Computational protocol: Variation in Soil Microbial Community Structure Associated with Different Legume Species Is Greater than that Associated with Different Grass Species

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[…] In order to evaluate the SMC structure with higher resolution, Miseq-sequencing of the bacterial 16S rRNA gene V3–V4 region and fungal ITS genes ITS1 region were conducted. For this, only three replicate samples were selected from four replicate samples for each treatment, according to the DGGE profiling. For bacteria and fungi, the 515F/806R (Peiffer et al., ) and ITS1F/ITS2 (Mueller et al., ) primer sets were used, with each reverse primers fused with a unique 6-mer barcode for each samples. Firstly, PCR reactions were carried out in 30 μl reactions with 15 μl of Phusion® High-Fidelity PCR Master Mix (New England Biolabs); 0.2 μmol l−1 of forward and reverse primers, and about 10 ng template DNA. Thermal cycling consisted of initial denaturation at 98°C for 1 min, followed by 30 cycles of denaturation at 98°C for 10 s, annealing at 50°C for 30 s, and elongation at 72°C for 60 s, finally 72°C for 5 min. Then the PCR products were electrophoresed on 2% agarose gel for detection, and samples with bright main strip between 400~450 bp were chosen for further experiments. PCR products were mixed in equal density ratios. Then, the mixture PCR products were purified with GeneJET Gel Extraction Kit (Thermo Scientific). Sequencing libraries were generated using NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA) following manufacturer's recommendations and index codes were added. The library quality was assessed on the Qubit 2.0 Fluorometer (Invitrogen) and Agilent Bioanalyzer 2100 system. Finally, the library was sequenced on an Illumina MiSeq platform and 300 bp paired-end reads were generated. Raw sequence reads were de-multiplexed, quality-filtered, processed and analyzed using QIIME (Caporaso et al., ). Sequences with ≥ 97% similarity were assigned to the same OUT by UCLUST clustering (Edgar, ). One representative sequence for each OUT were picked and used the RDP Classifier and Greengenes database (for bacterial 16S rRNA genes) and UNITE database (for fungal ITS1 genes) to annotate taxonomic information. Samples were rarified to 43,860 and 12,721 tags for bacteria and fungi, respectively, prior to downstream analyses. Because of low production of reads for SG3, we excluded this sample in the downstream analysis to avoid an underestimate with low reads number when randomly subsampling sequences to ensure a same sequence depth. [...] Analysis of variance (ANOVA) was conducted using IBM SPSS statistics software version 21 (SPSS Inc., Chicago, IL) to test significant difference of soil chemical properties, SMC alpha diversity, and copies of bacterial 16S rRNA genes and fungal ITS genes (n = 4, except for sequencing data n = 3), and t-tests were also conducted for legume and grass (n = 12, expect for sequencing data n = 9). Principle component analysis (PCA) on the basis of soil chemical property was performed using IBM SPSS statistics software to reveal the influence of plants. Bacterial and fungal taxa at phylum level selected by plants were plotted in SigmaPlot 12.5. To further uncover biomarkers, linear discriminant analysis (LDA) effect size (LEfSe) was performed to find significantly abundant bacterial and fungal taxa within three groups (legume, grass and control). The factorial Kruskal-Wallis sum-rank test (α = 0.05) was used to identify taxa with significant differential abundances between categories (using the more strict all-against-all comparisons), and then LDA was performed to estimate the effect size of each differentially abundant taxon. Then significant taxa and others were used to generate taxonomic cladograms reflecting differences within groups (Segata et al., ). Cluster analysis of DGGE profiles and sequencing data at phylum level were analyzed and visualized using IBM SPSS statistics software and Past 3.11 to evaluate variations of soil samples with UPGMA algorithm. […]

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