DOI: 10.5061/dryad.qfttdz0mx
title: Data from: The mechanism of promoting rhizosphere nutrient turnover for arbuscular mycorrhizal fungi attribute to recruited functional bacterial assembly

description: Symbiosis with arbuscular mycorrhizal (AM) fungi improves plant nutrient
 capture from the soil, yet there is limited knowledge about the diversity,
 structure, functioning, and assembly processes of AM fungi-related
 microbial communities. Here, 16S rRNA gene sequencing and metagenomic
 sequencing were used to detect bacteria in the rhizosphere of Lotus
 japonicus inoculated with and without AM fungi, and the L. japonicus
 mutant ljcbx (defective in symbiosis) inoculated with AM fungi in southern
 grassland soil. Our results show that AM symbiosis significantly increased
 bacterial diversity and promoted deterministic processes of bacterial
 community construction, suggesting that mycorrhizal symbiosis resulted in
 the directional enrichment of bacterial communities and established a
 stable rhizosphere bacterial community. AM fungi promoted the enrichment
 of nine bacteria, including Ohtaekwangia, Niastella, Gemmatimonas,
 Devosia, Sphingomonas, Novosphingobium, Opitutus, Lysobacter,
 Brevundimonas, which are positively correlated with NPK-related
 parameters. Through a functional identification experiment, we found that
 six of these genera, including Brevundimonas, Lysobacter, Ohtaekwangia,
 Sphingomonas, Devosia, and Gemmatimonas, demonstrated the ability to
 mineralize organophosphate and dissolve inorganic phosphorus, nitrogen,
 and potassium. Our study revealed that AM fungi can regulate rhizosphere
 bacterial community assembly and attract specific rhizosphere bacteria to
 promote soil nutrient turnover in southern grasslands.
description: <em>Greenhouse experiments</em>
 <em>Experiment 1</em> was designed to explore whether
 AM fungi inoculation affected the bacterial community in the rhizosphere.
 The factorial experimental design included the addition of AM fungi and
 the non-addition of AM fungi (CK). L<em>otus japonicus</em>
 (Miyakojima MG20) plants were selected and grown at 22°C to 24°C under a
 16-h-day/8-h-night cycle. We collected soil samples from a southern
 grassland in Zhaotong, Yunnan Province, Southwest China (103°63′E,
 27°66′N; Figure S1). The dominant native AM fungi in this soil were
 Acaulospora, as identified using special primers (Lee et al., 2008; Sato
 et al., 2005; Zhang et al., 2021) (Table S1). Surface soil (0–20 cm) was
 collected using a “five points” sampling strategy in a 25 m × 25 m field
 area. Soils were immediately transported for storage at 4°C. Soils were
 dried sufficiently to pass through a 2-mm sieve and plant tissues were
 removed. Soil samples were divided into two parts: one portion was used
 for chemical analysis, and another portion was used for the greenhouse
 experiment. Each pot contained 500 g of non-sterile soil. Three AM fungi,
 including <em>Glomus mosseae</em>, <em>G.
 intraradices</em> (alternatively <em>Rhizophagus
 intraradices</em>), and <em>G. versiforme</em>, were
 used in the experiment. These AM fungi are widely used
 in scientific research and practical applications and have different
 symbiotic abilities (<em>G.
 versiforme</em>&gt;<em>G.
 intraradices</em>&gt;<em>Glomus mosseae</em>) with
 <em>Lotus japonicus</em>. AM fungi spores were propagated on
 maize (<em>Zea mays</em> L.), <em>L.
 japonicus</em>, and white clover (<em>Trifolium
 repen</em>s L.) in a sand-ceramsite mixture (3:1w/w) for 4 months
 before the experiment. The matrix containing AM fungal propagation passed
 through sieves of 0.8mm, 0.25mm, and 0.055mm in turn. The inoculum in the
 bottom sieve including AM fungi spores and sand was used as AM fungi
 inoculum. Before use, we counted the spore density in the inoculum. Each
 gram of <em>Glomus mosseae</em>, <em>G.
 intraradices</em>, and <em>G. versiforme</em> inoculum
 contained approximately 60, 80, and 80 spores, respectively. A total of
 250 AM fungal spores were added per pot in AM fungi addition experiments.
 AM fungi inoculum was mixed with soil before planting plants. Eight
 replicates for each treatment were set.
 <em>Experiment 2</em> was designed to determine: (1)
 if mycorrhizal symbiosis can promote the absorption and utilization of
 nitrogen, phosphorus, and potassium by <em>L. japonicus</em>;
 (2) if mycorrhizal symbiosis shapes microorganisms in the rhizosphere.
 Plants <em>L. japonicus</em> (wild-type, Gifu-129) and
 mycorrhiza-defective mutant <em>ljcbx</em> plants (Gifu-129
 background) were selected to grow in non-sterile soil inoculated with
 <em>G. versiforme</em> (250 spores per pot), which have the
 strongest symbiotic ability with <em>L. japonicus</em>
 compared with <em>Glomus mosseae</em> and <em>G.
 intraradices</em>. Mycorrhiza-defective <em>L.
 japonicus</em> mutant <em>ljcbx</em> (30057597) plants
 were kindly provided by Dr. Stig U. Andersen and Jens Stougaard (Lotus
 Base, https://lotus.au.dk/) (Małolepszy et al., 2016). The growth
 conditions were the same as in <em>Experiment</em> 1. There
 were eight replicates for WT and mutant <em>ljcbx</em>
 treatments in <em>Experiment</em> 2.
 <em>Plant and soil analyses</em>
 In each treatment, plants were randomly collected at the final
 harvest and then divided into roots and shoots to analyze for nutrient
 uptake. Plant organs were dried at 105°C for 30 min and then oven-dried at
 75°C for 48 h to a constant weight (dry weight, DW). Samples were then
 ground to pass through a 0.25-mm sieve for chemical analysis. For plant N,
 P, and K measurements, samples were digested firstly in hot sulfuric acid
 with hydrogen peroxide as an additive (H2SO4–H2O2 method). In short,
 accurately weighed 0.1g ground sample in the digestion tube, added
 distilled water to fully wet it, then added 5 ml sulfuric acid to soak it
 overnight, and then digested it at 375℃ for 3h, cooled the digestion tube,
 added a few drops of hydrogen peroxide until it is completely digested.
 The volume of the digested solution to 50 ml. Took 10 ml supernatant to a
 volumetric flask, added water to 20 ml, added two drops of 0.3%
 2,4-dinitrophenol, and then sodium hydroxide solution was added to adjust
 the solution to yellow (pH 6-7). Total N, P, and K were measured using a
 CleverChem 380 discontinuous water quality analyzer (DeChem-Tech, Hamburg,
 Germany). For soil nutrient analysis, the soil was first passed through a
 2-mm sieve. Soil-available phosphorus (AP) was extracted with sodium
 bicarbonate and available potassium (AK) was extracted with ammonium
 acetate solution. Soil ammonium nitrogen (NH4+-N) and nitrate nitrogen
 (NO3-N) were extracted with 1 mol·l−1 KCl. Soil NH4+-N, NO3-N, AK, and AP
 were measured using a CleverChem 380 discontinuous water quality analyzer
 (DeChem-Tech, Hamburg, Germany). <em>Rhizosphere
 soil separation and 16S rRNA gene sequencing</em>
 Rhizosphere soil was collected according to established protocols
 (McPherson et al., 2018) after plants had grown in soil for 5 weeks. In
 brief, soil loosely attached to roots was removed with gentle shaking.
 Steps to collect rhizosphere soil were performed on ice. Roots with
 adhered soil (rhizosphere soil) were placed in a sterile 50-ml falcon tube
 containing 30 ml of sterile, pre-cooled PBS (phosphate-buffered saline)
 buffer (pH 7.5). The tube was mixed in a vortex apparatus for 15 s, and
 the turbid mixture was passed through a 100-μm aseptic nylon mesh strainer
 (to remove root fragments and large soil particles) into a new 50-ml tube,
 which was centrifuged for 5 min at 12,000 ×g at 4°C. The supernatant was
 discarded, and the collected soil, defined as rhizosphere soil, was frozen
 with liquid nitrogen and stored at −80°C. The total DNA of rhizosphere
 samples was extracted using a PowerSoil® DNA isolation kit according to
 the manufacturer’s instructions. Miseq sequencing targeting the bacterial
 16S rRNA gene V3-V4 region was performed on an Illumina MiSeq platform by
 the company Sangon Biotech (Shanghai, China). The 16S
 rRNA gene V3-V4 region was amplified using the16S rRNA gene primers 341F
 (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′). The
 amplification procedure is as follows: 94 °C for 3 min, followed by 5
 cycles consisting of 94 °C for 30 s, 45 °C for 20 s, 65℃ for 30 s, then 20
 cycles consisting of 94℃ for 20 s, 55℃ for 20 s, 72℃ for 30 s, finally,
 72 °C for 5 min. There was an average of 40000 reads per sample via 16S
 rRNA sequencing. Amplicon sequencing reads were quality-filtered and
 demultiplexed using QIIME (Caporaso et al., 2010) and USEARCH (Edgar,
 2010) pipelines. Demultiplexed files from each library were processed
 separately with the “DADA2” (Callahan et al., 2016) pipeline in R (version
 3.6.0). Amplicon sequence variants (ASVs) in each sample were inferred
 followed by trimming and filtering. Trim parameters were as follows: trim
 left 0 bases and truncate at base 250 for both forward and reverse reads.
 ASVs were then aligned to the SILVA database (Quast et al., 2013) for the
 taxonomic assignment. Sequences were clustered into OTUs at 97% identity
 using UPARSE algorithm. To detect genes related to
 metabolism by amplicon sequencing, the Kyoto Encyclopedia of Genes and
 Genomes (KEGG, http://www.kegg.jp) database and Pfam (Finn et al., 2013)
 database were used. DIAMOND (Buchfink et al., 2015) was used to compare
 the protein sequences of the gene set with KEGG database to obtain the
 KEGG orthology (KO), and the annotation of pathway and module of the
 sequence was obtained subsequently. The abundance of each functional level
 of KEGG in each sample was counted. Using KEGG and Pfam, functional genes
 were searched by protein domain. The functional gene set involved in N and
 P cycle and related KO information was constructed. Finally, according to
 the KO result of functional prediction of the amplicon through PICRUSt
 (version 1.1.4) using the KEGG database, the N and P functional genes were
 extracted and the heat map was drawn.
 <em>Metagenome sequencing</em>
 Metagenomic shotgun sequencing was performed on an Illumina HiSeq
 2500 platform at Sangon Biotech (Shanghai, China). A total of six samples
 were conducted metagenomic sequencing, including samples not inoculated
 with AM fungi (CK, three replicates) and samples inoculated with
 <em>G. versiforme</em> (three replicates). Finally, an average
 of 6 GB sequencing data were obtained from each sample. Raw sequencing
 data were evaluated with FastQC (version 0.11.8) software
 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/) and filtered
 using Trimmomatic (version 0.36) (Bolger et al., 2014) to obtain
 relatively accurate and effective data. IDBA_UD was used to splice clean
 reads of each sample to assemble long sequence contigs (Peng et al.,
 2012). Contigs were obtained according to overlapping relations between
 reads (minimum overlap=100). Assembly results of multiple K-mers were
 comprehensively evaluated, and the best K-mer assembly results (k-mer=31)
 were selected. Prodigal (version 2.60) (Liu et al., 2013) was used to
 predict ORFs (Open Reading Frames) of splicing results, and sequences with
 length ≥ 100 bp were selected and translated into amino acid sequences. We
 used CD-HIT software for de-redundancy to obtain a nonredundant gene set
 for the gene prediction results of each sample (Li & Godzik,
 2006). Bowtie2 was used to align clean reads of each sample to
 nonredundant gene set sequences (Langmead & Salzberg, 2012), and
 SAMtools was used to obtain aligned reads (Li et al., 2009). All reads
 sequences were mapped back to assembled contigs to obtain contig coverage,
 and then the contigs were clustered by integrating the information of GC
 content, nucleic acid composition, single copy gene, and other genomic
 characteristics (Zeng et al., 2019) to separate contig sequences belonging
 to different genomes. Finally, high-quality Binners (MAG, metagenome
 assembled genomes) were obtained. To further improve the quality and
 integrity of bins, we re-mapped the original sequence based on the
 obtained bins and optimized the assembly. Using SPAdes for fine assemble
 to remove contigs less than 1500 bp, and then evaluate the quality by
 checkM (version 1.1.3) (Parks et al., 2015). Each bin was annotated
 against the National Center for Biotechnology Information (NCBI)
 nonredundant Nucleotide Sequence Database (NT) using BLASTn (You et al.,
 2022). Species annotation was conducted by searching
 against NCBI non-redundant protein sequences (NR,
 http://ncbi.nlm.nih.gov/) database via DIAMOND blastp
 (e-value&lt;1e-5) (Buchfink et al., 2015). The translated proteins
 from all detected coding regions of each metagenome were annotated by
 searching against three custom databases. Kyoto Encyclopedia of Genes and
 Genomes (KEGG, http://www.kegg.jp) database was searched using GhostKOALA
 (e-value&lt;1e-5) (Kanehisa et al., 2016) to predict functional
 pathways and KEGG Orthology (KO) was obtained. Functional assignments were
 also conducted through the SEED database
 (http://www.theseed.org/wiki/Main_Page) using DIAMOND
 (e-value&lt;1e-5). The evolutionary genealogy of genes: Non-supervised
 Orthologous Groups (eggNOG, http://eggnogdb.embl.de/) database was
 searched via DIAMOND (e-value&lt;1e-5), and the Clusters of
 Orthologous Groups of proteins (COG) corresponding to the genes were
 obtained. On this basis, the gene sets were annotated and classified, and
 the abundance of each functional level of COG in each sample was counted.
 Then, using perl to retrieve COG annotation information corresponding to
 the core species. Gene abundances (gene counts per KO) were normalized to
 the number of amino acids detected in the metagenome of each
 sample. <em>Isolation and identification of
 rhizosphere bacteria</em> To isolate rhizosphere
 bacteria, we resuspended 1 g of rhizosphere soil in sterile phosphate
 buffer and incubated the mixture for 30 min with shaking at 150 rpm.
 Strains harvested from rhizospheres were cultured on three media: nutrient
 agar (NA), Reasoner’s 2A agar (R2A), and trypticase soy agar (TSA). A 1-ml
 suspension was taken from each mixture for serial dilution on NA, R2A, and
 TSA. One representative colony (determined by its morphology) from each
 plate was selected, purified, and stored at −80°C in R2A medium containing
 20% glycerol. The complete 16S rDNA was amplified via PCR with universal
 bacterial primers 27F (5′-AGA GTT TGA TCC TGG CTC AG-3′) and 1492R (5′-ACG
 GCT ACC TTG TTA CGA CTT-3′) (Defez et al., 2017). Genus-level
 classifications of isolates were confirmed by 16S rRNA gene sequencing,
 which was performed by Sangon Biotech. We analyzed resulting high-quality
 sequences with BLASTn (NCBI; http://blast.ncbi.nlm.nih.gov) to confirm the
 identity of each bacterium. Gram staining was performed with a three-step
 Gram stain procedure kit (Solarbio Co., Ltd., Beijing, China), in strict
 accordance with the standard procedure. Stained cells were observed with a
 compound microscope. <em>Determination of
 rhizosphere bacterial functions</em> Fresh soil
 samples (10 g) were suspended in 90 ml of sterile saline solution (NaCl,
 0.9% w/v), and mixtures were shaken (150 rpm) for 30 min at room
 temperature. Ten-fold dilutions were made in sterile saline solution, and
 0.1 ml was spread on the appropriate medium. Bacteria were cultured in
 standard NA (Cultimed, Spain) for 48 h at 30°C. Cultivable bacteria and
 organophosphorus-degrading bacteria (OPDB) were estimated using a standard
 dilution-plating procedure. The OPDB were cultivated in modified
 Pikovskaya’s agar medium (pH 7.0–7.5) containing the following
 (l<sup>−1</sup>): glucose, 10.0 g; (NH4)2SO4, 0.5 g; NaCl,
 0.3 g; KCl, 0.3 g; yeast extract, 0.5 g; MgSO4·7H2O, 0.3 g; FeSO4·7H2O,
 0.03 g; MnSO4·4H2O, 0.03 g; CaCO3, 5.0 g; soybean lecithin (P-lecithin),
 15.0 g; and Bacto-agar, 15.0 g. The phosphate-solubilizing ability of each
 isolate was tested using insoluble tricalcium phosphate (Ca3(PO4)2) as the
 sole P source in Pikovskaya’s medium. Phosphate solubilization was
 assessed by spot-inoculating individual bacterial isolates on Pikovskaya’s
 medium containing the following (l<sup>−1</sup>): yeast
 extract, 0.50 g; dextrose, 10.0 g; calcium phosphate, 5.00 g; ammonium
 sulfate, 0.50 g; potassium chloride, 0.20 g; magnesium sulfate, 0.10 g;
 manganese sulfate, 0.0001 g; ferrous sulfate, 0.0001 g; and Bacto-agar,
 15.00 g. Media pH was adjusted to 7.0 before autoclaving. Mixtures were
 poured into sterile petri plates (25 ml of agar per plate). Plates were
 incubated for 4 to 5 d at 28 ± 1°C. A clear zone around a bacterial colony
 was considered a positive indication of phosphate solubilization (Pande et
 al., 2017). To test the bacterial ability to solubilize K, all colonies
 growing on plates were screened on modified Aleksandrov agar medium
 containing the following (l−1): glucose, 3.50 g; MgSO4.7H2O, 0.50 g;
 CaCO3, 0.10 g; FeCl3, 0.0005 g; Ca3PO4, 2.00 g; insoluble mica powder, 1.0
 g, as the K source; and agar, 15.00 g. Colonies selected from plates were
 spot-inoculated on new plates and incubated at 28°C for 7 d.
 Nitrogen-fixing bacteria were isolated using solid N-free medium (NFM)
 containing the following (l<sup>−1</sup>): K2HPO4, 0.20;
 KH2PO4, 0.50 g; MgSO4.7H2O, 0.20 g; FeSO4·7H2O, 0.10 g; Na2MoO4·2H2O,
 0.005 g; NaCl, 0.20 g; glucose, 10.00 g; and noble agar, 15.00 g. Serial
 dilutions were spread on freshly prepared solid NFM in triplicate. Plates
 were incubated for 5 to 7 d at 28°C. Colonies were selected based on
 morphology and were streaked three times on NFM to ensure purity before
 they were isolated.
description: <em>Data analysis</em> All
 analyses and data visualization were performed in R version 3.6.0 (Hu et
 al., 2022; Team, 2019). Differences in the alpha diversity richness were
 estimated using one-way and two-way ANOVAs. Differences in the taxonomic
 composition of bacterial communities were visualized using non-metric
 multidimensional (NMDS) analyses. Alpha diversity and NMDS were calculated
 using the ‘vegan’ package (version 2.0-10) (Oksanen et al., 2013) in R. To
 describe potential co-occurrence between all OTUs in the rhizosphere,
 co-occurrence networks of each sample were constructed using the ‘cooccur’
 package in R. Using psych package in R, the genus level abundance matrix
 was transformed into species correlation matrix. Then, the Gephi
 (https://gephi.org/) was used to calculate the information of nodes and
 edges of the network. At last, network characteristic data was exported to
 draw figures. For Sloan neutral model (Rezki et al., 2017; Sloan et al.,
 2006) 95% binomial confidence intervals for the neutral model based on the
 Wilson method using the ‘Hmisc’ package in R were constructed (Morris et
 al., 2013). The deterministic processes (selection and environmental
 filtering) and stochastic processes (dispersal and drift) were assessed by
 comparing the βNTI values: deterministic processes (|βNTI|≥2), stochastic
 processes (−2 &lt; βNTI &lt; +2) (Dini-Andreote et al., 2015).
 Redundancy analysis was performed using ‘vegan’ package (version 2.0-10).
 In addition, linear discriminant analysis effect size (LEfSe) analysis was
 performed using LEfSe software to identify specific bacterial genera that
 are associated with AM fungi. <em>Data
 availability</em> Raw transcriptome data have
 been deposited in the National Center for Biotechnology Information (NCBI)
 SRA database with the BioProject accession number PRJNA791779
 (SRR17335095-SRR17335100). The 16S rRNA gene sequencing raw data were
 deposited in the NCBI SRA database under accession number PRJNA792500
 (SRR17344331-SRR17335100). Metagenomic sequences were deposited in the SRA
 database, which is available at NCBI with accession number PRJNA792495
 (SRR17333937-SRR17333942). <em>Code
 availability</em> Scripts used for computational
 analyses described in this study are available at
 https://github.com/xuyunjian1992/R-code-and-software, to ensure
 replicability and reproducibility of these results. No unpublished
 algorithms or methods were used.
description: # The mechanism of promoting rhizosphere nutrient turnover for arbuscular
 mycorrhizal fungi attribute to recruited functional bacterial assembly
 Brief summary of dataset contents, contextualized in experimental
 procedures and results: Symbiosis with arbuscular mycorrhizal (AM) fungi
 improves plant nutrient capture from the soil, yet there is limited
 knowledge about the diversity, structure, functioning, and assembly
 processes of AM fungi-related microbial communities. Here, 16S rRNA gene
 sequencing and metagenomic sequencing were used to detect bacteria in the
 rhizosphere of Lotus japonicus inoculated with and without AM fungi, and
 the L. japonicus mutant ljcbx (defective in symbiosis) inoculated with AM
 fungi in southern grassland soil. Our results show that AM symbiosis
 significantly increased bacterial diversity and increased deterministic
 processes of bacterial community construction, suggesting that mycorrhizal
 symbiosis resulted in the directional enrichment of bacterial communities
 and established a stable rhizosphere bacterial community. AM fungi
 promoted the enrichment of nine key bacteria, including Ohtaekwangia,
 Niastella, Gemmatimonas, Devosia, Sphingomonas, Novosphingobium, Opitutus,
 Lysobacter, Brevundimonas, which are positively correlated with
 NPK-related parameters. Through a functional identification experiment, we
 found that six of these genera, including Brevundimonas, Lysobacter,
 Ohtaekwangia, Sphingomonas, Devosia, and Gemmatimonas, demonstrated the
 ability to mineralize organophosphate and dissolve inorganic phosphorus,
 nitrogen and potassium. Our study revealed that AM fungi can regulate
 rhizospheric bacterial community assembly and attract specific
 rhizospheric bacteria to promote soil nutrient turnover in southern
 grasslands. ## Description of the data and file structure For Data part:
 Contain Amplicon fastq sequences For Software part: Contain R scripts:
 CircleHeatmap, heatmap, Plot_of_accumulation_of_species_abundance,
 plot_pca_pls, Sankey For Supplemental information part: Contain
 Supplemental_Information: Figures and tables