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.