2. Materials and Methods
2.1 Study site
The study was carried on three sampling locations located in Southeast
Georgia, including 1) Acacia (A)
Farm (Latitude 32°34.784N, Longitude 82°32.313W), 2) Honeydew (H) Farm
(32°32.354N, 81°50.053W), and 3) Strickland (S) Farm” (32°19.231N,
81°41.554W). At each of the location, three sites with different
vegetation types and disturbance intensity levels. The first site, site
one (1) is representative of a conventional tomato (Lycopersicon
esculentum ) crop field, defined as highly disturbed agricultural crop
production (C) soil. The second site is a transitional region, where a
secondary successional event is occurring (dominated byAndropogon spp.) and defined as moderate disturbed transitional
environment (T). The third site is representative of a recently
undisturbed forest habitat, containing both deciduous hardwood
(Quercus spp.). and coniferous pine forests (Pinus spp.)
and defined as less-disturbed native forests (F). At a location,
sampling sites were no more than 100 meters apart. Physical and chemical
soil properties including soil organic (%),
NO3-, P, Mg, Ca, soil pH, and cation
exchange-capacity, were determined by Waters Agricultural Laboratories,
Inc. Camilla, GA, and soil ammonium was determined at Georgia Southern
University by extracting soils with 2M KCl and followed with the
salicylate method (Nelson 1983).
2.2 Sample collection and DNA extraction
Soil samples were collected from there locations (A, H, and S) under
three sites with different vegetation types and disturbance intensity
levels (C, T, and F). Three replicates of the samples were collected
from each of the three vegetation types of three different locations,
thus a total of 27 samples were obtained. At each site, one
approximately 6 x 50-meter plot was randomly selected, and a grid with
30-centimeter intervals was established on each plot. We collected soil
samples from total of 30 generated coordinates and used a stratified
random sampling regime. Total of thirty soil cores (3.4 cm diameter; 10
cm deep) from the rhizosphere of each site were collected with a core
sampler and hand mixed in a single sterile plastic bag. We mixed ten
soils cores into a single sample, thus each site yielding 3 samples. A
grand total of 27 samples were collected with three sites at each of the
tree locations. The DNA was extracted using PowerSoil DNA Isolation Kit
from each sample (Mo Bio Laboratories, Inc., Carlsbad, CA).
2.3 Total bacterial DNA quantification
A NanoDrop spectrophotometer, ND-1000 (NanoDrop Technologies,
Wilmington, DE) was used to quantify total DNA of each sample. Soil
bacterial DNA was quantified by Real-time quantitative-PCR (Q-PCR) as
the indicator of relative soil bacterial abundance (Fierer et al. 2005).
The Q-PCR was performed in a the QuantStudio™ 6 Flex Real-Time PCR
System (Life Technologies, Carlsbad, CA, USA) using the conditions as
described previously (Wu et al. 2015). In brief, 16S rRNA gene was
amplified with the primers 27F (5’-AGAGTTTGATCMTGGCTCAG-3’) and 355R
(5’-GCTGCCTCCCGTAGGAGT-3’) for bacterial quantification. We extracted
DNA from the pure culture of Micrococcus lutus (Item # 155160
from Carolina Biological Supply Company, Burlington, NC, USA) as the
standard DNA. The standards with serial diluted DNA concentration
extracted from pure culture was quantified using NanoDrop
spectrophotometer, ND-1000 (NanoDrop Technologies, Wilmington, DE).
2.4. Illumina sequencing
We used the NanoDrop ND-1000 UV-Vis Spectrophotometer (NanoDrop
Technologies, Wilmington, DE, USA) to quantify the above extracted DNA.
Primers 27F (5’-
AGAGTTTGATCMTGGCTCAG-3’) and 355R (5’- GCTGCCTCCCGTAGGAGT-3’) were used
to amplify the V1–V2 hyper variable region of the 16S rRNA bacterial
gene. DNA sequencing was conducted at University of Georgia Genomics and
Bioinformatics Cores (Athens, Georgia, USA), using the MiSeq platform
(Illumina, Inc., USA). QIIME software was selected for the purpose of
integrating the original FASTQ format sequencing data (Caporaso et al.
2010). The USEARCH tool (version 7.0; http://drive5.com/usearch/) was
used to vet and remove chimeric sequences. The operational taxonomic
unit (OTU) partition threshold was identified at a 97% sequence
similarity of classification results, which was subsequently used for
the calculation of bacterial community diversity and relative abundance.
To obtain species classification data corresponding to each OTU, the 16S
Metagenomics from Illumina Sequence Hub (Illumina, Inc., San Diego, CA
USA) was applied to analyze DNA from amplicon sequencing of prokaryotic
16S small subunit rRNA genes.
2.5 Quantitative PCR (qPCR) and Denaturing Gradient Gel Electrophoresis
(DGGE) for N functional genes
Quantitative PCR amplification of selected nitrification and
denitrification genes (AOB amo A and nir K genes) with
corresponding primers (Wu et al. 2020) was performed using the
QuantStudio™ 6 Flex Real-Time PCR System (Life Technologies, Carlsbad,
CA, USA). The fluorescent dye SYBR-Green I which binds to
double-stranded DNA was applied to quantify the relative abundance of
nitrification AOB amo A and denitrification nir K genes.
Each of the PCR mixtures contained 12.5 µL of 2× GoTaq® Colorless Master
Mix (Promega, USA), 0.5 µL of 10 µM forward and reverse primer, 1 µL
BSA, 2 µL SYBR® of 1×, 6 µL of nuclease-free water, and 2 µL of DNA
template, which make the recommended 25 µL protocol. To control for
mechanical and technical errors, the Q-PCR of each sample was run in
triplicate, and the mean of all three DNA quantities was used for
statistical analysis.
The GC clamp was added to the corresponding primer position for DGGE
analysis. Amplified gene products (AOB amo A and nir K) were
run on 8% (w/v) acrylamide with a linear chemical gradient ranging from
40%–70% using a DGGEK-1001 Cipher DGGE Kit (C.B.S. Scientific, Del
Mar, CA, USA) and obtained DGGE band patterns were exported for further
nonparametric multivariate analyses of soil microbial and N functional
gene communities as described previously (Wu et al. 2020).
2.6 Link soil microbial community structure with environmental factors
We used PRIMER-E including PERMANOVA+ statistical software (PRIMER-E,
Plymouth, UK) to generate soil bacterial and N functional gene
similarity matrices of each sample from three different locations and
three different vegetation types with different disturbance intensity
levels. Bio-Env function and Principal Coordinates Analysis (PCO) with
Spearman’s correlations of variables with the PCO axes of PRIMER-E were
used to correlate the environmental factors with the similarity matrix
(Wu et al. 2015, Wu et al. 2020).
2.7 Statistical analysis of operational taxonomic unit (OTUs) to test
diversity and similarity
Univariate analysis of operational taxonomic unit (OTUs) by different
fragment lengths or 97% sequence similarity was used to characterize
diversity indices. Richness (S) was expressed as the total number of
different OTUs identified. Diversity was calculated using the
Shannon–Weiner (Weaver) index using the equation of Diversity (H): H’ =
- ∑ (pi) (loge pi),
where pi is the proportion of an individual OTU relative to the sum of
OTUs detected in a sample. Evenness was calculated by Pielou’s evenness
index using the equation of J′=H′/Log (S). Univariate analyses of
richness, richness and Shannon diversity indices were performed with
JMP® Pro 12.1.0 (SAS Institute Inc., USA). Cluster analysis was used to
compare soil bacterial communities and N functional genes from there
locations (A, H, and S) under three sites with different vegetation
types and disturbance intensity levels (C, T, and F). The analysis of
similarities (ANOSIM) procedure was applied to statistically
discriminate the soil bacterial communities and N functional genes. The
bacterial 16S rDNA OTUs obtained from Illumina sequencing from there
locations under three sites with different vegetation types and
disturbance intensity levels were determined using the similarity
percentages (SIMPER) procedure. PRIMER-E including PERMANOVA+
statistical software (Primer-E, Plymouth Marine Laboratory, UK) was
applied for all nonparametric multivariate analysis procedures,
including calculation of Bray–Curtis similarity matrices, cluster
analysis, SIMPER analysis, and ANOSIM.