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Unveiling the hidden world: How arbuscular mycorrhizal fungi and its regulated core fungi modify the composition and metabolism of soybean rhizosphere microbiome

Environmental Microbiome volume 19, Article number: 78 (2024) Cite this article

Abstract

Background

The symbiosis between arbuscular mycorrhizal fungi (AMF) and plants often stimulates plant growth, increases agricultural yield, reduces costs, thereby providing significant economic benefits. AMF can also benefit plants through affecting the rhizosphere microbial community, but the underlying mechanisms remain unclear. Using Rhizophagus intraradices as a model AMF species, we assessed how AMF influences the bacterial composition and functional diversity through 16 S rRNA gene sequencing and non-targeted metabolomics analysis in the rhizosphere of aluminum-sensitive soybean that were inoculated with pathogenic fungus Nigrospora oryzae and phosphorus-solubilizing fungus Talaromyces verruculosus in an acidic soil.

Results

The inoculation of R. intraradices, N. oryzae and T. verruculosus didn’t have a significant influence on the levels of soil C, N, and P, or various plant characteristics such as seed weight, crude fat and protein content. However, their inoculation affected the structure, function and nutrient dynamics of the resident bacterial community. The co-inoculation of T. verruculosus and R. intraradices increased the relative abundance of Pseudomonas psychrotolerans, which was capable of N-fixing and was related to cry-for-help theory (plants signal for beneficial microbes when under stress), within the rhizosphere. R. intraradices increased the expression of metabolic pathways associated with the synthesis of unsaturated fatty acids, which was known to enhance plant resistance under adverse environmental conditions. The inoculation of N. oryzae stimulated the stress response inside the soil environment by enriching the polyene macrolide antifungal antibiotic-producing bacterial genus Streptomyces in the root endosphere and upregulating two antibacterial activity metabolic pathways associated with steroid biosynthesis pathways in the rhizosphere. Although inoculation of pathogenic fungus N. oryzae enriched Bradyrhizobium and increased soil urease activity, it had no significant effects on biomass and N content of soybean. Lastly, the host niches exhibited differences in the composition of the bacterial community, with most N-fixing bacteria accumulating in the endosphere and Rhizobium vallis only detected in the endosphere.

Conclusions

Our findings demonstrate that intricate interactions between AMF, associated core fungi, and the soybean root-associated ecological niches co-mediate the regulation of soybean growth, the dynamics of rhizosphere soil nutrients, and the composition, function, and metabolisms of the root-associated microbiome in an acidic soil.

Highlights

AMF R. intraradices improved soybean stress resistance by recruiting specific PGPR and upregulating plant resistance promotion metabolic pathway.

The pathogenic fungus N. oryzae stimulated the stress response by enriching the antifungal antibiotic production bacteria and upregulating the antibacterial reaction-associated metabolic pathways.

The utility evaluation of some microbial agents on host plants should consider their potential impact on Olsen-P content in acidic soils with limited P availability.

The sampling compartments (i.e., host niches of the soybean rhizosphere) exerted greater influence on the assembly and shift of the bacterial community than the application of microbial agents.

AbstractSection Graphical Abstract

Introduction

Root-soil interactions are highly complex in natural conditions, involving a multitude of active microbes [1]. Rhizosphere microorganisms are indispensable regulators of plant adaptability and productivity, which play important roles in plant water and nutrients absorption, as well as in plant resistance to biotic and abiotic stresses, and were considered as the ‘second genome of plant’ [2, 3]. The rhizosphere microbiome along the soil-plant continuum participates in the plant growth, nutrition, health, and yield mainly through the interaction between plants and microbes and between microbes and microbes [4,5,6]. Mycorrhizal fungi and nitrogen-fixing bacteria enable plants to obtain up to 80% of their nitrogen (N) and 75% of their phosphorus (P) resources [7]. As one of the key components of the rhizosphere microbial community, arbuscular mycorrhizal fungi (AMF) directly enhance the absorption of essential nutrients such as N and P by plants, increase their resistance to biotic/abiotic stresses (e.g., drought and pathogens), and promote the colonization of N-fixing rhizobia and other plant growth promoting rhizobacteria (PGPR) in the rhizosphere of host plants [8,9,10]. The AMF also facilitated bacterial translocation and boosted the combination of plants with beneficial fungi and bacteria via a wide network of extraradical mycelium (ERM) [11]. The AMF and Rhizobium spp. have also been reported to influence the composition and abundance of other rhizosphere bacteria, and further enhance the fatty acid content, seed size, and yield of soybean grown in a semi-arid environment [12]. Other rhizosphere microbes were also closely related to plant health and nutrition, for example, root-associated microorganisms with growth-inhibitory siderophores can suppress pathogens as compared to the members with growth-promotive ones [13]. Some phosphorus solubilizing bacteria/fungi (PSB/PSF) can also dissolve insoluble minerals and organic phosphates in soil to promoting phosphorus (P) uptake of plants [14]. Some endophytic fungi can also increase the diversity of nodular culturable endophytic bacteria due to their mycelia, which are the ideal dispersal networks for rhizobia enrichment in the legume rhizosphere soil [15, 16]. Some other non-symbiotic PGPBs can promote the absorption and utilization of nutrients by improving the root structure [17], while some specific rhizosphere microorganisms can also indirectly improve the absorption of nutrients by plants and enhance their stress resistance by changing the structure and function of functional microbial communities through the interaction between microbes and microbes [18, 19]. Thus, the rhizosphere microbiome plays an important role in plant nutrition, growth, development and environmental adaptability [5, 20].

As one of the typical soil type in tropical and subtropical regions, acidic soil occupy approximately 30%/50% of the global/potential-cultivated land area [21]. Crop growth is inhibited in acid soils due to low pH value, low bio-available P and high aluminum (Al) toxicity. High levels of Al3+ in soil can reduce crop growth and yield by immobilizing soil P, damaging root tips, stunting roots, and inhibiting water and nutrient absorption [22, 23]. Due to the demand for the increase in crop production, some chemical fertilizers such as N fertilizers are often applied excessively, and their long-term application deteriorates soil quality, leading to further aggravation of soil acidification [24]. As a major oil and protein crop globally, soybean is also used as ‘pioneer crop’ to improve acidic soils because of N-fixing of its symbiotic rhizobia. The exudates of soybean, such as some organic acids like citric acid and malic acid, were found to chelate Al and closely related to the tolerance of soybean to acidic soil [25], and their root exudates had a notable impact on soil microscale environments and the composition of root-associated bacteria community [26, 27]. However, its productivity is also significantly hampered by Al stress and low available P due to the major soybean-producing regions are predominantly located in regions with acidic soils [28,29,30]. Moreover, nutrient-poor or extreme environments, such as acidic soils, can severely affect the composition and function of root-associated microbiomes. For instance, acidic soil inhibits soil respiration and nitrification, and influences microbial degradation of organic C and soil enzyme activity, and thereby suppresses the microbial-mediated nutrient cycles, which affects plant growth indirectly [31]. The interactions between plants and microorganisms were also directly affected by acidic soil. For example, acidic soil was reported to inhibit the signal exchange between legumes and rhizobia, reduces the process of rhizobia infecting root hairs, interferes nodulation and ultimately influences N-fixing [30]. In summary, it was urgent for us to alleviate the stress of acidic soil on plants and microorganisms through crop improvement and microbial inoculation.

Crop diversity has been successfully exploited for genetic improvement in modern breeding, which significantly contributes to yield and quality improvement in adverse environment and practical agricultural production, and produces satisfactory economic and social benefits for a long time [32, 33]. Nowadays, the application of composite microorganism agents increasingly becomes a promising new approach for crop growth promotion and soil improvement [34,35,36]. The synthetic communities (SynComs) constructed by soybean root associated functional microorganisms can significantly promote soybean N and P acquisition and ultimately soybean yield (up to 36.1%) [37]. The SynComs isolated from alkaline soil can regulate the growth of rhizobia specifically, and alleviated the impact of salt alkali stress on rhizobia nodulation and its colonization in nodules [38]. Special SynComs including bacteria and fungi helped soybeans resist Al toxicity by enriching plant growth promoting microorganisms [39]. Recent study indicated that the addition of AMF Rhizophagus intraradices agent promoted soybean biomass and increased plant C and N content by recruiting specific PGPR, thereby enhancing soybean tolerance to acidic soil in a host dependent manner [40]. Moreover, AMF R. intraradices were surprisingly found to reduce the abundance of pathogenic fungus Nigrospora oryzae while enriched P-solubilizing fungus Talaromyces verruculosus in the rhizosphere soil [40]. As a P-soluble microorganisms, T. verruculosus is a beneficial endophytic fungus, which can improve the utilization rate of insoluble phosphorus, promote plant growth and improve plant stress resistance [41]. However, how the AMF R. intraradices and its regulated core fungi N. oryzae and T. verruculosus reshapes root-associated microbial community, alters soil N and P nutrient cycling and ultimately enhances soybean tolerance to acidic soil remains to be clarified.

To address these issues, an Al-sensitive soybean was used to evaluate the alterations in the nutrient dynamics, the root-associated microbes, and the soil metabolism spectrum in response to the inoculation of the AMF and its regulated fungi. Our overall hypothesis was that the inoculation of SynComs would alter the metabolism and composition of root-associated bacterial community and soil nutrient dynamics by reshaping specific functional microbiota. We anticipate that host niches along the soil-plant continuum would determine the differentiation of root-associated bacterial community following SynComs application by enriching/reducing special functional plant-growth promoting or pathogenic microorganism under environmental stress.

Materials and methods

Plant, strain and soil materials

Seeds of Bendi 2 (Al-sensitive, BD2) genotype were used as model soybean plants in the present study. The AMF strain, Rhizophagus intraradices (national microbial resources platform number: 1511C0001BGCAM0062, Ri) was purchased from Beijing Academy of Agriculture and Forestry Sciences (Beijing, China). The Nigrospora oryzae (CGMCC: 3.13798, No) and Talaromyces verruculosus (CGMCC: 3.5693, Tv) were purchased from the China General Microbiological Culture Collection Center (Beijing, China). The acidic soil was collected from the Ecological Experimental Station of Red Soil, Chinese Academy of Sciences (Yingtan, Jiangxi, China) (28.208° N, 116.937° E) and was diluted with sterilized sand at V(soil): V(sand) = 4:1, which with a water holding capacity of 29.3% and a pH of 4.54 (± 0.21) [40].

Planting and sampling methods

The healthy soybean seeds were first surface-disinfected 16 h with chlorine (100 mL 10% NaClO + 4 mL 12 mol/L HCl), and washed 5 times with sterile water. Then the seeds were sterilized 30 s with 70% ethanol, 5 min with 2.5% sodium hypochlorite, followed by washing 5 times with sterile water again. The rhizobox was developed and improved by our laboratory (patent number CN102175487) with a length*width*height of 20 cm*15 cm*20 cm. Approximately 3.6 kg of acidic soil was placed in each rhizosbox. Soybeans were planted in the central compartment of the rhizosbox, which was separated with the two-side compartments with two layers of nylon mesh, to prevent roots from growing into the compartments on both sides [26, 40]. Then the AMF strain R. intraradices (10 g mixed culture powder in each rhizobox) was added to approximately 2 cm below the surface of soil before planting. Then 50 mL N. oryzae (cultured in Potato Dextrose Agar (PDA) medium) and T. verruculosus (cultured in Malt Extract Agar (MEA) medium) were resuspended in PBS, and 20 mL suspension was applied on the day 20 and 30 after planting. Original soil was unsterilized to maintain ‘natural’ with its native microbiota. The rhizoboxes was located in Nanjing University, Nanjing, China (32.125 N, 118.965 E). Each of the four treatments (in inoculation of No, Tv, No + Ri and Tv + Ri, respectively) had five biological replicates by using total 20 rhizoboxes. Soil samples were collected from two rhizosphere host niches from the outside to the interior of the roots, as well as two sampling stages. The experimental plant and soil were collected at the flowering stage (R2 stage, 59 days after planting) and maturing stage (R8 stage, 104 days after planting). The soils and roots materials were immediately placed in plastic bags with pre-freezing by using chemical ice packs after sampling [26, 40]. Two sampling compartments (i.e., two host niches) were collected, namely rhizosphere soil (Rh) and root endosphere (Rt). Rh samples were the soil tightly adhered to the root surface, which were collected by brushing off with phosphate buffered saline (PBS). The roots were further washed with PBS twice and placed in a 15 ml tube with 5 ml PBS (maximum at 6 ml). Then the tubes were sonicated for 20 cycles consisting of 30 s pulses and breaks of 30 s (Bioruptor Pico), centrifuged at 4000 × g for 2 min, followed by washing with PBS once more, and then the Rt samples were collected by grinding with liquid nitrogen [40]. The Rh and Rt samples for DNA extraction and metabolomics analysis were stored at -80 °C [26, 40].

Part of Rh samples were pre-treated by being air-dried and filtering through a 50 μm mesh sieve and stored at 4 °C for the detection of soil element contents and soil enzyme activities. The activities of six soil enzyme [i.e., nitrate reductase (S-NR, EC 1.7.99.4), nitrite reductase (S-NiR, EC 1.7.99.3), urease (S-UE, EC 3.5.1.5), sucrase (S-SC, EC 3.2.1.26), acid phosphatase (S-ACP, EC 3.1.3.2) and alkaline phosphatase (S-AKP/ALP, EC 3.1.3.1)], which involved in the carbon, nitrogen and phosphorus cycle, were measured according to the detailed instructions in kits (Solarbio Science & Technology Co., Ltd, Beijing, China) [40]. The other physicochemical properties of plants (e.g., plant height, plant fresh weight, 100-seed weight, crude fat (Soxhlet method) and crude protein content (Kjeldahl method) of seed) were also measured in the study [42].

DNA extraction, DNA amplicon sequencing and analysis

The Rh and Rt samples for DNA extraction analysis were first stored at -80 °C. Approximately 0.30 g of rhizosphere soil and 0.40 g root powder were used for DNA extraction via the DNeasyPowerSoil Pro Kit (QIAGEN, Hilden, Germany) after ground by a tissue grinder (Grinder-48, Gallop technology, Chengke Instrument Co., Ltd, Shanghai, China) at 60 HZ for 600 Sects. [26, 40]. The extracted DNA samples were verified with a Qubit Fluorometer (Invitrogen, Carlsbad, USA) to ensure more than 10 ng/µl [43]. The primers of the amplicons of V3-V4 hypervariable region of the 16 S rRNA gene (~ 468 bp) was 338 F/806R, and sequencing was performed on an Illumina NovaSeq 6000 with two paired-end read cycles of 250 bases each by OE Biotech. Co., Ltd (Shanghai, China) [40, 44]. The accession number of total 40 clean sequencing data in NCBI Sequence Read Archive database is SRP424056.

Raw sequencing data were in FASTQ format. Paired-end reads were then preprocessed using Trimmomatic software (version 0.35) and paired-end reads were assembled using FLASH software (version 1.2.11) [45, 46]. Obtain the clean tags sequence by using the split_ibraries (version 1.8.0) software in QIIME [47]. Use UCHIME (version 2.4.2) software to remove chimeras from clean tags and obtain valid tags [48]. Clean reads were subjected to primer sequences removal and clustering to generate operational taxonomic units (OTUs) using Vsearch software (version 2.4.2) with a 97% similarity cutoff [49]. The numbers of clean tags ranged from 70,817 to 74,073, and valid tags ranged from 46,980 to 64,659 with the average length between 407.09 and 415.6 bp. The number of OTUs in each sample were range from 1107 to 4204. Data was analyzed by using databases silva138/16s (http://www.arb-silva.de). Three indices, Chao1, Shannon, and Good’s coverage were used to reflect the richness, diversity and coverage of microbial communities in the analysis alpha diversity, and the principal coordinate analysis (PCoA) based on Bray-Curtis distance was used to evaluate the species complexity in beta diversity [50, 51]. The functional genes analysis was conducted via the software PICRUSt. All the bioinformatic analyses were performed on OECloud platform (https://cloud.majorbio.com).

Non-targeted metabolomics analysis

The Rh samples for metabolomics analysis was first stored at -80 °C. All chemicals and solvents were analytical or HPLC grade. For the Gas Chromatograph-Mass Spectrometer (GC-MS), 500 mg soil sample was combined with 1 mL of methanol-water (V methanol: V water = 1:1) solution, which supplemented with 2 µg/mL L-2-chlorophenylalanine. The sample was ground at 60 HZ for 2 min after being placed at -40 ℃ for 2 min, then transferred to a centrifuge tube, followed by a 30 min ultrasonic in ice-water bath. Transferred the homogenized sample to a clean 15 mL centrifuge tube, followed by transferring tube wall residue twice by using a 1 mL methanol: water solution. About 1.2 mL supernatant was freeze-dried in a clean centrifuge tube after being centrifuged for 10 min (7700 rpm, 4 ℃). Then resuspended in 300 µL methanol-water, vortex for 1 min, ultrasonic for 3 min and centrifuged for 10 min (12,000 rpm, 4 ℃). About 150 µL supernatant was prepared for GC-MS analysis. A 30 m x 0.25 mm x 0.25 μm HP-5MS fused-silica capillary column (Agilent J & W Scientific, Folsom, CA, USA) was utilized to separate the derivatives. The injector temperature was maintained at 260 ℃. The program contained 60 ℃ for 0.5 min, ramped to 210 ℃ at a rate of 8 ℃/min, to 270 ℃ at a rate of 15 ℃/min, to 305 ℃ at a rate of 20 ℃/min, and finally held at 305 ℃ for 5 min. The temperature of the MS quadrupole and ion source (electron impact) was set to 150 ℃ and 230 ℃, respectively. The collision energy was 70 eV. Mass data was acquired in a full-scan mode (50–500 m/z) [52,53,54]. The obtained GC/MS raw data in .D format were transferred to .abf format via software Analysis Base File Converter for quick retrieval of data. All peak signal intensities were segmented, normalized, and redundancy removal and peak merging were conducted to obtain the data matrix.

For the Liquid Chromatograph-Mass Spectrometer (LC-MS), 500 mg soil sample was combined with 1 mL of 1 mL methanol water. Pre-cooling at -40 ℃ for 2 min and grinding with a grinder (60 Hz, 2 min). Use 1 mL methanol water to transfer the homogenized sample and repeat the above operation once. Centrifuge for 10 min (7700 rpm, 4 ℃) and 1.2 mL of supernatant was dried in a clean centrifuge tube in a freeze concentration centrifugal dryer. Use 300 µL methanol water (V: V = 1:4, containing L-2-chlorophenylalanine, 2 µg/mL) re-solution, vortex for 1 min, ultrasonic for 3 min, and stand at 40 ℃ for 30 min. After centrifugation for 10 min (12000 rpm, 4 ℃), a 150 µL volume of the supernatant was used for LC-MS analysis. The instrument information, program, and protocol followed the descriptions of Lai et al. and Yang et al. [55, 56]. The original LC-MS data was normalized by software Progenesis QI V2.3 (Nonlinear, Dynamics, Newcastle, UK) and the positive and negative ion data were combined to data matrix.

The overall distribution among the samples and the distinction of the metabolites between groups (containing both GC-MS and LC-MS) were observed by using partial least-squares-discriminant analysis (PLS-DA) and principal component analysis (PCA). Bioinformatic analysis was performed using the OECloud tools at https://cloud.oebiotech.com. Differential metabolites (p-values < 0.05) were selected with variable importance of projection (VIP) values greater than 1.0. All the raw data of metabolomics along with their detailed descriptions were listed in project MTBLS7299 at database MetaboLights [57].

Statistical analyses

The statistical analyses of physicochemical properties of soils and plants, enzyme activities, alpha diversity indices and differential composition of the bacterial taxa and metabolites were evaluated according to one-way ANOVA in Graphpad Prism, followed by the Tukey test (*p < 0.05) [40, 58]. The PERMANOVA analysis (Adonis) and similarities (ANOSIM) were performed by using the vegan package of R software (v3.1.3) based on the Bray-Curtis distance metric [59].

Results

Physicochemical properties of soils and plants

The radar chart mainly reflected the effects of the application of different Syncoms on the physicochemical properties of plants and soil at flowering and maturing stages. The results showed that the co-application of pathogenic fungus N. oryzae and AMF R. intraradices seemed to have improvement effects on the height and fresh weight of soybean at flowering stage (Fig. 1A), as well as the height and N content of soybean at the maturing stage (Fig. 1C). Then the addition of P-solubilizing fungus T. verruculosus was found to increase the C/N ratio of soybean at both flowering and maturing stages (Fig. 1AC), while the co-application of T. verruculosus and R. intraradices promoted the P contents of soybean at maturing (Fig. 1C). As shown in Table 1, the results of statistical analyses showed that the addition of T. verruculosus significantly decreased the plant height, the N and P contents of soybean at the flowering stage. Although the co-application of N. oryzae and R. intraradices decreased the P contents of soybean at flowering, they significantly increased the plant height at both flowering and maturing stages (Table 1). However, the addition of all fungal agents had no obvious impacts on C, N, P contents or available P (Olsen-P) content of soil, as were the hundred seed weight, crude fat and crude protein content of seed (Fig. 1; Table 1). The activities of sucrase, nitrate and nitrite reductase, acid and alkaline phosphatase in the rhizosphere soil were unaffected by the application of N. oryzae, T. verruculosus and R. intraradices, whereas only the activity of urease under co-application of N. oryzae and R. intraradices was higher than under co-application of T. verruculosus and R. intraradices (Fig. 2).

Fig. 1
figure 1

The basic physicochemical properties of plants and soils. (A) The radar map of six physicochemical properties of plant at flowering stage. (B) The radar map of five physicochemical properties of soil at flowering stage. (C) The radar map of five physicochemical properties of plant at maturing stage. (D) The radar map of three physicochemical properties of seed at maturing stage. No, Tv and Ri represent inoculation of Nigrospora oryzae, Talaromyces verruculosus and Rhizophagus intraradices, respectively

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Fig. 2
figure 2

The activities of six key enzymes involved in the carbon, nitrogen and phosphorus cycle in the rhizosphere soil. S-SC (A), S-NR (B), S-NiR (C), S-UE (D), S-ACP (E) and S-AKP (F) represent sucrase (EC 3.2.1.26), nitrate reductase (EC 1.7.99.4), nitrite reductase (EC 1.7.99.3), urease (EC 3.5.1.5), acid phosphatase (EC 3.1.3.2) and alkaline phosphatase (EC 3.1.3.1), respectively. No, Tv and Ri represent inoculation of Nigrospora oryzae, Talaromyces verruculosus and Rhizophagus intraradices, respectively. The error bars represent the standard deviation and the asterisk (*p < 0.05) indicates a significant difference by the Tukey test according to one-way ANOVA

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Table 1 Basic physicochemical properties of plants and soils
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Alpha and Beta diversity of soybean rhizosphere bacterial community

The rarefaction curves of the observed OTU numbers of samples indicated that the sequencing depth and the OTU coverage of the samples included sufficient detectable species in bacterial communities and captured the diversity of bacterial communities (Figure S1). The addition of different SynComs (i.e., N. oryzae, T. verruculosus and R. intraradices) had no significant impacts on the richness and abundance of bacterial community in both the rhizosphere soil and root endosphere (Fig. 3ABC). However, the values of Chao1 and Shannon indices in the root endosphere were lower than those in the rhizosphere soil, while the Good’s coverage was higher in the root endosphere than rhizosphere soil (Fig. 3 ABC). Thus, the selection effect of soybean roots reduced the richness and diversity and increased the community coverage of root-associated microbiome.

Fig. 3
figure 3

The alpha and beta diversity of root-associated bacterial community (n = 5). The boxplot of alpha diversity was analyzed through Chao1 (A), Shannon (B) and Good’s coverage (C) indices. The beta diversity of all groups (D), treatment groups (E) and host niches groups (F) were analyzed through PCoA. N, T and R represent Nigrospora oryzae, Talaromyces verruculosus and Rhizophagus intraradices, respectively. Rh and Rt represent rhizosphere soil and root endosphere, respectively. Other treatment details were shown in Fig. 2. Different superscript letters indicate the significant difference (p < 0.05) between groups by the Tukey test according to one-way ANOVA

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The PCoA charts revealed that the rhizosphere bacterial community was not dominated by the SynComs treatments, but clearly segregated by two different sampling compartments (i.e., rhizosphere soil and root endosphere) (Fig. 3DEF). Then the ANOSIM and Adonis analyses showed that there exhibited no difference between the groups with and without the application of R. intraradices (Table 2). Statistical analysis also indicated substantial variations between N. oryzae and T. verruculosus treatments, and host niches had an appreciable effect on the composition of soybean rhizosphere microbiome (p-value < 0.05) (Table 2).

Table 2 Statistical analyses of bacterial community structure of rhizosphere soil and root endosphere
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Comparison of the compositions and functions of rhizosphere bacterial community

The comparison of the top abundant 20 phyla of the bacterial community revealed that the addition of SynComs exhibited no significant impact on the compositions of soybean rhizosphere microbiome at phylum level (Figure S2AB). In addition, when making comparison between different sampling compartments, the abundances of Chloroflexi, Acidobacteriota, Myxococcota and Gemmatimonadetes were significantly lower in the root endosphere than in the rhizosphere soil (Figure S2AB). Moreover, the relative abundance of Sphingomonas was lower in the root endosphere, whereas Bradyrhizobium, Streptomyces, Thermosporothrix and Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium were higher in the root endosphere than in the rhizosphere soil at genus level (Figure S2CD). The genus Burkholderia-Caballeronia-Paraburkholderia had no difference in abundance between two host niches according to one-way ANOVA, even though it seemed to be enriched in the rhizosphere soil (Figure S2CD). When making comparison between different SynComs application groups, the genus Bradyrhizobium and Streptomyces were found to be enriched under N. oryzae groups than T. verruculosus groups in the root endosphere according to one-way ANOVA (Figure S2CD). The genus Burkholderia-Caballeronia-Paraburkholderia and the species Trinickia symbiotica (which belongs to Burkholderia sensu lato), seemed abundant in T. verruculosus treatment than in N. oryzae treatment; however, no significant difference in statistical analysis was found between different treatments (Figure S2C-F).

Then the comparison of the abundance of all classified N-fixing bacteria at genus level showed that the application of SynComs exhibited no significant effects on most free-living, associative and symbiotic N-fixing bacteria (Fig. 4AB). However, N. oryzae increased the abundance of Bradyrhizobium in root endosphere (Fig. 4B and Figure S2CD). Additionally, although the SynComs had no significant influence on 3 of 4 classified bacteria species that were closely related to N-fixing, the co-application of T. verruculosus and R. intraradices was found to enrich Pseudomonas psychrotolerans in the rhizosphere soil (Fig. 4C). Lastly, the Rhizobium vallis was detected exclusively in the root endosphere of soybean (Fig. 4C).

Fig. 4
figure 4

The relative abundance of major nitrogen-fixing bacteria phyla and species in soybean root-associated bacterial community. (A) The relative abundance of major free-living and associative nitrogen-fixing bacteria at genus level. (B) The relative abundance of major symbiotic nitrogen-fixing bacteria at genus level. (C) The abundance of nitrogen-fixing bacteria at species level. Genera Rhizobium represent Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium in database. N, T, NR and TR represent N. oryzae, T. verruculosus, N. oryzae + R. intraradices, and T. verruculosus + R. intraradices respectively. Rh and Rt represent rhizosphere soil and root endosphere

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The analysis of predicted functional genes by PICRUSt implied that, different SynComs treatments exhibited no significant impact on the overall composition and abundance of functional genes (Figure S3A). The amino acid transport and metabolism being the most prevalent functional genes, followed by transcription and signal transduction mechanisms (Figure S3A). Seven COG functional classifications were found to be closely related to N-fixing, three of them, COG0347 (nitrogen regulatory protein PII), COG1348 (nitrogenase subunit NifH) and COG2710 (nitrogenase molybdenum-iron protein), were shown to be enriched in the root endosphere than in rhizosphere soil (Figure S3B). Six COG functional classifications were identified directly related to acid/alkaline phosphatase, four of them, COG0496 (acid phosphatase), COG1368 (alkaline phosphatase superfamily), COG1785 (alkaline phosphatase), and COG3540 (phosphodiesterase/alkaline phosphatase D), were also enriched in the root endosphere (Figure S3B). Lastly, the COG2710 and COG3540 were abundant in the endosphere under the N. oryzae addition as compared to T. verruculosus addition (Figure S3BC).

Metabolic response of the application of synthetic communities

The GC-MS non-targeted metabolomics analysis identified a total of 177 metabolites in four groups of rhizosphere soil samples. The PCA and PLS-DA analyses indicated that the soil metabolism spectrum changed significantly in response to SynComs application according to the obvious differences in the aggregation positions of the different treatment groups (Figure S4). The metabolites responsible for the separation between different groups were screened by VIP scores (> 1), and the number of identified differentially expressed metabolites (DEMs) was shown in the volcano plots and heatmap plots (Figure S5). A total of 18 DEMs were found in the AMF addition groups (w-Ri, with Ri inoculation, i.e., No + Ri and Tv + Ri groups) as compared to non-AMF addition groups (wo-Ri, without Ri inoculation, i.e., No and Tv groups), 12 DEMs such as hydroquinone, glyceric acid, behenic acid, butanedioic acid, arachidic acid and tetracosanoic acid were upregulated, while 6 DEMs such as ciliatine, aconitic acid, beta-sitosterol and oleic acid were downregulated (Figure S5C). Additionally, when making comparison with N. oryzae application group, the T. verruculosus application group had 1 upregulated metabolite 1,5-anhydroglucitol and 11 downregulated metabolites such as beta-hydroxymyristic acid, sucrose, and malic acid (Figure S5D).

The mechanism of metabolites changes under different SynComs additions was likely a result of reprogramming of multiple metabolic pathways [60]. Results of pathway enrichment analyses were shown in Figure S6 (p < 0.05). The application of R. intraradices significantly enriched five metabolic pathways, for example, biosynthesis of unsaturated fatty acids, glycerolipid metabolism, and glyoxylate and dicarboxylate metabolism (p < 0.01) (Figure S6A). In addition, the pathway analysis of T. verruculosus group revealed the significant enrichment of several metabolic pathways, including taste transduction, steroid biosynthesis, renal cell carcinoma and bacterial chemotaxis pathways, as compared to N. oryzae group (Figure S6B). The comparison of the abundance of metabolites involved in key metabolic pathways showed that, three DEMs (arachidic acid, behenic acid, and tetracosanoic acid) belonged to biosynthesis of unsaturated fatty acids were enriched, while the oleic acid was depleted in AMF addition groups as compared to non-AMF addition groups (Fig. 5A and Figure S5C). As shown in Fig. 5A, the T. verruculosus group had lower oleic acid abundance as compared to N. oryzae group. The AMF addition groups had a higher abundance of glucose-1-phosphate and glyceric acid which were involved in glycerolipid metabolism pathway, than the non-AMF addition groups (Figure S5C); however, there exhibited no difference between the single-variable comparison groups (Fig. 5B). Two DEMs involved in steroid biosynthesis pathway, beta-sitosterol and stigmasterol, were downregulated under T. verruculosus treatments when making comparison to N. oryzae addition groups (Figure S5D), and only beta-sitosterol in was found to have lower abundance in T. verruculosus group as compared to N. oryzae group (Fig. 5C). Lastly, the butanedioic acid was abundant in R. intraradices group and malic acid was abundant in N. oryzae group, and these two DEMs directly involved in the citrate cycle (TCA cycle) (Fig. 5D and Figure S5CD).

Fig. 5
figure 5

The relative abundance of identified differential metabolites between groups (n = 5) in key KEGG metabolic pathways. These KEGG metabolic pathways were descripted as biosynthesis of unsaturated fatty acids (A), glycerolipid metabolism (B), steroid biosynthesis (C) and citrate cycle/TCA cycle (D). No, Tv and Ri represent Nigrospora oryzae inoculation, Talaromyces verruculosus inoculation and Rhizophagus intraradices inoculation. Statistical assessment in box plots was performed using Wilcoxon-rank-sum test, asterisks indicate the significant difference (*P < 0.05, **P < 0.01) between groups. Other treatment details were shown in Fig. 2

Full size image

The LC-MS analysis then identified a total of 4475 metabolites. Compared with GC-MS, the LC-MS analysis displayed about 25-folds identified metabolites, and approximately 10-folds identified DEMs (Table S1). Although the GC-MS analysis found more upregulated than downregulated DEMs in the AMF addition groups, the LC-MS analysis showed an opposite result (Table S1). AMF application decreased the abundance of almost 4/5 metabolites (98/120) such as polidocanol, pentaethylene glycol and heptaethylene glycol monododecyl ether. Then the detailed comparison of the differential metabolites in the rhizosphere soil between AMF addition groups and non-AMF addition groups showed that there exhibited a positive correlation between 3-Ketosphingosine, 5’-Carboxy-gamma-chromanol and [(2 S,4R,5R,6R,14 S,16R)-14-Hydroxy-7,11-dimethyl-6-(2-oxopyran-4-yl)-3-oxapentacyclo [8.8.0.02,4.02,7.011,16]octadecan-5-yl] acetate, while there three metabolites were negatively correlated with most other metabolites (Figure S7A). Other metabolites were positively correlated with each other (Figure S7A). The Pearson-correlation analysis indicated a positive correlation (p value < 0.05, correlation > 0.95) when analyzing the co-occurrence patterns of major metabolites (Figure S7B). Three differential metabolites, pyrimidine metabolism, ABC transporters, and pentose phosphate pathway were upregulated, while four metabolites such as valine, leucine and isoleucine biosynthesis, and propanoate metabolism pathway were downregulated in AMF addition groups than in non-AMF addition groups according to enrichment plots (p value < 0.05) (Figure S7CE).

Microbial co-occurrence network analysis

RDA analysis revealed the relationship between the environmental factors and the structure of bacterial community. As shown in Fig. 6A, the red and blue arrows represented 4 different environmental factors and 10 major bacterial genera. The results showed that the total C and N content of soil were positively correlated with some main N-fixing bacterial genera such as Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, meanwhile the soil C and N content were negatively correlated with available P content of soil (Fig. 6A). Then the Pearson correlation analysis of plant physicochemical properties and the 10 major bacterial genera implied that, at flowering stage, the Acidipila had negative correlation with total C (%) (r=-0.574, p = 0.0081) and total P (g/kg) (r=-0.452, p = 0.0455) of soybean, as were Thermosporothrix with total N (%) (r=-0.481, p = 0.0319) of soybean (Fig. 6B). The total N (%) of soybean at maturing stage exhibited co-occurrence correlations with Acidipila (r = 0.520, p = 0.0188) and Bradyrhizobium (r = 0.461, p = 0.0405) (Fig. 6B).

Fig. 6
figure 6

Correlation analysis between plant and soil physicochemical properties, composition of rhizosphere bacterial community and rhizosphere metabolites. (A) RDA analysis plot of the 10 major bacterial genera in rhizosphere and 4 soil physicochemical properties. (B) The Pearson correlation results of 6 plant physicochemical properties and top 10 bacterial genera in rhizosphere. (C) The Pearson correlation results of top 20 bacterial genera associated with the rhizosphere soil of soybean. The Pearson correlation results of the 10 major bacterial genera in rhizosphere and 10 major metabolites of GC-MS (D) and LC-MS (E). Significant correlations (*P < 0.05, **P < 0.01, and ***P < 0.001) and the positive and negative correlation were shown in figures. Other treatment details were shown in Fig. 2

Full size image

To understand the feedbacks between microorganisms that occur among soybean rhizosphere soil, the co-occurrence patterns of the 20 major bacterial genera associated with the rhizosphere soil of soybean were also analyzed (Fig. 6C). As shown in Fig. 6C, there existed significant positive correlation (r > 0, p < 0.05) between Occallatibacter, Dyella, Puia, Burkholderia-Caballeronia-Paraburkholderia and Acidipila. The Saccharimonadales also had positive co-occurrence correlations with Novosphingobium (r = 0.881, p = 2.98 × 10− 7) and Chitinophaga (r = 0.686, p = 0.0008) (Fig. 6C). Further analysis of the relationship between composition of soil microbiome and rhizosphere metabolites via 10 major bacterial genera and 10 major metabolites revealed that, the Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium was found to have positive correlations with sorbitol (r = 0.450, p = 0.0466), Dyella was negatively correlated with stearic acid (r=-0.458, p = 0.0422) while Burkholderia-Caballeronia-Paraburkholderia was negatively corelative to L-alanine-alanine (r=-0.488, p = 0.0291) (Fig. 6D). The Streptomyces had positive correlations with piracetam (r = 0.489, p = 0.0286), while Sphingomonas was negatively correlated with porson (r=-0.458, p = 0.0422), armillaripin (r=-0.624, p = 0.0033) and palmitic amide (r=-0.449, p = 0.0468) (Fig. 6E). The tetrahydrobungeanool were found to have positive correlations with Burkholderia-Caballeronia-Paraburkholderia (r = 0.451, p = 0.0460) and Bradyrhizobium (r = 0.525, p = 0.0175), and was negatively correlative to Actinospica (r=-0.582, p = 0.0071) (Fig. 6E).

Discussion

Microbial inoculants modified the structure and function of rhizosphere microbiome

SynCom always represent viable, complex and stable community selected and engineered from a core microbiota [61]. Our recent study found that the abundance of pathogenic fungus N. oryzae and P-solubilizing fungus T. verruculosus was regulated by AMF R. intraradices. Thus, in this study, we used SynCom to represent these three artificial microbial agents above. In our study, host niches displayed differentiation of associated bacterial community following Syncoms inoculations. Both the single-strain inoculation and combined inoculations had no significant effects on bacterial alpha diversity. Endosphere had lower microbial richness (Chao1) and Shannon diversity than the rhizosphere, while higher Good’s coverage than rhizosphere (Fig. 3ABC). The beta diversity using PCoA revealed that endopshere and rhizosphere niches were clearly separated following both single strain and co-inoculation (Fig. 3DEF). Moreover, taxonomic studies revealed higher abundances of Chloroflexi, Acidobacteriota, Myxococcota and Gemmatimonadetes in the rhizosphere than in the endosphere (Figure S2AB). Furthermore, the Rhizobium vallis was only detected in the root endosphere (Fig. 4C). A recent study suggests that the rhizosphere microbiome is divided into the environment-dominated and plant genetic-dominated components, while the physical, chemical, and biological characteristics of rhizosphere soil mainly determine the assembly of the environment-dominated microbiome (up to 96.5%) [62]. Although the bacterial community was found to exhibit significant differences mainly between two host niches, significant statistical differences were also detected between N. oryzae and T. verruculosus treatments according to the analyses of ANOSIM and Adonis (Table 2). In addition, the co-inoculation of T. verruculosus and R. intraradices was found to increase the relative abundance of a N-fixer, Pseudomonas psychrotolerans, in the rhizosphere (Fig. 4C). The potential P-soluble fungus T. verruculosus was also reported to have cellulolytic characteristic [41, 63]. Pseudomonas populations were one of the famous plant growth-promoting rhizobacteria (PGPR), which can promote photosynthetic capacity by improving the plant chlorophyll content [64], and are directly involved in nutrient metabolism of C, N, and P in soil [65, 66]. Therefore, inoculation with T. verruculosus not only improved soil P nutrition, but also had direct and indirect effects on C nutrition. Moreover, the N. oryzae inoculation enriched Bradyrhizobium and Streptomyces in endosphere (Figure S2CD). The famous efficient antimycotic and antiprotozoal agent, polyene macrolide antibiotic, was produced by several soil bacterial species of the genus Streptomyces [67, 68]. Thus, the addition of pathogenic fungi might directly stimulate the enrichment of anti-pathogenic fungal microorganisms in the soybean rhizosphere. Moreover, a new study suggested that, the carbon compounds exuded by AMF genus Rhizophagus were acquired by the bacterium which could mineralize organic P, while Streptomyces inhibited the bacteria with weak P-mineralizing ability and enhanced AMF to acquire P [69], which proved that the role of Streptomyces in the AMF-mediated rhizosphere microbial community needed deeper exploration. Functional classification revealed the itrogenase molybdenum-iron protein (COG2710) and phosphodiesterase/alkaline phosphatase D (COG3540) were enriched in the endosphere upon N. oryzae inoculation (Figure S3BC). Our findings thus suggest that fungal inoculation could modulate the interactions among bacteria residing in the host plant niches, thus affecting the structure, function and dynamics of the resident bacterial community. These results also corroborate previous findings that the microbial inoculants could alter the diversity, network stability, structure and functionality of soil and plant microbiome [70], modify metabolite-soil-microbial interactions [71], and regulate secondary metabolites exudation and rhizosphere expansion [72].

Microbial inoculants adjusted the metabolic spectrum in the rhizosphere microenvironment

The composition of the plant-associated microbiome is influenced by various factors, including host genotype, root morphology, and root exudates [73]. In turn, changes in plant metabolic profiles caused by microbial inoculations might have an impact on the patterns of root exudation [74]. Thus, understanding the chemical characteristics of rhizosphere soil can give us insights into its effects on microbial community structure [75]. LC-MS has higher detection sensitivity and is more sensitive to trace substances, while GC-MS has better separation and detection effect and a more comprehensive database. In our investigation, the effect of single strain or co-inoculation on the assembly and shift of soil microbes could be due to their ability to modify the metabolic profile of host plant via interfering root exudation patterns. To prove this, GC-MS of rhizosphere soil samples was performed, and it was observed that AMF application upregulated DEMs which belong to biosynthesis of unsaturated fatty acids, such as arachidic acid, behenic acid, and tetracosanoic acid, as compared to non-AMF addition groups (Fig. 5A and Figure S5C). Unsaturated fatty acids are known to have a significant influence on enhancing plant resistance under adverse environmental conditions [76]. One famous plant growth promoting rhizobacteria, Pseudomonas psychrotolerans [77], was found to be enriched in the rhizosphere under the co-inoculation of T. verruculosus and R. intraradices, which might be co-related to the upregulation of the expression levels of biosynthesis of unsaturated fatty acids. Pseudomonas species in soil have been proved to be closely related to soybean stress resistance by exudating key metabolites such as purine, which also supports the previously reported cry-for-help theory [78, 79]. These findings imply that the inoculation of AMF R. intraradices may improve soybean resistance to acidic soil stress by modifying the chemical and microbiological composition of the soybean rhizosphere. Two DEMs involved in the steroid biosynthesis pathway, beta-sitosterol and stigmasterol, were found to have higher abundance in N. oryzae addition groups as compared to T. verruculosus treatments (Figure S5D). The steroid hormones are considered to be regulators of plant growth, development, and stress responses, and beta-sitosterol and stigmasterol are also known for their antibacterial activity [80,81,82]. Thus, the application of N. oryzae activated the antibacterial reaction in the soil microenvironment, which was also consistent with our finding in the composition of the bacterial taxa, that the polyene macrolide antibiotic production bacterial genus Streptomyces was abundant in endosphere under N. oryzae inoculation (Figure S2CD). Some metabolic pathways, such as taste transduction and renal cell carcinoma, existed significant differences between different treatments (Figure S6B), which might be closely related to protozoon such as Caenorhabditis elegans [83, 84]. Lastly, the LC-MS displayed about 25-fold identified metabolites and approximately 10-folds identified DEMs as compared with GC-MS, and the results of LC-MS indicated that AMF application decreased the abundance of almost 4/5 metabolites, which were also worth further study (Table S1).

The application of microbial agents needs to consider the complex interactions in the rhizosphere microenvironment

The N. oryzae inoculation enriched Bradyrhizobium in endosphere (Figure S2CD). The Pearson correlation analysis showed that the total N of soybean at maturing stage exhibited co-occurrence correlations with Acidipila and Bradyrhizobium (Fig. 6B). Bradyrhizobium is the dominant rhizobia in acidic soils, thus, the colonization of dominant rhizobia in acidic soil might directly increase plant N content. In addition, the co-inoculation of N. oryzae and R. intraradices significantly increased the activity of urease (Fig. 2). Through catalysing the breakdown of urea into CO2 and NH3, urease has an important role in the N cycle by generating accessible N for plant growth and might be a good index of soil quality [85]. However, the N. oryzae application was found to have no significant effect on biomass and N content of soybean (Table 1). The RDA analysis showed that soil C and N content were negatively correlated with available P content of soil (Fig. 6A). A previous study also proved that soil acidification induced by N addition decreased available P concentrations, and was associated with reductions in the relative abundance of phytase [86]. Thus, only the improvement of N nutrition in the rhizosphere microenvironment might not directly cause the plant growth promotion in available P-deficient acidic soil. Cooperation is common in nature and is a strategy conducive to community stability [87,88,89]. Previous studies also proved that, the bacterial community had more complex and compact associations under PGPB inoculants, the enhanced co-occurrence associations in the PGPB-inoculated bacterial network may contribute to the plant growth-promoting effects of PGPB [90], and microbial interactions may contribute to soil functions more than species diversity [91]. Thus, we believed that the addition of AMF and the enrichment of some PGPRs enhanced the stability of the rhizosphere environment and were beneficial for coping with acidic soil stress. Moreover, an increasing number of studies indicate that, in addition to complex mutual cooperation, competition may dominates the interactions among microbiome [92]. The disappearance of either cooperative party can lead to the collapse of the community, making cooperation a high-risk strategy [93]. In this study, the metabolism and absorption of N and P nutrients in plants seem to not always cause a win-win situation under environmental stress, and simply considering the enrichment/de-enrichment of certain species to evaluate the differential changes in the entire microenvironment ecology is far from enough. That’s why it is necessary to consider the reconciliation roles of complex Syncoms in the rhizosphere microenvironment through multi omics techniques.

More comprehensive analysis method could lead to more stable conclusions. One significant limitation of our study was that only the composition, function and metabolism of soybean rhizosphere soil were evaluated. Whether and how the effects of Syncoms inoculation on the transcription and expression of soybean roots under stressful environments should also be examined in further studies, to better evaluate the mechanism of Syncoms regulating the rhizosphere microenvironment. Ecological fertilizer technology such as the use of Syncoms, is an important supplement to traditional synthetic chemical fertilizer technology to promote plant productivity and contribute to the abolition of hunger, but their application in ecological agriculture still needs to require extensive exploration of the complex cooperation and competition interactions in the rhizosphere microenvironment [94,95,96].

Conclusions

Experimental results showed that the inoculation of AMF strain R. intraradices, pathogenic fungus N. oryzae and P-solubilizing fungus T. verruculosus modulated the interactions among bacteria residing in the host plant niches by affecting the structure, function, and dynamics of the resident bacterial community. The inoculation of AMF R. intraradices improved soybean resistance to acidic soil stress by upregulating the key metabolic pathway related to plant resistance promotion under adverse environmental conditions and recruiting specific PGPR. The N. oryzae application stimulated the stress response in the soil microenvironment through upregulating two antibacterial activity metabolic pathways in the rhizosphere and enriching the polyene macrolide antifungal antibiotic production bacterial genus Streptomyces in endosphere. However, although the addition of pathogenic fungus N. oryzae enriched Bradyrhizobium and increased soil urease activity, it had no significant effect on biomass and N content of soybean. Therefore, it is necessary for us to consider the potential effect on available P content of soil when applying some microbial agents to enhance plants growth in acidic soil. Lastly, the host niches had greater impact on the assembly and shift of soybean rhizosphere microbes than the microbial agents in this short-term study. Together, our results showed that microbial agents and host niches co-mediated the fine-tuning of the compositions, functions, and metabolisms of soybean rhizosphere microbiome and further improved the survival of Al-sensitive soybean BD2 in an acidic soil. Our findings also suggested that the application of microbial agents needs to consider the complex microbe-microbe and plant-microbe interactions in specific soil environments to achieve better application effects in ecological agriculture.

Data availability

The clean sequencing data of 16 S rRNA V3-V4 hypervariable region were uploaded to NCBI Sequence Read Archive database, and the accession number is SRP424056 (BioProject ID: PRJNA937876; reviewer link: https://dataview.ncbi.nlm.nih.gov/object/PRJNA937876?reviewer=u32bc58l2m37c9gsgaj2bia8d3). The raw data of Metabolomics were uploaded to database MetaboLights, and the project number is MTBLS7299 (link: www.ebi.ac.uk/metabolights/MTBLS7299).

Abbreviations

Al:

Aluminum

C:

Carbon

N:

Nitrogen

P:

Phosphorus

PBS:

Phosphate buffered saline

AMF:

Arbuscular mycorrhizal fungi

Ri:

Rhizophagus intraradices

No:

Nigrospora oryzae

Tv:

Talaromyces verruculosus

PGPR:

plant growth promoting rhizobacteria

Rh:

Rhizosphere soil

Rt:

Root endosphere

PCoA:

Principal coordinate analysis

VIP:

Variable importance of projection

GC-MS:

Gas Chromatograph-Mass Spectrometer

LC-MS:

Liquid Chromatograph-Mass Spectrometer

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Acknowledgements

The authors thank the Home for Researchers (www.home-for-researchers.com) for the grammar modifications.

Funding

This work was supported by grants from the National Natural Science Foundation of China (32101383, 42377327), the China Postdoctoral Science Foundation (2023T160299, 2023M731604, 2022M711562) and the Program for Changjiang Scholars and Innovative Research Team in University from the Ministry of Education of China (IRT_14R27).

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Authors and Affiliations

  1. State Key Laboratory of Pharmaceutical Biotechnology, Institute for Plant Molecular Biology, School of Life Sciences, Nanjing University, Nanjing, 210023, China

    Minkai Yang, Yuhang Song, Hanke Ma, Zhenghua Li, Jiawei Ding, Kechang Niu, Shucun Sun, Jinliang Qi, Guihua Lu, Aliya Fazal, Yonghua Yang & Zhongling Wen

  2. Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing, 210037, China

    Minkai Yang, Yuhang Song, Hanke Ma, Zhenghua Li, Jiawei Ding, Tongming Yin, Jinliang Qi, Guihua Lu, Aliya Fazal, Yonghua Yang & Zhongling Wen

  3. School of Life Sciences, Huaiyin Normal University, Huaian, 223300, China

    Guihua Lu

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  1. Minkai Yang
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Contributions

Conceptualization and Methodology: ZW, MY, and YY; Validation and Investigation: MY, ZW, YS, HM, ZL, JD, and AF; Software, Data Curation and Formal analysis: ZW, MY, AF, YS, HM, ZL, JD, TY, KN, SS, JQ and GL; Writing and Visualization: YY, ZW, MY, and AF; Supervision, Project administration and Funding acquisition: ZW, MY, and YY. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Aliya Fazal, Yonghua Yang or Zhongling Wen.

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Yang, M., Song, Y., Ma, H. et al. Unveiling the hidden world: How arbuscular mycorrhizal fungi and its regulated core fungi modify the composition and metabolism of soybean rhizosphere microbiome. Environmental Microbiome 19, 78 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s40793-024-00624-y

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Keywords

Environmental Microbiome

ISSN: 2524-6372