Microbial community structures of methane hydrate-bearing sediments in the Ulleung Basin, East Sea of Korea

Marine and Petroleum Geology 47 (2013) 136e146

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Microbial community structures of methane hydrate-bearing sediments in the Ulleung Basin, East Sea of Korea Jin-Woo Lee a, d, Kae Kyoung Kwon a, b, Aqil Azizi a, b, Hyun-Myung Oh a, Wonduck Kim a, Jang-Jun Bahk c, Dong-Hun Lee d, Jung-Hyun Lee a, b, * a

Marine Biotechnology Research Division, Korea Institute of Ocean Science & Technology, P.O. Box 29, Ansan 425-600, South Korea University of Science and Technology, 217 Gajunro, Yuseong-gu, Daejeon 305-333, South Korea Petroleum and Marine Resources Research Division, Korea Institute of Geoscience and Mineral Resources, 124 Gwahang-no, Yuseong-gu, Daejeon 305-350, South Korea d Department of Microbiology, Chungbuk National University, Chungbuk 361-763, South Korea b c

a r t i c l e i n f o

a b s t r a c t

Article history: Received 22 January 2013 Received in revised form 1 June 2013 Accepted 3 June 2013 Available online 13 June 2013

Microbial communities of a methane hydrate-bearing deep sediment cores, collect from Site UBGH2-10 from the Ulleung-Basin (East Sea of Korea), were characterized by phylogenetic analysis on the basis of 16S rRNA gene cloning. Quantification of key functional genes related to sulfate reduction (dsrA) and methanogenesis (mcrA) and that of bacteria and archaea were conducted using molecular approaches. On the basis of geochemical data, four subsections were selected to compare the microbial diversity relative to the presence of methane hydrate. Marine benthic group B (MBGB) and candidate division JS1 (JS1) were the dominant taxa in the archaeal and bacterial libraries, respectively, throughout the core, except in the deep layer. Interestingly, ANME-1b members and mcrA were detected only in sulfate methane transition zone (SMTZ). However, the proportions of sulfate-reducing Deltaproteobacteria clones and dsrA copy numbers differed (i.e., a very low proportion of clones but a sufficiently high dsrA copy number relative to bacteria). These results suggests that anaerobic oxidation of methane may occur mainly in SMTZ of Ulleung Basin sediment, where it is performed by ANME-1b via an unidentified oxidization process. Ó 2013 Elsevier Ltd. All rights reserved.

Keywords: Methane hydrate Sulfate methane transition zone (SMTZ) ANME-1b Marine benthic group B (MBGB) Candidate division JS1 (JS1) The Ulleung Basin

1. Introduction Marine sediments are the largest global reservoir of methane (Kvenvolden, 1988; Valentine, 2002), and significant levels of methane are produced each year (75e320 Tg year
1) by biogenic and thermogenic processes (Thauer et al., 2008). The methane produced is dissolved in sediment pore water and can be transformed or trapped in methane hydrates, while a proportion of the methane is released upward into the sediment surface where it is oxidized (Orphan et al., 2001). This oxidation process is mainly

accompanied by sulfate reduction (CH4 þ SO2
4 / HCO3 þ HS þ H2O), known as Anaerobic Oxidation of Methane (AOM), which is mediated by a syntrophic partnership between anaerobic methanotrophs (ANME) and sulfate-reducing bacteria (SRB) (Hoehler et al., 1994; Valentine and Reeburgh, 2000). AOM occurs mainly

* Corresponding author. Marine Biotechnology Research division, Korea Institute of Ocean Science & Technology, P.O. Box 29, Ansan 425-600, South Korea. Tel.: þ82 31 4006243; fax: þ82 31 4062495. E-mail address: [email protected] (J.-H. Lee). 0264-8172/$ e see front matter Ó 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.marpetgeo.2013.06.002

in the sulfate methane transition zone (SMTZ), which is defined as the horizon where sulfate and methane coexist, thereby leading to a significant increase in inorganic carbon (HCO
3 ), alkalinity, and hydrogen sulfide (Harrison et al., 2009; Knittel and Boetius, 2009; Reeburgh, 2007). The ANME clade is divided into three phylogenetic clusters, i.e., ANME-1, ANME-2, and ANME-3 (Boetius et al., 2000; Knittel and Boetius, 2009). Although these diverse microorganisms are widely distributed in marine sediments, their roles in the biogeochemical cycle are not clearly understood. The East Sea of Korea (also known as the Japan Sea) shares many features with the ocean, although it is smaller. Therefore, it is known as a mini-Ocean. The Ulleung Basin in the East Sea of Korea has recently attracted attention because it contains deposits of methane hydrate (Ryu et al., 2009) and shallow gas (Lee and Chough, 2003). The presence of methane hydrate in the Ulleung Basin was confirmed by drilling expedition (Ryu et al., 2009). In previous studies of Ulleung Basin sediments, the archaeal communities varied with depth and among sites (slope and basin), depending on the geochemical properties of the sediments (Kim et al., 2010). The candidate division JS1 (JS1) is a major component of the methane hydrate-bearing sediment in the Ulleung Basin

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(Jeong et al., 2010), which appears to be related to the presence of methane (Webster et al., 2004). However, insufficient information is available on the vertical distribution of microorganisms in the deep core samples that bear methane hydrate in the Ulleung Basin. Thus the objective of this study was to investigate the microbial community structure of the sediment core and its relationships with the depth and presence of methane hydrate. We also attempted to elucidate the partnership between the ANME and SRB groups during AOM process in this sediment from the Ulleung Basin in the East Sea of Korea. 2. Materials and methods 2.1. Site description and sampling Sediments from the Site UBGH2-10 (36 550 35.1200 N, 130 540 00.3600 E), which is located in the north eastern part of the UlleungBasin, ware obtained during a research cruise by the Second Ulleung Basin Gas Hydrate Drilling Expedition (UBGH2) in the East Sea of Korea. Subsurface sediments were collected using a steel core barrel containing a 6.7 cm inter diameter (i.d.) polycarbonate liner. Immediately after retrieval, the liner was capped and cut into 15e 20 cm sections using a pipe cutter. The samples collected were stored at
80  C during the cruise and at
20  C after returning to the laboratory until the nucleic acids were extracted. The geochemical parameters, such as methane, sulfate, and hydrogen sulfide concentrations, were obtained on board during the expedition using an entire core from the UBGH2-10 sediments, and the sections used for microbiological analysis were selected on the basis of those geochemical properties (Fig. 1 and Supplementary Table 1). The geochemical parameters of the sediments from different depths used for microbiological analysis were as follows:

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sulfate concentrations in the surface layer were approximately 24.3 mM; no sulfate was detected in the high methane and deep layers; methane was not detected in the surface layer; methane concentration increased to 40.5 mM in the high methane layer; and hydrogen sulfide was detected only in SMTZ (1.6 mM). 2.2. DNA extraction Total genomic DNA was extracted from the four selected depths in the core (approximately 10 g of sediment) (Fig. 1) using a PowerMaxÒ Soil DNA Isolation kit (MoBio Laboratories, USA). DNA was eluted in 3 ml of 10 mM Tris buffer (pH 8.0), concentrated by the addition of ethanol (volume, 2.5) and sodium acetate (final concentration, 0.3 M), and centrifuged at 14,000  g for 30 min at 4  C. The genomic DNA was re-purified to remove any residual humic acid using a PowerCleanÒ DNA Clean-Up kit (MoBio Laboratories), according to the manufacturer’s instructions. Total DNA was measured using a Quant-iT PicoGreen kit (Invitrogen, USA) on a StepOnePlusÔ Real-Time PCR system (Applied Biosystems Inc., Foster City, CA, USA) according to the manufacturer’s instructions. 2.3. Quantitative PCR (Q-PCR) of 16S rRNA genes and functional genes To determine the abundance of microorganisms (archaeal and bacterial) and key functional genes related to sulfate reduction (dsrA) and methanogenesis (mcrA) in the extracted DNA, Q-PCR was performed using SYBR Green I fluorescent dye as described previously (Lipp et al., 2008), with specific primer sets (Supplementary Table 2). Plasmids that carried an archaeal 16S rRNA gene (AB731373), bacterial 16S rRNA gene (Escherichia coli strain KCTC 2441), mcrA (this study), or dsrA (this study) were used as standards. Q-PCR amplification was performed in a total volume of 20 ml, which contained 10 ml of SYBR Green Real-time PCR Master Mix (TOYOBO, Japan), 0.3 mM of each primer, and 2 ml of the 1:10 diluted DNA templates, and deionized water was added to make the volume up to 20 ml. Thermal cycling was performed using the StepOnePlusÔ RealTime PCR system with the following parameters: 95  C initial hold for 15 min to activate Taq polymerase, followed by 40 cycles of amplification, where each cycle comprised denaturation at 95  C for 15 s, 20 s of annealing at primer specific temperatures (Supplementary Table 2), and an extension step of 20e30 s at 72  C. Fluorescence was measured at the end of each amplification cycle. Final melt curve analysis was performed to determine the presence or absence of non-specific amplification products. After Q-PCR was complete, temperature was ramped from 72  C to 95  C, increasing by 0.3  C each step, with a wait of 1 min on first step and 5 s after each subsequent step. The standard DNA and samples were serially diluted 10-fold to produce 103e108 copy equivalents and used as templates to generate standard curves by plotting the Ct value against the logarithm of the copy numbers of 16S rRNA genes and functional genes in each dilution. DNA extracts from all sediments with each gene copy number were spiked with the plasmid DNA at a concentration of 106e107 copies ng DNA
1 and each gene copy number was compared with that in samples without and with spiked DNA to test for the potential presence of PCR inhibition. 2.4. 16S rRNA gene amplification and sequence analysis

Figure 1. Profiles of geochemical parameters of methane concentration (closed circle), sulfate concentration (open circle) and hydrogen sulfide ion concentration (open triangle) for select UBGH2-10 sediment. (A) Was zoom in from the surface to SMTZ. (B) Was overall profiles of UBGH2-10. The dashed lines indicate the sampling depth.

The archaeal 16S rRNA genes were amplified using oligonucleotide primer set arch21F (50 -TCC GGT TGA TCC YGC CGG-30 ) and arch958R (50 -YCC GGC GTT GAM TCC AAT T-30 ) (DeLong, 1992), while the bacterial 16S rRNA genes were amplified using the bacterial 27F (50 -AGA GTT TGA TCM TGG CTC AG-30 ) and bacterial

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1518R (50 -AAG GAG GTG ATC CAN CCR CA-30 ) oligonucleotide primer set (Giovannoni, 1991). PCR amplification was performed using an out with 50 ml reaction mixture, which contained 1  PCR buffer, 160 mM of dNTPs, 0.3 mM of each primer, 1.5 units of Ex Taq DNA polymerase (Takara, Japan), and 100e150 ng of the template DNA. PCR amplification was performed using a T1 Thermocycler (Biometra, Germany) with an initial denaturation step at 94  C for 5 min, followed by 30 cycles of at 94  C for 60 s, 58  C for 60 s, and 72  C for 90 s, and a final extension step at 72  C for 10 min. The PCR products were electrophoresed on an agarose gel (1%) and purified using a QIAquick Gel Extraction kit (Qiagen, Germany), according to the manufacturer’s instructions. The PCR products of the 16S rRNA genes were ligated into the pGEMÒ T-Easy vector system (Promega, USA) and cloned into E. coli DH5a. The cloned 16S rRNA gene sequences were determined with arch21F or bacterial 27F and a BigDyeÒ Terminator v3.1 Cycle sequencing Kit (PE Applied Biosystems, Inc.) on an ABI 3730XL DNA Analyzer (Applied Biosystems, Inc.). The sequences were compared with 16S rRNA sequences in the GenBank database (http://www. ncbi.nlm.nih.gov) and the EzTaxon-e database (Kim et al., 2012) using BLAST searches. Closely related sequences were retrieved from the databases, aligned automatically using CLUSTAL X (Thompson et al., 1997), and then adjusted manually on the basis of 16S rRNA secondary structure information using PHYDIT (Chun, 1995). The evolutionary distance matrix was generated using the Jukes and Cantor method (1969), and the phylogenetic trees were inferred by the neighbor-joining method (Saitou and Nei, 1987). The phylogenetic trees were constructed using MEGA version 5.05. The robustness of the neighbor-joining tree topology was validated on the basis of bootstrap analysis with 1000 replicates (Felsenstein, 1985).

hydrate-bearing sediment of the Ulleung Basin was determined by Q-PCR. The calculated Q-PCR efficiencies for archaea, bacteria, mcrA, and dsrA were 72%, 108%, 80% and 87%, respectively. Linear plots of the log and Ct values for archaea, bacteria, mcrA, and dsrA had correlation coefficients (r2) of 0.994, 0.998, 0.998, and 0.987, respectively (Supplementary Fig. 1). The possible presence of PCR inhibitors in the sediment samples was tested using Q-PCR by spiking DNA extracts from all sediments with control plasmid DNA. The average recovery rates for archaea, bacteria, mcrA, and dsrA were 127%, 105%, 122%, and 97%, respectively. These results indicate that detectable Q-PCR inhibitors were not present in this assay. The copy number of bacterial 16S rRNA genes was 6.7  104 copies ng DNA
1 in the surface layer, which decreased gradually with the depth to 4.4  104 copies ng DNA
1 in the deep layer (Table 1). However, the abundance of bacterial 16S rRNA genes was very low (1.4  103 copies ng DNA
1) in the high methane layer. In contrast, the estimated archaeal numbers were higher than the copy numbers of bacteria in SMTZ (1.2  105 copies ng DNA
1). Significant numbers of dsrA were detected in the surface layer and SMTZ, i.e., 4.8  102 and 3.0  102 copies ng DNA
1, respectively. Methyl coenzyme M reductase is a key enzyme during the methanogenesis and AOM. Recent studies showed that mcrA is used for methane oxidation in the ANME group (Hallam et al., 2004; Nunoura et al., 2006). mcrA was detected only in SMTZ (1.8  103 copies ng DNA
1), which suggests that AOM occurs exclusively in this layer. 3.2. Basic information and statistical analysis of libraries In total, 401 sequences of partial archaeal and bacterial 16S rRNA genes were detected in the sediments, which represented 129 unique microbial phylotypes (Supplementary Table 3). OTUs and diversity indices were determined at the 3% 16S rRNA sequence difference level using the DOTUR program. On average, 15 OTUs were detected in each archaeal library, whereas bacterial libraries contained considerably higher number of OTUs, with an average 22 OTUs per library. Sequences from archaea and bacteria were clustered into 48 and 81unique OTUs, respectively. Diversity indices are valuable tools for quantifying the diversity of a community and can be used to describe its numerical structure by combining the richness and evenness of the components (Table 2). Coverage (C) is a quantitative estimate of how well the sample size reflects the apparent diversity within each library. The archaeal and bacterial C values were 62.2%e77.8% and 43.8%e 68.0%, respectively. The lowest C values for archaea and bacteria were found in the high methane layer. The Shannon index and the reciprocal of Simpson’s index were used to assess the diversity of the microbial community. For the bacterial libraries, both indices detected lower diversity in the deep layer. In contrast, archaeal diversity was highest in the deep layer. Interestingly, the archaeal Shannon and Simpson’s diversity indices were very low in SMTZ, and this implied that specific archaeal taxons might dominate this layer. The ACE index showed that the high methane layer might have the highest archaeal and bacterial diversities which was also

2.5. Statistical analysis Microbial diversity indices were calculated on the basis of the number of operational taxonomic units (OTUs). First, PHYLIP output format files produced using CLUSTAL X were used to calculate the distance matrices with the DNADIST program in Phylip 3.69. The distance-based OTU and richness program version 1.3 (DOTUR) (Schloss and Handelsman, 2005) was used to assign OTUs with 97% 16S rRNA gene sequence similarity as the standard. The coverage of each clone library was calculated using the following formula; C ¼ [1
(n1/N)]  100, where n1 is the number of unique OTUs and N is the total number of clones in a library (Dang et al., 2008). DOTUR was also used to calculate the rarefaction curves and diversity indices, including the Shannon diversity (H0 ), Simpson’s diversity, abundance-based coverage estimator (ACE) (Chao et al., 1993), and Chao1 (Chao, 1984). 3. Results 3.1. Quantification of prokaryotes and functional genes The abundance of total prokaryotic cells (archaeal and bacterial 16S rRNA genes) and key functional genes mcrA and dsrA in the

Table 1 Archaeal and bacterial 16S rRNA genes and the two functional gene copies derived from four samples. Sample layer

Copies ng DNA
1

Surface SMTZ High methane Deep layer

1.2 1.2 1.1 1.0

Archaeal 16S rRNA gene

a

Not detected.

   

104 105 105 103

   

1.3 1.2 1.5 4.4

   

103 104 104 101

Bacterial 16S rRNA gene 6.7 4.8 1.4 4.4

   

104 104 103 104

   

7.0 1.4 1.7 4.4

   

103 103 101 102

mcrA

dsrA

NDa 1.8  103  1.0  102 ND ND

4.8  102  5.0  101 3.0  102  5.4  101 ND ND

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Table 2 Diversity indices of archaeal and bacterial libraries. Domain

Section

No. of clones

No. of OTUsa

C (%)b

ACEc

Chao1d

Shannone

1/Simpsone

Archaea

Surface SMTZ High methane Deep layer Surface SMTZ High methane Deep layer

51 54 44 57 49 48 48 50

15 13 16 15 25 23 27 17

70.6 75.9 63.6 73.7 49.0 52.1 43.8 66.0

25.0 23.4 33.1 17.3 101.1 118.6 147.8 51.3

43.0 18.0 25.0 18.0 120.0 74.0 153.5 39.0

2.3 1.9 2.2 2.5 2.6 2.5 2.7 2.0

9.0 4.5 6.3 11.6 7.9 7.7 7.9 3.9

Bacteria

a b c d e

Calculated at the 0.03 difference level. Coverage (C) ¼ [1
(n1/N)]  100, where n1 is the number of unique OTUs and N is the total number of clones in a library. Nonparametric statistical prediction of the total OTUs based on the distributions of abundant and rare OTUs. Nonparametric statistical prediction of the total OTUs based on the distributions of singletons and doubletons. A higher number indicates greater diversity.

supported by rarefaction analysis. However, Chao1 produced a high value for archaeal diversity in the surface layer. In contrast to the rarefaction curves of bacterial libraries, the curves of archaeal libraries almost reached an asymptote, demonstrating that the archaeal libraries were less diverse (Supplementary Fig. 2). 3.3. Archaeal community structure The archaeal community compositions of the Ulleung Basin sediment were determined by the analysis of 16S rRNA gene clone libraries. In total, 207 sequences were generated from four archaeal libraries and seven different phylogenetic groups were identified (Fig. 2A). The major groups recovered from the archaeal libraries were the Marine Benthic Group B (MBGB) and the Miscellaneous Crenarchaeotal Group (MCG). The other archaeal groups were

ANME-1b, the MBGD, the South African Gold Mine Euryarchaeotal Group (SAGMEG), and Thaumarchaeota. All of the Crenarchaeota clones belonged to MBGB and MCG (Fig. 3). MBGB members have been detected in a wide range of deep sea marine environments. In the archaeal 16S rRNA gene libraries, MBGB members represented 58.8%, 35.2%, and 55.6% of the total in the surface layer, SMTZ, and high methane layer, respectively. Most of the MBGB sequences were related to sequences from methane containing sediment environments with methane hydrate and/or cold seeps (Beal et al., 2009; Dang et al., 2010, 2009; Inagaki et al., 2006, 2003; Knittel et al., 2005). MBGB has previously been reported as a predominant group in methane containing sediments (Inagaki et al., 2003; Reed et al., 2002) In contrast, a high proportion of MCG members were detected in the deep layer, where they represented 49.1% of the total, whereas they were absent from SMTZ. Interestingly, 42.6% of the archaeal clones were closely related to ANME-1b in the SMTZ clone library (Fig. 2A). This phylotype, which is closely associated with a role in AOM, was not present in the other layers. The sequences belonging to ANME-1b were closely related to the Indian Ocean subsurface biofilm clone slm_arc_201 (HQ700679) sequence (99.0% similarity) (Briggs et al., 2011) (Fig. 4). The 16S rRNA gene sequences affiliated with MBGD were widely distributed throughout the core and represented the most diverse group (10 OTUs among 27 clones) in the UBGH2-10 sediments. The SAGMEG phylotypes were predominant in the deep layer, where they represented 49.1% of the total. SAGMEG was originally discovered in the deep terrestrial subsurface, but various phylotypes have been retrieved from marine environments. The sequences analyzed in the present study were affiliated with SAGMEG reported from deep marine sediments containing methane hydrates (Reed et al., 2002). 3.4. Bacterial community structure

Figure 2. Microbial community structures based on 16S rRNA genes of archaea (A) and bacteria (B) from UBGH2-10 sediments. Relative abundance was calculated as follows: (n/N)  100, where n is the number of clones belonging to the same phylum and N is the total number of clones.

The community structures inferred on the basis of the bacterial 16S rRNA gene clone libraries contained diverse bacterial phylogenetic groups such as JS1, Chloroflexi, Planctomycetes, Proteobacteria, Firmicutes, and several candidate divisions (Fig. 2B). JS1 was dominant in the bacterial clone libraries and was detected throughout the sediment core with the highest proportion in SMTZ where it accounted for 58.3% of the library. They were differentiated into 11 phylotypes, although 70.3% of the clones were classified as the same phylotype. The sequences related to JS1 shared high similarity (91%e99%) with sequences that originated from a cold-seep area in the Japan Trench, a hydrate-bearing sediment from the Forearc Basin, and a macroscopic biofilm from the Indian Ocean (Briggs et al., 2011; Reed et al., 2002) (Fig. 5). The next major phylogenetic groups in the Ulleung Basin sediment were Chloroflexi and Planctomycetes. The majority of the Chloroflexi sequences

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Figure 3. Phylogenetic analysis of the nucleotide sequences related to Crenarchaeota based on archaeal 16S rRNA genes from UBGH2-10 sediments. Names in bold indicate clones obtained during this study. The tree was generated using a neighbor-joining algorithm on the basis of the Jukes and Cantor model with 1000 bootstraps replicated. Bootstrap values <50% are not shown. The scale bar represents 5% sequence divergence. The 16S rRNA gene sequence of E. coli (Z83204) was used as an outgroup (not shown).

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Figure 4. Phylogenetic analysis of the nucleotide sequences related to Euryarchaeota based on archaeal 16S rRNA genes from UBGH2-10 sediments. Names in bold indicate clones obtained during this study. The tree was generated using a neighbor-joining algorithm on the basis of the Jukes and Cantor model with 1000 bootstraps replicated. Bootstrap values <50% are not shown. The scale bar represents 5% sequence divergence. The 16S rRNA gene sequence of E. coli (Z83204) was used as an outgroup (not shown).

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Figure 5. Phylogenetic analysis of the nucleotide sequences related to JS1, Chloroflexi, and Planctomycetes based on bacterial 16S rRNA genes from UBGH2-10 sediments. Names in bold indicate clones obtained during this study. The tree was generated using a neighbor-joining algorithm on the basis of the Jukes and Cantor model with 1000 bootstraps replicated. Bootstrap values <50% are not shown. The scale bar represents 5% sequence divergence. The 16S rRNA gene sequence of Thermococcus hydrothermalis AL662 (AY099179) was used as an outgroup (not shown).

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were related to uncultured clones from methane rich or AOM associated sediments and were classified into three classes (Anaerolineae, Caldilineae and Dehalococcoidetes). The phylotypes related to Anaerolineae and Caldilineae were mainly distributed in the surface layer. In contrast, most of the Dehalococcoidetes sequences were detected in SMTZ or the deep layer. Approximately 7.2% of the total clones were affiliated with a wide range of Planctomycetes and were distributed evenly in three layers (surface layer, SMTZ, and high methane layer), most of which were closely related to uncultured clones derived from marine environmental samples. The majority of the SMTZ clones were affiliated with the class Phycisphaerae. In the present study, all of the Alphaproteobacteria clone sequences were affiliated with the Rhizobiales clade (Fig. 6) and were closely related to clones recovered from deep sea surface sediments from the South Atlantic Ocean (Schauer et al., 2010). Most of the phylotypes that grouped within Gammaproteobacteria were present in the deep layer clone library. Two of the Gammaproteobacteria phylotypes were closely related to cultured representatives of the genera Methylophaga and Enhydrobacter with which they shared 98% and 99% similarity, respectively. The sequences related to Deltaproteobacteria, some of which can utilize sulfate as an electron acceptor to produce energy were not very abundant (one sequence each in the surface layer, SMTZ, and deep layer). The sequences affiliated to Firmicutes and Actinobacteria were only found in the deep layer, i.e., 50.0% and 10.0%, respectively. The Firmicutes and Actinobacteria phylotypes represented a single phylotype, which was affiliated with Tumebacillus permanentifrigoris strain Eur1 9.5 (99%e100% similarity) (NR_043849) and Dietzia psychralcaliphila strain ILA-1 (99%e100% similarity) (NR_024767), respectively. In the surface and high methane layers, 12 and 14 clone sequences (24.5% and 29.2%, respectively) were affiliated with the uncultured candidate division TM6. Small numbers of the candidate divisions OP1, OP8, OP11, OD1, CD12, ANW, GN02, and WS1 were also found in the bacterial libraries. 4. Discussion The geochemical conditions in marine sediments have important effects on the microbial community structure and diversity (Edlund et al., 2008; Kuehl et al., 1996). Previous studies reported apparent relationships between the microbial distribution, diversity and geochemical properties in methane hydrate-bearing environments (Heijs et al., 2007; Inagaki et al., 2006; Knittel and Boetius, 2009). UBGH2-10 sediments have a clear SMTZ where sulfate and methane coexist, while below this there is a distinct layer of high methane sediment. The present study was based on the clone libraries of microbial communities obtained directly from four different geochemical sediments in the Ulleung Basin. We found that the copy number of bacterial 16S rRNA genes in the high methane layer was lower than that in the other layers, whereas the copy number of archaeal 16S rRNA genes increased dramatically in SMTZ and the high methane layer. This result suggests that archaeal and bacterial abundances were affected by geochemical conditions, including the presence of methane and sulfate. Q-PCR analysis could not determine whether the origin of mcrA was in the methanogen or ANME group. The results of Q-PCR analysis and the archaeal clone library showed that the methanogen group was not present in the high methane and deep layers. This result was unexpected because the methane hydrate present in the Ulleung Basin originates from biogenic sources. This unexpected result might have been observed because the number of methanogens present was below the detection limit; therefore, only a low number of clones were present in archaeal community analysis (Colwell et al., 2008).

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The ANME-1b clones and mcrA copy numbers were detected only in SMTZ which suggests that mcrA originated from the ANME1b group of archaea. The syntrophic associations between the ANME and SRB groups in the Desulfosarcina/Desulfococcus branch of Deltaproteobacteria are well known (Knittel and Boetius, 2009), but we found that only one clone was affiliated with Deltaproteobacteria and there was a lower copy number of dsrA than mcrA (16.4% of mcrA) in SMTZ (Table 1). In addition, the Deltaproteobacterial clone detected in the present study was closely related (94% sequence similarity) to Syntrophus gentianae. Members of the genus Syntrophus cannot utilize sulfate as an electron acceptor (Kuever et al., 2005), which suggests that Deltaproteobacteria might not be a sulfate-reducing syntrophic partner of ANME-1b. Recent studies suggested that some ANME-1 members oxidize methane anaerobically without any syntrophs or with a relatively low abundance of SRB. Archaea may possess a unique AOM metabolism pathway that consumes methane and sulfate together within their cells (Knittel et al., 2005). Another study supports this hypothesis because it showed that the ANME/SRB ratio in the ANME-2 dominated layer was 1:3 in sediment samples (Orcutt and Meile, 2008), whereas the ratio in ANME-1 dominated sediments was 16.1e86:1 in the Nyegga sediments (Roalkvam et al., 2011). In the present study, the ANME/SRB ratio was 6:1 in SMTZ. Therefore, in the present study, the microbial community structure detected in SMTZ suggests that ANME-1b may perform AOM without syntrophs or that they may have syntrophic relationships with sulfatereducing microorganisms other than Deltaproteobacteria. The archaeal members of MBGB predominated in the UBGH2-10 sediment. MBGB members predominate in several marine environments, such as deep sea sediments, sediments overlaying shallow gas hydrates, and SMTZ of the Santa Barbara Basin and Peru Margin (Fry et al., 2008; Harrison et al., 2009; Inagaki et al., 2006; Sorensen and Teske, 2006; Vetriani et al., 1999; Wang et al., 2010). The metabolism of MBGB representatives remains unknown, but several hypotheses have been postulated regarding their phylogenetic affiliations. Previous study speculated that the MBGB group might be responsible for sulfate reduction and methane oxidation (Inagaki et al., 2006). Thus, MBGB members are possible partners of AMNE-1b during AOM. Candidate division JS1 was a predominant bacterium in the hydrate-bearing sediment from the Ulleung Basin. The JS1 group is an uncultivated bacterial group, which was previously obtained from Japan Sea sediments (Rochelle et al., 1994). It has also been recovered from various deep marine sediments, cold seeps and tidal flats such as the Forearc Basin, Guaymas Basin, Gulf of Mexico, Japan Trench and North German coast (Dhillon et al., 2003; Inagaki et al., 2003; Leloup et al., 2009; Orcutt et al., 2010; Webster et al., 2007). Webster et al. (2004) reported that it may be relatively more abundant in hydrate-bearing sediments (20%e50%) than in non-hydrate-bearing sediments (11%e23%). Recent studies suggested that JS1 is a major methane-associated group; thus, it could be useful as an indicator of methane hydrate (Inagaki et al., 2006). However, some studies suggested that JS1 is location-specific, and therefore cannot be used as a universal indicator of methane (Liao et al., 2009). The exact biogeochemical role of JS1 is unknown; however, we suggest that it may play an important role in hydratebearing sediments. In the present study, MCG members were not identified in SMTZ clone library. MCG members are widely distributed throughout marine subsurface sediments, and they have a wide range of habitats, including various terrestrial and marine surface and subsurface environments (Lloyd et al., 2013). Many studies suggested that this group have negative relationships with AOM, although their physiologies remain unknown. Sorensen and Teske (2006) reported that MCG dominated the overall core sediment, whereas they could

Figure 6. Phylogenetic analysis of the nucleotide sequences related to candidate division groups, Proteobacteria, Actinobacteria, and Firmicutes based on bacterial 16S rRNA genes from UBGH2-10 sediments. Names in bold indicate clones obtained during this study. The tree was generated using a neighbor-joining algorithm on the basis of the Jukes and Cantor model with 1000 bootstraps replicated. Bootstrap values <50% are not shown. The scale bar represents 5% sequence divergence. The 16S rRNA gene sequence of Thermococcus hydrothermalis AL662 (AY099179) was used as an outgroup (not shown).

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not be detected in the narrow SMTZ. Previous studies showed that MCG members are metabolically active heterotrophs in deep subsurface sediments that can utilize complex organic materials such as buried organic carbon (Biddle et al., 2006). The phylotype of MBGD equivalent to Marine Group III, as defined by DeLong (1998) was found in the subsurface sediments of ODP sites 1227and 1251 (Inagaki et al., 2006), and they are metabolically active in subsurface environments (Sorensen and Teske, 2006). Most of the MBGD sequences retrieved in the clone libraries were affiliated to Thermoplasmatales, which might be scavengers in their environment that utilize components from decaying microorganisms (such as oligopeptides) as carbon and/or energetic sources for growth (Takano et al., 2010). Recently, extracellular protein-degrading enzymes encoded by MCG and MBGD groups may be involved with protein remineralization in anoxic sediments (Lloyd et al., 2013). Chloroflexi have been obtained mainly from anoxic and organicrich environments such as sediments, subsurfaces, hot springs, and anaerobic waste water sludges (Sekiguchi et al., 2003). This phylum, previously known as green non-sulfur bacteria, mainly comprises uncultured microorganisms. The three clones related to Anaerolineae were only present in the surface. In contrast, the sequences affiliated with Caldilineae were detected in the surface and high methane layers. Both species can grow anaerobically and chemoorganotrophically using a number of carbohydrates and amino acids in the presence of yeast extract. Interestingly, the growth of Anaerolinea and its relatives can be stimulated in coculture with hydrogenotrophic methanogens (Sekiguchi et al., 2003). Sequences affiliated to Dehalococcoidetes were also identified, including Dehalococcoidetes ethenogenes, which are known to be hydrogenotrophic and heterotrophic anaerobes that can metabolize the refractory substrate, tetrachloroethene (MaymoGatell et al., 1997). However, the roles of this group have not been clarified in methane hydrate-bearing sediments because of a lack of isolates from deep sediment environments. Specific members of Firmicutes, Actinobacteria, and SAGMEG were rare the deep layer clone library. The Firmicutes and Actinobacteria phylotypes were closely related to the spore-forming bacterium T. permanentifrigoris strain Eur1 9.5 (NR_043849) and the alkaliphilic bacterium D. psychralcaliphila strain ILA-1 (Steven et al., 2008; Yumoto et al., 2002), respectively. The abundance of Firmicutes is unknown in the deep layer, but their ability to form spores may be beneficial under these restricted conditions. The limited organic carbon availability and high pressure in the deep layer may constrain the range of microbes that can withstand these harsh environmental conditions. Of the many candidate divisions of bacterial communities, the candidate division TM6 was present in the surface and high methane layers, but the reason for its abundance in these two layers is unknown. This group may be related to biofilm formation and acidophilic characteristics, because most of the candidate TM6 sequences in the NCBI database originate from biofilms and acidic environmental conditions. 5. Conclusion In this study, we analyzed the archaeal and bacterial abundance and diversity in methane hydrate-bearing sediments from four different depths in the Ulleung Basin using Q-PCR and 16S rRNA gene clone libraries. Six and nine major phylogenetic groups were detected in each of the four archaeal and bacterial libraries, respectively. ANME-1b group members and mcrA were detected only in SMTZ, but the ratio of dsrA was only 16.4% relative to mcrA. Sulfate-reducing Deltaproteobacteria were not detected in SMTZ. This result suggests that AOM is mediated solely by ANME-1b or with partners other than sulfate-reducing Deltaproteobacteria in the Ulleung Basin, East Sea of Korea. The roles of MBGB members and

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JS1 were not determined in the methane hydrate-bearing sediment, but the distributions of these two groups related to methane hydrate suggest that they may be involved directly or indirectly with biogeochemical processes in the methane hydrate-bearing sediment. Determining the physiological functions of these groups and ANME-1b using cultivation or metagenomic studies, including single-cell genomics (Siegl et al., 2011) will enhance our understanding of AOM and its importance in high methane sediments. Acknowledgments This study was supported by the Marine and Extreme Genome Research Center Program of the Ministry of Oceans and Fisheries, Republic of Korea, and the Korea Institute of Ocean Science and Technology (KIOST) in-house program (PE98993). We would like to thank the officers, crews, and shipboard scientific staff of the D/V Fugro Synergy during the UBGH2. Special appreciation is extended to the Korea Institute of Geoscience and Mineral Resources and Han Yang University. We also acknowledge the editor and reviewers. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.marpetgeo.2013.06.002. References Beal, E.J., House, C.H., Orphan, V.J., 2009. Manganese- and iron-dependent marine methane oxidation. Science 325, 184e187. Biddle, J.F., Lipp, J.S., Lever, M.A., Lloyd, K.G., Sorensen, K.B., Anderson, R., Fredricks, H.F., Elvert, M., Kelly, T.J., Schrag, D.P., Sogin, M.L., Brenchley, J.E., Teske, A., House, C.H., Hinrichs, K.U., 2006. Heterotrophic Archaea dominate sedimentary subsurface ecosystems off Peru. Proc. Natl. Acad. Sci. U. S. A. 103, 3846e3851. Boetius, A., Ravenschlag, K., Schubert, C.J., Rickert, D., Widdel, F., Gieseke, A., Amann, R., Jorgensen, B.B., Witte, U., Pfannkuche, O., 2000. A marine microbial consortium apparently mediating anaerobic oxidation of methane. Nature 407, 623e626. Briggs, B.R., Pohlman, J.W., Torres, M., Riedel, M., Brodie, E.L., Colwell, F.S., 2011. Macroscopic biofilms in fracture-dominated sediment that anaerobically oxidize methane. Appl. Environ. Microb. 77, 6780e6787. Chao, A., 1984. Non-parametric estimation of the number of classes in a population. Scand. J. Stat. 11, 783e791. Chao, A., Ma, M.C., Yang, M.C.K., 1993. Stopping rules and estimation for recapture debugging with unequal failure rates. Biometrika 80, 193e201. Chun, J., 1995. Computer-assisted Classification and Identification of Actinomycetes. Ph.D. thesis. University of Newcastle, Newcastle upon Tyne, United Kingdom. Colwell, F.S., Boyd, S., Delwiche, M.E., Reed, D.W., Phelps, T.J., Newby, D.T., 2008. Estimates of biogenic methane production rates in deep marine sediments at Hydrate Ridge, Cascadia margin. Appl. Environ. Microb. 74, 3444e3452. Dang, H.Y., Li, T.G., Chen, M.N., Huang, G.Q., 2008. Cross-Ocean distribution of Rhodobacterales bacteria as primary surface colonizers in temperate coastal marine waters. Appl. Environ. Microb. 74, 52e60. Dang, H.Y., Luan, X.W., Chen, R.P., Zhang, X.X., Guo, L.Z., Klotz, M.G., 2010. Diversity, abundance and distribution of amoA-encoding archaea in deep-sea methane seep sediments of the Okhotsk Sea. Fems Microbiol. Ecol. 72, 370e385. Dang, H.Y., Luan, X.W., Zhao, J.Y., Li, J., 2009. Diverse and novel nifH and nifH-like gene sequences in the deep-sea methane seep sediments of the Okhotsk Sea. Appl. Environ. Microb. 75, 2238e2245. DeLong, E.F., 1992. Archaea in coastal marine environments. Proc. Natl. Acad. Sci. U. S. A. 89, 5685e5689. DeLong, E.F., 1998. Everything in moderation: archaea as ‘non-extremophiles’. Curr. Opin. Genet. Dev. 8, 649e654. Dhillon, A., Teske, A., Dillon, J., Stahl, D.A., Sogin, M.L., 2003. Molecular characterization of sulfate-reducing bacteria in the Guaymas Basin. Appl. Environ. Microb. 69, 2765e2772. Edlund, A., Hardeman, F., Jansson, J.K., Sjoling, S., 2008. Active bacterial community structure along vertical redox gradients in Baltic Sea sediment. Environ. Microbiol. 10, 2051e2063. Felsenstein, J., 1985. Confidence-limits on phylogenies e an approach using the bootstrap. Evolution 39, 783e791. Fry, J.C., Parkes, R.J., Cragg, B.A., Weightman, A.J., Webster, G., 2008. Prokaryotic biodiversity and activity in the deep subseafloor biosphere. Fems Microbiol. Ecol. 66, 181e196. Giovannoni, S.J., 1991. The polymerase chain reaction. In: Stackebrandt, E., Goodfellow, M. (Eds.), Nucleic Acid Techniques in Bacterial Systematics. John Wiley and Sons Ltd., Chichester, United Kingdom, pp. 177e203.

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