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: hlyuan@cau.edu.cn

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

S

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July 2016⎪Vol. 26⎪No. 7

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

1322 Deng et al.

J. Microbiol. Biotechnol.

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

1324 Deng et al.

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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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.

1326 Deng et al.

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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