1. No category

Applied Soil Ecology 86 (2015) 165–173
Contents lists available at ScienceDirect
Applied Soil Ecology
journal homepage: www.elsevier.com/locate/apsoil
Effects of salinization and crude oil contamination on soil bacterial
community structure in the Yellow River Delta region, China
Yong-chao Gao a,b , Jia-ning Wang b , Shu-hai Guo a , Ya-Lin Hu a , Ting-ting Li a ,
Rong Mao c, De-Hui Zeng a, *
a
b
c
State Key Laboratory of Forest and Soil Ecology, Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110164, China
Provincial Key Laboratory of Applied Microbiology, Institute of Biology, Shandong Academy of Sciences, 19 Keyuan Road, Jinan 250014, China
Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130012, China
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 8 March 2014
Received in revised form 13 October 2014
Accepted 15 October 2014
Available online 6 November 2014
Soil salinization is a predominant environmental character in oil fields, especially in coastal regions.
However, information about the coupling effect of crude oil contamination and salinization on soil
biological characteristics is lacking. Therefore, the objective of this study was to examine soil bacterial
community changes in response to different gradients of salinity and total petroleum hydrocarbon (TPH)
concentration. Fifteen soil samples collected from the Yellow River Delta region of China with different
gradients of salinity and TPH concentration were used for analyzing soil physicochemical properties,
microbial biomass and denaturing gradient gel electrophoresis (DGGE) profiles. The results showed that
salinity negatively affected soil microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN),
but little affected bacterial Shannon and evenness indices. TPH concentration was correlated negatively
with soil MBC, positively with MBN and Shannon index, but had no effect on evenness index. Canonical
correspondence analysis showed that salinity and TPH concentration were the main factors causing the
shift of soil bacterial community structure. Soil salinity had a suppress effect on most bacterial
populations without changing their dominance, while soil TPH influenced the bacterial diversity
selectively. By extraction of main bacterial clusters from the dendrogram tree of DGGE profiles, the most
active bacterial species involved in the shift of bacterial community structure were identified under the
single or dual stresses of salinization and oil contamination. Actinobacteria, g-Proteobacteria, Firmicutes,
Deinococcus–Thermus and some unclassified bacteria were the dominant bacteria participating in crude
oil degradation in dual stresses of salinization and oil contamination. Our results provide new insight and
useful information in the screening of cultivable bacteria for bioremediation of crude oil contaminated
saline.
ã 2014 Elsevier B.V. All rights reserved.
Keywords:
Microbial biomass
Bacterial community
Denaturing gradient gel electrophoresis
(DGGE)
Bacterial diversity
Saline soil
1. Introduction
Environmental factors have great influence on soil bacterial
community. For crude oil contaminated soil, soil structure and
physicochemical and biological characteristics, e.g., soil organic
matter content, bulk density, porosity, permeability, soil respiration and material transfer processes, can be altered by the high
hydrophobicity of hydrocarbons (Liang et al., 2012). Bacterial
communities tend to be dominated by the strains that can survive
in hydrocarbon-rich environments and degrade the oil contaminants for growth (Zucchi et al., 2003). Saline and hypersaline
environments are frequently accompanied with crude oil
* Corresponding author. Tel.: +86 24 83970220; fax: +86 24 83970394.
E-mail address: [email protected] (D.-H. Zeng).
http://dx.doi.org/10.1016/j.apsoil.2014.10.011
0929-1393/ ã 2014 Elsevier B.V. All rights reserved.
contamination as a result of industrial activities (Oren et al.,
1992). Microbial community composition is also susceptible to soil
salinization due to differential tolerance of microbial genotypes
(Mandeel, 2006; Pankhurst et al., 2001). The transition zone of
different environments are ideal systems for exploring the
succession of microbial community as the abiotic factors strongly
impact the distribution patterns of species (Herlemann et al., 2011;
Campbell and Kirchman, 2013). The bacterial community structure
at the phylum and subphylum levels changes predictably with
gradients in salinity and other environmental factors (Campbell
and Kirchman, 2013). As for crude oil contaminated saline soil,
microbial degradation is the main mechanism for natural
decontamination, and a better understanding of the microbial
community structure in soil along a gradient of both oilcontamination and salinization and soil microbial responses to
their stresses could provide clues about the functional
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Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
microorganisms that adapt to such habitats. It is also helpful to find
out the microorganisms with different metabolic functions and to
monitor the process of bioremediation. However, most of the
previous studies focused more on the effect of crude oil
contamination or bioremediation on the microbial community
(Bordenave et al., 2007; Evans et al., 2004; Paissé et al., 2008;
Röling et al., 2004; Yu et al., 2011). Information about the coupling
effect of crude oil contamination and salinization on soil biological
characteristics is lacking. Kleinsteuber et al. (2006) investigated
the diversity and dynamics of bacterial community in an exploited
oil field soil with high salinity in Argentina. Their results showed
that the bacterial community shifted during long-term incubation
with diesel fuel at four salinities between 0 and 20% NaCl, and the
most active species changed with the alteration of salinity
accordingly. Wang et al. (2011a) studied the responses of archaeal
communities to different petroleum hydrocarbon concentrations
in saline–alkali soil in China; however, they did not consider soil
salinity as an independent factor due to the minor difference
among the sampled soils.
In this study, we evaluated soil bacterial community changes in
response to different gradients of salinity and total petroleum
hydrocarbon (TPH) concentration in the Yellow River Delta region
of China. The objectives of this study were: (1) to analyze the
effects of salinization and crude oil contamination on soil microbial
biomass carbon (MBC), microbial biomass nitrogen (MBN) and
bacterial diversity; (2) to clarify the main soil environmental
factors affecting the bacterial community of oil-contaminated
saline soil; (3) to analyze the succession of soil bacterial
community structure along a gradient of soil salinity and TPH
concentration and to find out the main bacterial populations
participating in crude oil degradation in the saline soil. We
attempted to clarify the bacterial roles in natural attenuation of oil
contaminated saline soil and throw a new light on the screening of
the cultivable bacteria.
2. Materials and methods
2.1. Site description
The sampling sites are located in Shengli Oilfield (118 070 –
119100 E, 36 550 –38 100 N) in the Yellow River Delta region which
was formed by the fluvial sedimentation of the Yellow River, the
second largest river in China. With the continuous massive
deposition at the mouth of the river of silt from erosion of the
Loess Plateau in central China, the delta is still under expansion at a
rate of 2000 ha per year (Liu and Drost, 1997). The annual average
temperature is 11.7–12.6 C, and annual precipitation 530–
630 mm. Due to the low and flat terrain, high groundwater table,
high evaporation/precipitation rates, poor drainage conditions,
and infiltration of seawater, soil salinization in this area has been
severe. The dominant soils along the seashore are commonly Salic
Fluvisols and Gleyic Solonchaks (Fang et al., 2005). According to a
previous study (Wang et al., 2011a), the proportions of alkane,
aromatic, polar N-, S-, O-containing compounds and asphaltene
fractionated from the TPH of the chronically contaminated saline
soil were about 35%, 18%, 25% and 22% in the Yellow River Delta
region.
2.2. Soil sampling
In May 2010, five chronically oil-contaminated sites were
selected according to their distances to the shoreline, with an
interval of about 20 km between neighboring sites. One oil well
was selected at each site. Considering the wellhead as the center,
we sampled soils in cross directions. In each direction, four
quadrats with a size of 1 m 1 m were installed at 5-m intervals.
Five soil cores (F = 2.5 cm) were collected from the 0–20 cm layer
of each quadrat with a shape of “W” and composited, and
altogether 76 samples were obtained. The samples were sealed in
plastic bags, stored in an ice box and sent to the laboratory within
6 h after sampling. Each field-moist soil sample was sieved (2 mm)
and divided into two subsamples stored at 4 C and 20 C,
respectively, until analysis.
2.3. Soil property and soil microbial biomass analyses
The soil samples used for the test of electrical conductivity (EC)
and pH were air-dried. Soil EC and pH were measured in a 1:5
sample/water mixture with a DDS-307W microprocessor conductivity meter (Shanghai Lida Instrument Factory, China) at 25 C
after shaking for 30 min. The conversion relationship between the
EC1:5 and the salinity of coastal saline soil was calculated using the
following formula (Liu et al., 2006):
EC1:5 ¼ 0:3658St 0:0152 r2 ¼ 0:988; P < 0:01
where EC1:5 is the EC of 1:5 soil–water extract in dS ml, and St is
the soil salinity in g kgl.
According to the US Soil Salinity Laboratory (Richards, 1954),
soil samples with EC above 4 mS cm1 (salinity 11 g kg1, according
to the EC1:5 and salinity conversion formula above mentioned)
were considered to be saline. The soil water-holding capacity was
measured according to Alef and Nannipieri (1995, p. 106). Crude oil
in the soil samples were extracted with n-hexane (EPA 3550B,
1996) and the organic extracts were analyzed in terms of TPH (EPA
8015B, 1996). The soil samples with TPH concentration above
1.6 g kg1 were considered to be contaminated with crude oil
(Wang et al., 2011b; Gao et al., 2013). Fifteen of the seventy-six soil
samples with a broad gradient of both soil salinity and TPH were
selected for further laboratory analysis (Sections 2.4 and 2.5) to
reveal relationships between environmental stresses and soil
microbial diversity and community structure. Soil total nitrogen
(TN) concentration was analyzed by the Kjeldahl method, and soil
total phosphorus (TP) was determined by the molybdenum–
stibium colorimetry method with a continuous-flow analyzer
(AutoAnalyzer III, Bran + Luebbe GmbH, Germany) after the
samples were digested with H2SO4. Soil MBC was determined
by the chloroform fumigation–extraction method (Vance et al.,
1987) and soil MBN was determined by the ninhydrin-reactive N
(N-nin) measurements described by Joergensen and Brookes
(1990).
2.4. PCR-DGGE analysis
Total genomic DNA was extracted from the soil samples using the
E.Z.N.A.TM Soil DNA Kit (Omega Biotek, USA) according to the
manufacturer’s instructions. The quantity of the extracted DNA was
determined using Scientific NanoDrop 2000 spectrophotometer
(Thermo, USA), then the DNA was diluted to about 15 ng mL1 for
further PCR amplification. The variable V3 region of 16S
rRNA was amplified by PCR using a pair of universal primers,
338F
50-ACTCCTACGGGAGGCAGCAG-30
and
534 R
50 -ATTACCGCGGCTGCTGG-30 ,
to
which
a
GC
clamp
(CGCCCGCCGCGCGCGGCGGGCGGGGCGGGGGCACGGGGGG) was
attached at the 50 -terminus (Muyzer et al., 1993). The PCR mixture
consisted of 5 mL of DNA template, 2 mL of 338 F/534R (10 mM)
primers each, 25 mL of Tiangen 2 Taq PCR Master Mix and 16 mL
ddH2O comprising a total volume of 50 mL (Tiangen Biotech, Beijing).
A modified touch-down PCR procedure was used for cycling
amplification in a VeritiTM PCR thermal cycler (Applied Biosystems,
Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
167
Table 1
Physicochemical properties of the 15 soil samples.
Sample code
Grouping
A
B
I
8.1
4.6
C
D
E
F
II
G
H
Salinity
(g kg1)
TN
(g kg1)
TP
(g kg1)
pH
Water content
(%)
1.58
1.39
0.31
0.36
0.31
0.36
8.55
8.43
9.03
14.11
5.5
8.0
7.1
6.5
10.24
7.12
19.88
23.16
0.56
0.32
0.41
1.03
0.56
0.52
0.48
0.51
8.66
8.66
8.32
8.11
22.42
21.37
8.54
9.22
III
14.0
10.8
0.76
1.30
0.29
0.45
0.29
0.45
8.39
8.03
16.17
15.64
I
J
K
IV
18.8
15.3
13.4
7.27
8.38
27.84
0.30
0.42
0.38
0.31
0.43
0.55
8.19
8.42
8.09
14.32
14.45
15.12
L
M
V
31.1
21.8
0.93
0.05
0.23
0.19
0.24
0.18
8.23
8.51
18.23
18.21
N
O
VI
22.2
21.1
11.47
13.21
0.27
0.23
0.26
0.23
8.43
8.25
18.82
17.18
TPH
(g kg1)
TPH: total petroleum hydrocarbon; TN: total nitrogen; TP: total phosphorus. Group I: slightly saline soil without oil contamination; Group II: slightly saline soil with oil
contamination; Group III: moderately saline soil without oil contamination; Group IV: moderately saline soil with oil contamination; Group V: strongly saline soil without oil
contamination; Group VI: strongly saline soil with oil contamination.
USA). Touch-down amplification was performed with an initial step
of 10 min at 94 C, followed by 10 cycles of denaturation for 1 min at
94 C, annealing for 1 min with temperatures decreasing from 60 C
at 0.5 C per cycle, and primer extension for 1.5 min at 72 C. This step
was similarly followed by 25 cycles of denaturation for 1 min at 94 C,
annealing for 1 min at 55 C, and primer extension for 1.5 min at
72 C, followed by a final extension at 72 C for 10 min. Denaturing
gradient gel electrophoresis (DGGE) analysis was performed with 8%
(w/v) polyacrylamide gels (ratio of acrylamide to bis-acrylamide
37.5:1) in 1 TAE buffer (40 mM Tris-acetate,1 mM Na-EDTA, pH 8.0)
with a gradient ranging from 40 to 60% (where 100% denaturant was
defined as 7 M urea and 40% formamide) at a constant voltage of 65 V
and 60 C for 16 h (Bio-Rad Dcode System, USA). Gels were silver
stained according to Ning et al. (2009). Finally, the DGGE gels were
scanned using Gel Doc 2000 gel image analysis system (Bio-Red USA)
and analyzed by Quantity One image analysis software (version 4.1;
Bio-Rad Laboratories, Hercules, CA, USA). Prominent DGGE bands
were excised with a sterile razor blade, resuspended in 50 mL
sterilized ddH2O, stored at 4 C overnight, reamplified, cloned in the
pGEM-T Easy vector (Promega, Madison, WI), and sequenced by
using an ABI Prism Big Dye terminator cycle sequencing reaction kit,
version 3.1 (PerkinElmer Applied Biosystems, Foster City, CA, USA),
and an ABI 3700 DNA sequencer (PerkinElmer Applied Biosystems,
Foster City, CA, USA) following the manufacturer’s instructions. The
sequences were edited and assembled using the BioEdit software,
inspected for the presence of ambiguous base assignments.
2.5. Biodiversity and phylogenetic analyses
We used Shannon index (H') and Pielou’s evenness index (E) to
assess the bacterial diversity of the oil-contaminated saline soil.
The H' value was calculated as follows:
H0 ¼ S
X
Pi lgPi
of distinct bands in a lane (Dunbar et al., 2000; Kirk et al., 2005).
The E value was calculated from each standardized band by using
number and height of peaks in each band profile as representatives
of the number and relative abundance of different phylotypes in
each line. The formula for E value was calculated as follows:
E¼
H0
Hmax
where Hmax = lgS (Thavamani et al., 2012).
16S rRNA gene sequences were blasted against the GenBank
database (www.ncbi.nlm.nih.gov/BLAST). All sequences with
similarities greater than 97% were included in a phylogenetic
analysis (Eckburg et al., 2005; Xing et al., 2010). All sequences used
for phylogenetic analysis have been deposited in the GenBank
nucleotide sequence database under accession numbers from
JQ277113 to JQ277158. The phylogenetic trees were constructed by
the neighbor-joining method with the Molecular Evolutionary
Genetics Analysis (MEGA) software (Tamura et al., 2007).
2.6. Statistical analyses
The relationships of the intensity values of the 43 bands
detected with the main soil environmental factors, the Shannon
index and the evenness index, and the relationships of soil salinity
and TPH with MBC, MBN, the Shannon index and the evenness
index, were all analyzed using Pearson’s correlation analysis. For
selecting proper multivariate analysis method, the initial
detrended correspondence analysis (DCA) was applied to examine
the data under analysis. The results demonstrated that the data
Table 2
Pearson’s correlation coefficients of soil salinity, TPH concentration with soil MBC
and MBN, Shannon and evenness indices.
i¼1
where, Pi is the relative peak area intensity of a DGGE band,
calculated from ni/N,ni is the peak area of the band, N is the sum of
all peak areas in the densitometry curve, and S is the total number
Salinity
TPH
MBC
MBN
Shannon index
Evenness index
0.55*
0.55*
0.55*
0.79**
0.26
0.58*
0.39
0.08
TPH: total petroleum hydrocarbon; MBC: microbial biomass carbon; MBN:
microbial biomass nitrogen. Significant differences between parameters were
indicated by * P < 0.05, ** P < 0.01.
168
Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
(499 permutations) with unrestricted permutation, was performed to investigate the statistical significance. Ordination
triplots including the environmental variables, soil samples and
DGGE bands were used to explain our data. A dendrogram analysis
of the DGGE fingerprints was constructed based on the Dice
similarity coefficient using unweighted pair group method
clustering with the Quantity One software.
3. Results
3.1. Physicochemical characteristics of the soil
Fig. 1. DGGE of 16S rRNA amplification fragments and corresponding bands isolated
for sequencing. PCR fragments were separated on a DGGE using a denaturant
gradient of 40–60%. The columns from A to O represent the 15 soil samples with
differences in salinity and crude oil contamination. The numbers from 1 to
43 inserted in the lanes represent bands successfully isolated and sequenced.
exhibited unimodal rather than linear responses to the environmental variables (Lepš and Šmilauer, 2003), so we performed
canonical correspondence analysis (CCA) by CANOCO 5.0 (Biometris, Wageningen, Netherlands) to explain our data. The analysis
was performed without transformation of data and focus scaling
on intersample distances. Manual selection of environmental
variables, applying a partial Monte Carlo permutation test
Soil samples A–F with salinity varying from 4.6 to 8.1 g kg1,
were considered not or slightly saline (Table 1), soil samples G–K
with salinity ranging from 10.8 to 18.8 g kg1 were classified to be
moderately saline, and soil samples L–O with salinity above
20.0 g kg1 were classified to be strong saline (Richards, 1954). The
TPH concentrations of soil samples A–B, G–H and L–M were below
1.6 g kg1, and considered to be not oil contaminated according to
previous studies (Wang et al., 2011b; Gao et al., 2013). The soil
samples were classified into six groups according to the salinity
and TPH content. Samples A and B were classified as group I,
representing the “slightly saline soil without oil contamination”;
samples C–F were classified as group II, representing the “slightly
saline soil with oil contamination”; samples G–H were classified as
group III, representing the “moderately saline soil without oil
contamination”; samples I–K were classified as group IV,
representing the “moderately saline soil with oil contamination”;
samples L–M were classified as group V, representing the “strong
saline soil without oil contamination”; and samples N–O were
classified as group VI, representing the “strong saline soil with oil
contamination”. Soil pH had little variation among the 15 soil
samples.
3.2. Relationships of soil environmental factors with soil MBC, MBN,
Shannon index, evenness index and the DGGE bands detected
Soil salinity had a significant negative relationship with either
MBC or MBN (Table 2). TPH had different effects on MBC and
MBN. Soil MBC was inhibited by TPH, while the MBN had a
significant positive relationship with TPH. Soil salinity had no
Fig. 2. A dendrogram representation of a hierarchical cluster analysis of the DGGE profiles. The capital letters A–O represent the 15 soil samples with differences in salinity
and crude oil contamination. The Roman numerals I–VI in Fig. 2 and Table 1 have the same meanings.
Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
effects on soil Shannon and evenness indices. Soil TPH
concentration positively correlated with Shannon index; however, it had no relationship with bacterial evenness index. The
relationships of main environmental factors with the DGGE bands
showed that soil salinity had significant negative effects on
unclassified bacteria (band 5), g-Proteobacteria (band 9) and
a-Proteobacteria (band 22), while positively affected on Firmicutes (band 8), g-Proteobacteria (bands 19, 20) and unclassified
bacteria (band 26) (Table 3). Soil TPH had positive relationships
with g-Proteobacteria (bands 6, 14, 43), Actinobacteria (bands 7,
17), unclassified bacteria (bands 15, 31) and Firmicutes (band 41),
but negatively correlated with g-Proteobacteria (band 10). Soil TN
had positive relationships with a-Proteobacteria (bands 11, 22)
and Firmicutes (band 36). Soil TP had positive relationships with
Actinobacteria (band 17) and unclassified bacteria (band 24),
while negatively related with Firmicutes (bands 8, 35) and
unclassified bacteria (band 26). Soil pH had positive relationships
with a-Proteobacteria (band 9) and Actinobacteria (band 13), but
negative related with unclassified bacteria (band 31) and
Actinobacteria (band 33). Soil water content had a significant
negative effect on g-Proteobacteria (band 43).
169
3.3. PCR-DGGE and cluster analysis
Reamplification products and clones obtained were screened by
DGGE analysis of the respective 16S rRNA fragments (Fig. 1). The
profile showed that most of the main bands had a similar pattern
but differed in intensity among the 15 soil samples. The
dendrogram analysis of the DGGE profiles showed that all soil
samples except sample D were consistent with the group
classification according to salinity and TPH concentration (Fig. 2,
Table 1). The DGGE profiles of the soil samples with similar
environmental stresses were clustered together.
3.4. Phylogenetic analysis
Comparing the variation of the bacterial community of the soil
samples in the DGGE profiles, the sequences of these bands fell into
corresponding operational taxonomic units (OTUs) based on a
threshold of 97% similarity (Kocherginskaya et al., 2001). The
phylogenetic distributions of the OTUs were divided into the
following groups (Fig. 3): Proteobacteria (g-Proteobacteria,
a-Proteobacteria), Actinobacteria, Deinococci, and Firmicutes
Table 3
Pearson’s correlation coefficients of the 43 DGGE bands detected with the main environmental factors, Shannon and evenness indices.
Band number
Salinity
TPH
TN
TP
pH
Water content
Shannon index
Evenness index
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
0.19
0.36
0.34
0.19
0.53*
0.01
0.20
0.74**
0.55*
0.03
0.47
0.22
0.03
0.02
0.36
0.45
0.27
0.28
0.69**
0.57*
0.50
0.54*
0.31
0.44
0.16
0.86**
0.00
0.15
0.02
0.07
0.04
0.28
0.02
0.01
0.42
0.47
0.24
0.39
0.20
0.10
0.10
0.22
0.09
0.06
0.09
0.01
0.29
0.26
0.54*
0.82**
0.13
0.32
0.54*
0.24
0.16
0.09
0.88**
0.58*
0.16
0.68**
0.14
-0.38
0.19
0.19
0.13
0.06
0.18
0.17
0.22
0.19
0.00
0.01
0.05
0.78**
0.17
0.19
0.49
0.17
0.17
0.39
0.16
0.28
0.18
0.63*
0.31
0.58*
0.08
0.18
0.19
0.05
0.49
0.26
0.47
0.29
0.02
0.28
0.72**
0.49
0.04
0.27
0.17
0.02
0.04
0.41
0.33
0.41
0.20
0.61*
0.40
0.07
0.39
0.49
0.04
0.07
0.19
0.27
0.36
0.22
0.02
0.03
0.39
0.54*
0.18
0.27
0.04
0.26
0.04
0.19
0.20
0.08
0.39
0.28
0.09
0.34
0.01
0.48
0.56*
0.18
0.47
0.27
0.21
0.19
0.27
0.05
0.11
0.62*
0.09
0.51
0.44
0.06
0.31
0.05
0.56*
0.04
0.74**
0.15
0.02
0.03
0.15
0.27
0.38
0.01
0.38
-0.53*
0.36
0.00
-0.42
-0.25
0.01
0.46
-0.29
0.33
0.16
0.06
0.11
0.33
0.34
0.17
0.05
0.24
0.61*
0.25
0.15
0.28
0.53*
0.47
0.23
0.28
0.10
0.09
0.01
0.14
0.00
0.29
0.03
0.46
0.45
0.08
0.14
0.02
-0.48
0.02
0.58*
0.09
0.53*
-0.26
0.12
0.16
0.05
0.05
0.31
0.09
0.26
0.13
0.35
0.11
0.06
0.06
0.02
0.11
0.34
0.17
0.18
0.00
0.19
0.18
0.08
0.42
0.22
0.11
0.05
0.02
0.49
0.27
0.29
0.27
0.47
0.26
0.26
0.46
0.36
0.43
0.26
0.15
0.19
0.18
0.28
0.01
-0.06
0.33
0.34
0.03
0.21
0.13
0.38
0.28
0.09
0.55*
0.13
0.01
0.44
0.07
0.28
0.58*
0.59*
0.55*
0.33
0.46
0.06
0.02
0.17
0.56*
0.60*
0.03
0.33
0.07
0.10
0.37
0.41
0.36
0.08
0.17
0.49
0.31
0.35
0.28
0.12
0.21
0.41
0.01
0.02
0.12
0.22
0.52*
0.62*
0.57*
0.48
0.28
0.25
0.51
0.23
0.52*
0.46
0.12
0.11
0.25
0.29
0.01
0.18
0.39
0.36
0.05
0.62*
0.39
0.19
0.15
0.47
0.16
0.13
0.61*
0.53*
0.02
0.10
0.09
0.26
0.23
0.55*
0.18
0.06
0.19
0.00
0.16
0.28
0.15
0.25
0.62*
0.23
0.08
0.13
0.15
0.11
0.25
0.07
0.05
TPH: total petroleum hydrocarbon; TN: total nitrogen; TP: total phosphorus. Significant differences between parameters were indicated by * P < 0.05, ** P < 0.01.
170
Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
Fig. 3. Phylogenetic tree of sequences obtained from 16S rRNA amplification fragments separated by DGGE. DGGE bands are designated as shown in Fig. 4 and the accession
number for each sequence is enclosed in brackets. The reference sequences used are shown with their species names and GenBank accession numbers. The scale bar
corresponds to 0.02 substitution per nucleotide position.
Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
(Negativicutes and Bacilli). Some OTUs could not be classified and
were designated as “unclassified”. Proteobacteria, Actinobacteria
and Firmicutes were the three dominant groups in the
oil-contaminated saline soil. Comparing the DGGE profile,
dendrogram and the phylogenetic tree (Figs. 1, 2, 3) and the
grouping according to soil physicochemical properties (Table 1),
a-Proteobacteria (bands 9, 11, 22, 30-2), g-Proteobacteria (bands 4,
23), Firmicutes (bands 30-1, 36), Actinobacteria (bands 13, 16),
Deinococcus–Thermus (band 27) and unclassified bacteria (bands
2, 5, 10, 12, 18) were the dominant bacteria in the soil with slight
salinization but no oil contamination (group I). Actinobacteria
(bands 7, 17), g-Proteobacteria (band 6), Deinococci (band 27),
Firmicutes (band 40) and unclassified bacteria (bands 24, 25, 31)
were enhanced in the slightly oil-contaminated saline soil (group
II). Actinobacteria (bands 16, 33), a-Proteobacteria (band 22),
g-Proteobacteria (bands 10, 30-2), Firmicutes (band 36) and
unclassified bacteria (bands 12, 29) were the dominant bacteria in
moderately saline soil without oil contamination (group III).
Actinobacteria (bands 1-2, 7, 16, 21, 34), Firmicutes (bands 8, 41),
Deinococci (band 37), g-Proteobacteria (band 43) and unclassified
bacteria (bands 15, 25, 31) were enhanced in the moderately
oil-contaminated saline soil (group IV). Firmicutes (bands 8, 30-1,
36), a-Proteobacteria (band 22), g-Proteobacteria (bands 4, 10, 19,
20), Actinobacteria (bands 13, 21) and unclassified bacteria (bands
2, 5, 12, 25, 26, 31) were the dominant bacteria in strongly saline
soil without oil contamination (group V). Actinobacteria (bands 7,
21), Firmicutes (bands 8, 40), g-Proteobacteria (bands 4, 6, 14),
Deinococci (band 37) and unclassified bacteria (bands 15, 25, 26,
31, 42) were the main bacteria in the soil with the dual stresses of
salinization and oil contamination (group VI).
3.5. CCA analysis of the bacterial community
The CCA analysis was used to correlate environmental variables
with the bacterial community structure. The key environmental
variables determining the bacterial community structures were
deduced (Fig. 4). The first two axes of CCA plots respectively
accounted for 20.7% and 13.0% of the total variance of the DGGE
data. Both axes presented high species-environment correlation
171
values (0.96 and 0.94 for axis 1 and axis 2, respectively) (P < 0.01).
TPH and salinity were the strongest determinants of the first axis.
These two environmental factors had negative relations with axis
1. TN and pH had positive relations with axis 1, but did not play the
leading role. TP showed a strong positive relationship with axis 2.
Soil salinity and water content had negative relationships with
both axes. The acute angle between the lines of water content and
axis 2 indicated that water content had a more negative effect on
axis 2. Comparing the bacterial community succession of the
6 groups, the species in the ellipse region of oil degrading bacteria
(Fig. 4), such as Actinobacteria (bands 7, 17, 21, 34), g-Proteobacteria (bands 6, 14, 43), Deinococci (band 37), Firmicutes (bands 40,
41) and unclassified bacteria (bands 15, 25, 42), were the bacteria
with the ability of oil degradation. The species in the ellipse region
of halophilic bacteria (Fig. 4), such as Actinobacteria (bands 7, 21),
g-Proteobacteria (bands 3, 6, 19, 20, 38), Firmicutes (bands 8, 35,
39, 40), Deinococcus–Thermus (band 37) and unclassified bacteria
(bands 15, 25, 26, 31, 42), were the halophilic bacteria. The species
in the overlapping region (Fig. 4), such as Actinobacteria (bands 7,
21), g-Proteobacteria (band 6), Firmicutes (band 40), DeinococcusThermus (band 37) and unclassified bacteria (bands 15, 25, 31, 42),
were the bacteria with high oil degradation ability in saline soil.
4. Discussion
4.1. Effects of salinity and TPH on soil MBC and MBN
Previous studies showed that MBC and MBN were positively
correlated with the TPH concentration in oil contaminated soil
(Joergensen et al., 1995; Margesin et al., 2000). In our study, TPH
concentration was negatively correlated with the MBC, but
positively correlated with the MBN, possibly due to the opposing
effects of salinity and TPH concentration on soil microorganisms
(Table 2). Besides, contamination age might be another important
factor leading to the above outcomes. Most of the laboratory
experiments were conducted using soils artificially contaminated
by fresh crude oil. In reality, the bioavailable carbon from oil could
stimulate microbial activities of the soil (Viñas et al., 2005),
consequently leading to the depletion of bioavailable components
of crude oil after long-term attenuation (Mulligan and Yong, 2004),
and soil microbial biomass would descend to the level of precontaminated state correspondingly. Salinity as a stressful
environmental factor for soil microorganisms has been reported
by Yuan et al. (2007), who found that EC (ranging from 0.32 to
23.05 mS cm1) had a significant negative exponential relationship
with MBC and MBN. During the bioremediation of oil-contaminated saline soil, high salinity would reduce the metabolic activity
of many microorganisms and inhibit microbial oil degradation
(Rhykerd et al., 1995).
4.2. The relationship of main environmental factors with bacterial
diversity
Fig. 4. CCA triplot based on binary data of bacterial diversity, soil samples and
environmental factors of the soil samples. The total inertia of the matrix was
4.24 and the selected variables explained 53.7% of the variance of the DGGE data set
(sum of canonical eigenvalues: 2.28). The arrows indicate the direction of maximum
correlation, and the length of the arrow reflects the strength of the correlation. TPH:
total petroleum hydrocarbon; TN: total nitrogen; TP: total phosphorus; Water: soil
water content.
The H' and E are two general diversity indices that are closely
correlated with species richness and relative abundance. No
obvious correlation existed between the H' value and soil salinity in
our study (Table 2). Yu et al. (2012) found a similar result when
investigating the shifts of microbial community function and
structure along a successional gradient of coastal wetlands in the
Yellow River Estuary, indicating that microbial diversity was not
dramatically decreased with a reduction of microbial biomass. In
our study, the H' value of bacterial diversity had a positive
relationship with TPH (Table 2). This is consistent with the result of
Nie et al. (2009), who investigated the rhizosphere effects of
petroleum contamination and salinization on soil bacterial
abundance and diversity in the Yellow River Delta region. Their
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Y.- Gao et al. / Applied Soil Ecology 86 (2015) 165–173
result showed that TPH had positive effects on soil bacterial
diversity of both rhizosphere and bulk soils. Xenobiotic contaminants, such as TPH, diesel and gasoline, are substances that can be
used directly or indirectly as carbon sources. The introduction of
these contaminants could induce the proliferation of the
corresponding bacteria with degradation ability (Bordenave
et al., 2007; Evans et al., 2004; Kleinsteuber et al., 2006; Viñas
et al., 2005).
Microbial communities with less variation in species abundance are considered to be more homogeneous than communities
with more variation in species abundance. In our study, the E value
of bacterial diversity had no correlation with salinity and TPH. The
soil microbes we studied might have undergone long-term
competition and have been balanced under the two environmental
stresses. It had been reported that the dynamics of the microbial
community were a continuous process during the initial period of
oil contamination (Ding et al., 2012; Kaplan and Kitts, 2004). The
effect of a disturbance on microbial community function depends
on its duration and specificity. After a transient disturbance, the
system function might eventually return to its former state,
whereas a permanent disturbance would result in a new, altered
state (Müller et al., 2002).
4.3. Succession of bacterial community and the main bacterial
populations participating in crude oil degradation in saline soil
The main populations of soil bacterial community would
change when the soil suffered crude oil contamination. A previous
report showed that the dominant phyla were Proteobacteria,
Bacteroidetes, Firmicutes, Actinobacteria, Acidobacteria, Chloroflexi, and Verrucomicrobia in coastal wetlands of the Yellow River
Delta without oil contamination (Yu et al., 2012). When the soil
was contaminated by heavy crude oil, the dominant bacteria were
g-Proteobacteria and Bacteroidetes (Yu et al., 2011). In Spain,
Alonso-Gutierrez et al. (2009) found that a-Proteobacteria and
Actinobacteria were the prevailing groups of bacteria in two
different types of long-term fuel contaminated soil. Our study
showed that g-Proteobacteria, Actinobacteria, Firmicutes, Deinococcus–Thermus and some unclassified bacteria were always the
dominant populations in the oil-contaminated saline soil at
different salinity; all the bacteria identified belong to the halophilic
bacteria (Zhuang et al., 2010). Furthermore, the main bacterial
phylotypes were also shifted with different bioremediation phases
of the PAH contaminated soil (Viñas et al., 2005). A microcosm
experiment on the succession of bacterial community with the
removal of crude oil contaminants using saline soil sampled from
the Yellow River Delta, China showed that g-Proteobacteria was
the dominant bacteria responsible for the biodegradation of TPH at
the initial stage. Subsequently, the bacteria belonging to a-Proteobacteria became the dominant oil-degraders to degrade the
remaining recalcitrant constituents of the heavy crude oil (Yu et al.,
2011). An Acinetobacteria strain was reported to be capable of
utilizing n-alkanes of chain length C10—C40 as a sole source of
carbon (Throne-Holst et al., 2007). In addition, the main bacteria at
different soil salinity were also different. At low salt concentrations, Acidovorax delafieldii and Pseudomonas sp. (Proteobacteria) were the main bacteria that could degrade the benzene,
toluene, ethylbenzene, and xylene (BTEX). Halobacillus salinus and
Bacillus simplex (Firmicutes) were the bacteria suitable for
bioremediation in hypersaline conditions (Nicholson and Fathepure, 2005). The ascertaining of the key bacteria responsible for oil
degradation in different environmental conditions is of great
importance to the bioremediation techniques, such as bioaugmentation and biostimulation. In view of the bioaugmentation, it is
more targeted in screening of the cultivable bacteria with high
degradation ability and environmental adaptability (Zhuang et al.,
2010). To the uncultivable bacteria, the addition of inorganic or
organic nutrients, such as nitrogen, phosphorous, potassium,
calcium or manure, has positive effects on soil bacterial
communities (Evans et al., 2004; Xu and Obbard, 2003; Yu
et al., 2011), though the precise control of biostimulation on the
functional populations is still difficult. Understanding the succession of bacterial communities under different environmental
gradients provides more references on the weighting factors
influencing bacterial community succession in the crude oil
contaminated saline soil.
In conclusion, soil salinity and TPH concentration were the two
main environmental factors affecting soil bacterial diversity and
community structure. Soil salinity had a suppress effect on most
populations without changing their dominance, while soil TPH
influenced the community diversity selectively even though some
species were strengthened with the aggravation of salinization.
Actinobacteria, g-Proteobacteria, Firmicutes, Deinococcus–Thermus and some unclassified bacteria were the dominant bacteria in
dual stresses of salinization and oil contamination. These findings
provide new insight and useful information in the screening of
cultivable bacteria for bioremediation of crude oil contaminated
saline soil.
Acknowledgments
We thank Richard P. Dick and two anonymous referees for their
valuable suggestions and Gui-Yan Ai and Jing-Shi Li for their help in
laboratory analyses. This work was funded by the National High
Technology Research and Development Program (“863” Program)
of China (2013AA06A210), the National Environmental Protection
Special Fund for Public Welfare Industry of China (No. 201109022),
the Natural Science Foundation of Shandong Province (No.
ZR2011DQ002) and Shandong Province Science and Technology
Development Fund (No.2014GSF117019).
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