Research Article jmb Review - Semantic Scholar · 2017. 10. 19. · methane, ethane, propane, and...
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J. Microbiol. Biotechnol.
J. Microbiol. Biotechnol. (2016), 26(7), 1320–1332http://dx.doi.org/10.4014/jmb.1602.02045 Research Article jmbReview
Universal Indicators for Oil and Gas Prospecting Based on BacterialCommunities Shaped by Light-Hydrocarbon Microseepage in ChinaChunping Deng1, Xuejian Yu1, Jinshui Yang1, Baozhen Li1, Weilin Sun2, and Hongli Yuan1*
1State Key Laboratory of Agrobiotechnology, College of Biological Sciences, China Agricultural University, Beijing 100193, P.R. China2National Research Center for Geoanalysis, Beijing 100037, P.R. China
Introduction
Hydrocarbon seepage is prevalent in the carbon cycle on
Earth [13]. A large body of studies have addressed the
microbial community structures and diversity of functional
genes at hydrocarbon macroseeps (active seeps with large
concentrations of migrated hydrocarbons) [56], such as
oil-spill areas and oil-contaminated soil, seawater, and
sediment, using high-throughput sequencing of 16S rRNA
genes, clone libraries of the pmoA, alkB, and nah genes [53,
57, 58], and GeoChip [25, 40, 48]. These reports indicate
that high concentrations of hydrocarbons have significant
effects on the abundance and diversity of microbial and
functional populations in these environments.
Driven by the pressure of subterranean oil and gas
reservoirs, low-molecular-weight hydrocarbons, such as
methane, ethane, propane, and butane, can vertically
penetrate faults and fractures in the reservoirs and migrate
upward to the near-surface soils [8], and be utilized
by indigenous hydrocarbon-oxidizing microorganisms as
potential carbon and energy sources [37]. Thus, the
anomalous enrichment of these bacteria caused by long-
term and continuous light-hydrocarbon microseeps (passive
seeps with low concentrations of migrated hydrocarbons)
Received: February 22, 2016
Revised: April 1, 2016
Accepted: April 21, 2016
First published online
April 27, 2016
*Corresponding author
Phone: +86-10 62733349;
Fax: +86-10 62733349;
E-mail: [email protected]
upplementary data for this
paper are available on-line only at
http://jmb.or.kr.
pISSN 1017-7825, eISSN 1738-8872
Copyright© 2016 by
The Korean Society for Microbiology
and Biotechnology
Light hydrocarbons accumulated in subsurface soil by long-term microseepage could favor
the anomalous growth of indigenous hydrocarbon-oxidizing microorganisms, which could be
crucial indicators of underlying petroleum reservoirs. Here, Illumina MiSeq sequencing of the
16S rRNA gene was conducted to determine the bacterial community structures in soil
samples collected from three typical oil and gas fields at different locations in China.
Incubation with n-butane at the laboratory scale was performed to confirm the presence of
“universal microbes” in light-hydrocarbon microseepage ecosystems. The results indicated
significantly higher bacterial diversity in next-to-well samples compared with background
samples at two of the three sites, which were notably different to oil-contaminated
environments. Variation partitioning analysis showed that the bacterial community structures
above the oil and gas fields at the scale of the present study were shaped mainly by
environmental parameters, and geographic location was able to explain only 7.05% of the
variation independently. The linear discriminant analysis effect size method revealed that the
oil and gas fields significantly favored the growth of Mycobacterium, Flavobacterium, and
Pseudomonas, as well as other related bacteria. The relative abundance of Mycobacterium and
Pseudomonas increased notably after n-butane cultivation, which highlighted their potential as
biomarkers of underlying oil deposits. This work contributes to a broader perspective on the
bacterial community structures shaped by long-term light-hydrocarbon microseepage and
proposes relatively universal indicators, providing an additional resource for the improvement
of microbial prospecting of oil and gas.
Keywords: Light-hydrocarbon microseepage, bacterial community structure, hydrocarbon-
oxidizing bacteria, linear discriminant analysis effect size
S
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could be used as an indicator for petroleum prospecting
[38, 49]. Studies of the ecological characteristics of bacteria
in these ecosystems could be of great importance for the
improvement of petroleum prospecting technology. Until
now, most studies on the microbial community structures
in these environments have been based on culture-
dependent approaches, usually with short-chain alkanes
(C1–C6) as the sole carbon sources [37, 38], with the results
showing higher numbers of light-hydrocarbon oxidizers in
hydrocarbon prospective areas than non-prospective areas.
However, the vast majority of bacteria are viable but
uncultivable in the laboratory. The development of culture-
independent molecular biotechnology, especially next-
generation high-throughput sequencing, which is known
to be superior for detecting rarer bacterial populations at
unprecedented depths [5, 51], has greatly facilitated our
knowledge of microbial community structures. Nevertheless,
only a few studies of microbial communities in hydrocarbon
microseepage ecosystems have been conducted with
culture-independent methods, such as the clone library
approach [32, 60] and denaturing gradient gel electrophoresis
(DGGE) analysis [55]. To date, knowledge of the patterns of
microbial communities in these ecosystems is still lacking.
The overall ecological characteristics of bacteria are
influenced by geographic location and various environmental
factors, such as soil type, vegetation, pH, and nutrients [7,
24]. Thus, oil and gas fields with different geographic
locations and significant heterogeneity could comprise
distinct microbial community structures, and only co-
occurring taxa enriched in different oil and gas fields could
be useful as “universal indicators” for microbial prospecting
of oil and gas reservoirs. However, existing studies have
been limited to only one individual oil or gas field, such as
the Ban 876 gas and oil field [60], Beihanzhuang oil field in
China [55], and a sedimentary basin in Brazil [32]. Thus, it
is difficult to find shared taxa enriched in different fields
based on the previous studies. Research on the microbial
compositions in subsurface soil among different oil and gas
fields is much needed to determine universal microbes.
The general method for determining microbial community
structures is sampling, high-throughput sequencing, and
data analysis [3, 31]. Laboratory simulations are important
for the verification of microorganisms with special functions,
such as hydrocarbon degradation [16, 41]; however, samples
obtained from light-hydrocarbon microseepage ecosystems
have been rarely addressed. Despite the fact that methane
is the most abundant gas hydrocarbon in petroleum
reservoirs, the presence of C2–C4 n-alkane-oxidizing bacteria
seems to be more indicative of microseepage from subsurface
deposits [46]. Among them, butane originates only from
gas and oil fields. Thus, the existence of anomalously high
densities of butane-oxidizing bacteria in soil could be an
indicator of subterranean petroleum or gas deposits [63].
Only a few studies have been conducted on the anaerobic
degradation of butane by marine sulfate-reducing bacteria
at marine seeps [20-22]. The community structure of butane-
utilizing bacteria above terrestrial oil and gas fields is quite
poorly understood.
To address the above issues, we conducted a study to
determine the response of microbial communities to long-
term light-hydrocarbon microseepage at two typical oil
fields and one gas field in China with relatively distinct
climates, using high-throughput sequencing and laboratory-
simulated incubation with n-butane. The aims of this study
were to (i) determine the bacterial community structures in
the oil and gas fields; (ii) determine the impact of geographic
location and environmental factors on the bacterial
community structure; and (iii) propose universal microbial
taxa that are enriched by light hydrocarbons at different oil
and gas fields compared with the background. Our results
provide comprehensive information about the microbial
community structures at long-term light-hydrocarbon
microseeps, and suggest a highly potential basis for the
microbial prospecting of oil and gas.
Materials and Methods
Sample Collection
Soil samples were collected from two typical oil fields and one
gas field at different geographic locations in China with the help
of the National Research Center for Geoanalysis, Chinese Academy
of Geological Sciences: Jianghan (JH) oil field in central China,
Shengli (SL) oil field in north China [26], and Puguang (PG) gas
field in southwest China (Fig. 1). The three regions have relatively
different climates. JH has a subtropical humid monsoon climate,
SL has a warm temperate continental semihumid monsoon climate
[25], and PG has a subtropical monsoon climate. Soil samples
named OJH, OSL, and OPG were taken adjacent to crude oil or
gas pumping wells from the JH, SL and PG sites, respectively.
Corresponding background samples named NJH, NSL, and NPG
were acquired from the same sites but far away from the known
oil and gas fields. Details of the samples are given in Table S1. All
the soil samples were collected in five replicates (200 g each) from
a depth of about 50 cm to avoid anthropogenic disturbance, mixed
thoroughly, and packaged in sterile bags and stored at 4°C and
-20°C for incubation experiments and DNA extraction, respectively.
Gas samples from the subsurface soil (about 80 cm below the
ground) were collected using a 615C penetration type sampler
equipped with a hand vacuum pump (Eijkelkamp, the Netherlands)
at each sampling site. About 12 ml of gas was pumped into each
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downward serum bottle (15 ml) prefilled with saturated saltwater.
An air sample above each site was also collected. The bottles were
sealed with a butyl rubber stopper and kept bottom-up to prevent
gas leakage.
Geochemical Analysis
All liquid and solid chemicals were of analytical reagent grade
and purchased from Sinopharm Chemical Reagent Co., Ltd
(China). Mixed standard gas (4.55% methane, 0.25% ethane, 0.054%
propane, 0.0537% n-butane, 0.151% iso-butane, and 94.941%
nitrogen) was purchased from Beiwen Gas Manufacturer (China)
with ≥99.99% purity. The pH values of the samples were
determined using a 1:2.5 (w/v) soil:deionized water suspension
and twin pH B-212 (Horiba, Japan). The concentrations of total
nitrogen (TN), available nitrogen (NO3
--N and NH4
+-N), total
organic carbon (TOC), and total phosphorus (TP) were determined
according to a Chinese handbook [12], and the concentrations of
water-soluble salts (K+, Ca2+, and Mg2+) were determined at the
Institute of Plant Nutrition and Resources, Beijing Academy of
Agriculture and Forestry Sciences.
The compositions of short-chain alkanes in the gas samples (1 ml)
were measured using a gas chromatography–flame ionization
detection (Finnigan TraceGC Ultra; Germany) according to the
method described previously [63], with slight modifications.
Specifically, the gas chromatograph was run at a column temperature
of 35°C for 4 min and then the temperature was increased to 160°C
(20°C/min) and held for 3 min. The inlet and detector temperatures
were 200°C and 300°C, respectively. Room air was used as a blank
control. Total solvent extractable matter (TSEM) was prepared
from 100 g of soil according to the Ultrasonic-Soxhlet extraction
gravimetric method [19].
Butane Incubation Microcosms
The three OJH samples and two NJH samples collected from
Jianghan oil field were chosen for subsequent incubation. For one
set of samples, n-butane was used as the sole carbon source and
another set of samples were used as controls (room air). For each
set, 10 g of fresh, homogenized, and 2-mm-sieved soil was added
to a 50 ml serum bottle. The bottles were tightly sealed with butyl
rubber stoppers and aluminum crimp caps, and then injected with
6 ml of n-butane or 6 ml of air with a gas-tight syringe (SGE,
Australia). Each set was cultured in the dark at room temperature.
The concentration of n-butane in the headspace of each microcosm
was measured every week (7, 14, 21, and 26 days) by gas
chromatography–flame ionization detection, as previously described,
to make sure there was sufficient n-butane. Destructive sampling
was also performed at each time point and soils were stored at
-20°C until DNA extraction.
DNA Extraction
Metagenomic DNA was extracted from the soil samples using
an E.Z.N.A. Soil DNA Kit (Omega, USA), according to the
manufacturer’s instructions. Two or three replicates of each soil
sample were individually prepared for subsequent analysis.
16S rRNA Amplification and DGGE Analysis of Incubated
Samples
The variable V3 region of the bacterial 16S rRNA gene was
amplified using primers 2 and 3 [33]. Each PCR mixture contained
2.5 µl of 10× Taq buffer, 1.5 µl of 25 mM MgCl2, 0.5 µl of 2.5 mM
dNTP mixture (TaKaRa Co., Shiga, Japan), 0.5 µl of 10 µM each
primer, 0.25 µl of 5 U/µl Taq DNA polymerase (Fermentas,
Waltham, MA, USA), and approximately 100 ng of genomic DNA
Fig. 1. Map of sampling sites in two oil fields and one gas field located in different areas across China.
JH = Jianghan oil field in Hubei province, central China; SL = Shengli oil field in Shandong province, eastern China; PG = Puguang gas field in
Sichuan province, southwest China. Sample names with the letter O are samples obtained adjacent to oil or gas pumping wells and those with the
letter N are background samples. Details of the sampling sites are given in Table S1.
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as the template. Deionized water was added to a total volume of
25 µl. The PCR program included an initial denaturation step at
95°C for 5 min, 19 touchdown cycles of 95°C for 1 min, annealing
temperature decreased every cycle by 0.5°C (from 65°C to 55.5°C)
for 1 min, and extension at 72°C for 3 min, followed by 5 cycles
consisting of 95°C for 1 min, 65°C for 1 min, and 72°C for 3 min,
and a final extension step of 10 min at 72°C. The PCR products
were concentrated and purified using Wizard SV Gel and the PCR
Clean-Up System (Promega, USA) following the manufacturer’s
instructions.
An approximately 250 ng aliquot of each PCR product was
separated on 6% (w/v) denatured polyacrylamide gel using a
Dcode System apparatus (Bio-Rad, USA) with a 45–70% denaturing
gradient (100% denaturant corresponded to 7 M urea and 40%
deionized formamide). DGGE was performed in 0.5× Tris-acetate-
EDTA (TAE) buffer at a constant voltage of 50 V and a
temperature of 60°C for 15 h. The DNA bands were stained with
SYBR green I (Amresco, USA) for 30 min and photographed
through a UV gel documentation system (Bio-Rad, USA).
Illumina MiSeq Sequencing of Bacterial 16S rRNA Genes and
Data Analysis
The diversity and composition of the bacterial communities in
each of the samples were determined using a protocol described
previously [4]. PCR amplifications were conducted using the
515f/806r primer set specific to the V4 region of the 16S rRNA
gene [28]. Sequencing was conducted on an Illumina MiSeq
platform by Novogene (China).
Sequencing reads were assigned to each sample according to
the 6 bp unique barcode of each sample, and the barcodes and
primers were trimmed after that. Pairs of reads from the original
DNA fragments were merged using FLASH [10]. Raw tags were
filtered by Quantitative Insights Into Microbial Ecology (QIIME)
quality filters with default settings [2, 4]. Chimeric sequences
were removed according to previous publications [11, 17]. The
sequencing data were analyzed with the QIIME software package
[52] and UPARSE pipeline [10], in addition to custom Perl scripts
to analyze alpha diversity and beta diversity. Sequences with
≥97% similarity were assigned to the same operational taxonomic
units (OTUs). The OTU table was rarified to eliminate the effect of
sequencing depth on the indices. The small percentage of archaeal
sequences was removed [61].
Principal component analysis (PCA) of the geochemical data
and the relative contributions of soil geographic location and
geochemical parameters to the variations in the bacterial
communities in samples (variation partitioning analysis, VPA)
were conducted using the vegan package ver. 2.2-1 in the R
computing environment [23, 34]. The significance test was carried
out by Monte Carlo permutation (999 times). The Pearson’s
correlation coefficients of the diversity of the bacterial communities
with soil geochemical parameters and the Duncan tests for
statistical significance were calculated using SPSS 18.0 software. A
non-metric multidimensional scaling analysis (NMDS) of bacterial
composition based on the Bray-Curtis distance matrix was
performed with PAST ver. 3.0 [47]. The characterization of bacterial
features differentiating the samples obtained from oil and gas
fields and background areas was carried out using the linear
discriminant analysis (LDA) effect size (LEfSe) method, a useful
tool that emphasizes both biological relevance and statistical
significance [45]. The Kruskal-Wallis rank sum test was used with a
significance alpha value of 0.05 to detect features with significantly
different abundance among classes, and the threshold on the
logarithmic LDA score for discriminative features was 2.5. A more
strict strategy for multiclass analysis was set in this study. The
relative abundance of each taxon was standardized by subtracting
the mean relative abundance in objective samples and dividing
the difference by its standard deviation. After that, the hierarchical
cluster analysis of these samples was performed using the R
package of pheatmap ver. 1.0.8 [36] based on the normalized
matrix.
Nucleotide Sequence Accession Number
The sequences obtained by Illumina MiSeq sequencing were
deposited in the National Center for Biotechnology Information
Sequence Read Archive (SRA) under the accession number
SRP063715.
Results
Geochemical Analysis of Soil Samples
The geochemical parameters of the soil samples are shown
in Table S1. The concentration of TSEM among samples
ranged from 20 to 541.6 µg/g. Methane was present at all
the sampling sites in Jianghan (JH) with a concentration of
259.55–424.20 ppm in OJH (oil field) and 291.80–372.91 ppm
in NJH (background) samples (Table S2). Notably, n-butane
and iso-butane were only detected in the OJH2, OJH3, and
OJH5 samples obtained next to the oil and gas wells. A
relatively low concentration of methane (1.65–25.2 ppb)
was detected in the Puguang (PG) and Shengli (SL) samples.
Thus, the subsequent analysis did not include information
on light hydrocarbons.
PCA of the geochemical parameters in all samples
revealed that samples were distinctly separated based on
geographic location, especially for samples collected from
PG and JH (Fig. 2). Moreover, the key environmental
factors were distinct among the different oil and gas fields.
Specifically, JH samples (OJH and NJH) contained relatively
high-level nutrient factors (TOC, TN, TP, and C/N) and
moisture, and PG samples (OPG and NPG) were mainly
positively affected by altitude and NO3
--N. The TSEM,
Mg2+ and Ca2+ contents, pH value, and location were
positively correlated with the OSL samples, whereas weak
correlations were found between NSL samples and the
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J. Microbiol. Biotechnol.
environmental variables.
Bacterial Diversity and Correlation with Geochemical
Parameters
For all the initial samples, a total of 2,198,466 clean reads
were obtained after rigorous quality control through
Illumina MiSeq sequencing, and were affiliated with 4,554
OTUs on average at 97% sequence identity.
The α-diversity indices are shown in Table 1. Samples
OJH and OPG revealed significantly higher richness and
diversity than samples NJH and NPG, respectively. The
phylogenetic diversity index exhibited the same trend. In
order to compare the effect of environmental factors on the
bacterial diversity between the next-to-well samples and
background samples, Pearson’s correlation analysis was
performed. The result showed that the diversity of the
background samples was significantly influenced by
almost all the environmental factors detected in our study
(Table 2). Unlike the background samples, the diversity of
next-to-well samples was significantly influenced by the
major nutrient factors (TOC, TN, NH4
+-N, and TP), whereas
the geographic location (latitude, longitude, and altitude)
and pH values showed a nonsignificant effect. Notably, the
bacterial diversity of the next-to-well and background
samples did not show a significant correlation with TSEM
concentration.
Comparison of Bacterial Assemblages and the Influence
of Environmental Factors
The NMDS analysis of the bacterial compositions among
the samples showed that samples OJH2, OJH3, and OJH5
were separated from NJH6 and NJH7 by NMDS2 (Fig. 3).
The bacterial compositions of pristine samples NPG10,
NPG11, and NPG13 were remarkably different from
samples OPG5 and OPG7. Samples OSL5 and OSL7 were
considerably dissimilar to samples NSL1, NSL2, and NSL4.
These results indicated that the bacterial communities of
samples from the two oil fields and one gas field were
notably different from those in the corresponding
background samples. Surprisingly, the detected bacterial
compositions in samples OPG5 and OPG7 were relatively
similar to the OSL samples, implying that samples collected
Fig. 2. Principal component analysis of environmental data in
soil samples.
Only significant environmental factors are presented (p < 0.05). Lon =
longitude, Alt = altitude, Lat = latitude, TSEM = total solvent
extractable matter, TN = total nitrogen, TP = total phosphorus, TOC =
total organic carbon, C/N = ratio of TOC to TN. Numbers in brackets
indicate the percentage of the total variance explained by each axis.
Table 1. Alpha-diversity indices of samples in the present study.
Sample Chao1 richness OTU numbers Shannon index Phylogenetic diversity
OJH 10,661.35 (986.99)d 4,972.33 (475.00)c 9.23 (0.83)bc 380.54 (30.62)d
NJH 8,915.04 (1,197.43)b 4,255.25 (641.97)ab 8.52 (1.39)ab 327.31 (39.37)b
OPG 8,940.66 (1,246.87)b 4,481.67 (625.03)b 9.32 (1.07)bc 332.24 (33.56)b
NPG 7,600.76 (472.73)a 3,845.33 (173.31)a 8.29 (0.24)a 299.00 (11.27)a
OSL 9,460.00 (376.87)bc 4,655.50 (219.62)bc 9.98 (0.34)cd 341.34 (12.58)bc
NSL 10,160.72 (418.42)cd 5,105.25 (149.12)c 10.41 (0.07)d 364.98 (6.96)cd
Standard deviations are in parentheses.
Sample names with the letter O are samples obtained adjacent to oil or gas pumping wells, and those with the letter N are background samples.
OJH represents the mean diversity of OJH2, OJH3, and OJH5.
NJH represents the mean diversity of NJH6 and NJH7.
OSL represents the mean diversity of NSL1, NSL2, and NSL4.
NSL represents the mean diversity of OSL5, OSL6, and OSL7.
OPG represents the mean diversity of OPG5, OPG7, and OPG8.
NPG represents the mean diversity of NPG10, NPG11, and NPG13.
Values with different lowercase letters in the same column are significantly (p < 0.05) different from each other, according to Duncan’s test.
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July 2016⎪Vol. 26⎪No. 7
from distant regions with light-hydrocarbon microseepage
could have similar bacterial compositions, which suggests
the possibility of the presence of universal indicators for oil
and gas prospecting.
To quantify the contributions of geographic location and
soil environmental variables to the bacterial community
variation in the OS samples, VPA was carried out. The
result revealed that a total of 82.93% of the variation was
explained by the detected environmental parameters.
Geographic location and geochemical parameters were
able to independently explain 7.05% and 36.23% of the total
variation, respectively. Interactions between the two
components showed more influence (39.65%) than the
individual components did, implying a strong correlation
between them that should not be ignored. Only 17.07% of
the total variation could not be explained.
Bacterial Composition and Discriminating Taxa
The relative abundance of the top 14 phyla in all the
samples is shown in Fig. 4A. Samples obtained from
different areas had similar dominant phyla, but varied in
their relative abundance. Proteobacteria was the most
abundant phylum among all the samples, ranging from
22.81% to 41.83%. Notably, Firmicutes was the second
dominant phylum among JH and PG samples, ranging
from 6.71% to 28.57% and represented mostly by bacilli.
Acidobacteria, Actinobacteria, Chloroflexi, Bacteroidetes,
and Gemmatimonadetes were prevalent among these
samples as well. Other phyla, such as Planctomycetes,
Nitrospirae, Verrucomicrobia, WS3, Cyanobacteria, Chlorobi,
and Armatimonadetes, were found to account for only small
proportions in the samples. The bacterial communities at
the phylum level varied substantially between the next-to-
well and background samples at each site, and were even
more distinct among the three sites (Fig. 4B).
The dominant taxa in all samples were analyzed using
LEfSe to identify specific phylotypes as biomarkers between
next-to-well and background samples. The cladogram
showed that Bacteroidetes was the discriminating phylum,
with a significantly higher abundance in next-to-well
samples compared with background samples (Fig. 5 and
Table S3; p < 0.05), whereas the phyla Acidobacteria and
Verrucomicrobia were much less in proportion. Moreover,
the class Acidimicrobiia within the phylum Actinobacteria,
classes Cytophagia, Flavobacteriia, and Sphingobacteriia
within the phylum Bacteroidetes, and class Anaerolineae
within the phylum Chloroflexi were significantly enriched
in next-to-well samples. At the order level, next-to-well
samples had a remarkably higher abundance of Cytophagales,
Table 2. Statistical analysis of the relationship between richness
and diversity of bacterial communities and soil physicochemical
parameters based on Pearson’s correlation coefficients.
Sample ID OS NS
Community index Richnessa Shannon Richness Shannon
Latitude -0.320 -0.143 0.840** 0.865**
Longitude -0.140 -0.313 0.897** 0.845**
Altitude -0.030 0.368 -0.683** -0.571**
K+ -0.434* -0.372 0.646** 0.562**
Ca2+ -0.323 -0.130 0.626** 0.720**
Mg2+ -0.271 -0.088 -0.482* -0.401
Moisture 0.530** 0.008 -0.644** -0.733**
pH -0.152 -0.219 0.780** 0.699**
TOC 0.418* -0.030 0.733** 0.725**
TN -0.050 -0.594** 0.468* 0.443*
C/N 0.465* 0.195 0.417 0.466*
NH4
+-N 0.504* -0.034 -0.058 -0.062
NO3
--N -0.201 0.095 0.103 0.254
TP 0.172 -0.443* -0.209 -0.342
TSEM -0.315 -0.222 -0.039 0.068
aObserved operational taxonomic unit numbers.
OS represents samples collected adjacent to crude oil or gas pumping wells,
including OJH, OSL, and OPG; NS refers to samples obtained from the
corresponding background area (NJH, NSL, and NPG).
*p < 0.05; **p < 0.01; number of permutations: 999.
TOC = total organic carbon, TN = total nitrogen, C/N = ratio of TOC to TN, TP =
total phosphorus, TSEM = total solvent extractable matter.
Fig. 3. Analysis of bacterial community compositions using
non-metric multidimensional scaling (NMDS) analysis based
on the Bray-Curtis distance matrix.
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J. Microbiol. Biotechnol.
Flavobacteriales, Desulfuromonadales, Alteromonadales,
Oceanospirillales, Pseudomonadales, and Thiotrichales,
whereas background samples favored the growth of orders
Acidobacteriales, Solibacterales, Actinomycetales, Bacteroidales,
Fig. 4. Relative abundance (A) and hierarchical clustering (B) of dominant phyla based on the averages of 2-3 replicate soil
samples at each site.
B, black indicates a higher relative abundance and white indicates a lower relative abundance.
Fig. 5. Cladogram representing the discriminating taxa between samples obtained adjacent to the oil and gas pumping wells (OS)
and background samples (NS) using the linear discriminant analysis (LDA) effect size method based on the dominant 1% of genera
in all samples.
Red and green represent taxa with a significantly higher relative abundance in OS and NS, respectively (yellow = nonsignificant). The size of each
circle is proportional to the taxon’s relative abundance. Only taxa with an LDA score >2.5 are shown.
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and Thermogemmatisporales. The abundant families
Cytophagaceae, Mycobacteriaceae, Pirellulaceae, Geobacteraceae,
Campylobacteraceae, Alteromonadaceae, Ectothiorhodospiraceae,
and Piscirickettsiaceae occupied a notably larger proportion
in next-to-well samples. It is worth mentioning that LEfSe
highlighted several genera that were significantly more
abundant in next-to-well samples compared with background
samples, such as Mycobacterium, Flavobacterium, Geobacter,
Pseudomonas, Arenimonas, and Lysobacter, suggesting their
potential application as distinguishing biomarkers of
underlying oil and gas deposits. In contrast, the relative
abundance of the genera Nocardioides and Enterococcus was
significantly less in next-to-well than in background samples.
Response of Key Taxa under Laboratory Simulation with
n-Butane
To confirm the response of taxa enriched in next-to-well
samples to light hydrocarbons, laboratory-simulated
incubation with n-butane was carried out. Samples collected
from JH were selected as representatives based on the
relatively high concentration of light hydrocarbons detected
in these sites. To determine the appropriate incubation
time, DGGE analysis was conducted on samples OJH5 and
NJH7 as representatives to uncover the bacterial community
structure dynamics during the cultivation. The DGGE
fingerprints showed remarkably different microbial
community structures between treatments with or without
n-butane after incubation for 14 days and an even longer
time (e.g. 21 and 26 days; see Fig. S1). Considering that
long-term incubation might raise the possibility of cross-
feeding to a large extent [16], samples treated for 14 and 21
days with n-butane or an equal volume of air were chosen
for Illunima MiSeq sequencing to investigate the potential
bacteria able to utilize n-butane.
The hierarchical clustering map at the genus level
showed that the microbial community structure of samples
obtained from the oil field were distinct from those from
pristine soil, even after the n-butane stimulation (Fig. 6). n-
Butane greatly favored the growth of Azoarcus (the relative
abundance increased from 0.42% to 1.16%), Hydrogenophaga
(from 0.08% to 0.24%), Mycobacterium (from 0.51% to 0.74%),
Pseudomonas (from 0.58% to 1.02%), Rhodococcus (from
0.04% to 0.15%), and Rubrivivax (from 0.17% to 0.32%) in
OJH samples in 14 or 21 days, indicating an immediate
response of these genera to butane stimulation. For the
NJH samples, the relative abundance of Methylibium,
Hydrogenophaga, Nocardioides, Sphingomonas, Pseudonocardia,
Polaromonas, Nevskia, and Rubrivivax was notably increased
after incubation with n-butane compared with the
treatment with air. However, the relative abundance of
Nocardia, Lactococcus, Bacillus, Steroidobacter, Acinetobacter,
and Carnobacterium increased considerably even without n-
butane.
Discussion
Bacterial Community Structures in Light-Hydrocarbon
Microseepage Ecosystems and Their Correlation with
Environmental Factors
A large body of studies have addressed the influence of soil
pH on the diversity and richness of bacterial communities
[14, 62]; however, no significant correlation was found
Fig. 6. Hierarchical cluster analysis of dominant genera based
on the average of relative abundance of replicate incubations.
OB14 and OB21 represent OJH samples and NOB14 and NOB21
represent NJH samples, both incubated with n-butane for 14 and 21
days, respectively. ON14 and ON21 represent OJH samples and
NON14 and NON21 represent NJH samples, both incubated with air
for 14 and 21 days, respectively. Red indicates a higher relative
abundance and green indicates a lower relative abundance.
1328 Deng et al.
J. Microbiol. Biotechnol.
between pH values and the diversity of next-to-well samples
in this study. The diversity of next-to-well samples was
found to be significantly correlated with the major nutrient
factors (TOC, TN, and TP), whereas the diversity of
background samples was affected by almost all the detected
environmental variables. The notable distinction between
the next-to-well and background samples was probably
because of the continuous migration of light hydrocarbons
from subsurface petroleum reservoirs in the next-to-well
samples. Light hydrocarbons can be utilized as a carbon
source by hydrocarbon-oxidizing bacteria [37], which might
result in the change of TOC. As nitrogen and phosphorus
are the key nutrient elements for microbes [9, 44], the
utilization of extra light hydrocarbons in next-to-well
samples might be mainly affected by these nutrient factors.
Although the diversity of OSL was lower than NSL, an
unexpected outcome in our work was the significantly
higher diversity in next-to-well samples compared with
background samples. Oil contamination has been reported
to decrease the diversity of microbes and functional genes
in previous studies [1, 25, 35, 57]. This is probably because
relatively high concentrations of oil are highly toxic,
mutagenic, and/or carcinogenic to microorganisms [42].
In contrast, microbial communities at long-term light-
hydrocarbon microseeps might have acclimatized to trace
and continuous light hydrocarbons through horizontal gene
transfer [27]. In addition, there was no significant correlation
between the bacterial diversity and the concentration of
TSEM. Although this could be attributed to the much lower
extent of oil contamination in our samples compared with
previous reports [25, 57], this further illustrated the
relatively different eco-environments of light-hydrocarbon
microseepage ecosystems in the present study and oil-
contaminated ecosystems. Wu et al. [55] suggested that light-
hydrocarbon microseepage could change the composition
of bacterial communities, but showed no significant
influence on bacteria in the Beihanzhuang oil field using
the DGGE method. However, Man et al. [29] found
relatively higher α-diversity at oil and gas fields compared
with the control area by PCR-DGGE, which showed a
similar trend with our study. Our results showed notably
different trends in bacterial diversity stimulated by relatively
long-term light-hydrocarbon microseepage compared with
previous reports in short-term oil contamination, suggesting
that bacterial diversity could be a new indicator for
microbial prospecting of oil and gas. Considering that
scant information about the bacterial diversity at light-
hydrocarbon microseeps compared with corresponding
background samples is available, further work is urgently
required.
Understanding the factors that influence microbial
community structures could be of great help to unravel the
patterns of microbial distribution, which is crucial in
microbial ecology. In the present study, PCA suggested
that samples collected from different oil and gas fields
comprised remarkably different geochemical properties.
Surprisingly, NMDS analysis indicated that samples
obtained from distant fields could have similar bacterial
community structures, such as samples OPG and OSL.
Moreover, there was no significant correlation between the
diversity of the next-to-well samples and the geographic
location. Furthermore, VPA showed that only 7.05% of the
bacterial community in next-to-well samples was explained
by geographic location independently, whereas it explained
33.5% in five oil-contaminated fields in China [25]. These
results imply the possibility of the presence of “core
microbes” stimulated by long-term light-hydrocarbon
microseepage, even at distant geographic locations with
great heterogeneities, which could be used as potential
“universal indicators” for subsurface oil and gas reservoirs.
The bacterial community structures between samples OSL
and NSL were not significantly different compared with
other samples, which might be because of the anthropogenic
disturbances at this area with developed industry. Previous
study indicated that anthropogenic disturbance has great
effect on the biosphere and global biogeochemical processes,
which substantially altered the community structures and
their ecological functions [6]. However, further research is
still needed.
Proposed “Universal Indicators” in Oil and Gas Fields
The few reports on microbial communities in light-
hydrocarbon microseepage ecosystems have illustrated
relatively different results at different phylogenetic levels.
Based on the 16S rRNA gene clone libraries, Chloroflexi
and Gemmatimonadetes were found to be dominant in the
Ban 876 gas and oil field in China [60] and Actinobacteria,
Proteobacteria, and Acidobacteria were the most dominant
phyla, whereas Gemmatimonadetes, Bacteroidetes, Chloroflexi,
Cyanobacteria, and Firmicutes were the least numerous
phyla in petroliferous soil from a sedimentary basin in
Brazil [32]. Nocardioides, Aciditerrimonas, sulfate-reducing
bacteria, and Chloroflexi were proposed to be novel
indicators for microbial prospecting of oil and gas in the
Beihanzhuang oil field using the DGGE method [55].
Methylocystaceae might act as a potential indicator for an
unexploited gas resource, and Methylophaga and Alcanivorax for
oil using PCR-DGGE [29]. Considering that this previous
Bacterial Communities at Light-Hydrocarbon Microseeps 1329
July 2016⎪Vol. 26⎪No. 7
work was limited by methods with relatively low resolution,
and only one oil or gas field was considered in each study,
there is still insufficient information to determine universal
indicators in different oil and gas fields for oil and gas
prospecting.
In this study, LEfSe analysis indicated that the phylum
Bacteroidetes was significantly enriched in next-to-well
samples compared with background samples, which is
relatively consistent with previous reports, suggesting the
possibility of Bacteroidetes as an indicator. However,
hierarchical clustering of dominant phyla indicated that
samples were grouped based on geographic location
(Fig. 4B). As a high phylogenetic level, one phylum comprises
many taxa, which could be affected by many environmental
variables [7, 18]; this might decrease their reliability as a
universal phylum among different fields.
At a relatively low phylogenetic level, the genera
Mycobacterium and Pseudomonas were found to be
significantly enriched in next-to-well samples through the
LEfSe method. Interestingly, both genera notably bloomed
in OJH samples after n-butane incubation. Mycobacterium
and Pseudomonas have been reported to be capable of
degrading short-chain hydrocarbons [46], as well as
other alkanes [50], and have also been detected in other
light-hydrocarbon microseepage ecosystems, such as a
sedimentary basin in Brazil [32]. In addition, primers were
designed based on several strains of Mycobacterium and
Pseudomonas for the detection of propane and butane-
oxidizing microorganisms (e.g., Cano et al., 2013. USA Patent).
Our results, in combination with previous reports, further
underpin their possibility as “universal indicators” for
subsurface oil and gas reservoirs, despite the dramatically
different environmental characteristics among these study
sites. Still, more extensive samples should be studied to make
these findings more confirmative. Moreover, Lysobacter,
Geobacter, and Arenimonas were found to be more abundant
in the oil field samples, implying that they may take part in
hydrocarbon metabolism directly or indirectly and their
potential application as biomarkers. Further work based on
DNA-SIP (stable-isotope probing) or RNA-SIP is necessary
to further confirm their hydrocarbon-oxidizing ability and
explore more hydrocarbon-degrading bacteria.
Nocardia and Rhodococcus are also capable of utilizing
light hydrocarbons [39]. In this study, although Rhodococcus
was not significantly enriched in next-to-well samples, the
relative abundance indeed increased notably after n-butane
incubation for 21 days, indicating that Rhodococcus could be
proposed as an assistant indicator. However, the relative
abundance of Nocardia increased considerably even without
n-butane, as well as Lactococcus, Bacillus, Steroidobacter,
Acinetobacter, and Carnobacterium, indicating that these
genera have abilities other than butane degradation.
Nocardioides has been reported to be able to degrade short-
chain hydrocarbons [46] and has been proposed as a new
indicator [55]. However, our results showed a much lower
abundance of Nocardioides in next-to-well samples than in
background samples, which might be due to their metabolic
versatility [59]. Therefore, they should not be adopted as
reliable indicators for petroleum deposits.
Contemporary environmental disturbances and historical
contingencies are considered to be the two main factors
shaping microbial communities [30]. Previous reports
have suggested that the relative influence of historical
contingencies and environmental factors on bacterial
communities is scale dependent [15, 43, 54]. At the present
scale (about 1,000 km), VPA showed that the bacterial
communities in next-to-well samples were mainly explained
by environmental factors other than geographic location
independently, which is relatively consistent with Wu et al.
[54], implying the potential application of our findings for
microbial prospecting of oil and gas deposits at a regional
spatial scale (about 1,000 km). Still, more extensive samples
at a much larger scale should be studied.
One of the final goals of investigating microbial populations
and distribution patterns in long-term light-hydrocarbon
microseepage environments is to reduce the drilling risks
in petroleum exploration. For the first time, our results have
illustrated remarkably higher diversity at light-hydrocarbon
microseeps compared with the background area at two of
the three sites, based on high-throughput sequencing.
Owing to the notable enrichment of Mycobacterium and
Pseudomonas in next-to-well samples and their substantial
increase in abundance after laboratory simulation with n-
butane, these genera are proposed as potential universal
indicators for the microbial prospecting of oil and gas
reservoirs. Further work on the ecological characteristics of
microbial communities in light-hydrocarbon microseeps
among different areas at much broader scales will be
necessary. These results, integrated with geological,
geophysical, and geochemical evidence, could be invaluable
in achieving higher success in forecasting the existence of
oil and gas deposits.
Acknowledgments
This work was financially supported by the National
Natural Science Foundation of China (No. 31270533). The
authors declare that they have no conflict of interest. This
1330 Deng et al.
J. Microbiol. Biotechnol.
article does not contain any studies with human participants
or animals performed by any of the authors.
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