DNA extraction and PCR amplification
Total DNA of the sand samples was extracted using the soil DNA extraction kit (Qiagen, Hilden, Germany) according to its manual. The total DNA concentration was detected by 1% agarose gel electrophoresis and a NanoDrop 2000, and 10 ng DNA templates were used for PCR amplification. The 16S rRNA genes (V3-V4) were amplified using the universal bacterial primers 338F-806R. PCR amplification system was composed of 5× FastPfu Buffer (4 µL), 2.5mM dNTPs (2 µL), 5 µM forward primer (0.8 µL), 5 µM reverse primer (0.8 µL), FastPfu polymerase (0.4 µL), BSA (0.2 µL), DNA template (10 ng), and by adding ddH20 to 20 µL. PCR reaction cycling was composed of an initial denaturation at 94°C for 5 min, followed by 30 cycles of denaturation at 95°C for 30 s, annealing at 95°C for 30 s, elongation at 72°C for 45 s, and final elongation at 72℃ for 10 min.
Illumina sequencing and bioinformatics analyses
DNA sequencing was performed on an Illumina Miseq PE300 platform in the Majorbio Bio-Pharm Biotechnology Company (Shanghai, China). Raw reads were quality filtered by the QIIME software package (Caporaso et al., 2010). The reads were clustered into the operational taxonomic units (OTUs) at 97% similarity level. The OTU sequences were annotated by the SILVA database. One-way ANOVA with Tukey’s test was performed by SPSS software (version 23.0) to test the environmental factors and α-diversity index differences of different samples. Redundancy analysis (RDA) in the vegan package in R3.6.1 was used to calculate the correlation among the environmental variables and the bacterial communities. Analysis of similarity (ANOSIM) was performed to examine the significance among the groups. The number of permutations was set at 999. Linear discriminant analysis effect size (LEfSe) analysis was employed to identify the significant difference of bacterial community between surface and subsurface samples (Segata et al., 2011). Principal coordinates analysis (PCoA) was used to analyze the bacterial community similarity of surface samples based on the Bray-Curtis distance. Variance partitioning analysis (VPA) was applied in the quantitative evaluation of the individual and common degree of interpretation between variant of environmental factors and bacterial communities using the vegan package in R3.6.1. The correlation between environmental factors and different phyla was shown as heatmaps using the spearman correlation coefficient from surface samples.