biomass, increasing the growth hormone level, and enhancing ACC deaminase activity; or for preparing a composition or kit for promoting the growth of leguminous plants, increasing biomass, increasing the growth hormone level, and enhancing ACC deaminase activity.

[0034] In another aspect of the present invention, there is provided a method for promoting the growth of leguminous plants, increasing biomass, increasing the growth hormone level, and enhancing ACC deaminase activity, including: applying (such as inoculating) the above-mentioned synthetic microbial community or the composition to leguminous plants.

[0035] In another aspect of the present invention, there is provided a kit for promoting the growth of leguminous plants, increasing biomass, increasing the growth hormone level, and enhancing ACC deaminase activity, which includes the above-mentioned synthetic microbial community or the composition.

[0036] In another aspect of the present invention, there is provided a method for analyzing the ecology of soil flora (including rhizosphere flora) under different fertilization methods (the existing situation, including determining the characteristics and functions of core bacteria (including potential functions)), comprising: (1) fertilizing plants (such as leguminous plants) with different fertilization methods; (2) obtaining the abundances of core ASVs in treatment groups with different fertilization methods; (3) for the abundances of core ASVs obtained in (2), analyzing the differences in the abundances of core ASVs in different treatment groups by an absolute quantification method; identifying biomarker taxa (core bacteria biomarker taxa) in different fertilization treatment groups or different developmental stages (including different time points), preferably by using linear discriminant analysis (LEfSe) and / or the random forest method; (4) analyzing the functions of the flora by using a flora function annotation database; preferably, the flora function annotation database includes the FAPROTAX database; (5) analyzing the differences in the functions of the flora in different fertilization treatment groups by metagenomic sequencing and / or assembly.

[0037] In one or more embodiments, in (3), for the flora (such as rhizosphere soil flora), using linear discriminant analysis to analyze the differential species in treatment groups with different fertilization methods or at different developmental stages; preferably, performing the above analysis on the flora from phylum to species.

[0038] In one or more embodiments, in (3), using the random forest method for analysis and establishing a random forest prediction model; preferably, establishing a random forest prediction model for the flora at the family, genus, and / or species levels.

[0039] In one or more embodiments, in (5), the metagenomic analysis includes (but is not limited to): sequencing, classification, gene (function) annotation; annotating to databases (including KEGG, COG, and CAZyme), calculating the functional diversity between samples in treatment groups with different fertilization methods, including α-diversity and β-diversity; analyzing the abundance differences of functional genes related to microbial nitrogen, phosphorus, and potassium cycles in treatment groups with different fertilization methods.

[0040] In one or more embodiments, the method further includes: performing co-occurrence network analysis and / or ecological cluster function analysis to obtain the flora communities that play a decisive role under different fertilization methods; preferably, further including isolating the flora communities that play a decisive role.

[0041] In one or more embodiments, the co-occurrence network analysis includes: calculating the Spearman correlation between each ASV based on the absolute abundances of the rhizosphere and endorhizal core ASVs, and using the data with a correlation coefficient r > 0.8 and a significance P < 0.05 to construct the bacterial co-occurrence networks of the treatment groups with different fertilization methods in the rhizosphere and endorhiza, and calculating the basic network topological characteristics including the number of nodes, the number of edges, the average degree, and the robustness of each network; preferably, the robustness of the rhizosphere or endorhizal microbial communities of the treatment groups with different fertilization methods is further analyzed by randomly removing 50% of the nodes in the network.

[0042] In one or more embodiments, the microbial communities that play a decisive role include (but are not limited to): the microbial communities that improve the soil properties of the fertilized soil and the soil microbiota, the microbial communities that promote the symbiotic nitrogen fixation of rhizobia and leguminous plants, and the microbial communities that promote the growth of leguminous plants, increase biomass, increase the level of growth hormones, and improve the ACC deaminase activity.

[0043] In one or more embodiments, the different fertilization methods include (but are not limited to): changing the fertilization methods of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer; preferably, the change includes an increase or decrease (including reducing to 0) in the application rates of nitrogen fertilizer, phosphorus fertilizer, and / or potassium fertilizer.

[0044] In one or more embodiments, changing the fertilization methods of nitrogen fertilizer, phosphorus fertilizer, and potassium fertilizer forms different fertilization treatment groups.

[0045] In one or more embodiments, after obtaining the microbial communities that play a decisive role under different fertilization methods, it further includes: artificially preparing the microbial communities to form a synthetic community, and performing re-inoculation to verify the functions of the microbial communities; preferably, after re-inoculation, finally achieving the improvement of the soil properties of the fertilized soil and the microbial communities of the soil microbiota, the promotion of the symbiotic nitrogen fixation of rhizobia and leguminous plants, and / or the promotion of the growth of leguminous plants and the microbial communities that increase biomass.

[0046] In another aspect of the present invention, there is provided the use of the method for analyzing the ecology of soil microbiota (including rhizosphere microbiota) under different fertilization methods (the existing situation, including determining the characteristics and functions (including potential functions) of the core bacteria), so as to obtain the microbial communities that play a decisive role under different fertilization methods; preferably, the microbial communities include (but are not limited to): the microbial communities that improve the soil properties of the fertilized soil and the soil microbiota, the microbial communities that promote the symbiotic nitrogen fixation of rhizobia and leguminous plants, and the microbial communities that promote the growth of leguminous plants, increase biomass, increase the level of growth hormones, and improve the ACC deaminase activity.

[0047] Other aspects of the present invention will be apparent to those skilled in the art from the disclosure herein. Description of the Drawings

[0048] Figure 1 Design and use of internal standard plasmids

[0049] Figure 2 Overall experimental design. (A) Randomized block design of the black soil fertility location experiment. (B) Schematic diagrams of each growth and development stage of soybean. (C) Schematic diagram of soybean rhizosphere and each sequencing project

[0050] Figure 3 Soil physical and chemical properties. (A - F) are the available nitrogen content, available phosphorus content, available potassium content, organic matter content, soluble organic carbon content, and pH value of non-rhizosphere soil in each fertilization treatment, respectively. Error bars represent standard deviation SD, and asterisks indicate significant differences compared with the control NPK treatment (*P < 0.05, **P < 0.01, ***P < 0.001).

[0051] Figure 4 Soybean yield and nodule phenotypes. (A and B) are the soybean yields in two consecutive planting seasons in 2020 and 2017, respectively. (C and D) are the nodule number and nodule diameter of soybean at the early flowering stage (D42), respectively. Error bars represent standard deviation SD, and asterisks indicate significant differences compared with the control NPK treatment (*P < 0.05, **P < 0.01, ***P < 0.001).

[0052] Figure 5 Relationship between the addition amount of internal standard plasmid and the number of sequences. (A - C) are the relationships between the addition amount of internal standard plasmid and the number of internal standard sequences in the sequencing results in non-rhizosphere soil, rhizosphere soil, and root samples, respectively

[0053] Figure 6 Diversity of bacterial communities. All results are calculated based on absolute abundances. (A - C) are the Shannon diversity indices of bacterial communities in non-rhizosphere soil, rhizosphere soil, and roots, respectively. (D - F) are the β-diversity difference analyses of bacterial communities in non-rhizosphere soil, rhizosphere soil, and roots, respectively. (G) Relationship between the time distance and Bray - Curtis distance among different samples in each fertilization treatment in rhizosphere soil (left) and root samples (right). (H) Relationship between the development time of each sample and the Bray - Curtis distance of PK, NK, and NP samples relative to the NPK sample in rhizosphere soil (left) and root samples (right).

[0054] Figure 7 Composition of bacterial communities. (A - C) are the absolute abundances of bacteria (upper) and the relative abundances of the main 9 phyla (lower) in non-rhizosphere soil, rhizosphere soil, and root samples, respectively. Lowercase letters represent significant differences in bacterial absolute abundances among different development stages (P < 0.05).

[0055] Figure 8 Composition of the rhizobial community. (A - C) show the absolute abundances (upper) and relative abundances (lower) of rhizobia in non-rhizosphere soil, rhizosphere soil, and nodule samples, respectively. Lowercase letters represent significant differences in the absolute abundances of rhizobia among different developmental stages (P < 0.05).

[0056] Figure 9 Dynamic change trends of bacterial abundances over time. (A - B) show the dynamic change trends of the absolute and relative abundances of bacteria (including Proteobacteria, Actinobacteria, Bacteroidetes, Firmicutes, Acidobacteria, and Chloroflexi) in rhizosphere soil and endophyte samples during plant development.

[0057] Figure 10 Dynamic change patterns of rhizosphere ASVs during developmental stages. (A - D) represent the dynamic change patterns between the relative abundances (left) and absolute abundances (right) of high-abundance rhizosphere bacterial ASVs in NPK, PK, NK, and NP treatments during plant development. (E) shows the proportions of ASVs with similar change trends (green) and opposite change trends (red) in the dynamic changes of relative and absolute abundances for each fertilization treatment.

[0058] Figure 11 Differences in rhizosphere core ASVs among different fertilization treatments. All results were calculated based on absolute abundances. (A) shows the types (left) and proportions of absolute abundances (right) of rhizosphere core ASVs in total rhizosphere ASVs. (B) shows the relationship between the abundances of rhizosphere core ASVs and total rhizosphere ASVs in four different fertilization treatments. (C) shows the absolute abundances of rhizosphere core ASVs in each fertilization treatment and at each developmental stage.

[0059] Figure 12 Rhizosphere differential core ASVs in each fertilization treatment. All results were calculated based on absolute abundances. (A - C) show the rhizosphere differential core ASVs between PK, NK, and NP treatments and the normal fertilization treatment. Red represents excluded, blue represents enriched, and the colors of the clades represent different bacterial phylum-level classifications. All ASVs in the figure are significantly different (P < 0.05).

[0060] Figure 13 Analysis of biomarker taxa in each fertilization treatment. All results were calculated based on absolute abundances. The figure shows biomarker taxa (at the family level) in different fertilization treatments, P < 0.05, LDA score > 6.

[0061] Figure 14, Analysis of biomarker taxa at each developmental stage. All results were calculated based on absolute abundances. The figure shows biomarker taxa (family level) at different growth and developmental stages, P<0.05, LDA score>6.

[0062] Figure 15 , Random forest analysis of rhizosphere samples based on fertilization treatments. All results were calculated based on absolute abundances, and all data were analyzed at the family taxonomic level. (A) The importance of 16 biomarker taxa is arranged from high to low, the column colors represent the phylum-level classification of different bacteria, and the inset shows the 10-fold cross-validation error rate. (B) The absolute abundances of 16 biomarker taxa in different fertilization treatments.

[0063] Figure 16 , Random forest analysis of rhizosphere samples based on developmental stages. All results were calculated based on absolute abundances, and all data were analyzed at the family taxonomic level. (A) The importance of 16 biomarker taxa is arranged from high to low, the column colors represent the phylum-level classification of different bacteria, and the inset shows the 10-fold cross-validation error rate. (B) The absolute abundances of 16 biomarker taxa in different developmental stages.

[0064] Figure 17 , Functional differences of rhizosphere core ASVs among different fertilization treatments. All results were calculated based on functional abundance ratios. The figure shows the abundance differences of 16 bacterial functions with relatively high abundances among different fertilization treatments in the rhizosphere, and the asterisks indicate significant differences compared with the normal fertilization treatment (*P<0.05, **P<0.01, ***P<0.001).

[0065] Figure 18 , Functional differences of rhizosphere core ASVs among different developmental stages. All results were calculated based on functional abundance ratios. The figure shows the abundance differences of 16 bacterial functions with relatively high abundances among different developmental stages in the rhizosphere, and the lowercase letters represent significant differences in the relative abundances of bacterial functions among different developmental stages (P<0.05).

[0066] Figure 19 , Phylogenetic analysis and functional annotation of bacterial bins. Phylogenetic analysis of 140 bacterial bins obtained by binning rhizosphere samples and functional gene annotation related to nitrogen, phosphorus, and potassium cycles. The branch colors represent the phylum-level classification of bacteria, and the small square colors represent various gene functions.

[0067] Figure 20, Diversity analysis and differential analysis of bacterial functions. All results were calculated based on relative abundance (TMP). (A - C) show the α - diversity analysis (left) and β - diversity analysis (right) of KEGG, COG, and CAzy functions in each fertilization treatment, respectively. (D) shows the differential analysis of the abundances of each functional gene between the PK, NK, and NP fertilization treatments and the normal fertilization treatment. Asterisks indicate significant differences compared with the control NPK (*P < 0.05, **P < 0.01, ***P < 0.001).

[0068] Figure 21 , Co - occurrence network analysis of rhizosphere bacteria in each fertilization treatment. All results were calculated based on absolute abundance. (A) Co - occurrence networks and related network characteristics of rhizosphere soil bacteria in the NPK, PK, NK, and NP fertilization treatments. The point color represents different bacterial phylum - level classifications, and the point size represents degree. (B) Differential analysis of the robustness between different networks. (C) Differential analysis of the average degree at the order level between different networks. Error bars represent standard deviation SD, and asterisks indicate significant differences compared with the control NPK (*P < 0.05, **P < 0.01, ***P < 0.001).

[0069] Figure 22 , Co - occurrence network analysis of endophytic bacteria in each fertilization treatment. All results were calculated based on absolute abundance. Co - occurrence networks and related network characteristics of endophytic bacteria in the NPK, PK, NK, and NP fertilization treatments. The point color represents different bacterial phylum - level classifications, and the point size represents degree.

[0070] Figure 23 , Co - occurrence network analysis of rhizosphere bacterial communities. All results were calculated based on absolute abundance. (A) Co - occurrence network of rhizosphere bacterial communities. The point color represents different modules, and the point size represents degree. (B) Differential analysis of the cumulative absolute abundance of samples between different fertilization treatments in each module. (C) Species composition of bacteria in each module at the phylum classification level. Error bars represent standard deviation SD, and asterisks indicate significant differences compared with the control NPK (*P < 0.05, **P < 0.01, ***P < 0.001).

[0071] Figure 24 , Phylogenetic analysis of purified bacteria. (A) Phylogenetic analysis of 16S sequences of 1011 monoclonal strains obtained by isolation and purification. The color of the clade represents bacterial phylum - level classification. The strains indicated by triangles are the strains used in the subsequent synthetic community re - inoculation experiment. The blue triangle is the control synthetic community, and the yellow and white triangles are the experimental synthetic communities. (B) Coverage of the isolated strains in the rhizosphere core ASVs.

[0072] Figure 25Growth phenotypes of soybeans after inoculation with synthetic communities. (A) Phylogenetic analysis of bacterial 16S sequences in the co-occurrence network of rhizosphere bacterial communities. The colors of the bars represent different modules, and the pentagrams represent synthetic communities. Among them, the blue pentagrams represent the 5 control synthetic strains in M1, the yellow pentagrams represent the 5 experimental synthetic strains in M2, and the white pentagrams represent the other two experimental synthetic strains in M2. (B) Growth phenotypes of soybeans after 3 weeks of growth under nitrogen addition (left) and no nitrogen addition (right) conditions. The scale bar is 10 cm. (C and D) Phenotypic differences in plant height (left) and dry weight (right) of soybeans after inoculation with synthetic communities under nitrogen addition and no nitrogen addition conditions. Error bars represent standard deviation SD, and lowercase letters represent significant differences in the growth phenotypes of soybeans among different treatments (P < 0.05).

[0073] Figure 26 Diagram of important conclusion patterns. (A) Differences in rhizosphere bacterial abundance and diversity among different developmental stages. (B) Differences in soil properties, plant phenotypes, rhizosphere bacterial functions, and network characteristics among different fertilization treatments. Compared with the normal fertilization treatment, the blue upward arrow represents an increase, and the orange downward arrow represents a decrease.

[0074] Figure 27 Bacterial beta-diversity based on absolute abundance. All results were calculated based on absolute abundance. (A) Beta-diversity differences in bacterial communities among all samples (each fertilization treatment, each niche, each developmental stage). (B) Bray-Curtis distances between rhizosphere soil and endorhiza bacteria at each developmental stage. (C) Influence degree of fertilization on beta-diversity of rhizosphere soil and endorhiza bacteria at each developmental stage. Lowercase letters represent significant differences among different growth and developmental stages (P < 0.05).

[0075] Figure 28 Shannon-Wiener curves of bacterial 16S data. The sequencing results of the 16S rRNA gene are rarefaction curves based on the Shannon index.

[0076] Figure 29, Bacterial diversity based on relative abundance. All results were calculated based on relative abundance. (A - C) are the Shannon diversity indices of the bacterial communities in non-rhizosphere soil, rhizosphere soil, and endophytic bacteria, respectively. (D) shows the differences in β-diversity of bacterial communities among all samples (each fertilization treatment, each niche, each developmental stage). (E) represents the Bray-Curtis distance between rhizosphere soil and endophytic bacteria at each developmental stage. (F) shows the degree of influence of fertilization on the β-diversity of rhizosphere soil and endophytic bacteria at each developmental stage. (G) shows the relationship between the temporal distance and the Bray-Curtis distance among different samples in each fertilization treatment in rhizosphere soil (left) and endophytic samples (right). (H) shows the relationship between the developmental time of each sample and the Bray-Curtis distance of PK, NK, and NP samples relative to the NPK sample in rhizosphere soil (left) and endophytic samples (right). Asterisks indicate significant differences between each fertilization treatment and the control (*P < 0.05, **P < 0.01, ***P < 0.001), and lowercase letters represent significant differences between different developmental stages (P < 0.05).

[0077] Figure 30 , Accuracy of the random forest model. All results were calculated based on absolute abundance, and all data were analyzed at the family taxonomic level. (A and B) represent the accuracy of the random forest models established based on rhizosphere soil samples with different fertilization treatments and different developmental stages, respectively.

[0078] Figure 31 , Co-occurrence networks of bacteria in rhizosphere soil and endophytic bacteria in each fertilization treatment. All results were analyzed based on absolute abundance on the MENA website. (A and B) represent the co-occurrence networks and related network characteristics of bacteria in rhizosphere soil and endophytic bacteria in NPK, PK, NK, and NP fertilization treatments, respectively. The dot color represents the phylum-level classification of bacteria, and the dot size represents the degree.

[0079] Figure 32 , Verification of the growth-promoting function of the synthetic community. (A) Verification of the functions of each strain in the synthetic community, such as IAA production, ACC deaminase activity, inorganic phosphorus solubilization, and nitrogen fixation. (B and C) represent the IAA concentration and ACC deaminase activity produced by each synthetic community, respectively. Detailed implementation methods

[0080] Through in-depth research and based on long-term soil incubation experiments, the inventors of the present invention for the first time used absolute quantification methods to explore the dynamic assembly patterns of rhizosphere and endophytic microbiomes throughout the plant life cycle, and studied the response characteristics of rhizosphere microbiomes to different fertilization treatments. Combining methods such as metagenomics, microbial isolation and cultivation, and synthetic communities, the functional adaptations of rhizosphere microorganisms under different fertilization conditions and their mechanisms affecting the growth of leguminous plants were revealed. On this basis, the present invention provides methods for improving the soil properties and soil flora of fertilized soil, or promoting symbiotic nitrogen fixation between rhizobia and leguminous plants; and also provides synthetic microbial communities useful for promoting the growth of leguminous plants and increasing biomass.

[0081] As used herein, the term "leguminous plant" refers to a leguminous plant with root nodules, including "nodule plants". The "nodule plants" include plants that can be invaded by rhizobia and stimulated to form nodules on the roots.

[0082] As used herein, the "nodule plants" also include "nodule-like plants", which refer to plants with nodule-like or nodule-shaped structures.

[0083] As used herein, the term "improvement" refers to improving the characteristics of the soil or plants.

[0084] As used herein, "improving the soil" includes improving the soil properties and soil flora in fertilized soil, including but not limited to: reducing soil acidification (increasing the soil pH value, such as increasing by 0.5 units or 1 unit or more), increasing the number and diameter of root nodules in the soil, and increasing the abundance of rhizobia in the soil.

[0085] As used herein, the "fertilized soil" refers to the soil used for planting plants that has been fertilized or needs to be fertilized.

[0086] As used herein, "improving plants" includes but not limited to: increasing the number of root nodules in leguminous plants, increasing the size of root nodules in leguminous plants, promoting the nitrogen fixation ability of root nodules in leguminous plants, promoting the utilization of nutrients by leguminous plants, reducing the nitrogen fertilizer requirement of leguminous plants, increasing the biomass of leguminous plants, increasing the yield of leguminous plants, etc.

[0087] As used herein, "improvement of plant traits", "improved traits", "improved plant traits", "trait improvement", etc. can be used interchangeably, and refer to the beneficial changes in the performance of plants modified by the technical solutions of the present invention compared with unmodified plants (such as wild-type plants).

[0088] As used herein, the term "biomass" may relate to the vegetative biomass of plants. "Biomass" may include measurement indicators such as plant height and plant weight.

[0089] As used herein, the terms "enhanced", "improved", "elevated", "promoted", or "augmented" are interchangeable and shall be meant in the applied sense to mean an "enhancement", "improvement", "elevation", "promotion", or "augmentation" of, for example, 2 to 100%, such as at least 2%, 3%, 4%, 5%, 6%, 7%, 8%, 9% or 10%, preferably at least 15% or 20%, more preferably 25%, 30%, 40%, 60%, 80%, 90% or higher, as compared to the soil or plant before the implementation of the regulation or modification, or to the control (group).

[0090] There is a close connection among soil nutrients, rhizosphere microorganisms, and plant development. Together, they determine the health and yield of plants, and microorganisms play a crucial role throughout the process. In the present invention, the community structure and function of soil microorganisms under different fertilization conditions, the interaction between plants and rhizosphere microorganisms at different developmental stages, and the interaction network among rhizosphere microorganisms were studied. This has positive significance for the protection of land resources and the improvement of food production, and provides a new approach for exploring better fertilization systems for agricultural ecosystems and finding new plant-beneficial microorganisms.

[0091] In recent years, the extensive use of fertilizers such as nitrogen, phosphorus, and potassium has been the main reason for crop yield increase. Intensive agricultural production over-relies on chemical fertilizers and pesticides, which not only causes many environmental problems but also seriously damages the structure and function of soil microorganisms, affecting the sustainable development of agricultural ecosystems. The present invention studied the characteristics of rhizosphere microbial communities in response to different soil fertilities and their relationship with plant growth phenotypes under long-term different fertilization measures, especially under the conditions of nitrogen deficiency, phosphorus deficiency, or potassium deficiency in the soil.

[0092] In a specific embodiment, four different fertilization treatments were selected for the study, including normal fertilization (NPK, applying nitrogen, phosphorus, and potassium fertilizers), no nitrogen fertilizer application (PK, applying phosphorus and potassium fertilizers), no phosphorus fertilizer application (NK, applying nitrogen and potassium fertilizers), and no potassium fertilizer application (NP, applying nitrogen and phosphorus fertilizers) treatments. Based on the absolute quantification method of unit sample mass, a series of metagenomic analyses were performed on the collected non-rhizosphere soil, rhizosphere soil, roots, and root nodule samples.

[0093] In specific embodiments, the physical and chemical properties of the soil and the yield under different fertilization treatments were first analyzed. Then, based on 16S rRNA and rpoB gene amplicon sequencing, the diversity and community composition structure of rhizosphere and endophytic bacterial communities were analyzed, and the dynamic response characteristics of bacterial communities to different fertilization treatments at different growth and development stages of leguminous plants were further explored. For the first time, the quantitative microbiome characteristics of the whole life cycle of leguminous plants under different nutrient conditions were systematically described. At the same time, the similarities and differences between traditional relative abundance and absolute abundance in characterizing microbial community characteristics were comprehensively compared, and the influence of sequence concentration on amplification and sequencing was evaluated. Then, through differential analysis, the changes in the abundance of the core microbial community in different fertilization treatments were explored, and biomarker taxa of microorganisms in different fertilization treatments and different growth and development stages were identified by methods such as LEfSe analysis and random forest prediction. Then, the functions of bacterial communities were predicted using the FAPROTAX database. Finally, through metagenomic assembly, the abundance differences of rhizosphere microbial functional genes between different fertilization treatments were studied, and the characteristics and functions of rhizosphere core microorganisms in leguminous plants under different fertilization treatments and different growth and development stages were comprehensively explored, revealing the functional adaptation of rhizosphere microorganisms in leguminous plants under different nutrient conditions. Finally, co-occurrence network analysis was used to analyze the interactions between rhizosphere core bacterial communities in each fertilization treatment, and potential core ASVs that may potentially play a growth-promoting function were identified in the ecologically enriched clusters specific to the nitrogen-free fertilization condition through modular analysis. Then, through the isolation and cultivation of rhizosphere bacteria, its growth-promoting function was verified under greenhouse conditions, and the potential relationship between the functional adaptation of rhizosphere microbial communities and the growth and development of leguminous plants under different fertilization treatments was further explored.

[0094] The results of soil physical and chemical property analysis showed that compared with the soil under normal fertilization (NPK), the available nitrogen content in the nitrogen-free fertilization (PK) soil decreased by 35%, and the pH value increased by 1.1 units; the available phosphorus content in the phosphorus-free fertilization (NK) soil decreased by 95%; the available potassium content in the potassium-free fertilization (NP) soil decreased by 61%. Based on the statistical analysis of the yield in the last two consecutive planting seasons, it was found that the lack of phosphorus seriously affected the productivity of leguminous plants, and the yields in the two seasons decreased by 25% and 29% respectively; the lack of potassium slightly reduced the yield of leguminous plants, but the effect was not significant; while there was no significant difference in the yield of leguminous plants without nitrogen fertilizer compared with the control, indicating that additional nitrogen fertilizer is not required for growing leguminous plants in the black soil of Northeast China, and the available phosphorus content is the main limiting factor for the productivity of leguminous plants.

[0095] Results of microbiome analysis based on absolute quantification showed that the abundances of rhizosphere and endophytic bacteria tended to increase continuously with plant development, and the niche, plant development stage, and fertilization treatment all significantly affected the bacterial community structure. Specifically, the α-diversity of rhizosphere bacteria in the PK treatment was higher than that in the normal fertilization treatment during the vegetative growth stage, and the β-diversity was significantly different from that of other treatments; while the NK treatment reduced the abundance of rhizosphere bacteria and the turnover rate with plant development, and its α-diversity was higher than that in the normal fertilization treatment during the reproductive growth stage. Proteobacteria and Actinobacteria were dominant in both rhizosphere and endophytic bacteria, among which Actinobacteria was dominant in the early stage of legume plant development, while Proteobacteria was dominant in the later stage of legume plant development. Bacteroidetes mainly accumulated in the later stage of legume plant growth, and its abundance in the NK treatment was significantly lower than that in the control. In addition, long-term nitrogen fertilizer omission significantly increased the abundance of rhizobia in the rhizosphere.

[0096] Furthermore, 573 core ASVs (Amplicon Sequence Variants) were identified in the rhizosphere. These ASVs accounted for only 2.4% of the rhizosphere bacterial species but contributed 74.6% of the abundance. The results of differential analysis showed that there were 172, 67, and 6 differential core ASVs in the PK, NK, and NP treatments, respectively, compared with the normal fertilization treatment. The results of functional prediction of core ASVs showed that the nitrogen fixation function of bacteria in the PK treatment was significantly enhanced, while the carbon metabolism function of bacteria in the NK treatment was reduced and the nitrogen metabolism function was enhanced. Metagenomic functional analysis found that there were obvious differences in the functional diversity of rhizosphere bacteria among different fertilization treatments, and the functional diversity of bacteria in the PK treatment was significantly lower than that in the normal fertilization treatment. At the same time, the nitrogen mineralization ability was enhanced in the PK treatment, and functions such as nitrogen reduction and inorganic phosphorus solubilization were reduced. Functions such as nitrification, denitrification, inorganic phosphorus solubilization, and phosphorus starvation response were enhanced in the NK treatment, and the nitrogen reduction function was reduced. While the potassium transport function was enhanced in the NP treatment, indicating that nutrient stress induced the functional adaptation of microorganisms to the rhizosphere environment.

[0097] Compared with the normal fertilization treatment, both the PK and NP treatments significantly increased the complexity and stability of the rhizosphere bacterial co-occurrence network, in which rhizobia and Sphingomonas played important roles. In contrast, the NK treatment had no significant effect on the bacterial network complexity. In addition, five bacteria (Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., and Phyllobacterium sp.) with close connections in the rhizosphere network were identified in the ecological clusters specifically enriched by the PK treatment. The results of the pot experiment showed that compared with the inoculated control synthetic community (SynCtrl), in the nitrogen-added and nitrogen-free treatments, the plant height of leguminous plants increased by 15% and 6%, respectively, and the dry weight increased by 38% and 18%, respectively, after inoculating the synthetic community (SynCom5), indicating that the microbial clusters enriched in the plant rhizosphere under environmental stress can synergistically play a growth-promoting function and have the potential for further application.

[0098] Plant rhizosphere microorganisms have broad application prospects in aspects such as biofertilizers, biopesticides, promoting nutrient absorption, stress resistance, and yield increase, and are one of the key bases for the development of sustainable agriculture. Based on the new findings of the present invention, the present invention also provides a series of synthetic microbial communities, including the synthetic microbial community SynCom5 or SynCom7. Applying the synthetic microbial community to leguminous plants can promote the growth of leguminous plants and increase the biomass.

[0099] The present invention also provides a composition containing the synthetic microbial community, which contains: an effective amount of the synthetic microbial community or its culture. More preferably, the composition further contains a pharmaceutically acceptable carrier.

[0100] In the present invention, the Rhodococcus sp. (Rhodococcus), Lysobacter sp. (Lysobacter), Terrabacter sp. (Terrabacter), Arthrobacter sp. (Arthrobacter), Phyllobacterium sp. (Phyllobacterium), preferably also includes Bosea sp. (Bosea) and Aeromicrobium sp. (Aeromicrobium). These strains are known in the art, and those skilled in the art can obtain the corresponding strains according to the information provided by the present invention.

[0101] The compositions of the present invention (such as pesticidal compositions) can be formulated into any suitable dosage form. For example, the forms of said compositions include (but are not limited to): powders, granules, oils, emulsions, wettable powders, microcapsules, non-woven fabric fungicides. When preparing the compositions, suitable solid diluents include (but are not limited to): diatomaceous earth, corn cobs, tricalcium phosphate, cork powder, clays such as kaolin, bentonite or attapulgite, or water-soluble polymers. Solid compositions may contain one or more compatible wetting agents, dispersants, emulsifiers or pigments, which may also act as diluents in the solid state. Liquid compositions may be in the form of solutions, suspensions or emulsions, may also be encapsulated in natural or synthetic polymers, and may contain wetting agents, dispersants or emulsifiers. Such emulsions, suspensions or solutions can be prepared with aqueous, organic or water-organic diluents (as well as mixtures of the above diluents). In addition, the diluents may contain, for example, the ionic or non-ionic wetting agents, dispersants or emulsifiers described above or mixtures thereof. The compositions may be in concentrated or diluted form.

[0102] Suitable methods for applying the compositions described above to growing plants include: solid broadcasting, foliar spraying, liquid irrigation, etc. The preferred application method is the method of applying near the roots of the plants.

[0103] Those skilled in the art can see that the application of the compositions may vary with external conditions such as temperature, humidity, the area to be treated and the plants to be treated, etc. Therefore, the application ratio can vary within a relatively wide range. The frequency of use of the compositions of the present invention can be selected by farmers, pest control professionals or other persons skilled in the art according to the expected effects.

[0104] The synthetic microbiota-based compositions of the present invention are artificially prepared combinations of strains from nature, are harmless to other animals and plants, and belong to an environmentally friendly composition.

[0105] Based on the method of absolute quantification, the microbiome assembly rules and response characteristics of leguminous plants throughout their life cycles under different fertilization conditions were first described in the present invention, and it was first proposed that the bacterial ecological clusters specifically enriched in the rhizosphere under low nitrogen conditions can significantly promote the development of leguminous plants. Using the analysis method based on absolute quantification can combine absolute abundance with relative abundance to more comprehensively and accurately reflect the true quantity and differences of microorganisms.

[0106] The present invention proposes a new scheme for the fertilization strategy of soil. According to the analysis results of the present invention, when leguminous plants are planted in black soil, no additional nitrogen fertilizer needs to be applied, and only appropriate amounts of phosphate fertilizer and potassium fertilizer need to be input to ensure the yield, which will be beneficial to reducing the use of chemical fertilizers and promoting the sustainable development of agriculture.

[0107] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. For the experimental methods without specific conditions noted in the following embodiments, they are generally carried out under conventional conditions such as those described in "Molecular Cloning: A Laboratory Manual", Third Edition, edited by J. Sambrook et al., Science Press, or according to the conditions recommended by the manufacturer.

[0108] Materials and Methods

[0109] Plant Materials

[0110] Soybean (Glycine max): Cultivar "Heinong 84".

[0111] Rotated maize (Zea mays): Cultivar "Tiannong 9".

[0112] Rotated wheat (Triticum aestivum): Cultivar "Longfumai 196".

[0113] In the field experiment, all plant materials were grown under natural conditions, and throughout the growth period, unified irrigation and weeding were carried out in all plots. In the synthetic community re-inoculation experiment, all plant materials were grown in a phytotron with growth conditions of 22 - 24 °C, 16 h of light and 8 h of darkness.

[0114] Strain Materials

[0115] 1) Internal standard cloning strain:

[0116] Escherichia coli TOP10 strain.

[0117] 2) SynCtrl control combination community strains: PK85 (Brevundimonas sp.); NK173 (Sediminibacterium sp.); NP149 (Mycobacterium sp.); NP94 (Herbaspirillum sp.); NK151 (Sphingomonas sp.).

[0118] 3) SynCom7 experimental combination community strains: PK167 (Rhodococcus sp.); PK216 (Bosea sp.); PK260 (Lysobactersp.); NPK195 (Terrabacter sp.); NK218 (Arthrobacter sp.); NPK112 (Aeromicrobium sp.); PK237 (Phyllobacterium sp.).

[0119] 4) The SynCom5 experimental combination forms a community strain: PK167 (Rhodococcus sp.); PK260 (Lysobacter sp.); NPK195 (Terrabacter sp.); NK218 (Arthrobacter sp.); PK237 (Phyllobacterium sp.).

[0120] Experimental design and sample collection

[0121] The long-term fertility experiment of the Northeast Black Soil started in 1979. The experimental site is located in Minzhu Township, Daowai District, Harbin City, Heilongjiang Province (126°51′E, 45°50′N). There are a total of 24 different fertilization treatments in the experimental site, and a randomized block design is adopted, with 3 replicates for each treatment. Each experimental plot is 36 square meters, and different plots are separated by cement walls (15 cm wide and 1.1 m deep), and they are uniformly managed by the Heilongjiang Academy of Agricultural Sciences. These experimental plots have been implementing a rotation system of soybean, corn, and wheat, and only one crop is planted per year. Four experimental treatments in the soybean season are selected in the present invention, namely normal fertilization (NPK, applying nitrogen, phosphorus, and potassium fertilizers), no nitrogen fertilizer application (PK, applying phosphorus and potassium fertilizers), no phosphorus fertilizer application (NK, applying nitrogen and potassium fertilizers), and no potassium fertilizer application (NP, applying nitrogen and phosphorus fertilizers) (the same fertilization strategy is also adopted in other planting seasons). The nitrogen fertilizer used is urea, the phosphorus fertilizers are triple superphosphate and diammonium phosphate, and the potassium fertilizer is potassium sulfate. The specific fertilization amounts are as follows: in the wheat and corn seasons, it is 150 kg N ha -1 , 75 kg P2O5 ha -1 , 75 kg K2O ha -1 ; in the soybean season, it is 75 kg N ha -1 , 150 kg P2O5 ha -1 , 75 kg K2O ha -1 . Nitrogen, phosphorus, and potassium fertilizers are all applied after the harvest of autumn crops, and 50% of the nitrogen fertilizer in the corn season is applied at the jointing stage.

[0122] The crop planted in 2020 is soybean, and it was sown on May 11, 2020. Before sowing, 3 non-rhizosphere soil samples were collected from each experimental plot as controls, and then some of the air-dried non-rhizosphere soil was sent to the Institute of Environment and Safety, Wuhan Academy of Agricultural Sciences for testing soil physical and chemical properties (including alkaline hydrolyzable nitrogen, available phosphorus, available potassium, organic matter, soluble organic carbon, and pH, etc.). The soil type belongs to typical black soil, and the relevant physical and chemical properties are shown in Table 1.

[0123] Table 1. Soil physical and chemical properties

[0124] Sample AHN (mg / kg) AP (mg / kg) AK (mg / kg) OM (g / kg) DOC (mg / kg) pH NPK - 1 164 133 376 28.6 29 5.34 NPK - 2 172 143 398 30.2 28.7 5.06 NPK - 3 180 159 361 29.4 29.1 5.21 PK - 1 109 161 421 28.1 22.6 6.28 PK - 2 101 144 403 27.8 24.1 6.33 PK - 3 125 146 388 28 22.5 6.39 NK - 1 166 7.1 371 29.4 27.5 5.12 NK - 2 133 5.4 356 29.6 28.5 5.34 NK - 3 147 8.6 348 30.6 27.4 5.3 NP - 1 146 104 147 28.1 27.1 5.64 NP - 2 166 132 135 29.3 30.5 5.31 NP - 3 132 146 156 29.3 28.2 5.47

[0125] Note: AHN is available nitrogen, AP is available phosphorus, AK is available potassium, OM is organic matter, DOC is dissolved organic carbon, and pH is hydrogen ion concentration index.

[0126] All soybeans germinated on May 22, 2020. Sampling was carried out at 8 stages after soybean germination, namely 1 day, 4 days, 7 days, 14 days (nodulation began), 28 days (early growth stage), 42 days (beginning of flowering stage), 60 days (full flowering stage), and 72 days (pod-setting stage) after germination. At each sampling stage, 3 plants with the same growth vigor were selected in the corresponding treatment plots, and their roots (5 - 15 cm) were dug out. Then, the soil attached to the outer periphery of the roots was removed, and finally, the roots, root nodules, and the soil attached to the rhizosphere were stored in 50 mL sterile centrifuge tubes and taken back to the laboratory for further processing. After the soybeans matured, the grain yields of each plot were also measured, and the specific yields are shown in Table 2.

[0127] Table 2. Soybean Yields (The yield in 2017 was the yield of the previous season's soybeans, and the yield in 2020 was the yield of the current season's soybeans)

[0128]

[0129] Sample Processing

[0130] The specific operations are as follows:

[0131] 1) Add 25 mL of PBS buffer to the 50 mL sterile tube storing the samples and shake well.

[0132] 2) Use an ultrasonic cleaner (40 Hz, 1 min) to ultrasonically clean the samples to completely separate the rhizosphere soil from the root tissues.

[0133] 3) Transfer the rhizosphere soil suspension to a new 50 mL sterile tube (try to remove plant tissues), centrifuge (9000 rpm, 5 min), and then remove the supernatant.

[0134] 4) Resuspend the rhizosphere soil precipitate in 1 mL of PBS buffer in a 2 mL centrifuge tube, centrifuge again (12000 rpm, 2 min), and then try to remove the supernatant completely, retaining the rhizosphere soil precipitate.

[0135] 5) Use a vacuum centrifugal concentrator (1400 rpm, 2 h) to completely dry the rhizosphere soil at room temperature.

[0136] 6) Use a multi-sample tissue grinder (40 Hz, 1 min) to homogenize the dried rhizosphere soil.

[0137] 7) Weigh approximately 100 mg of the dried and homogenized rhizosphere soil sample and store it for future use.

[0138] 8) Wash the roots and nodules ultrasonically and by shaking twice in a 50 mL sterile tube with PBS buffer.

[0139] 9) Disinfect the surfaces of the roots and nodules with 5% (w / v) NaClO solution for 3 min.

[0140] 10) Wash the roots and nodules ultrasonically and by shaking three times again with an equal volume of PBS buffer.

[0141] 11) Weigh approximately 200 mg of the roots and nodules respectively. After absorbing the moisture with sterile absorbent paper, use a vacuum centrifugal concentrator (1400 rpm, 2 h) to completely dry them.

[0142] 12) Weigh and record the dried root and nodule samples, then grind and homogenize them under liquid nitrogen for storage for later use.

[0143] 13) After drying and homogenizing the rhizosphere soil in a similar manner as above, weigh approximately 100 mg as a control soil sample for storage for later use.

[0144] After all samples are processed, store them in a -80 °C refrigerator, list the sample inventory, and record the dry weights.

[0145] DNA Extraction

[0146] Extract the microbial genomic DNA from all samples using the FastDNA SPIN Kit for Soil according to its instructions.

[0147] Design and Addition of Internal Standards

[0148] Thirteen internal standards (Synthetic spike-in, SynSpike) were designed according to the previous principles and methods (Tkacz et al., 2018; Wang et al., 2020). Twelve of them are different 16S SynSpikes, which are designed based on the V5-V7 variable region of the bacterial 16S rRNA gene. The other one is rpoB SynSpike, which is designed based on the rpoB gene of symbiotic rhizobia. Synthesize these internal standard genes, fuse these internal standard genes with the plasmid pUC57, and finally transfer them into Escherichia coli TOP10 for propagation and cloning.

[0149] All internal standard plasmids were extracted using the TIANpure Midi Plasmid Kit according to the instructions. After the extraction of internal standard plasmids, the concentration and purity of DNA were measured using NanoDrop. Then, all internal standards were combined into SynSpike for Soil and Nodule at different concentrations, and after diluting it 10-fold, SynSpike for Root was obtained. The combination and detailed concentrations of internal standards are shown in Table 3. Finally, 30 μL of microbial DNA and 10 μL of the corresponding SynSpike DNA were transferred to a new centrifuge tube, mixed well, and stored in an -80 °C refrigerator for subsequent DNA amplification, library construction, and sequencing. Finally, the absolute abundance of microorganisms in the unit mass of the sample was calculated based on the copy number of SynSpike plasmids using the Qiime2 process ( Figure 1 ).

[0150] Table 3. Length and addition amount of each internal standard plasmid

[0151]

[0152] Note: Synthetic DNA Length is the length of each internal standard DNA, and Plasmid Length is the length after each internal standard DNA is fused to the pUC19 plasmid backbone (2699 bp). Soil and Nodule are the addition amounts of each internal standard plasmid in soil, rhizosphere soil, and nodule samples, and Root is the addition amount of each internal standard plasmid in root samples.

[0153] DNA amplification

[0154] The V5-V7 variable region of the bacterial 16S rRNA gene was amplified using 799F (5’-AAC MGG ATT AGA TAC CCK G-3’) and 1193R (5’-ACG TCA TCC CCACCT TCC-3’) (Bai et al., 2015); the rpoB gene of rhizobia was amplified using rpoB1479F (5’-GAT CGA RAC GCC GGA AGG-3’) and rpoB1831R (5’-TGC ATG TTC GAR CCCAT-3’) (Wang et al., 2020). The 16S rRNA gene amplification included all soil samples and root samples, and the rpoB gene amplification included all soil samples and nodule samples.

[0155] After amplification, 1% agarose gel electrophoresis was used to detect the quality of DNA amplification, and the amplified DNA was stored in an -80 °C refrigerator for subsequent library construction and sequencing.

[0156] High-throughput sequencing

[0157] 1) Amplicon sequencing

[0158] The amplified samples were library - constructed and sequenced. Amplicon sequencing was performed on the MGISEQ platform with a single - end read length of 400 bp (SE400). The list of sequencing samples and the raw data have been uploaded to the NCBI SRA database (Project number: PRJNA922226; 16S rRNA amplicon data sample numbers: SAMN32647504 - SAMN32648110; rpoB amplicon data sample numbers: SAMN32650334 - SAMN32650836).

[0159] 2) Metagenomic sequencing

[0160] The genomic DNA of rhizosphere microorganisms at three representative time points (1 day, 42 days, and 72 days after soybean germination) was selected for metagenomic sequencing. To save costs, 10 μL of DNA from each of the three replicate samples in the same plot were taken and mixed into 1 sample, and then the DNA of the metagenomic sequencing sample was library - constructed and sequenced. Metagenomic sequencing was performed on the DNBSEQ platform with a paired - end read length of 100 bp (PE100). The list of sequencing samples and the raw data have been uploaded to the NCBI SRA database (Project number: PRJNA922226; sample numbers: SAMN32727371 - SAMN32727406).

[0161] Isolation and purification of bacteria

[0162] 1) Culture media: LB and R2A agar media.

[0163] 2) Pour culture dishes. After the culture media were sterilized, 20 mL of agar medium was accurately measured with a measuring cup and poured into a 10 - cm - diameter round dish in a sterile operation table. After the medium cooled and solidified, the lid was covered and kept for later use.

[0164] 3) Prepare soil dilution. Under sterile conditions, 3 g of fresh rhizosphere soil (including four treatments: NPK, PK, NK, and NP) were weighed respectively. The weighed rhizosphere soil was put into a 50 - mL sterile tube, and then 30 mL of PBS buffer was added. Then, it was ultrasonicated and shaken for 30 min to fully mix the sample and the solution. The mixed liquid was the soil suspension of 10 -1 dilution gradient. 1 mL of the soil suspension was pipetted into a sterile tube containing 9 mL of PBS buffer and fully mixed to obtain the soil suspension of 10 -2 dilution gradient. According to this step, continue to prepare 10 -3 、10 -4 、10 -5 、10 -6Soil dilution solutions with different concentrations, etc.

[0165] 4) Prepare the soil dilution solution of the dried sample. To better isolate and purify Bacillus, weigh 3 g of fresh rhizosphere soil separately again, put it in a desiccant for preservation. Take it out after two weeks, put it into a sterile centrifuge tube containing 30 mL of PBS buffer, shake well and heat at 80 °C for 30 min. Finally, obtain the soil gradient dilution solution of the dried sample according to step 3.

[0166] 5) Coating. First, make corresponding marks on the culture dishes, then pipette 50 μL of the soil dilution solution into the agar media (LB and R2A) respectively, and then evenly coat it with a sterile spreading rod. Finally, seal it with a sealing film.

[0167] 6) Incubation. Invert the coated culture dishes and incubate them in an incubator at 30 °C for 48 h, and pay attention to observing the growth status of the colonies during this period.

[0168] 7) Pick single colonies. Use an inoculation loop to pick the single colonies with good growth on the medium, streak them on a new same medium, and then invert and continue to incubate them in an incubator at 30 °C for 48 h for subsequent identification.

[0169] 8) Strain preservation. Pick the single colonies on the streaked plate into a strain preservation tube containing 0.4 mL of sterile 50% glycerol and 0.6 mL of LB liquid medium, make marks, mix well and store them in a -80 °C refrigerator.

[0170] Bacterial identification

[0171] Use the universal primers 27F (5’-AGA GTT TGA TCC TGG CTC AG-3’) and 1541R (5’-AAG GAG GTG ATC CAG CCG CA-3’) of the bacterial 16S rRNA gene to identify the purified bacteria and perform PCR reactions.

[0172] After the PCR reaction, mix 4 μL of the PCR product with 2 μL of Loading buffer, and then perform 1% agarose gel electrophoresis (120 V, 30 min) for detection. Select the PCR amplification products with normal bands (20 μL of the PCR stock solution) for Sanger sequencing. Use BLAST version 2.12.0 to align the sequencing results to the 16S rRNA gene database Greengenes 13_8 99% OTUs (McDonald et al., 2012) to finally obtain the annotation information.

[0173] Amplicon analysis

[0174] The amplicon sequencing results were analyzed using QIIME 2 (Bolyen et al., 2019), version 2021.11, including the following:

[0175] 1) The sequencing results were quality controlled using FastQC (https: / / www.bioinformatics.babraham.ac.uk / projects / fastqc), version 0.11.9, and MultiQC (Ewels et al., 2016), version 1.11, to remove low-quality samples that did not meet the requirements;

[0176] 2) The raw sequence data was quality filtered using the q2-demux plugin, and then the amplification primer sequences were removed using the q2-cutadapt (Martin, 2011) plugin;

[0177] 3) The sequences were denoised using the q2-DADA2 (Callahan et al., 2016) plugin, and the sequences were truncated at the specified length according to the results of the previous quality filtering to remove low-quality bases in the sequences (the retained length of the 16S sequence was 305 bp, and the retained length of the rpoB sequence was 304 bp), and finally Amplicon Sequence Variants (ASVs) were obtained;

[0178] 4) The ASVs were classified using the q2-feature-classifier (Bokulich et al., 2018) plugin. For the bacterial 16S rRNA Greengenes 13_8 99% OTUs reference sequences (McDonald et al., 2012), the classify-sklearn naive bayes classifier was used to classify the 16S ASVs; for the rpoB gene reference sequences of rhizobia by Wang et al. (Wang et al., 2020), the classify-consensus-blast classifier was used to classify the rpoB ASVs (identity was set to 0.977);

[0179] 5) Based on the classification annotation results, use the q2-taxa plugin to filter the feature tables and representative sequences according to the following rules: For 16S data, only retain the data annotated at the phylum level, and filter out the data annotated as Unassigned, Archaea, Chloroplast, mitochondria, or SynSpike. In addition, since the root tissue samples may contain some small, incompletely removed root nodules, which will lead to an overly high abundance of rhizobia in the samples, thus masking the true composition structure of the endophytic bacterial community, the 16S root tissue sample data also needs to be additionally filtered to remove the data annotated as Bradyrhizobiaceae. For rpoB data, filter out the data annotated as Unassigned or SynSpike, and only retain the data annotated at the species level and belonging to nitrogen-fixing rhizobia.

[0180] 6) Annotate the filtered representative sequences again according to step 4.

[0181] 7) Use the q2-phylogeny plugin to align the ASVs through MAFFT (Katoh et al., 2002), and then construct a phylogenetic tree through FastTree (Price et al., 2010);

[0182] 8) After rarefying the 16S samples to 1030 sequences using q2-diversity, calculate its α-diversity indices (such as Shannon index) and β-diversity indices (such as Bray-Curtis dissimilarity).

[0183] Metagenome assembly

[0184] Assemble the metagenomic data with reference to the Easy Metagenome metagenomic analysis pipeline published by Liu et al. (Liu et al., 2021c), including:

[0185] 1) Use FastQC and MultiQC to perform quality control on the sequencing results and remove the low-quality samples that do not meet the requirements;

[0186] 2) Use KneadData (https: / / huttenhower.sph.harvard.edu / kneaddata) version 0.10.0 to filter the low-quality data and remove the sequences of the host plant Glycine max;

[0187] 3) The high-quality data filtered from 36 samples were assembled individually using the default parameters of MEGAHIT (Li et al., 2015) version 1.2.9 (k-mers = 21, 33, 55, 77, 99, 127).

[0188] 4) The QUAST (Gurevich et al., 2013) version 5.0.2 was used to evaluate the quality of genome assembly.

[0189] 5) All Reads were aligned back to the assembled Contigs using Bowtie 2 (Langmead & Salzberg, 2012) version 2.4.4, and then the sequence coverage (Reads coverage) of each Contig was calculated.

[0190] 6) The sequences longer than 1000 bp were screened using SeqKit (Shen et al., 2016) version 2.1.0 as the assembly results.

[0191] 7) The open reading frames of each Contig were predicted using Prodigal (Hyatt et al., 2010) version 2.6.3, and the complete genes were extracted.

[0192] 8) The predicted gene sequences of all samples were clustered using CD-HIT (Li & Godzik, 2006) version 4.8.1 to construct a non-redundant gene set.

[0193] 9) The assembled non-redundant gene set was quantified using Salmon (Patro et al., 2017) version 1.6.0 to obtain the original Reads data and the normalized TPM (Transcripts Per Kilobase Million) data.

[0194] 10) The non-redundant genes were annotated to databases such as GO, KEGG, and COG using eggNOG-mapper (Cantalapiedra et al., 2021) version 2.1.6, and the databases were annotated to the CAZy database (Drula et al., 2022) using DIAMOND (Buchfink et al., 2021) version 2.0.13.

[0195] Metagenomic binning

[0196] The Metagenomic data were binned and purified using MetaWRAP (Uritskiy et al., 2018) version 1.3.2, and the specific steps are briefly described as follows:

[0197] 1) Use two algorithms, MaxBin 2 (Wu et al., 2016) and MetaBAT 2 (Kang et al., 2019) in the binning module to bin contigs with lengths greater than 2000 bp;

[0198] 2) Use version 1.0.12 of CheckM (Parks et al., 2015) in the bin_refinement module to purify the obtained bins, and retain bins with integrity greater than or equal to 70% and contamination less than 10%;

[0199] 3) Merge the bins of all samples into the same directory, and then use dRep (Olm et al., 2017) version 3.2.2 to remove redundancy of all bins at the species level based on Average Nucleotide Identity (ANI) (detailed parameters: -sa 0.95 -nc 0.10 -comp 70 -con 10), and finally obtain a non-redundant genome set;

[0200] 4) Use version 1.7.0 of GTDB-Tk (Chaumeil et al., 2019) to perform species annotation and phylogenetic tree construction on the non-redundant genome set;

[0201] 5) Use version 1.13 of Prokka (Seemann, 2014) to annotate the genes in each non-redundant genome set;

[0202] 6) Use Salmon to calculate the relative abundance of each bin in the samples.

[0203] Random forest analysis

[0204] First, filter out ASVs with an average relative abundance less than 0.1% in each group of data. Then, take two samples in each block at each growth stage and each fertilization treatment as the training set of the random forest model, and take the remaining one sample in each block as the test set. Then use the randomForest function of version 4.7 of the randomForest (https: / / CRAN.R-project.org / package=randomForest) package in R language version 4.1.0 to train the training set data of each group, then use the predict function to test the test set data of each group, and finally use the rcfv function to perform 10× cross-validation on the error rate.

[0205] Co-occurrence network analysis

[0206] Use the co-occurrence network to reveal the potential interaction relationships between microbial communities in various treatments and compare the differences in relevant network properties between different treatments. The specific steps are as follows:

[0207] 1) Select ASVs with an average relative abundance of not less than 0.1% at each developmental time point for the next network analysis;

[0208] 2) Use the absolute abundance data of ASVs from a total of about 576 samples (including 2 ecological niches, 4 treatments, 8 time points, and 9 replicates) to construct the network;

[0209] 3) Use the corr.test function in the psych (https: / / CRAN.R-project.org / package=psych) package version 2.2.5 of R version 4.1.0 to calculate the pairwise Spearman correlation matrix, and use the FDR (False Discovery Rate) method to correct the P values;

[0210] 4) Select datasets with a P value less than 0.05 and a correlation greater than 0.8 or less than -0.8 according to the correlation results for network construction;

[0211] 5) Use the igraph (Csardi & Nepusz, 2006) package version 1.2.6 in R to calculate the relevant properties of the network and nodes in each network;

[0212] 6) Calculate the robustness of each network according to the method described by Yuan et al. (Yuan et al., 2021);

[0213] 7) Finally, use Gephi (Bastian et al., 2009) version 0.9.5 to visualize the co-occurrence network relationships between microbial communities in each treatment. The point size represents the degree, and the point color represents the phylum-level classification of bacteria or different modules.

[0214] Synthetic community back-inoculation

[0215] Based on the results in the rhizosphere microbial community network, the ASV sequences in the three modules were separately aligned to the previously isolated bacterial sequences by Blast (identity > 95%). Seven ASVs were identified in module 2 that was specifically enriched in the PK treatment. Then these seven ASVs were used as the experimental synthetic community. In addition, five ASVs were screened in module 1 as the control synthetic community (including strains: Brevundimonas sp., Sediminibacterium sp., Mycobacterium sp., Herbaspirillum sp., Sphingomonas sp.). The synthetic community re-inoculation experiment was carried out in non-nutrient soil (vermiculite), including two nutrient treatments (nitrogen application and no nitrogen application) and four inoculation treatments (experimental synthetic community SynCom7 composed of seven bacteria, experimental synthetic community SynCom5 composed of five bacteria, control synthetic community SynCtrl composed of five control bacteria, and sterile water control Control). The specific operation process is as follows:

[0216] 1) Sterilize the vermiculite (121 °C, 20 min) and place it in a 10 cm × 10 cm square plant culture box. Then pour an appropriate amount of sterile water into the bottom tray to make all the soil evenly wet;

[0217] 2) Disinfect soybean seeds with 5% (w / v) NaClO solution for 5 min, and then wash them 5 times with sterile water before sowing;

[0218] 3) Sow 4 soybean seeds in each small box, and then spread a thin layer of vermiculite on its surface and wait for germination;

[0219] 4) After the seeds have fully germinated, cut off the plants with too poor or too good growth so that all the seedlings are in the same size state, then take pictures for recording and wait for inoculating the synthetic community;

[0220] 5) Streak the selected strains stored in the -80 °C refrigerator on solid LB medium and incubate them upside down at 30 °C for 48 h;

[0221] 6) Pick a single colony into a 50 mL sterile centrifuge tube containing 20 mL of liquid LB medium for expansion culture (30 °C, 48 h);

[0222] 7) After centrifugation (9000 rpm, 2 min), collect the bacterial pellet, then resuspend it with 30 mL of sterile water, and measure the OD value using a spectrophotometer (OD600);

[0223] 8) Prepare a 100 mL mixed bacterial solution with a final concentration of OD = 0.1 for each bacterium according to the required synthetic community;

[0224] 10) Inoculate the root of each seedling with 1 mL of the corresponding inoculation solution according to the corresponding design;

[0225] 11) Seven days after the first inoculation, add another inoculation once more in the same way;

[0226] 12) During the entire experimental period, water with the corresponding nitrogen-deficient nutrient solution (-N MFP, without NH4NO3) and nitrogen-containing nutrient solution (MFP); The MFP formula (1 L) is: 132.3 mg CaCl2·2H2O, 123.2 mg MgSO4·7H2O, 95.3 mg KH2PO4, 113.6 mg Na2HPO4·12H2O, 4.9 mg FeC6H5O7, 40 mg NH4NO3, 100 μg MnCl2·2H2O, 100 μg CuSO4·5H2O, 100 μg ZnCl2, 100 μg H3BO4, 100 μg Na2MoO4.

[0227] 13) After the plants have grown for 3 weeks, take pictures and record, and measure their plant height, dry weight and other phenotypes.

[0228] Data statistics

[0229] All statistical analyses were performed using R version 4.1.0 or Linux (CentOS 7.6 version). For all statistical tests, P < 0.05 was considered significant, and the significance in each figure was specified in the figure legend.

[0230] Statistical analysis of soil physical and chemical properties, soybean yield and nodule phenotypes

[0231] All data were first tested for normality using the Shapiro-Wilk test (Shapiro & Wilk, 1965) and for homogeneity of variance using the Bartlett test. For data that were normally distributed and had homogeneous variances, one-way ANOVA was used for single-factor variance testing, and the Dunnett post hoc test was used for multiple comparison analysis. All data that were not normally distributed or had heterogeneous variances were analyzed using the Kruskal-Wallis rank sum test, and the Dunn post hoc test was used for multiple comparisons.

[0232] Statistical analysis of alpha diversity

[0233] A linear mixed model was constructed using the lmer function in the lme4 (https: / / CRAN.R-project.org / package=lme4) package version 1.1 for three-way ANOVA to statistically analyze the effects of ecological niche, fertilization treatment, developmental stage, and the interactions between these factors on bacterial α-diversity. The Kruskal-Wallis rank sum test was used for the analysis of α-diversity differences between different developmental stages and different fertilization treatments, and the Dunn post hoc test was used for multiple comparisons. The differences in α-diversity between different developmental stages were represented by lowercase letters, and the differences between different treatments at the same developmental stage were represented by asterisks (only compared with the normal fertilization treatment).

[0234] Statistical analysis of beta diversity

[0235] The adonis2 function in the vegan (https: / / CRAN.R-project.org / package=vegan) package version 2.5 was used to perform permutational multivariate analysis of variance (PERMANOVA) on bacterial β-diversity to statistically analyze the explanatory power and significance of ecological niche, fertilization treatment, developmental stage, and the interactions between these factors on bacterial β-diversity. The Kruskal-Wallis rank sum test and the Dunn post hoc test were used for multiple comparisons to analyze the differences in Bray-Curtis distances between rhizosphere samples and endophytic samples at different developmental stages. Univariate regression analysis was used to test the relationship between the soybean growth time distance between samples and the Bray-Curtis distance between samples in different fertilization treatments, and the relationship between the developmental stage of each sample and the Bray-Curtis distance of samples in PK, NK, NP treatments relative to samples in the normal fertilization treatment.

[0236] Analysis of differences in species abundance between samples

[0237] The same method was used for the analysis of differences in the relative and absolute abundances of species between samples. The Kruskal-Wallis rank sum test was used for the analysis of abundance differences between different developmental stages and different fertilization treatments, and the Dunn post hoc test was used for multiple comparisons.

[0238] LEfSe absolute abundance difference analysis

[0239] The identification of microbial biomarker taxa mainly involves performing differential analysis on the absolute abundances of each sample through the LEfSe software (version 1.0.8) (ASVs with an average relative abundance less than 0.1% in each group of data are filtered out in advance). First, a non-parametric Kruskal-Wallis rank sum test is performed on each group of samples in each taxonomic attribute. Then, based on the species with significant differences in the previous step, a Wilcoxon rank sum test is performed pairwise to detect differences between groups. Finally, a linear discriminant model is established to evaluate the influence degree (LDA score) of significantly different species between different groups.

[0240] Differential analysis of the relative abundances of bacterial ASV functional genes

[0241] Differential analysis of the relative abundances of bacterial ASV functional genes between different growth and development stages is performed using the Kruskal-Wallis test with Dunn's post hoc test for multiple comparisons. For the differential analysis of the relative abundances of functional genes between different fertilization treatments, first, the average relative abundances of bacterial ASV functional genes for each treatment and each growth stage are calculated, and then the Wilcoxon rank sum test is used to perform paired tests on the data for each development stage to detect differences between each treatment and the normal fertilization treatment.

[0242] Differential analysis of the absolute abundances of rhizosphere core ASVs between different fertilization treatments

[0243] First, the average absolute abundances of bacterial core ASVs for each treatment and each growth stage are calculated. Then, the Wilcoxon rank sum test is used to perform paired tests on the ASV abundances for each development stage to detect differences between each treatment and the normal fertilization treatment. Finally, ASVs with an abundance 1.5 times higher or lower than the control in all development stages of each fertilization treatment are regarded as differential ASVs between the corresponding fertilization treatments.

[0244] Differential analysis of the basic characteristics of co-occurrence networks

[0245] For the differential analysis of the robustness between each network and the average degree of nodes at the order level, as well as the differential analysis of the cumulative absolute abundances of samples in each module of the rhizosphere network, the Kruskal-Wallis test with Dunn's post hoc test is used for multiple comparison analysis to compare the differences between each treatment network or module and the normal fertilization treatment network or module, respectively.

[0246] Differential analysis of metagenomic functional genes

[0247] For the analysis of the α-diversity differences in the metagenomic functions among different fertilization treatments, multiple comparison analyses were all performed using the Kruskal-Wallis test with Dunn's post hoc test; for the analysis of β-diversity differences, canonical correspondence analysis (CCA) was used to calculate the explanatory power of fertilization treatments for β-diversity, and then ANOVA-like permutation tests were used to calculate the significance.

[0248] Phenotypic analysis of the synthetic community re-inoculation experiment

[0249] All data were first tested for normality and homogeneity of variance using the Shapiro-Wilk test and Bartlett test. If the data conformed to a normal distribution and had homogeneous variance, ANOVA one-way analysis of variance was used, and multiple comparison analyses were performed using LSD post hoc test; otherwise, the Kruskal-Wallis rank sum test was adopted, and multiple comparisons were performed using Dunn's post hoc test.

[0250] Example 1. Soil physical and chemical properties and soybean growth phenotypes

[0251] An experiment was conducted in a black soil experimental field in Harbin, Heilongjiang Province. It included a total of 4 different fertilization treatments, namely normal fertilization (NPK, applying nitrogen, phosphorus, and potassium fertilizers), no nitrogen fertilizer application (PK, applying phosphorus and potassium fertilizers), no phosphorus fertilizer application (NK, applying nitrogen and potassium fertilizers), and no potassium fertilizer application (NP, applying nitrogen and phosphorus fertilizers). Each treatment had 3 replicates, and a randomized block design ( Figure 2 A) was used. Sampling was carried out at 8 time points after soybean germination, namely 1 day, 4 days, 7 days, 14 days (nodulation began), 28 days (early growth stage), 42 days (beginning of flowering stage), 60 days (full flowering stage), and 72 days (pod-setting stage) ( Figure 2 B). 3 plants were collected from each plot, with a total of 9 replicates. Then, amplicon analysis (including 16S rRNA and rpoB genes) and metagenomic analysis based on relative and absolute abundance quantification were performed on the collected non-rhizosphere soil (36 samples), rhizosphere soil (287 samples), roots (284 samples), and root nodules (180 samples) ( Figure 2 C).

[0252] Analyze the physical and chemical properties of soils with different fertilization statuses (including long-term non-application of N, P, and K fertilizers), as well as the yield and root nodule phenotypes of soybeans.

[0253] 1. Soil physical and chemical properties

[0254] Non-rhizosphere soil was collected before sowing, and the soil physical and chemical properties were measured. The contents of alkali-hydrolyzable nitrogen, available phosphorus, and available potassium in the normal fertilization treatment were 172 mg kg-1 、145 mg / kg -1 and 378 mg / kg -1 。Compared with the normal fertilization treatment, the available nitrogen content in the PK treatment decreased by 35% to 112 mg / kg -1 ( Figure 3 A); the available phosphorus in the NK treatment decreased by 95% to 7 mg / kg -1 ( Figure 3 B); the available potassium content in the NP treatment decreased by 61% to 146 mg / kg -1 ( Figure 3 C), indicating that the lack of chemical fertilizers during long-term tillage reduced the content of corresponding available nutrients in the soil. However, in all treatments, the content of organic matter did not change significantly( Figure 3 D), but compared with the control, the content of dissolved organic carbon (DOC) in the PK treatment decreased significantly( Figure 3 E). In addition, it was also found that the soil pH value in the PK treatment was 1.1 units higher than that in the normal fertilization treatment( Figure 3 F), indicating that long-term nitrogen application acidifies the soil.

[0255] 2. Yield and nodule phenotype of soybean

[0256] After the soybean matured, the grain yield of the soybean was counted. According to the yield results of two consecutive soybean planting seasons, compared with normal fertilization, the soybean yields in the NP and NK treatments both showed a decreasing trend, with the NK treatment being the most obvious, decreasing by 25% and 29% in 2020 and 2017 respectively. However, there was no significant difference in soybean yield between the PK treatment and the normal fertilization treatment( Figure 4 A and Figure 4 B). In addition, since rhizobia can symbiotically form nodules with soybeans for nitrogen fixation, the nodule phenotype of soybeans at the early flowering stage (D42) was also counted. Similar to the yield results, the number and diameter of nodules in soybeans in the NK treatment were significantly lower than those in the normal fertilization treatment, suggesting that low phosphorus conditions inhibited the symbiotic nitrogen fixation process of soybean nodules and may lead to a decrease in soybean yield. However, in the PK treatment, the number and diameter of nodules were significantly higher than those of the control, while there was no obvious difference in the NP treatment compared with the control( Figure 4 C and Figure 4 D), indicating that in the black soil of Northeast China, low nitrogen promoted the symbiotic nitrogen fixation of soybeans and rhizobia and had no significant effect on soybean yield.

[0257] Example 2. Dynamic response characteristics of bacterial communities with plant development

[0258] The dynamic response characteristics of rhizosphere and endophytic bacterial communities under different fertilization treatments at each growth stage of soybean were explored based on amplicon sequencing, and the diversity and composition of rhizosphere and endophytic bacterial communities were analyzed. Meanwhile, the similarities and differences between traditional relative abundance and absolute abundance in characterizing microbial community characteristics were also compared.

[0259] 1. Calculation of the absolute abundance of bacterial communities

[0260] When analyzing the microbial composition of samples by conventional amplicon sequencing, only the relative abundance (i.e., proportion) of microorganisms in the samples is considered. Therefore, it can only describe the community composition and diversity of microorganisms, but cannot characterize the true number of each microorganism in the sample and the true differences between samples, thus causing a wrong understanding of the true changes in the microbial flora between samples. To better characterize the specific quantity of the microbial community and obtain the true change results of the community between samples, according to the method of the inventors based on adding artificially synthesized spike-in plasmids to sample DNA (Wang et al., 2020), it is used to quantitatively detect the copy number of bacterial 16S rRNA genes per unit mass of the sample. However, since only a single spike-in plasmid was added to the sample DNA before, it was impossible to judge whether the concentration of the internal standard plasmid affected the amplification efficiency. Therefore, on this basis, the inventors improved the previous method and added 12 16S spike-in plasmids (16S SynSpike) to the sample DNA to test the influence of DNA concentration on the amplification efficiency.

[0261] The amount of 16S SynSpike added to the DNA samples increased in a gradient manner (Table 3). Through the univariate regression analysis between the amount of 16S SynSpike added and the number of 16S SynSpike sequences in the sequencing results, it was found that in the non-rhizosphere soil, rhizosphere soil and endophytic samples, the relationship between the amount of SynSpike added and the number of sequences was fitted to a straight line passing through the origin. This indicates that the number of SynSpike sequences in the sequencing results is proportional to its added amount, and the sequence concentration has no obvious influence on the amplification efficiency of the spike-in plasmid ( Figure 5 ). Based on this result, the proportional relationship between the total amount of all 16S SynSpike added and the total number of sequences annotated as 16S SynSpike in each sample was used as the basis for calculating the absolute abundance of each ASV. Finally, the absolute abundance of each ASV was estimated by calculating the copy number of the internal standard plasmid.

[0262] 2. Bacterial community analysis

[0263] Through 16S amplicon high-throughput sequencing, 12,349 - 801,472 sequences were obtained for each endorhizosphere sample, and 198,300 - 1,213,079 sequences were obtained for each rhizosphere soil sample. After filtering based on taxonomic annotation, 1,030 - 502,193 sequences remained for endorhizosphere samples, and 96,164 - 577,954 sequences remained for rhizosphere soil samples.

[0264] First, the diversity of bacterial communities was analyzed based on absolute abundance. The results of the Shannon diversity index showed that long-term different fertilization treatments had no significant effect on the α-diversity of bacteria in non-rhizosphere soil ( Figure 6 A). The results of three-way ANOVA found that niche, plant developmental stage, and different fertilization treatments could all significantly affect the α-diversity of bacterial communities (P < 0.001), and there was also a significant interaction among these three factors (Table 4). For example, in terms of niche, the α-diversity of bacteria was significantly higher in the rhizosphere (6.1) than in the endorhizosphere (4.5). During the entire growth cycle, the α-diversity of rhizosphere bacteria was the highest 4 days after seed germination. As the plant grew, the α-diversity showed a slow downward trend and was the lowest at the full-bloom stage (D60) and pod-setting stage (D72), while the α-diversity of bacteria in the NK treatment remained relatively stable ( Figure 6 B). In addition, the α-diversity of endorhizosphere bacteria was the highest at the beginning and then showed a downward trend, reaching the lowest value 14 days after seed germination, and then increasing and remaining relatively stable ( Figure 6 C), which may be due to the enrichment of a large number of rhizobia in the endorhizosphere of soybeans at the D14 stage (beginning to nodulate), thus reducing the α-diversity of the remaining bacteria. Among different fertilization treatments, the α-diversity of rhizosphere bacteria in the PK treatment was significantly higher than that of the normal fertilization treatment during the early vegetative growth stage (D1, D4, and D14), while the α-diversity of rhizosphere bacteria in the NK treatment was significantly higher than that of the normal fertilization treatment during the later reproductive growth stage (D42, D60, and D72) ( Figure 6 B). In the endorhizosphere, except that the α-diversity of bacteria in the NP treatment was significantly lower than that of the control at 72 days after seed germination, there was no obvious change in the α-diversity among different fertilization treatments at the other growth stages ( Figure 6 C).

[0265] Table 4. Three-way ANOVA of bacterial α-diversity based on absolute abundance (the results were calculated based on absolute abundance)

[0266] Variable F P Compartment 2385.3 1.9E-193 Stage 39.7 1.9E-44 Treatment 13.6 1.6E-08 Compartment × Stage 22 5.0E-26 Compartment × Treatment 8.2 2.3E-05 Stage × Treatment 5.7 5.4E-14 Compartment × Stage × Treatment 3 1.3E-05

[0267] The results of PCoA (Principal Co-ordinates Analysis) based on the Bray-Curtis distance of bacterial β-diversity showed that there were obvious differences in bacterial communities between rhizosphere soil and roots. Figure 27A). The PERMANOVA results showed that the niche (R 2 = 0.241, P < 0.001) was the main driver of bacterial β-diversity, followed by the plant developmental stage (R 2 = 0.114, P < 0.001) and the fertilization treatment (R 2 = 0.040, P < 0.001). The interactions among these factors also had a significant impact on bacterial β-diversity, especially the interaction between the niche and the plant developmental stage (R 2 = 0.103, P < 0.001) (Table 5).

[0268] Table 5. Contributions of various factors to the differences in bacterial β-diversity based on absolute abundance (the results were calculated based on absolute abundance)

[0269] Variable <![CDATA[R 2 > F P Compartment 0.241 312.6 0.001 Stage 0.114 21.0 0.001 Treatment 0.040 17.4 0.001 Compartment × Stage 0.103 19.1 0.001 Compartment × Treatment 0.032 13.9 0.001 Stage × Treatment 0.043 2.6 0.001 Compartment × Stage × Treatment 0.037 2.3 0.001

[0270] The Bray-Curtis distance between rhizosphere and endophytic bacteria decreased gradually with plant development ( Figure 27 A and Figure 27 B), and during each stage of plant development, the effect of fertilization on the β-diversity of rhizosphere bacteria was significantly higher than that on endophytic bacteria ( Figure 27 C). In non-rhizosphere soil, the fertilization treatment explained 58.6% of the differences in bacterial β-diversity, and there were significant differences in the bacterial β-diversity between the PK treatment and the control ( Figure 6 D). In rhizosphere soil, except for the NK treatment, the β-diversity of bacteria in each fertilization treatment showed obvious divergence over time (R 2 = 0.271), and the bacterial community structure of the PK treatment was significantly different from that of the control (R 2 = 0.199) ( Figure 6 E). Similarly, the β-diversity of endophytic bacteria also diverged over time (R 2 = 0.293), but the effect of the fertilization treatment on bacterial β-diversity was relatively small (R 2 = 0.042) ( Figure 6 F). The time decay pattern showed that the NK treatment decreased the regression slope of rhizosphere bacteria, from 0.0043 in the normal fertilization treatment to 0.0014, but there was no difference in endophytic bacteria ( Figure 6 G and Table 6). During the entire plant development process, the Bray-Curtis distance between rhizosphere bacteria between the PK treatment and the normal fertilization treatment was relatively stable (Slope = 0.0005). The differences between the normal fertilization treatment and the NK (Slope = 0.0040) and NP (Slope = 0.0028) treatments increased significantly over time, while the range of changes in the Bray-Curtis distance between bacteria in each treatment in endophytic and the control over time was relatively small ( Figure 6H and Table 7).

[0271] Table 6. Relationship between time distance and Bray-Curtis distance based on absolute abundance ( Figure 6 G statistic results, calculated based on absolute abundance)

[0272]

[0273] Table 7. Relationship between development time and beta distance based on absolute abundance among treatments ( Figure 6 H statistic results, calculated based on absolute abundance)

[0274]

[0275] Meanwhile, to compare whether there are differences in bacterial diversity results calculated based on relative abundance and absolute abundance, bacterial diversity based on traditional relative abundance was also analyzed. In the analysis based on relative abundance, to reduce the influence of sequencing depth among different samples and ensure the comparability between endophytic samples and rhizosphere samples, 1030 sequences were respectively extracted from each sample for downstream analysis. The rarefaction curve results based on the Shannon index showed that this sampling depth could well represent the Shannon index in each sample ( Figure 28 ). The calculation results based on this sampling depth found that the bacterial α-diversity in each niche was very similar to the calculation results based on absolute abundance ( Figure 29 A-C), which also indicated that niche, developmental stage, and fertilization treatment could all significantly affect the α-diversity of the bacterial community (P < 0.001) (Table 8).

[0276] Table 8. Three-way ANOVA of bacterial α-diversity based on relative abundance (calculated based on relative abundance)

[0277] Variable F P Compartment 1422.0 5.8E-149 Stage 36.6 2.2E-41 Treatment 12.0 1.4E-07 Compartment × Stage 18.1 1.6E-21 Compartment × Treatment 7.0 1.3E-04 Stage × Treatment 5.0 6.5E-12 Compartment × Stage × Treatment 2.6 1.5E-04

[0278] In the β-diversity analysis, it was also found that niche (R 2 = 0.136, P < 0.001) and plant developmental stage (R 2 = 0.133, P < 0.001) were the main driving factors of bacterial β-diversity ( Figure 29 D and Table 9), while the turnover rate of rhizosphere and endophytic bacteria ( Figure 29 G and Table 10) and the degree of change over time of the Bray-Curtis distance between each fertilization treatment and the control ( Figure 29 H and Table 11) were similar to the results based on absolute abundance. However, different from the calculation results based on absolute abundance, the Bray-Curtis distance based on relative abundance between rhizosphere and endophytic bacteria did not show a continuous decreasing trend with plant developmentFigure 29 E), and there were also no significant differences in the effects of fertilization on the relative abundance-based β-diversity of rhizosphere and endophytic bacteria at various stages of plant development ( Figure 29 F).

[0279] Table 9. Contribution of each factor to the difference in relative abundance-based bacterial β-diversity (calculated based on relative abundance)

[0280] Variable <![CDATA[R 2 > F P Compartment 0.136 132.0 0.001 Stage 0.133 18.4 0.001 Treatment 0.045 14.5 0.001 Compartment × Stage 0.066 9.2 0.001 Compartment × Treatment 0.018 5.9 0.001 Stage × Treatment 0.049 2.3 0.001 Compartment × Stage × Treatment 0.033 1.5 0.001

[0281] Table 10. Relationship between time distance and Bray-Curtis distance based on relative abundance (calculated based on relative abundance)

[0282]

[0283] Table 11. Relationship between developmental time and Bray-Curtis distance between treatments based on relative abundance (calculated based on relative abundance)

[0284]

[0285] These results indicate that although partially similar results can be obtained when calculating bacterial diversity based on absolute abundance and relative abundance, there are still significant differences.

[0286] In summary, nitrogen-free fertilization induced a new development pattern of the soybean rhizosphere bacterial community, with its α-diversity increasing in the early stage of soybean development and its β-diversity being significantly different from other fertilization treatments. In addition, phosphorus-free fertilization reduced the turnover rate of rhizosphere bacteria, which maintained a high α-diversity in the late stage of soybean development.

[0287] 3. Composition of the bacterial community

[0288] The 16S sequences after annotation filtering included 30,122 amplicon sequence variants (ASVs), of which 23,866 ASVs were in the rhizosphere and 11,690 ASVs were in the endosphere. Based on absolute quantification analysis, it was found that the niche, fertilization treatment, and developmental stage could all significantly affect the absolute abundance of bacteria (Table 12). There were no obvious differences in the abundance of bacteria in the non-rhizosphere soil under long-term different fertilization treatments ( Figure 7 A). With the development of the plant, the abundances of rhizosphere and endophytic bacteria both showed an increasing trend, being relatively stable in the early vegetative growth stage (D1 - D14) (average in the rhizosphere was 5.8×10 9 , and average in the endosphere was 1.5×10 8 ), then began to gradually increase, and reached the highest at the pod-setting stage (D72) (average in the rhizosphere was 2.3×10 10 , and average in the endosphere was 4.7×10 9)。However, in rhizosphere soil, the abundance of bacteria in the NK treatment did not show a similar changing trend, and its abundance was significantly lower than that in the normal fertilization treatment, especially in the later stage of development ( Figure 7 B), indicating that the soybean rhizosphere microbial community has a special dynamic response pattern under low phosphorus conditions, which may be caused by altered exudates or delayed development.

[0289] Table 12, Three-factor analysis of variance of bacterial absolute abundance (calculated based on absolute abundance)

[0290] Variable F P Compartment 730.8 3.4E-100 Stage 71.9 8.0E-72 Treatment 21.8 2.8E-13 Compartment × Stage 25.8 3.6E-30 Compartment × Treatment 16.4 3.4E-10 Stage × Treatment 9.7 4.3E-26 Compartment × Stage × Treatment 4.7 7.3E-11

[0291] Through sequence annotation, it was found that the abundances of the main 9 phyla of bacteria in rhizosphere soil and root samples accounted for 98.39%-99.68% and 98.58%-99.98% of the total abundance respectively. Among them, the most dominant bacterial groups were Proteobacteria (30.8%-58.8% in rhizosphere and 15.7%-75.2% in roots), Actinobacteria (14.4%-43.7% in rhizosphere and 16.4%-77.1% in roots), and Bacteroidetes (1.6%-31.7% in rhizosphere and 0.3%-11.5% in roots). During the development of soybean, the root-associated microorganisms were mainly Actinobacteria in the early stage and Proteobacteria in the later stage, and this phenomenon was most obvious in the roots. Interestingly, the relative abundance of Bacteroidetes was relatively low in the early stage of soybean growth, but at the pod-setting stage (D72), its abundance reached 23.2% and 5.7% in rhizosphere and roots respectively ( Figure 7 , Tables 13 and 14). Statistical analysis showed that the relative abundances of Bacteroidetes in rhizosphere and roots in the NK treatment were significantly lower than those in the normal fertilization treatment at the pod-setting stage (D72). In the PK treatment, the relative abundance of Acidobacteria in rhizosphere was significantly lower than that in the normal fertilization treatment at the four development stages (D4, D14, D28, and D72) of soybean, while the relative abundance of Firmicutes in roots was significantly higher than that in the normal fertilization treatment in the early stage of soybean growth (D4, D7, and D14) ( Figure 7 and Table 15).

[0292] Table 13, Differences in relative abundances of rhizosphere soil bacteria among different development stages (the numbers in the table represent the relative abundances (percentages) of bacteria in rhizosphere soil samples, and lowercase letters represent the significant differences in relative abundances of bacteria among different development stages in rhizosphere soil (P<0.05))

[0293]

[0294] Table 14, Differences in relative abundances of root bacteria among different development stages (the numbers in the table represent the relative abundances (percentages) of bacteria in root samples, and lowercase letters represent the significant differences in relative abundances of bacteria among different development stages in roots (P<0.05))

[0295]

[0296] Table 15. Differences in relative abundances of bacteria among different fertilization treatments (the numbers in the table represent the relative abundances (percentages) of bacteria in rhizosphere soil (left) or root (right) samples, and the lowercase letters represent the significant differences in the relative abundances of bacteria among different fertilization treatments in rhizosphere soil (left) or root (right) (P<0.05))

[0297]

[0298]

[0299] These results indicate that the assembly patterns of rhizosphere and endophytic bacteria are different among different fertilization treatments and vary dynamically with plant development.

[0300] 4. Composition of the rhizobia community

[0301] To explore the role of rhizobia during soybean growth, rpoB gene amplicon sequencing was performed on nodule and rhizosphere soil samples. As a result, 244,251 - 1,299,348 sequences were obtained for each nodule sample, and 89,563 - 1,522,022 sequences were obtained for each rhizosphere soil sample. After taxonomic annotation filtering, 30,155 - 629,691 sequences remained for nodule samples, and 132 - 157,398 sequences remained for rhizosphere soil samples. The filtered sequences included a total of 235 ASVs, among which 139 ASVs were present in nodules and 126 ASVs were present in rhizosphere soil.

[0302] Based on absolute quantitative analysis, it was found that in the non-rhizosphere soil under long-term different fertilization treatments, the abundance of rhizobia in the PK treatment was significantly higher than that in the normal fertilization treatment, while there were no obvious differences among the other treatments. With plant development, the abundance of rhizobia in the rhizosphere soil showed an increasing trend. Its abundance was relatively stable (5.5×10 6 ) during the early vegetative growth stage (D1 - D14), then began to gradually increase and reached the highest level (6.7×10 8 ) at the pod-setting stage (D72), which was consistent with the change trend of bacterial abundance. However, the abundance of rhizobia in nodules did not show an obvious change pattern and varied dynamically within the range of 3.3×10 10 to 2.0×10 11 during plant development( Figure 8 ).

[0303] Annotation of ASV sequences to the species level of rhizobia revealed significant differences in the rhizobial composition between the PK treatment and the normal fertilization treatment in the non-rhizosphere soil. Among them, the relative abundances of Sinorhizobium sp. and Bradyrhizobium jicamae were significantly higher than those in the normal fertilization treatment, while the relative abundances of Bradyrhizobium japonicum and Rhizobium anhuiense were lower than those in the normal fertilization treatment ( Figure 8 A and Table 16). The sum of the nine rhizobial sequences with the highest average abundances in the rhizosphere soil samples accounted for 99.17%-100% of all sequences in the samples, and the mainly enriched rhizobia were Bradyrhizobium japonicum (35.5%-91.8%), Rhizobium anhuiense (5.2%-50.8%), and Bradyrhizobium jicamae (0.2%-23.5%) ( Figure 8 B). Almost all sequences in the nodules were annotated as Bradyrhizobium japonicum (99.8%-100%), indicating that this rhizobium is the species that forms nodules in symbiosis with soybeans ( Figure 8 C).

[0304] Table 16. Differences in the relative abundances of rhizobia among different fertilization treatments (the numbers in the table represent the relative abundances (percentages) of rhizobia in the samples, and the lowercase letters represent the significant differences in the relative abundances of rhizobia among different fertilization treatments (P<0.05))

[0305]

[0306]

[0307] The results of the difference analysis found that there were significant differences in the composition of the rhizobial community between the PK treatment and the normal fertilization treatment. In the rhizosphere soil, the relative abundance of Bradyrhizobium jicamae in the PK treatment was significantly higher than that in the normal fertilization treatment throughout the vegetative growth period, and the relative abundance of Sinorhizobium sp. was also higher than that of the control in three stages (D4, D14, and D28) during the vegetative growth period. On the contrary, the relative abundance of Bradyrhizobium elkanii was lower than that of the control at D1, D4, D7, D14, and D42 ( Figure 8 B and Table 16). Importantly, compared with the vegetative growth stage, the relative abundance of Bradyrhizobium japonicum, which forms symbiosis with soybeans, was higher during the reproductive growth period (D42, D60, and D72) ( Figure 8B and Table 17). These results indicate that long-term nitrogen fertilizer omission increased the abundance of rhizobia in the soil, and there were more symbiotic rhizobia in the rhizosphere during the late growth stage of soybean.

[0308] Table 17. Differences in the relative abundance of rhizobia in rhizosphere soil among different developmental stages (the numbers in the table represent the relative abundance (percentage) of rhizobia, and lowercase letters represent significant differences in the relative abundance of rhizobia among different developmental stages (P<0.05))

[0309]

[0310] 5. Dynamic changes in bacterial abundance

[0311] By analyzing the dynamic change trend of bacterial abundance with plant development, it was found that there were significant differences in the dynamic change trends between the absolute abundance and relative abundance of bacteria, and the most obvious difference was in the phylum Actinobacteria. During the soybean growth cycle, the relative abundance of Actinobacteria in the rhizosphere decreased from 33.4% to 18.0%, while the absolute abundance increased from 1.9×10 9 to 3.7×10 9 ; the relative abundance of Actinobacteria in the roots decreased from 68.3% to 32.6%, while the absolute abundance increased significantly from 8.5×10 7 to 1.1×10 9 . In addition, the relative abundance of Acidobacteria in the rhizosphere decreased slowly, but the absolute abundance remained relatively stable; the relative abundances of Firmicutes, Acidobacteria, and Chloroflexi in the roots had a decreasing trend, while the absolute abundances had an increasing trend. The relative abundances and absolute abundances of Proteobacteria and Bacteroidetes in the rhizosphere and roots had similar dynamic change patterns and both showed a continuous increase ( Figure 9 Figure, Tables 13 and 14). These results indicate that relative abundance information cannot reflect the true number of microorganisms in the sample, which will cause serious misjudgment of the real changes in the microbial community between samples, thus affecting the experimental conclusions.

[0312] To further prove that there are significant differences in the dynamic change trends between the absolute abundance and relative abundance of bacteria, the dynamic change trend of the abundance of high-abundance bacteria in the rhizosphere with plant development was also analyzed at the ASV level. Through cluster heatmap analysis, it was found that among the rhizosphere samples of 4 different fertilization treatments, the trends of the relative abundances of these high-abundance bacterial communities changing with time were either the same or similar to the absolute abundance (green clustering clusters), or completely different from the absolute abundance (red clustering clusters) ( Figure 10 A-D). Further statistics showed that in the NPK, PK, NK, and NP treatments, the proportions of the relative abundances and absolute abundances of bacterial communities changing in completely opposite trends with time accounted for 38.1%, 46.5%, 44.9%, and 48.3% of the total bacterial communities in each treatment, respectively.Figure 10 E). These results further indicate that the absolute quantification method can present more realistic experimental results when characterizing microbial communities, while the analysis results based on relative abundance are somewhat "misleading". Therefore, the absolute quantification-based analysis method is mainly adopted next.

[0313] Example 3. Characteristics and functions of soybean rhizosphere bacteria

[0314] In this example, mainly based on the absolute quantification method, the abundance differences of core ASVs among different fertilization treatments were analyzed. The biomarker taxa in different fertilization treatments and different developmental stages were identified by methods such as LEfSe analysis and random forest analysis. Then, the functions of the bacterial community were predicted and analyzed using the FAPROTAX database (Louca et al., 2016). Finally, the differences in rhizosphere bacterial functions among different treatments were analyzed by metagenomic assembly, comprehensively exploring the characteristics and potential functions of core bacteria in the soybean rhizosphere.

[0315] 1. Differences in rhizosphere core ASVs among different fertilization treatments

[0316] After filtering out the low-abundance ASVs with an abundance ratio less than 0.1% in each rhizosphere sample, a total of 573 ASVs were obtained. Although these ASVs only accounted for 2.4% of the total ASV species in the rhizosphere, they accounted for 74.6% of the total abundance ( Figure 11 A). Further analysis found that the abundances of these 573 ASVs and the total rhizosphere ASV abundances had a very high correlation in each fertilization treatment (R2>0.977) ( Figure 11 B), so they were defined as core ASVs.

[0317] By counting the abundances of rhizosphere core ASVs in each fertilization treatment and developmental stage, it was found that except for the rhizosphere bacterial abundance in the NK treatment changing less with plant development, the rhizosphere bacterial abundances in the other fertilization treatments showed a slow increasing trend with plant development. Compared with normal fertilization, the rhizosphere bacterial abundance in the NK treatment was significantly reduced in the later stage of soybean development (D42-D72), while the rhizosphere bacterial abundance in the NP treatment was the highest at the pod-setting stage (D72) ( Figure 11 C), which was consistent with the results found in the bacterial community composition before ( Figure 7 B). Hierarchical clustering analysis was also used to further explore the changes of these rhizosphere core ASVs among different fertilization treatments. The results showed that compared with the normal fertilization treatment, many core ASVs had unique patterns in the PK treatment. Among them, the ASV in the 10th clustering cluster was specifically excluded in the PK treatment, while the ASV in the 15th clustering cluster was specifically enriched in the PK treatment ( Figure 11 C), similar to the diversity analysis results ( Figure 6B, E, and H), further demonstrating that long-term nitrogen omission significantly altered the developmental patterns of rhizosphere core ASVs in the PK treatment.

[0318] Next, the differential ASVs between each fertilization treatment and the normal fertilization treatment were compared ( Figure 12 ). By pairwise comparison of the absolute abundances of rhizosphere core ASVs among different fertilization treatments at each developmental stage, it was found that the abundances of 172 core ASVs in the PK treatment differed from those in the normal fertilization treatment. Among them, 69 ASVs were significantly enriched, mainly including Sphingomonadales and Rubrobacterales. More ASVs in bacterial taxa such as Actinomycetales, Burkholderiales, and Acidobacteriales were excluded by the PK treatment ( Figure 12 A). In the NK treatment, 67 core ASVs were significantly different from those in the normal fertilization treatment. The vast majority of ASVs (98.5%, 66) were significantly downregulated, including beneficial bacterial taxa such as Sphingomonas, Rhizobium, and Burkholderia ( Figure 12 B), indicating that long-term phosphorus omission disrupted the beneficial interaction between plants and rhizosphere bacteria, which may also be the reason for the reduction in soybean yield. In the NP treatment, there were fewer differential core ASVs compared to the control. Four ASVs belonging to Flavobacteriales were excluded ( Figure 12 C).

[0319] 2. Identification of core bacterial marker taxa

[0320] To obtain differential species among different fertilization treatments and different growth and development stages, LEfSe analysis (Linear discriminant analysis Effect Size) based on absolute abundance was performed on the rhizosphere soil bacterial community at six taxonomic levels from phylum to species. Ultimately, a large number of differential biomarker taxa were found both between different fertilization treatments (LDA > 6) and between different growth and development stages (LDA > 6). A total of 217 differential abundance clades were found between different fertilization treatments, and 83, 83, 1, and 50 bacterial biomarker taxa were identified in the NPK, PK, NK, and NP treatments, respectively. Among them, 20 families such as Micrococcaceae and Burkholderiaceae were identified in the normal fertilization treatment, 15 families such as Rhizobiaceae and Rubrobacteraceae were identified in the PK treatment, and 13 families such as Weeksellaceae and Enterobacteriaceae were identified in the NP treatment( Figure 13 ).

[0321] In addition, 199 differential clades were also found between different development stages, and 2, 8, 13, 1, 16, 70, 3, and 86 bacterial biomarker taxa were identified in the development stages such as D1, D4, D7, D14, D28, D42, D60, and D72, respectively. Among them, the Oxalobacteraceae and Alteromonadaceae families were identified in the D4 stage, 3 families such as Moraxellaceae and Chromatiaceae were identified in the D7 stage, 5 families such as Pseudomonadaceae and Bacillaceae were identified in the D28 stage, 16 families such as Caulobacteraceae and Streptomycetaceae were identified in the D42 stage, the Enterococcaceae family was identified in the D60 stage, and 22 families such as Weeksellaceae and Bradyrhizobiaceae were identified in the D72 stage( Figure 14 ). These results indicate that the rhizosphere bacterial community contains specifically enriched bacterial taxa in each fertilization treatment and different development stages. Therefore, it becomes possible to predict the nutrient types and plant development stages in farmland soil using these biomarker taxa.

[0322] 3. Random forest prediction of rhizosphere core bacterial biomarker taxa

[0323] To further analyze and identify the biomarker taxa of the rhizosphere core bacterial community under different fertilization treatments and different growth and development stages, a random forest prediction model was constructed at the phylum, class, order, family, genus, and species levels using the species composition characteristics of the rhizosphere core bacterial community based on absolute abundance. First, the error rate and accuracy of the random forest prediction model were evaluated at each taxonomic level, and it was found that the error rates at the family, genus, and species levels were relatively low and the accuracy was high (Table 18). To correspond to the LEfSe results and be comparable, the random forest model was trained and predicted at the family taxonomic level.

[0324] Table 18. Error rate and accuracy of the random forest prediction model (The results were calculated based on absolute abundance. They are the error rate and accuracy of the random forest prediction model constructed based on developmental stage or fertilization treatment data at the phylum, class, order, family, genus, and species taxonomic levels)

[0325]

[0326] At the family level, the trained random forest model had a prediction accuracy of 79.2% among different fertilization treatments and as high as 90.6% among different developmental stages ( Figure 30 ). Then, 10× cross-validation was used and it was found that using 16 families as biomarkers had relatively low cross-validation error rates among different fertilization treatments and different developmental stages, which were 19.9% ( Figure 15 A) and 14.1% ( Figure 16 A), respectively. Then, the absolute abundances of these 16 biomarker taxa were analyzed in each fertilization treatment and different growth and development stages. It was found that in different fertilization treatments, the PK treatment was enriched in bacterial taxa such as Actinospicaceae, Rubrobacteraceae, Sinobacteraceae, Dolo_23, and Propionibacteriaceae. Consistently, these biomarker taxa also appeared in the corresponding LEfSe results ( Figure 13 ), indicating that the prediction model had high accuracy.

[0327] In addition, among different growth and development stages, Oxalobacteraceae was enriched at D4, Moraxellaceae at D7, Bacillaceae and Methylophilaceae at D28, Halomonadaceae, Caulobacteraceae and Kouleothrixaceae at D42, and Paenibacillaceae, Bradyrhizobiaceae, Rhizobiaceae, Weeksellaceae and Sphingobacteriaceae at D72( Figure 16 B). Similarly, these biomarker taxa at each developmental stage were consistent with the corresponding LEfSe results( Figure 14 ). The above results further demonstrated that in the black soil farmland system in Northeast China, these biomarkers could be used to predict soil nutrient types and the developmental stages of soybeans, and also provided a theoretical basis for improving the structure and composition of the rhizosphere microbial community of soybeans.

[0328] 4. Functional analysis of the core bacterial community

[0329] To explore the potential functions of the rhizosphere core bacterial community in rhizosphere soil, functional annotation of all core bacterial ASVs was performed based on the FAPROTAX database. After functional prediction of 573 core bacterial ASVs, 45 related metabolic pathways were finally obtained. Then, 16 bacterial functions with relatively high abundance ratios were selected to compare their abundance differences among different fertilization treatments and developmental stages, including mainly 8 carbon metabolism-related pathways and 8 nitrogen metabolism-related pathways( Figure 17 and Figure 18 ).

[0330] To compare the differences in the potential functions of core bacterial ASVs among different fertilization treatments, pairwise differential comparison analysis was performed on the functional abundance ratios of core bacteria at each developmental stage in different treatments. The results showed that in the PK treatment, the abundance ratios of functional genes related to C metabolism (methylotrophy) and N metabolism (nitrogen fixation) were significantly higher than those in the normal fertilization treatment, while the abundance ratios of functional genes related to C metabolism (aromatic compound degradation and ligninolysis) and N metabolism (nitrate ammonification) were lower than those in the normal fertilization treatment. This indicates that low nitrogen conditions significantly altered the C and N metabolic functions of soybean rhizosphere bacteria, further verifying that long-term nitrogen deficiency induced a unique developmental pattern of soybean rhizosphere bacteria. Among them, the increase in the proportion of bacteria with nitrogen fixation function is beneficial to supplement the nitrogen nutrient content in the soil. In the NK treatment, compared with the normal fertilization treatment, the abundance ratios of C metabolic functional genes such as chemoheterotrophy, aerobic chemoheterotrophy, fermentation, and chitinolysis decreased, while the abundance ratios of most N metabolic functional genes increased significantly, including nitrite respiration, denitrification, nitrification, and nitrate ammonification. This indicates that low phosphorus reduced the C metabolic activities of soybean rhizosphere bacteria but significantly enhanced the N metabolism-related functions. In addition, there were not obvious changes in the rhizosphere bacterial functions between the NP treatment and the normal fertilization treatment ( Figure 17 ), indicating that the effect of low potassium conditions on rhizosphere bacteria was limited.

[0331] In addition, the differences in the potential functions of core bacterial ASVs among different growth and development stages were further compared through differential analysis. The inventors found that the abundance ratio of aromatic compound degradation functional genes in the D7 period was significantly higher than that in other development periods. The abundance ratio of fermentation functional genes in the D28 period was also significantly upregulated. The abundance ratio of chitin degradation functional genes in the D72 period was the highest among all development stages. These changes may be due to the alteration of secretions during soybean development. Importantly, the inventors also found that the abundance ratios of functional genes such as nitrite respiration, denitrification, nitrification, and nitrate ammonification in N metabolism were significantly higher in the early growth stage (D1 - D28) of soybean than in the late reproductive growth stage (D60 and D72), while the abundance ratios of functional genes such as nitrogen fixation and urea degradation continued to increase with soybean development. These results indicate that during the entire growth cycle of soybean, compared with the late development stage, rhizosphere bacteria have higher N metabolism activity in the early development stage, but the abundance ratios of nitrogen metabolism functions related to increasing soil nitrogen nutrients (nitrogen fixation and urea degradation) continue to increase with plant development, suggesting that more nitrogen nutrients are needed in the late plant development stage( Figure 18 ).

[0332] 5. Metagenomic functional analysis

[0333] To further explore the functions of the rhizosphere bacterial community under different fertilization treatments, metagenomic sequencing analysis was performed on rhizosphere soil samples from the early growth stage (D1), early flowering stage (D42), and podding stage (D72) of soybean. A total of 2166 GB of data was obtained after metagenomic sequencing of 36 rhizosphere soil samples. After quality control and assembly, each sample obtained an average of 2,081,141 contigs (786,987 - 3,223,911), with an average length of 793.8 bp (608.2 - 873.2 bp) and an average N50 of 1320 (981 - 1650). After gene prediction, each sample obtained an average of 470,823 complete genes (82,032 - 959,495). After removing redundancy from all genes, a total of 3,454,121 non-redundant genes were obtained. The contigs assembled in each sample were subjected to metagenomic binning, and then 142 binning results with a contamination level less than 10% and a completeness greater than 70% were retained after removing redundancy from the 655 bins obtained. After annotation, it was found that 140 bins were bacteria( Figure 19 ), and 2 bins were archaea.

[0334] First, the 140 bacterial bins obtained after binning were classified and functionally annotated. Similar to the species composition results obtained from previous 16S amplicon sequencing, Proteobacteria and Actinobacteria accounted for a high proportion in the rhizosphere, 55% (77) and 27.9% (39) of the total bins, respectively. Moreover, it was also found that most bacteria in the soybean rhizosphere had functional genes related to nitrogen mineralization, nitrogen-limitation adaptation, organic phosphorus mineralization, inorganic phosphorus solubilization, phosphorus transport, phosphorus starvation response, and potassium transport. Only a few bacteria had functional genes related to nitrogen reduction and denitrification, and extremely few bacteria had functional genes related to nitrification ( Figure 19 ). The Northeast Black Soil contains relatively high nutrient contents, which may be the reason why the rhizosphere bacteria of soybeans generally have functional genes for nutrient metabolism such as nitrogen, phosphorus, and potassium. At the same time, these bacteria may also further enhance the content and availability of relevant nutrients in the soil.

[0335] Next, all the obtained non-redundant genes were separately annotated to the KEGG, COG, and CAZyme databases, and the functional diversity among samples under different fertilization treatments was calculated. The results of the functional α-diversity index (Shannon index) showed that compared with the normal fertilization treatment, the functional α-diversity of the PK treatment was significantly reduced among all databases (KEGG, COG, CAZyme), while there were no significant differences between the NK and NP treatments and the control. The results of the Constrained Principal Coordinate Analysis (CPCoA) for calculating functional β-diversity indicated that except for the relatively high similarity in functional β-diversity between the normal fertilization treatment and the NP treatment, there were significant differences in the β-diversity of the PK and NK treatments and the normal fertilization treatment in the KEGG, COG, and CAZyme functions (P<0.05)( Figure 20 A-C). These results suggest that long-term unbalanced fertilization significantly changed the functional diversity of rhizosphere bacteria, which may exacerbate the functional adaptation of rhizosphere bacteria under different nutrient conditions.

[0336] Furthermore, the abundance differences of functional genes related to microbial nitrogen, phosphorus, and potassium cycling among different fertilization treatments were analyzed. Compared with the normal fertilization treatment, the genes related to functions such as nitrogen reduction (narB, narG, and narH), denitrification (nirK), inorganic phosphorus solubilization (gcd and ppa), and potassium transport (kdpA, ktrB, and kefB) were specifically reduced, while the functions related to nitrogen mineralization were enhanced. Compared with the 16S functional prediction results ( Figure 17) Similarly, it was found that the nitrogen fixation functional gene clusters (nifD, nifH, and nifK) consistently increased in the PK treatment. These results indicate that although low nitrogen conditions generally reduced the nutrient metabolism function of rhizosphere microorganisms, they enhanced their nitrogen fixation function. In the NK treatment, the abundances of inorganic phosphorus solubilization genes (gcd and ppa) and phosphorus starvation response gene (phoU) increased significantly, while the abundances of nitrification genes (hao and amoA), denitrification genes (nirK, nosZ, and nirS), and nitrogen limitation adaptation gene (glnA) involved in nitrogen metabolism-related functions also all increased significantly. In contrast, it was found that the abundances of nitrogen reduction-related genes (nirB, nirA, narB, and nasA) and potassium transport-related genes (kdpA and ktrB) showed a decreasing trend. These results suggest that low phosphorus conditions promoted the phosphorus response ability and supply ability of soybean rhizosphere microorganisms, while also enhancing nitrogen metabolism activities and reducing the transport of potassium nutrients. Compared with the normal fertilization treatment, the abundances of nutrient cycling-related functional genes changed little in the NP treatment, but the abundances of potassium transport-related genes increased, especially the Kup gene, indicating that low potassium conditions enhanced the potassium transport of soybean rhizosphere microorganisms( Figure 20 ). These results further demonstrate that the functions of soybean rhizosphere microorganisms are significantly different under different nutrient conditions, and the functional adaptation of rhizosphere microorganisms in different environments also enhances the adaptation ability of plants to various stresses, which is beneficial to the absorption of limiting soil nutrients by plants.

[0337] Example 4, Co-occurrence Network Analysis and Bacterial Ecological Cluster Function Analysis

[0338] Based on the absolute quantification method, the co-occurrence network of core bacteria in each fertilization treatment was analyzed, and ecological clusters that might function under low nitrogen conditions were found through modular analysis and the results of differential analysis. Then, their growth-promoting functions were verified through the isolation and culture of bacteria and the synthetic community re-inoculation experiment, comprehensively exploring the potential relationship between the functions of rhizosphere bacterial communities under different fertilization treatments and the growth and development of soybeans.

[0339] 1. Co-occurrence Network Analysis among Different Fertilization Treatments

[0340] To explore the direct or indirect interaction relationships among bacteria in each sample, co-occurrence networks analysis was performed on samples with different fertilization treatments in the rhizosphere and inside the roots. The Spearman correlation between each ASV was calculated based on the absolute abundances of the core ASVs in the rhizosphere and inside the roots. Finally, the data with a correlation coefficient r > 0.8 and a significance P < 0.05 were used to construct the bacterial co-occurrence networks for different fertilization treatments in the rhizosphere and inside the roots. At the same time, the basic network topological characteristics such as the number of nodes, the number of edges, the average degree, and the robustness of each network were also calculated. In the rhizosphere samples, the number of nodes in the normal fertilization treatment network was 293, the number of edges was 1096, and the average degree was 7.48. The number of nodes in the PK network and the NP network was comparable to that in the normal fertilization treatment network, being 289 and 266 respectively, but the number of edges was much higher than that in the normal fertilization treatment network, being 3249 and 3341 respectively. This also led to the average degrees of the PK network and the NP network being higher than that in the normal fertilization treatment network, being 22.48 and 25.12 respectively. The NK network had 270 nodes, comparable to the normal fertilization treatment network, and the number of edges and the average degree were 1442 and 10.68 respectively, slightly higher than the control ( Figure 21 A). These results indicate that under low nitrogen and low potassium conditions, the rhizosphere of soybean enhanced the interaction among bacteria, resulting in an increase in the complexity of the bacterial network. This is beneficial to promoting metabolic cooperation among microorganisms and enhancing the adaptability of microorganisms in nutrient-deficient environments.

[0341] The robustness of the rhizosphere bacterial networks in each fertilization treatment was further analyzed by randomly removing 50% of the nodes in the network. The results showed that the stabilities of the PK, NK, and NP networks were significantly higher than that of the normal fertilization treatment network ( Figure 21 B). Then, each network was segmented into subnetworks at the order level, and the average degree differences of the bacterial taxa with higher degrees among different fertilization treatments were calculated. The significance analysis results showed that the average degrees of the bacterial taxa Rhizobiales, Actinomycetales, Sphingomonadales, and Burkholderiales in the PK network and the NP network were significantly higher than those in the normal fertilization treatment network. In addition, the average degree of the bacterial taxon Gaiellales in the PK network was also higher than the control ( Figure 21 C). In summary, these results indicate that not applying nitrogen fertilizer or not applying potassium fertilizer significantly increased the complexity of the rhizosphere bacterial network, while the rhizosphere bacterial network under nutrient conditions lacking nitrogen, phosphorus, and potassium had higher stability. The complexity and stability of the network are important characteristics of ecosystem functions. The increase in complexity and stability may be the potential reasons for rhizosphere bacteria and plants to relieve stress.

[0342] Meanwhile, the co-occurrence networks of bacteria in the roots under each fertilization treatment were also analyzed. The number of nodes in the network of the normal fertilization treatment was 198, the number of edges was 584, and the average degree was 5.9. Similar to the results of the rhizosphere bacterial network, in the roots, the average degree of the PK network was 10.4, and its complexity was significantly higher than that of the normal fertilization treatment network. The average degrees of the NK network and the NP network were 7.76 and 6.63, respectively, which were also slightly higher than that of the normal fertilization treatment network( Figure 22 ).

[0343] To further verify the accuracy of the calculated basic network characteristics, the same dataset was used for repeated verification on the website MENA (Molecular Ecological Network Analysis Pipeline). Similar results were also observed among different fertilization treatments in the co-occurrence networks of rhizosphere and root bacteria( Figure 31 ).

[0344] 2. Co-occurrence network analysis of rhizosphere bacterial communities

[0345] Considering that there are also co-correlations among microbial groups under different fertilization treatments, and rhizosphere bacteria are more sensitive to fertilization treatments( Figure 27 ), a total co-occurrence network was constructed using all samples of different fertilization treatments in the rhizosphere to compare the changes in ecological clusters (i.e., modularity) caused by different fertilization treatments. The results showed that the rhizosphere bacterial network contained three main modules( Figure 23 A). The absolute abundance of bacteria in the PK treatment was significantly higher than that of the normal fertilization treatment in module 2, while it was significantly lower than that of the normal fertilization treatment in module 3. The absolute abundance of bacteria in the NK treatment was significantly lower than that of the control in all three modules, while there was no significant difference in the absolute abundance of bacteria between the NP treatment and the control( Figure 23 B). Through the classification and statistics at the phylum level of bacteria, it was found that Proteobacteria were mainly dominant in modules 1 and 3, while the bacterial groups in module 2 were mainly composed of Actinobacteria, and the proportion of Acidobacteria decreased significantly( Figure 23 C). Since the previous results showed that not applying nitrogen fertilizer did not reduce the yield of soybeans( Figure 4 ), and unique developmental patterns of rhizosphere bacteria of soybeans were formed under low nitrogen conditions( Figure 6 , Figure 11 and Figure 17 ), it was suggested that the core bacteria specifically enriched in the PK treatment-specific module (module 2) might affect the growth and development of soybeans.

[0346] 3. Isolation and purification of rhizosphere bacteria

[0347] To verify the above results, rhizosphere bacteria in different fertilization treatments were isolated and purified. A total of 1,011 monoclonal bacteria were finally isolated. After these monoclonal bacteria were purified by streak plate culture, PCR amplification and Sanger sequencing identification were performed using universal primers for the 16S rRNA gene. After annotating the sequencing results to the Greengenes database, it was statistically found that the isolated bacteria mainly belonged to Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Among them, the number of Proteobacteria was the largest, accounting for 45% (455) of the total number of isolated bacteria. Followed by Firmicutes and Actinobacteria, accounting for 24.4% (277) and 21.2% (214) of the total, respectively. The least was Bacteroidetes, only accounting for 4.6% (47) of the total number ( Figure 24 A). Then, the core ASVs of rhizosphere bacteria were sorted from high to low according to abundance, and the sequences of the isolated strains were aligned with the sequences of the core ASVs. The results showed that when the cumulative abundance of the core ASVs reached 60% and 80%, the total abundance of the isolated strains accounted for 58% and 49% of the total abundance of the core ASVs, respectively. This indicates that the isolated strains had a high coverage rate in the rhizosphere ASVs ( Figure 24 B).

[0348] In addition, Blast analysis was performed on the sequences of each module's ASVs obtained from the previous co-occurrence network analysis of the rhizosphere bacterial community and the sequences of the isolated strains, and the results with a similarity greater than 95% were selected. Combining the modular analysis and differential analysis results of the rhizosphere bacterial co-occurrence network, finally 7 isolated strains were screened in module 2 as experimental synthetic communities, and they were combined into two synthetic communities, SynCom7 (containing 7 species) and Syncom5 (containing 5 species), for back-inoculation experiments to verify the function of the ecological cluster in module 2. At the same time, 5 isolated strains were also screened from module 1 as control synthetic communities ( Figure 24 A).

[0349] 4. Synthetic community back-inoculation experiment

[0350] Based on the above experimental results and the isolation, purification, and identification of bacteria, the test strains for synthetic community back-inoculation were finally selected. The synthetic community back-inoculation experiment was carried out under the conditions of an artificial climate laboratory. Soybean seeds were disinfected and uniformly germinated in the native soil. Three days after the seeds completely germinated, plants with too fast or too slow growth were removed, and then 1 mL of inoculation solution was inoculated into the roots of the plants. The inoculation solutions mainly included 4 types, namely sterile water (Control), control synthetic community containing 5 strains from module 1 (SynCtrl), experimental synthetic community containing 7 strains from module 2 (SynCom7), and experimental synthetic community containing 5 strains from module 2 (SynCom5)( Figure 25A). Seven days after inoculation, 1 mL of the corresponding inoculum was supplemented at the roots of the plants again. After 3 weeks of soybean growth, phenotypes such as plant height and dry weight were measured.

[0351] Statistical analysis revealed that in nitrogen-supplemented vermiculite, there were no significant differences in plant height and dry weight between soybeans inoculated with the control synthetic community and those inoculated with sterile water. Compared with the control synthetic community, inoculation with two experimental synthetic communities, SynCom5 and SynCom7, increased the plant height and dry weight of soybeans. Among them, the effect was most obvious after inoculation with SynCom5, with the plant height of soybeans increasing by 15% and the dry weight increasing by 38%. In vermiculite without nitrogen supplementation, there was no difference in plant height between soybeans inoculated with the control synthetic community and those inoculated with sterile water as a control, but the dry weight was higher than the latter. Similarly, compared with the control synthetic community, after inoculation with SynCom5, the plant height of soybeans increased by 6% and the dry weight increased by 18%; after inoculation with SynCom7, the plant height and dry weight of soybeans were significantly higher than those after inoculation with sterile water ( Figure 25 B-D).

[0352] To further explore the pathways by which the bacteria in these synthetic communities promote soybean growth, the inventors detected the physiological functions of these bacteria. The results showed that compared with the bacteria in the control synthetic community, the bacteria in the experimental synthetic communities (SynCom5, SynCom7) could produce higher concentrations of IAA and more active ACC deaminase, and the inorganic phosphorus solubilization function was only found in the experimental synthetic communities ( Figure 32 ). IAA, as a plant growth hormone, effectively promotes plant growth. ACC deaminase effectively promotes biomass.

[0353] In summary, the experimental synthetic communities have the function of promoting soybean growth under pot conditions, and the growth-promoting effect of SynCom5 is the most obvious. The promotion of soybean growth by the specifically enriched ecological clusters in the PK treatment indicates that under low-nitrogen conditions, the bacterial communities in the rhizosphere other than rhizobia also have a significant growth-promoting function for soybeans, which further enhances the adaptability of soybeans in low-nitrogen environments and may also potentially promote soybean productivity.

[0354] Based on the above research results, it was found that as soybeans developed, the α-diversity of rhizosphere bacteria gradually decreased, while the abundance of bacteria gradually increased, mainly including bacterial groups such as Proteobacteria, Bacteroidetes, and Rhizobia. The α-diversity of bacteria in the PK treatment was significantly higher than that in the normal fertilization treatment during the early vegetative growth stage, while the α-diversity of bacteria in the NK treatment was significantly higher than that in the normal fertilization treatment during the reproductive growth stage. Figure 26A). In the black soil under long-term fertilization, compared with the soil under normal fertilization, the available nitrogen content in the PK soil decreased by 35%, the pH value increased by 1.1 units, and the number and diameter of soybean root nodules increased; the available phosphorus content in the NK soil decreased by 95%, the number and diameter of soybean root nodules decreased, and the soybean yield decreased by 25-29%; the available potassium content in the NP soil decreased by 61%. In the rhizosphere, different fertilization treatments induced the functional adaptation of microorganisms to the environment. The nitrogen fixation and nitrogen mineralization functions of microorganisms were enhanced in the PK treatment, the inorganic phosphorus solubilization and phosphorus starvation response functions of microorganisms were enhanced in the NK treatment, and the potassium transport function of microorganisms was enhanced in the NP treatment. In addition, the lack of nutrients such as N, P, and K enhanced the stability of the rhizosphere bacterial network, while the PK and NP treatments also enhanced the complexity of the rhizosphere bacterial network. Most importantly, the ecologically enriched clusters specific to the PK rhizosphere network could promote the growth of soybeans, increasing the dry weight and plant height of soybeans, which may be one of the key factors for maintaining its yield.( Figure 26 B).

[0355] Discussion

[0356] The rhizosphere microbial community is highly dynamic and is strongly regulated by soil nutrient conditions and plant growth and development. Plants can shape specific rhizosphere microbial communities to enhance their adaptability during different developmental processes and in different environments. Rhizosphere microorganisms are the "second genome" of plants and play roles in regulating plant growth and development, promoting plant nutrient uptake, improving plant resistance, and maintaining plant health. However, the long-term unreasonable use of chemical fertilizers not only exacerbates environmental pollution but also severely damages the structure and function of soil microorganisms, which is not conducive to the sustainable development of agriculture. Here, based on a long-term fertilization positioning experiment, the assembly dynamics and functional adaptation of the rhizosphere microbial community of soybeans under different fertilization treatments and at different developmental stages were studied, the quantitative microbiome characteristics of the whole life cycle of soybeans under different nutrient conditions were described for the first time, and the ecological mechanism by which rhizosphere microorganisms promote crop growth was revealed.

[0357] 1. Effects of fertilization on soil physical and chemical characteristics and crop yield

[0358] The present invention found that compared with the soil under normal fertilization, the available nitrogen content in the PK soil decreased by 35%, the available phosphorus content in the NK soil decreased by 95%, and the available potassium content in the NP soil decreased by 61%.( Figure 3A-C). The second soil census classified soil nutrient contents into six grades from high to low (Soil Census Office, 1993). The contents of available nitrogen, available phosphorus, and available potassium in the normally fertilized soil all belong to the first grade (extremely high). The content of available nitrogen in the PK soil and the content of available potassium in the NP soil both belong to the third grade (upper-middle), while the content of available phosphorus in the NK soil belongs to the fourth grade (lower-middle). The inventors' research shows that in the soybean-corn-wheat rotation system, although long-term nitrogen application reduction decreased the corresponding soil nutrient contents, the soybean yield did not decrease, and even increased by 18% in the 2020 growing season. In addition, the productivity of Northeast soybeans is mainly limited by the content of soil available phosphorus. The soybean yields in the non-phosphorus fertilizer treatment decreased by 25% and 29% respectively in the 2020 and 2017 growing seasons ( Figure 4 A and B).

[0359] Contrary to the results of the NK treatment, the nodule number and nodule diameter of soybeans in the PK treatment were significantly higher than those in the nitrogen application treatment ( Figure 4 C and D), and the content of soil available nitrogen remained at a high level. The higher nitrogen content in the nitrogen-free fertilizer soil may be due to the fact that soybeans can utilize both the nitrogen absorbed by the roots and the nitrogen biologically fixed. On the one hand, low nitrogen promotes nodulation, and the increase in nodule number can improve the nitrogen content in plants and soil; on the other hand, in the absence of exogenous nitrogen input, the soil and root-related microbial communities can also increase the nitrogen mineralization in the soil. In addition, it was also found that the pH value of the soil in the PK treatment was significantly higher than that of other treatments, indicating that long-term nitrogen application would acidify the soil. The continuous acidification of the soil would lead to soil compaction, decreased fertility, and reduced biological activity, seriously affecting the health and yield of crops. Although the content of soil soluble organic carbon in the PK treatment was lower than that in the normally fertilized treatment, the organic matter content showed no significant change.

[0360] 2. Temporal dynamics of soybean root-related microorganisms

[0361] Microorganisms are key regulators of soil organic matter decomposition and nutrient cycling, and their diversity and biomass are crucial for soil quality and health. The inventors found that there were no significant differences in the α-diversity of non-rhizosphere soil bacteria under long-term different fertilization treatments ( Figure 6 A and Figure 29 A), while the α-diversity of the rhizosphere bacterial community was significantly affected by the plant development stage and different fertilization treatments ( Figure 6 B and Figure 29 B). During the whole growth period, the α-diversity of rhizosphere bacteria was significantly higher in the vegetative growth stage than in the reproductive growth stage ( Figure 6 B and Figure 29B). The hint is that when plants transition from vegetative growth to reproductive growth, root exudates decrease, which in turn affects the diversity of the rhizosphere bacterial community. It was also found that in the rhizosphere, the α-diversity of the PK treatment was significantly higher than that of the normal fertilization treatment in the early stage of vegetative growth, while the α-diversity of the NK treatment was higher than that of the normal fertilization treatment in the late stage of reproductive growth( Figure 6 Band Figure 29 B). The diversity of rhizosphere bacteria in the NK treatment did not decrease during the reproductive growth period.

[0362] Plants recruit a large number of microorganisms throughout their life cycle, and the colonization pattern of microorganisms is driven by specific root exudates during plant growth, resulting in a highly dynamic rhizosphere-associated microbial community. The rhizosphere is a hot spot for soil microbial communities, and rhizosphere microorganisms are crucial for maintaining plant growth and productivity. Since there are significant differences in the abundances of rhizosphere microbial taxa at different plant developmental stages, the analysis method based on relative abundance may lead to bias in data interpretation. The inventors also found that there were significant differences in the dynamic change trends of the absolute abundances and relative abundances of bacteria during soybean development, with the most obvious differences in the phylum Actinobacteria( Figure 9 ), and 38.1%-48.3% of the high-abundance ASVs in the rhizosphere of different fertilization treatments also showed different change trends( Figure 10 ). The results of Quantitative Microbiome Profiling (QMP) based on absolute quantification showed that there were significant differences in the abundances, diversities, and community compositions of rhizosphere and endophytic bacteria( Figure 6 and Figure 7 ). Microorganisms need to penetrate the cell wall and overcome host immunity to successfully colonize the roots, so the endophytic microbial community is mainly regulated by its host genotype. Except for the NK treatment, the rhizosphere bacterial community had a relatively high turnover rate during plant growth and development (the absolute quantification result was 0.43-0.54%; the relative quantification result was 0.28-0.34%), which was significantly higher than that of non-rhizosphere soil. These results indicate that due to the gradual increase in the abundance of bacteria during plant growth and development, the turnover rate of the rhizosphere bacterial community also increased. In particular, the inventors found that the absolute abundances of bacteria in each treatment remained relatively stable throughout the vegetative growth period, and except for the NK treatment, the absolute abundances of bacteria increased sharply during the reproductive growth period( Figure 7 and Figure 11 C), suggesting that this was due to the change in root exudates and the increase in root deposits after soybean entered the flowering stage.

[0363] The bacteria associated with soybean roots are mainly composed of the phyla Actinobacteria, Proteobacteria, and Bacteroidetes. Actinobacteria dominated in the early stage of plant growth, while Proteobacteria and Bacteroidetes were dominant in the later stage( Figure 7 andFigure 9 )。The dominant positions of Proteobacteria, Actinobacteria, and Bacteroidetes in plant roots have long been widely recognized in Arabidopsis and various crops. Previous studies have shown that Actinobacteria can be selectively recruited by living plant cells but not by lignified cells. Many Actinobacteria taxa can produce a series of secondary metabolites with antibacterial activity and can improve the adaptability of the host to the environment. Considering that plants are more vulnerable to pathogen infection in the early growth stage, the inventors speculated that protective microorganisms in the early growth stage of plants are crucial for plant health and development. In contrast, Proteobacteria and Bacteroidetes are generally considered to be copiotrophs with a typical R strategy and can grow rapidly in resource-rich environments. The increase in the abundance of these taxa with plant growth is to some extent related to the changes in the types and amounts of plant root exudates. Many taxa belonging to Proteobacteria can promote plant growth through various ways such as nitrogen fixation, phosphorus solubilization, auxin production, and siderophore secretion. The role of Bacteroidetes in the rhizosphere has not been elucidated, but it has been reported that they are important participants in soil nutrient cycling. Bacterial taxa belonging to Bacteroidetes contain genes related to denitrification, and Bacteroidetes have a high response to the nitrogen and phosphorus cycling processes in rhizosphere soil. In the present invention, all fertilized soils are acidic soils (pH < 6.5)( Figure 3 F), and almost all sequences in the nodules are annotated as Bradyrhizobium japonicum( Figure 8 C). The composition of rhizobia in soybean nodules depends on soil conditions. Bradyrhizobium is the most abundant genus in natural acidic or neutral soils, while Sinorhizobium is dominant in nodules from alkaline soils. These results indicate that plant development drives the assembly of root-associated microbiomes and shows a close interaction with the environmental adaptability of the host.

[0364] 3. Effects of fertilization on rhizosphere microbial communities and functions

[0365] The inventors found that the NK treatment significantly reduced the temporal turnover of rhizosphere bacteria( Figure 6 G), and the abundance of rhizosphere bacteria was always lower than that of the normal fertilization treatment throughout the plant development stage, especially at the maturity stage( Figure 7 B), indicating that phosphorus deficiency delays the development of rhizosphere bacterial communities. Similarly, drought stress also significantly reduced the development of root microbial communities. Therefore, the inventors proposed a concept that when plants face abiotic stresses such as nutrient deficiency or drought, the postponement of root microbial development is common. In addition, it was found that the absolute abundances of most significantly different core ASVs in the NK treatment were lower than those of the normal fertilization treatment, especially taxa belonging to Actinomycetales, Burkholderiales, Rhizobiales, and Sphingomonadales( Figure 11 and Figure 12)。Consistently, compared with the control, the number and diameter of nodules in the NK treatment were also significantly reduced, indicating that the ability of soybean symbiotic nitrogen fixation was decreased under phosphorus-deficient conditions. Sphingomonas and Burkholderia are closely associated with plants, have multiple functions such as nitrogen fixation and pathogen inhibition, and can rapidly assimilate plant root exudates. Phosphorus deficiency led to a decrease in the abundance of these rhizosphere bacterial groups, indicating that long-term non-application of phosphorus fertilizer decoupled the beneficial interactions between plants and microorganisms. Therefore, maintaining a reasonable soil phosphorus nutrient level is crucial for ensuring the ecological functions of black soil.

[0366] Compared with the normal fertilization treatment, the soybean yield in the NK treatment decreased by about one-fourth. On the one hand, in the soil without phosphorus fertilizer application, the microbial functional genes encoding inorganic phosphorus solubilization and phosphorus starvation response were specifically upregulated ( Figure 20 D), and these genes were generally present in metagenome-assembled genomes (MAGs) ( Figure 19 ); on the other hand, other symbiotic microorganisms, such as arbuscular mycorrhizal fungi, may also promote the absorption of phosphorus nutrition by soybeans. The lower available phosphorus content in the soil may be due to the rapid turnover between microbial solubilization of phosphorus nutrients and plant absorption. In addition, the results based on metagenome and FARPROTAX functional annotation showed that N cycling processes, including nitrification and denitrification, were significantly enriched in the NK treatment ( Figure 17 and Figure 20 D). Due to the stoichiometric characteristics of plant nutrient absorption, the NK treatment may lead to nitrogen surplus, thus increasing the nitrogen transformation process in the rhizosphere, which is consistent with the reports based on soil results.

[0367] Different from phosphorus fertilizer, the rhizosphere bacterial community was more sensitive to nitrogen fertilizer, while potassium application had no significant effect on most bacterial groups and functional genes ( Figure 17 and Figure 20 D). The beta distance of rhizosphere bacteria between the PK and normal fertilization treatments was highly different at the early stage of plant growth and remained stable during the whole plant development process; while the beta distance between the NK and NP treatments and the normal fertilization treatment showed an increasing trend with plant growth, especially in the NK treatment ( Figure 6 H). These results indicate that although nitrogen fertilizer had no significant effect on the turnover rate of rhizosphere bacteria, it induced a unique development pattern of the rhizosphere bacterial community, which may be due to the strong impact of nitrogen on the biochemical processes of soils in different ecosystems. Compared with the normal fertilization treatment, the functional alpha-diversity of soybean rhizosphere microorganisms in the PK treatment was significantly reduced, while there was no obvious difference between the NK and NP treatments and the control ( Figure 20A-C). In addition, the CPCoA results showed that there were significant differences in the functional diversity of rhizosphere microorganisms between all fertilization treatments and the control, except for the NP treatment ( Figure 20 A-C). It was also found that the abundance of nitrogen availability-related functional genes in rhizosphere bacteria increased in the PK treatment, while the abundance of nitrogen reduction-related genes decreased ( Figure 20 D). The increase in the gene abundances of nitrogen fixation and organic nitrogen mineralization processes indicated that the rhizosphere microbial community promoted the functional adaptation between plants and microorganisms, thus facilitating the improvement of plant phenotypic plasticity. Similarly, compared with no fertilization, long-term application of nitrogen, phosphorus, and potassium fertilizers significantly reduced the nitrogen fixation activity in rhizosphere and non-rhizosphere soils. In addition, long-term nitrogen application also decreased the nitrogen metabolism process of archaea in the soil, while enhancing the processes of bacterial denitrification, assimilatory nitrate reduction, and organic nitrogen metabolism. Interestingly, there were almost no significantly different microbial groups and functional genes between the NP treatment and the normal fertilization treatment, but the genes involved in potassium transport were significantly enriched in the rhizosphere of the NP treatment, especially the Kup gene ( Figure 20 D). Kup is a protein located inside the cell membrane and is used for microbial potassium ion uptake, indicating that rhizosphere microorganisms have strong resilience to potassium supply. These results based on the microbial community composition and function showed that the rhizosphere microbial community can regulate multiple functions to promote the adaptation of plants and itself to different environments.

[0368] 4. The role of key bacterial communities in maintaining soybean growth

[0369] Rhizosphere microorganisms maintain their own growth and metabolism through interactions and improve the host's adaptability to the environment. Similar to the results of bacterial community composition, it was found that the response of the rhizosphere bacterial co-occurrence network to fertilization was higher than that in the roots, and the complexity of the rhizosphere bacterial network in the PK and NP treatments was significantly higher than that in the normal fertilization treatment ( Figure 21 A), and the complexity of the bacterial network in the roots of the PK treatment was also higher than that of the control ( Figure 22 ). It has been reported that microbial communities may enhance their interactions under environmental perturbations, such as under nutrient deficiency and pathogen invasion. Under nitrogen-deficient conditions, the microbial community strengthened their interaction network. A study on microbial interactions also showed that there was a high degree of metabolic cooperation among microorganisms under low-nutrient conditions. The complexity and stability of the network are important characteristics of ecosystem functions. The increase in these characteristics in the PK and NP treatments indicates that enhancing the metabolic interactions among microorganisms may be a potential cause of stress alleviation. Although the abundances of rhizobia and Sphingomonas did not change significantly between the NP treatment and the normal fertilization treatment, the NP treatment significantly enhanced the connectivity of these nodes in the network ( Figure 21C). This may be due to the close cooperation among potassium-solubilizing bacteria, which promotes the activation of soil potassium. Different from the higher network complexity in the PK and NP treatments, the NK treatment had no significant effect on network complexity but also increased the network stability ( Figure 21 B). Considering the lower phosphorus availability in the soil of the NK treatment, it is speculated that there may be strong competition between bacteria and plants in assimilating phosphorus, which to some extent limits the microbial biomass and the interactions among microorganisms.

[0370] The rhizosphere microbial community structure is the result of a series of interactions and feedbacks between plants and the soil environment, and plays an important role in plant growth, development, and health. Therefore, revealing plant-microbe interactions related to plant phenotypes is of great significance for sustainable agricultural development. The application of plant growth-promoting bacteria is considered an effective measure to improve agricultural productivity and soil and environmental health. Although many advances have been made in the field of plant rhizosphere microbiome research, the use of microbiome technologies to improve crop growth and development is still in its infancy. By analyzing the main ecological clusters (network modules) in the core microbial groups, it was found that there was a high positive correlation within these modules, but there were significant differences in abundance between different treatments ( Figure 23 A and B). Different ecological clusters may have specific metabolic functions and ecological properties. Therefore, revealing the key species in the microbial network can provide further guidance for the utilization of beneficial microorganisms. Key species refer to microorganisms that establish more connections with other microorganisms in the microbial network, and some of them play a key role in constructing the entire network. In the present invention, bacterial co-occurrence network analysis was used, and then through the isolation and purification of bacteria, bacterial synthetic communities (SynComs) from different ecological clusters were finally designed. Five bacteria were screened and identified from module 2 enriched in the PK treatment as the experimental synthetic community, namely Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., and Phyllobacterium sp. ( Figure 23 ). The results of the pot experiment showed that the synthetic bacterial community in module 2 could significantly promote the growth of soybeans. However, the synthetic bacterial community in module 1 had no significant effect on soybean growth ( Figure 25 ). The promotion of plant growth by the ecological cluster enriched in the PK treatment indicates that the bacterial community in the rhizosphere other than rhizobia also has a significant growth-promoting function for soybeans.

[0371] Biological material preservation

[0372] The representative cell strain PK167 (Rhodococcus sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCC M2024006. It has been detected as viable by the deposit center.

[0373] The representative cell strain PK260 (Lysobacter sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCC M2024011. It has been detected as viable by the deposit center.

[0374] The representative cell strain NPK195 (Terrabacter sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCC M2024007. It has been detected as viable by the deposit center.

[0375] The representative cell strain NK218 (Arthrobacter sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCC M2024009. It has been detected as viable by the deposit center.

[0376] The representative cell strain PK237 (Phyllobacterium sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCCM 2024010. It has been detected as viable by the deposit center.

[0377] The representative cell strain PK216 (Bosea sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCC M2024008. It has been detected as viable by the deposit center.

[0378] The representative cell strain NPK112 (Aeromicrobium sp.) of the present invention has been deposited at the China Center for Type Culture Collection (hereinafter referred to as CCTCC; Wuhan, China, Wuhan University). The deposit date is January 2, 2024, and the deposit number is CCTCCM 2024005. It has been detected as viable by the deposit center.

[0379] The above-described embodiments merely represent several implementation manners of the present invention. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the scope of the patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the appended claims. At the same time, all the documents mentioned in the present invention are cited in this application as references, just as each document is cited separately as a reference.

Claims

1. A method for improving the soil properties and soil flora of fertilized soil, or promoting symbiotic nitrogen fixation between rhizobia and leguminous plants, comprising: Adjust the fertilization methods of nitrogen fertilizer, phosphorus fertilizer and potassium fertilizer, and reduce the application rate of nitrogen fertilizer.

2. The method according to claim 1, characterized in that, The improvement of soil properties includes: reducing soil acidification, increasing the number and diameter of root nodules, and increasing the abundance of rhizobia; preferably, in leguminous plants, the development pattern of the microbial community changes, its α-diversity increases in the early stage of development, the nitrogen metabolism function of the microbial community is enhanced, and the nitrogen mineralization ability of the microbial community is enhanced; preferably, in leguminous plants, the complexity and stability of the co-occurrence network of the rhizosphere microbial community are improved.

3. The method according to claim 2, characterized in that The soil is planted with leguminous plants, and the fertilization method is: 75 kg N / ha -1 , 150 kg P2O5 / ha -1 , 75 kg K2O / ha -1 , where the dosage of N, P2O5 or K2O can fluctuate by 50% up and down.

4. The method according to claim 3, characterized in that The nitrogen fertilizer includes urea, the phosphorus fertilizer includes triple superphosphate and diammonium phosphate, and the potassium fertilizer includes potassium sulfate.

5. The method according to any one of claims 1-4, characterized in that The method further includes: preparing synthetic microbial community SynCom5 or SynCom7, applying it to leguminous plants to promote the growth of leguminous plants, increase biomass, increase the level of growth hormone, and improve the ACC deaminase activity; Among them, the SynCom5 includes the following strains: Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., Phyllobacterium sp.; Among them, the SynCom7 includes: the strains of the synthetic microbial community SynCom5, as well as Bosea sp. and Aeromicrobium sp.

6. The method according to claim 5, characterized in that, The increase in biomass includes: increasing the plant height of leguminous plants and increasing the plant weight of leguminous plants; or, the growth hormone includes: IAA.

7. The application of the method according to any one of claims 1-4 for improving the soil properties and soil microbial community of fertilized soil, or promoting symbiotic nitrogen fixation between rhizobia and leguminous plants.

8. Use of the method according to claim 5 or 6 for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and enhancing ACC deaminase activity; Preferably, the increase in biomass includes: increasing plant height and increasing plant weight; preferably, the growth hormone includes: IAA.

9. An artificial synthetic microbial community, selected from synthetic microbial community SynCom5 or synthetic microbial community SynCom7; The SynCom5 includes the following strains: Rhodococcus sp., Lysobacter sp., Terrabacter sp., Arthrobacter sp., Phyllobacterium sp.; The SynCom7 includes: The strains of the synthetic microbial community SynCom5, as well as Bosea sp. and Aeromicrobium sp.; Preferably, the artificial synthetic microbial community is contained in a composition; more preferably, the composition is a solution, a suspension medium.

10. The synthetic microbial community according to claim 9, wherein, The preservation numbers of the Rhodococcus sp. at the China Center for Type Culture Collection are CCTCC M 2024006, the preservation numbers of the Lysobacter sp. at the China Center for Type Culture Collection are CCTCC M 2024011, the preservation numbers of the Terrabacter sp. at the China Center for Type Culture Collection are CCTCC M 2024007, the preservation numbers of the Arthrobacter sp. at the China Center for Type Culture Collection are CCTCC M 2024009, the preservation numbers of the Phyllobacterium sp. at the China Center for Type Culture Collection are CCTCC M 2024010, the preservation numbers of the Bosea sp. at the China Center for Type Culture Collection are CCTCC M2024008, and the preservation numbers of the Aeromicrobium sp. at the China Center for Type Culture Collection are CCTCC M2024005.

11. A composition containing the synthetic microbial community according to claim 9 or 10, or a culture of the synthetic microbial community according to claim 9 or 10; preferably, it further includes an agriculturally acceptable carrier or culture medium components.

12. The composition according to claim 11, wherein The composition includes: a pesticide composition; preferably, the dosage form of the composition includes: solution, suspension, emulsion, oily or aqueous dispersion system, powder, granule, or microcapsule; preferably, the composition is a culture medium containing the synthetic microbial community.

13. Use of the synthetic microbial community according to claim 10 or the composition according to claim 11 or 12 for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and enhancing ACC deaminase activity; or, for preparing a composition or kit for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and enhancing ACC deaminase activity.

14. A method for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and enhancing ACC deaminase activity, comprising: Applying the synthetic microbial community according to claim 9 or 10 or the composition according to claim 11 or 12 to leguminous plants.

15. A kit for promoting the growth of leguminous plants, increasing biomass, increasing the level of growth hormone, and enhancing ACC deaminase activity, comprising: The synthetic microbial community according to claim 10 or the composition according to claim 11 or 12.

16. A method for analyzing the ecology of soil microbial communities under different fertilization methods, including: (1) Fertilizing plants with different fertilization methods; (2) Obtaining the abundances of the core ASVs of the treatment groups with different fertilization methods; (3) For the abundances of the core ASVs obtained in (2), analyzing the differences in the abundances of the core ASVs of different treatment groups by an absolute quantification method; identifying biomarker taxa in different fertilization treatment groups or different developmental stages, preferably analyzing using linear discriminant analysis and / or random forest method; (4) Analyzing the functions of the microbial community using a microbial community function annotation database; preferably, the microbial community function annotation database includes the FAPROTAX database; (5) Analyzing the differences in the functions of the microbial communities of different fertilization treatment groups through metagenomic sequencing and / or assembly.

17. The method according to claim 16, wherein (3) For the microbial community, linear discriminant analysis is used to identify differential species in treatment groups with different fertilization methods or at different developmental stages; preferably, the analysis is performed on the microbial community from phylum to species; and / or (3) The random forest method is used for analysis and a random forest prediction model is established; preferably, for the microbial community, a random forest prediction model is established at the family, genus, and / or species level.

18. The method according to claim 16, wherein (5) The metagenomic analysis includes: sequencing, classification, and gene annotation; annotating to a database and calculating the functional diversity among samples of treatment groups with different fertilization methods, including α-diversity and β-diversity; analyzing the abundance differences of functional genes related to microbial nitrogen, phosphorus, and potassium cycling in treatment groups with different fertilization methods.

19. The method according to claim 16, wherein, It also includes: performing co-occurrence network analysis and / or ecological cluster function analysis to obtain the microbial community that plays a decisive role under different fertilization methods; preferably, it also includes isolating the microbial community that plays a decisive role.

20. The method according to claim 19, wherein The co-occurrence network analysis includes: calculating the Spearman correlation between each ASV based on the absolute abundances of rhizosphere and root core ASVs, and using the data with correlation coefficient r > 0.8 and significance P < 0.05 to construct the bacterial co-occurrence network of treatment groups with different fertilization methods in the rhizosphere and roots, and calculating the basic network topology characteristics including the number of nodes, number of edges, average degree, and robustness of each network; preferably, the robustness of the rhizosphere or root microbial community network of treatment groups with different fertilization methods is further analyzed by randomly removing 50% of the nodes in the network.

21. The method according to claim 16, wherein The microbial community that plays a decisive role includes: the microbial community that improves the soil properties of fertilized soil and the soil microbial community, the microbial community that promotes the symbiotic nitrogen fixation of rhizobia and leguminous plants, and the microbial community that promotes the growth of leguminous plants, increases biomass, increases the growth hormone level, and enhances the ACC deaminase activity.

22. Use of the method according to any one of claims 16 - 21, for analyzing the ecology of soil flora under different fertilization methods, so as to obtain the flora community that plays a decisive function under different fertilization methods; preferably, the flora community includes: The microbial community that improves the soil properties of fertilized soil and the soil microbial community, the microbial community that promotes the symbiotic nitrogen fixation of rhizobia and leguminous plants, and the microbial community that promotes the growth of leguminous plants, increases biomass, increases the growth hormone level, and enhances the ACC deaminase activity.

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