ENVIRONMENTAL MICROBIOLOGY

Influence of Deglaciation on Microbial Communitiesin Marine Sediments Off the Coast of Svalbard, Arctic Circle

Soo-Je Park & Byoung-Joon Park & Man-Young Jung &

So-Jeong Kim & Jong-Chan Chae & Yul Roh &

Matthias Forwick & Ho-Il Yoon & Sung-Keun Rhee

Received: 9 November 2010 /Accepted: 20 April 2011 /Published online: 10 May 2011# Springer Science+Business Media, LLC 2011

Abstract Increases in global temperatures have been shownto enhance glacier melting in the Arctic region. Here, we haveevaluated the effects of meltwater runoff on the microbialcommunities of coastal marine sediment located along atransect of Temelfjorden, in Svalbard. As close to the glacierfront, the sediment properties were clearly influenced bydeglaciation. Denaturing gradient gel electrophoresis profilesshowed that the sediment microbial communities of thestations of glacier front (stations 188–178) were distinguish-able from that of outer fjord region (station 176). Canonicalcorrespondence analysis indicated that total carbon andcalcium carbonate in sediment and chlorophyll a in bottom

water were key factors driving the change of microbialcommunities. Analysis of 16S rRNA gene clone librariessuggested that microbial diversity was higher within theglacier–proximal zone (station 188) directly affected by therunoffs than in the outer fjord region.While the crenarchaeotalgroup I.1a dominated at station 176 (62%), Marine BenthicGroup-B and other Crenarchaeota groups were proportionallyabundant. With regard to the bacterial community, alpha-Proteobacteria and Flavobacteria lineages prevailed (60%)at station 188, whereas delta-Proteobacteria (largely sulfate-reducers) predominated (32%) at station 176. Considering noclone sequences related to sulfate-reducers, station 188 maybe more oxic compared to station 176. The distance-wisecompositional variation in the microbial communities isattributable to their adaptations to the sediment environmentswhich are differentially affected by melting glaciers.

Introduction

The Arctic region has been the focus of increasing attention asan ecosystem that is highly sensitive to changes associatedwith global warming [1]. In fact, over the past few decades,the amount of summer glacial meltwater runoff has increasedsubstantially (about ~450 km3 year−1) [2, 3]. The meltedglaciers would have a tremendous impact on ecosystems,including the composition and activity of microbial commu-nities in the region.

Svalbard located far north of the Arctic Circle (Fig. 1)consists of a collection of islands. This area is of particularinterest as global climate change has long been predicted to beparticularly perceptible at such latitudes. Glacial meltwaterforms channels that flush the cryoconite holes in fjords amongthe Svalbard islands [4], and this phenomenon results in thetransport of nutrients and microorganisms to Arctic coastal

Soo-Je Park and Byoung-Joon Park contributed equally to this work.

Electronic supplementary material The online version of this article(doi:10.1007/s00248-011-9860-5) contains supplementary material,which is available to authorized users.

S.-J. Park : B.-J. Park :M.-Y. Jung : S.-J. Kim : S.-K. Rhee (*)Department of Microbiology, Chungbuk National University,Cheongju 361-763 Chungbuk, Republic of Koreae-mail: rhees@chungbuk.ac.kr

J.-C. ChaeDivision of Biotechnology, Chonbuk National University,Iksan 570-752, Republic of Korea

Y. RohFaculty of Earth System and Environmental Science,Chonnam National University,Gwangju 500-757, Republic of Korea

M. ForwickDepartment of Geology, University of Tromsø,9037 Tromsø, Norway

H.-I. YoonKorea Polar Research Institute,Songdo Techno Park, 7-50,Inchon P.O. Box 32, 406-840, Republic of Korea

Microb Ecol (2011) 62:537–548DOI 10.1007/s00248-011-9860-5

areas. Thus, glacial meltwater runoff may potentially exert adirect effect on the microbial community in coastal seawaterand sediments [5]. Although many studies have beenconducted on the water column of the Arctic Ocean [6–8]and the sediments therein [9–11], little attention has been paidthus far to the microbial community of the coastal region ofthe Arctic Circle, which is profoundly affected by summerglacial meltwaters.

As temperature is one of the most important factors inmicrobial growth in Arctic environments [12], analyses ofchanges in the microbial community may provide us withimportant insights into factors indicative of global climatechanges [13, 14]. Arctic environments feature a variety ofdiverse niches which can be colonized by uncultivatedpsychrophilic microorganisms. The distribution or succes-sion of microbial communities associated with changes inenvironmental conditions can be estimated via statisticalapproaches performed after molecular community analyses,including denaturing gradient gel electrophoresis (DGGE),terminal-restriction fragment length polymorphisms (T-RFLP),and the cloning and sequencing of 16S rRNA genes.

Microbial successions occurring in conjunction with therecession of permafrost glaciers have been studied at a variety

of high altitude regions, such as the Alps [15], and have beenassumed to function as a barometer of global climate. Therewas no previous investigation on the coastal microbialcommunities in Arctic regions affected by summer glacialmeltwater. The objectives of this study, thus, were to monitorchanges of microbial community in coastal sediment affectedby glacial meltwater of Tempelfjorden, Spitsbergen (Svalbard)(Fig. 1) and to search for the principal environmentalvariables driving these changes. The information gained inthis study may provide basic knowledge useful in estimatingthe effects of global warming in the Arctic region.

Materials and Methods

Site Description and Sample Collection

All material was collected with R/V Jan Mayen 104 at theUniversity of Tromsø, Norway. Coastal marine sedimentswere collected from seven stations in the Arctic area(Tempelfjorden, Spitsbergen, Svalbard, Arctic Circle)(Fig. 1), using a giant box corer (50×50×60 cm), inAugust 2006. The corer had been employed to retrieve

Figure 1 Box corer sampling sites. a Overview map. b Map ofIsfjorden, Sassenfjorden, and Tempelfjorden. The stations studied aremarked by squares. The stations for the analysis of library of 16S rRNA

gene sequences are marked with closed squares. Locations mentioned inthe text are indicated

538 S.-J. Park et al.

undisturbed samples of the seafloor. After taking colorpictures, the uppermost 1 cm from three different points inthe box cores was independently sampled with a sterilespoon and stored in plastic and glass bottles. The plasticbottles were then stored at −80°C, whereas the material inthe glass bottles was prepared for subsequent biogeochem-ical analyses. In an effort to extract genomic DNA fromfrozen sediment samples, a Power Soil™ DNA Kit (Mo BioLaboratories, Carlsbad, CA) was employed. The genomicDNA concentration isolated from the each marine sedimentwas determined in triplicate, using an ND-1000 spectropho-tometer (Nanodrop Technologies, Wilmington, DE). Thequantities of the extracted DNA samples were analyzed bygel electrophoresis in 1% agarose in 1× Tris–acetate–EDTA(TAE) buffer stained with ethidium bromide (0.5 μg permilliliter). The genomic DNAs were visualized under UVlight and photographed with digital camera.

Properties of Water and Sediment of Sampling Stations

The physical properties of the water column were evaluatedalong a transect including eight stations in August 2006.Conductivity, temperature, and depth were measured usinga Seabird 911 CTD (Sea-Bird Electronics, Bellevue, WA).The values of physical properties were measured inreplicates from each water column and sediment. ASeaTech transmissometer with a path length of 25 cm(Wet Labs, Philomath, OR) was attached to the CTD forturbidity measurements [16]. The sediments were analyzedwith regard to their bulk mineral assemblages after drying.Geochemical analyses of the seafloor were performed onsurface samples (0–1 cm) obtained from seven box corers.Total carbon (TC) and total nitrogen (TN) contents weremeasured with powdered samples, using a Carlo Erba NA-1500 Elemental Analyzer (CE Elantech, Italy). Totalinorganic carbon (TIC) contents were determined using aUIC Carbon Dioxide Coulometer (UIC, Joliet, IL) after thedissolution of the powdered samples in phosphoric acid.Total organic carbon (TOC) and the contents of carbonateminerals were calculated as the difference between total andinorganic carbon [17].

Color images of the sediment surfaces were obtained with aline scan camera attached to a Geotek Multi-Sensor CoreLogger (MSCL) (GEOTEC, UK). Visual descriptions wereperformed, and the sediment color was adjudged on the basisof Munsell Soil Color Charts. Particle-size analyses of thesediment samples were conducted with 1-cm-thick slices fromselected depths from all box cores (with the exception of thecore of station 176 by gravity core). The particle sizedistribution of the sediment samples was plotted on a Polk'sdiagram to assess the sediment texture [18]. The propertiesof bottom water and sediments of sampling stations aredescribed in Table 1.

Quantification of 16S rRNA Gene Copy NumbersUsing Real-Time PCR

For quantification of bacterial 16S rRNA genes, primers518F [19] and 786R [20] were used. Bacterial 16S rRNAgene copies were quantified using the MiniOpticon real-time PCR detection system (Bio-Rad Laboratories, Hercules,CA) and built-in Opticon Monitor Software version 3.1 (Bio-Rad Laboratories, Hercules, CA). The thermal cyclingparameters were used: 15 min at 95°C; 40 cycles of 20 s at95°C, 20 s at 55°C, and 20 s at 72°C, and readings were takenbetween each cycle. Copy numbers were calculated using anexternal standard curve that describes the relationship betweena known copy number of gene and the cycle threshold(Ct) value, as previously described Park et al. [21].Specificity of real-time PCRs was identified by analyzingmelting curves, checking the size of PCR products usinggel electrophoresis.

PCR–DGGE of Archaeal and Bacterial 16S rRNA Genes

In order to compare community structures of archaea andbacteria in seven different sediment communities, 16SrRNA genes were amplified with genomic DNAs extractedfrom each site and analyzed via PCR–DGGE. PCRamplification of the bacterial 16S rRNA gene was con-ducted using the GC clamp338F-518R primer combination,as described by Muyzer et al. [19] and performed using thePCR conditions described by Henckel et al. [22]. Thereaction was started in a following mixture: 10 μl 2× PCRMaster mix Solution (Intron, Republic of Korea), 0.5 μM ofeach primer (final concentration), ~10 ng of genomic DNAas template, and water added to a final volume of 20 μl.The PCR cycles were as follows: 5 min at 95°C; 30 cyclesof 30 s at 95°C, 30 s at 55°C, and 90 s at 72°C; 10 min at72°C. PCR and DGGE experiments were carried for thetriplicate samples from each sampling station. The PCRproducts of archaeal 16S rRNA genes amplified with the20F-958R primer set [23] were used as templates for asecond round of amplification using a DGGE universalprimer set (GC clamp340F-517R), as described by Raskinet al. [24]. The final PCR products were purified with aPCR Purification Kit (Solgent, Korea) and then employedfor DGGE analysis. DGGE analysis was conducted usingD-code system (Bio-Rad Laboratories, Hercules, CA). Thepolyacrylamide concentration and denaturant gradient forthe 16S rRNA sequence were optimized as 8% polyacryl-amide (37.5:1=acrylamide:bisacrylamide) and 30–60%denaturant. Electrophoresis was conducted for 14 h at80 V in 1× TAE buffer at a constant temperature of 60°C.The gels were stained for 30 min in 1× TAE buffercontaining a 1:10,000 dilution of SYBR green dye(Bioneer, Korea).

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DGGE Analysis of Archaeal and Bacterial Diversities

The DGGE profiles and band intensities of sampling siteswere analyzed by BioNumerics software (BioSystematica,UK). For DGGE profile analysis, each lane was separatelyselected, and common bands were chosen as positions fornormalization in order to compensate for differences inmigration distance due to gel heterogeneity. The normalizedbanding patterns were then employed to generate dendro-grams via calculation of the Pearson correlation coefficientand by using the unweighted pair group method witharithmetic averages (UPGMA) clustering. To check thevariability of the DGGE profiles obtained by threeindependent DGGE experiments, we conducted a statisticalanalysis (ANOVA test).

Statistical Analysis

In order to determine correlations between communitystructures and environmental parameters, canonical corre-

spondence analysis (CCA) by CANOCO version 4.5(Biometrics, the Netherlands) or XLSTAT (Addinsoft,New York, NY) was employed after initial analysis viadetrended correspondence analysis (DCA), which chose theunimodal response model and weight correlation matrixrather than the linear response model (i.e., principalcomponent analysis, PCA) to the environmental variables[25, 26]. The DGGE profiles (i.e., band position andintensity) of study sites (station 176–188) were used asmicrobial community composition in CCA. This analysiswas conducted without data transformation and focusedscaling on intersample distance. The manual selection ofenvironmental variables, applying a Monte Carlo permuta-tion test (499 random permutations) with unrestrictedpermutation, was conducted in order to determine thestatistical significance (p<0.05).

The resultant ordination biplot approximated the weight-ed average of each species (DGGE banding intensity in thisstudy) with regard to each of the environmental variables,which were represented as arrows. The lengths of these

Table 1 Physicochemical properties of bottom water and sediments of sampling stations

Property Sampling station

176 178 180 182 184 186 188

Latitude (N) 78°21.54' 78°22.11' 78°22.02' 78°23.34' 78°24.96' 78°25.96' 78°26.45'

Longitude (E) 16°28.52' 16°40.56' 16°50.94' 16°58.97' 17°08.51' 17°17.27' 17°18.97'

DNA extraction yield (μg per gram sediment) 3.0 4.0 2.5 2.1 1.4 0.7 0.9

rRNA gene copy number (per gram sediment)c 5.4×108 5.2×108 3.9×108 2.9×108 7.0×107 5.5×105 3.4×106

Bottom water

Depth (m) 60 95 101 88 71 36 40

Salinity (psu) 34.2 34.4 34.9 34.9 34.6 34.2 34.1

Temperature (°C) −0.5 −0.7 −0.6 −0.6 −0.5 0.5 0.3

Transmissivity (V) 3.8 3.1 3.2 3.3 2.8 2.7 1.5

Chlorophyll a (μg/l, bottom water)a 0.2 0.4 0.4 0.3 0.4 0.5 0.3

Chlorophyll a (μg/l, transition zone)a 1.1 1.3 1.4 1.0 1.3 0.8 0.3

Sediment

Total nitrogen (%)d 0.15 0.14 0.13 0.14 0.11 0.05 0.04

Total carbon (%)d 2.5 3 2.8 3.2 4.9 5.3 4.7

Total inorganic carbon (%)d 0.49 0.8 0.9 1.3 1.2 3.4 2.5

Total organic carbon (%)d 2.2 2.3 1.8 2 3.7 1.6 2.3

Calcium carbonate (%)d 6 7 8 10 9 28 21

C/N ratio 15 16 15 15 36 33 52

Sedimentation rate (cm/ka)b 34 95 82 95 135 1,692 3,800

Color Grayishbrown

Grayishbrown

Grayishbrown

Light brownishgray

Light reddishbrown

Reddishbrown

Reddishbrown

Texture Silt Mud Sandy silt Silt Mud Mud Silt

a Data from [72]b Data from [40]c Data represent the means of bacterial 16S rRNA gene copy numbers from replicate experimentsd Sediments from each sampling station were analyzed in duplicate and average values are shown

540 S.-J. Park et al.

arrows indicated the relative importance of environmentalfactors explaining variations in the DGGE banding profiles,whereas the angle between the arrows and the axis reflectedthe degree to which they were correlated [27].

Cloning of Archaeal and Bacterial 16S rRNA Genes

Because the 16S rRNA gene amplicons from DGGE bandswere too short to provide appropriate phylogenetic informa-tion, larger fragments of 16S rRNA genes were amplified viaPCR using either bacterial- [27F and 1492R] [28, 29] orarchaeal-specific primers [20F and 958R] [23] to constructthe clone library. To analyze the effects of deglaciationrunoff on microbial community composition, the sedimentproximal to the glacier (the station 188) was selected, and thearchaeal and bacterial 16S rRNA gene sequence librariestherefore were constructed with the extracted DNA. Forcomparative analysis, archaeal and bacterial 16S rRNA genelibraries were also constructed from the sediment of thestation at the outer fjord (station 176), which was the leastprofoundly affected by deglaciation runoff among theselected sampling stations.

The PCR was performed in a total 50 μl, consisting of25 μl 2× PCR Master mix Solution (Intron, Republic ofKorea), 1 μM of each primer (final concentration), ~10 ngof genomic DNA as template and water added to a finalvolume of 50 μl. The following PCR cycles wereemployed: 5 min at 94°C; 30 cycles of 30 s at 94°C, 30 sat 56°C, 90 s 72°C; 7 min at 72°C; and holding at 10°C.Duplicate PCR products amplified from each sample werepooled and purified with a PCR purification Kit (Solgent,Korea), ligated into T&A Cloning Vector (T&A CloningVector Kit, Real Biotech Corporation, Taiwan), and trans-formed into Escherichia coli DH5α cells in accordancewith the manufacturer's instructions. Putative positiveclones were transferred to 96-well plates containingLuria–Bertani Broth with ampicillin (100 μg/ml), grownovernight at 37°C, and stored at −70°C prior to screening.

RFLP Analysis and Sequencing

Each of the library clones were directly PCR-screened for thepresence of inserts using the M13 universal primer set, M13F;5′-GTTTCCCAGTCACGAC-3′ and M13R; 5′-TCACA-CAGGAAACAGCTATGAC-3′, PCR was conducted underthe following conditions: 5 min at 94°C; 30 cycle of 30 s at 94°C, 30 s at 55°C, 90 s at 72°C; 5 min at 72°C; and maintenanceat 10°C. The positive clones from each library were randomlyselected and purified with a PCR Purification Kit (Solgent,Korea).

RFLP was conducted in order to avoid sequencing thesame phylotype [28] and maximize the number of differentsequences obtained in this study. Enzyme selection and

restricted fragment size were predicted using http://www.restrictionmapper.org/ or NEBcutter V2.0 software (http://tools.neb.com/NEBcutter2/). Ten microliters of PCR prod-ucts was digested in a 30-μl reaction volume containing 1×supplied buffer, 1× bovine serum albumin (BSA), and 10 Uof MnlI and RsaI endonucleases (New England BiolabsInc., Beverly, MA). The treated samples were incubated for16 h at 37°C in a water bath. The resultant products werethen separated via gel electrophoresis in 4.0% agarose(3.0% NuSieve and 1.0% SeaKem; Bio Whittaker MolecularProbes, Rockland, ME) gels which were stained with 0.5 μgof ethidium bromide per milliliter and visualized under UV.The stained gels were scanned and analyzed using BioNu-merics software (BioSystematica, UK). The similarity be-tween each pair of patterns was estimated via the applicationof the Dice coefficient [30].

PCR products of representative clones (102 for archaeaand 180 for bacteria) were directly sequenced using ABIPRISM_BigDye™ Terminator Cycle Sequencing ReadyReaction kits (Applied Biosystems, Foster City, CA) and anABI PRISM 3730xl DNA Analyzer (Applied Biosystems).

Phylogeny and Rarefaction Analysis

The sequences were screened for possible chimeras withthe CHIMERA_CHECK program at the Ribosomal Data-base Project website (http://rdp8.cme.msu.edu) and com-pared with associated 16S rRNA gene sequences viaBLAST analysis (http://www.ncbi.nlm.nih.gov). Multiplealignments with related taxa were conducted via theCLUSTAL X program [31]. The evolutionary distanceswere calculated with the Kimura two-parameter model [32],whereas the phylogenetic trees were constructed vianeighbor-joining [33] with MEGA 3 software [34], withbootstrap values based on 1,000 replications [35].

The estimation of species richness, diversity indices, andrarefaction curves of the archaeal and bacterial 16S rRNAgenes in each of the libraries were determined using ESTI-MATES software (http://viceroy.eeb.uconn.edu/EstimateS).We accounted the clones with the same RFLP pattern in theabundance estimate. The species rarefaction curve of theentire dataset from each group was computed via theindividual-based Coleman method with PAST software(http://folk.uio.no/ohammer/past). The bias-corrected Chaolestimator of species richness was calculated after 1,000randomizations of sampling, without replacement. The cover-age percentage was calculated by Good's method via thefollowing formula: [1−(n/N)]×100, where n is the number ofphylotypes in a sample represented by one clone (singletons)and N is the total number of sequences within that sample[36]. The diversity of the sampled sequence set was estimatedusing the Simpson and Shannon indices within the ESTI-MATES application. The Shannon index of evenness was

Prokaryotic Diversity Influenced by Deglaciation 541

calculated via the formula E=eD/N, where D is the Shannondiversity index. The operational taxonomic units (OTUs) ofthe 16S rRNA gene were defined as sequence groups inwhich sequences differed by 3% and 5%, respectively [37]. Inorder to calculate diversity indices and coverage, we countedall clones showing the same pattern of RFLP as the number ofclones for the species. Rarefaction Curves were plotted usingSigamPlot (V7.101, SPSS).

The UniFrac distance metric [38] and the P test [39]were used to calculate the distance and compare betweenmicrobial communities using the phylogenetic informationof 16S rRNA gene of clone libraries.

Nucleotide Sequence Accession Numbers

The sequences reported in this paper have been deposited inthe GenBank database (accession numbers GU292215–GU292303).

Results

Site Characteristics

As shown in the map of Tempelfjorden, Spitsbergen(Fig. 1), station 188 is in close proximity to the glacierfront, and is thus directly affected by deglaciation. Theeffects of meltwater runoff are diminished with greaterproximity to the outer fjord (Sassenfjorden, Spitsbergen)(station 176) along the sampling transects. Seawater instation 188 appeared turbid and yellow gray during thesampling cruise in October of 2006. The water columntransmissivity of station 188 was close to 0 at the surfacelayer and was less than 3.0 at the bottom of the watercolumn. The color of the sediment of station 188 was alsoreddish-brown in spite of the typical dark gray color seen atstation 176 because of the influence of terrestrial runoff dueto deglaciation. However, the sediment texture at the bothstation was silty (Table 1). The content of calciumbicarbonate, an indicator of the effects of terrestrial runoff,was significantly higher in station 188. In fact, thesedimentation rates were approximately 3,800 cm/ka inthe vicinity of the shore (station 188) and decreased withdistance outward from the glaciers (ca. 30 cm/ka at station176) [40]. These results demonstrated that the sediment inthe vicinity of glacier had been piled up freshly with soilstransported by deglaciation runoffs.

The quantity of genomic DNA extracted from thesediments and the copy number of bacterial 16S rRNAgene per gram sediment decreased from the outer basin tothe glacier–proximal basin (Table 1). The extraction yieldsof genomic DNAwere well correlated with copy number ofbacterial 16S rRNA gene (Spearman R=0.96, P<0.001).

PCR–DGGE Profiles of 16S rRNA Genes

DGGE experiments were performed triplicates, and the resultswere highly reproducible (data not shown). The results ofcluster analysis of the PCR–DGGE profiles of the archaealand bacterial 16S rRNA genes showed that the sedimentmicrobial community of station 176 was separated fromothers, constituting an out-group (Supplementary Fig. 1). Inparticular, the archaeal communities of station 178 to 188were highly similar to one another (>90% similarity) butdistinct from that of station 176 (Supplementary Fig. 1a).The diversity of bacterial communities was greater than thatof archaeal communities. The DGGE profiles of the bacterial16S rRNA gene sequences were divided into two clusters,group I (station 178, 180, 182, and 184) and group II (station186 and 188), with similarities of at least 83% and 78%,respectively (Supplementary Fig. 1b). These results demon-strate that the bacterial communities were affected substan-tially by meltwater runoff during deglaciation. Although thechanges in bacterial communities occurred gradually andwere correlated with the distance from the glacier front, thearchaeal communities (station 178–188) in proximity to theinner fjord were quite similar to one another.

Influence of Environmental Factors on MicrobialCommunity Compositions

CCAwas conducted using PCR–DGGE banding patterns of16S rRNA gene acquired from the sampling stations(Fig. 2). Reproducibility of DGGE experiments could besupported by clustering of data of triplicates. The eigenval-ues of Fig. 2a (0.662 and 0.293) and Fig. 2b (0.483 and0.166) were calculated to explain the archaeal and bacterialDGGE profiles, respectively. The axes explained 100% and87.3% of the species–environment relationships among thearchaeal and bacterial communities (Supplementary Ta-ble 1). This result demonstrated that the structures ofmicrobial communities could be affected by environmentalfactors (sediment properties). This was verified by thespecies–environment correlation values, which were highfor both axes (0.999 and 0.991 for axes 1 and 0.999 and0.907 for axes 2 of archaea and bacteria, respectively).Additionally, the results of the Monte Carlo significancetests for each axis revealed that they were reliable to asignificant degree (p<0.004). The total carbon (TC),calcium carbonate, and chlorophyll a (bottom water) levelexplained the largest amount of variation of microbialcommunities (Fig. 2), which was supported by its highintraset correlations in Supplementary Table 1. Especially,the most important factors influencing archaeal andbacterial DGGE banding patterns were calcium carbonateand chlorophyll a, respectively. Statistical analysis based onthe PCR–DGGE of the 16S rRNA gene sequences supports

542 S.-J. Park et al.

the notion that the microbial communities were alteredaccording to their distance from the glacier front, whichmay be a function of the input of meltwater runoff resultingfrom deglaciation.

Clone Library Analysis of 16S rRNA Gene Sequences

Archaeal Communities Approximately 120 clones repre-senting the two sampling sites (station 188 and 176) wererandomly selected and screened using RFLP patterns inorder to avoid sequencing the same phylotypes. Finally,

102 clones were sequenced and analyzed from station 188and 176. Our estimations of species richness and diversityvalues (Table 2) and rarefaction analysis (data not shown)revealed that the archaeal communities were more diverseat station 188 than at station 176.

The majority of total archaeal clones belonged to thecrenarchaeotal group (CG) I.1a in both stations. OtherCrenarchaeota groups were detected, and were affiliated withthe Miscellaneous Crenarchaeota Group (MCG) (5.5% and25.5%), Marine Benthic Group (MBG)-B (1.8% and 14.9%),MBG-A (1.8% and 0%) and MBG-C (5.5% and 8.5%)

Figure 2 Effect of environmen-tal factors and microbial com-munity composition, asmeasured by the DGGE bandingpatterns. a Archaea. b Bacteria.Canonical correspondence anal-ysis (CCA) ordination diagramof microbial communities asso-ciated with the environmentalvariables of chlorophyll a (bot-tom water and transition layer,respectively), sedimentationrate, total nitrogen (TN), totalcarbon (TC), total inorganiccarbon (TIC), total organic car-bon (TOC), calcium carbonate(CaCO3), and carbon/nitrogenratio (C/N). The stations wereindicated as closed diamond,176; open circle, 178; cross,180; closed circle, 182; closedtriangle, 184; closed square,186; open triangle, 188

Prokaryotic Diversity Influenced by Deglaciation 543

(stations 176 and 188, respectively) (Fig. 3). Additionally, afew Euryarchaeota groups belonging to the TerrestrialMiscellaneous Euryarchaeotal Group (TMEG) (21.8% and10.6%), VAL III (0% and 2.1%), Methanogen (0% and2.1%), and MBG-D (1.8% and 4.3%) were also noted(stations 176 and 188, respectively) (Fig. 3). The majority ofthese archaeal groups have been discovered in sub-seafloormarine sediments [41–43].

Interestingly, the proportion of CG I.1a clones (32%) atthe station 188 site was substantially lower than that ofstation 176 (62%) (Fig. 3). The representative clone A176T-6 of CG I.1a was closely related to “Ca. Nitrosopumilusmaritimus” (Supplementary Fig. 3) (with 95% 16S rRNAgene sequence similarity) which was identified as anammonia-oxidizing autotrophic archaeon isolated from anaquarium environment [44]. The CG I.1a was detected

predominantly in the surface layers of organic-poor marinesediment [44–48]. One of the significant differences inarchaeal community composition between the station 176and 188 is the proportion of MCG (5.4 and 25.5%,respectively) (Fig. 3). MCG is known to be a dominantarchaeal group in subsurface marine sediments (SupplementaryFig. 3) [41, 49, 50]. Another dominant group in station 188was MBGB (14.8%), which is also a major archaeal memberin subsurface marine sediments, which have been previouslyproposed to utilize organic carbons [49].

Bacterial Communities Approximately 200 clones repre-senting the two sampling sites (station 188 and 176) wererandomly selected and screened using RFLP patterns inorder to avoid sequencing the same phylotypes. Subse-quently, 180 clones of station 188 and 176 were sequencedand analyzed from station 188 and 176.

The proportions of singletons were 25.9% and 17.1% ofeach of the clone libraries for the station 176 and 188 sites,respectively (Table 2). Our estimations of species richnessand diversity value demonstrated that the bacterial commu-nity at station 188 also maintained greater diversity than didthe bacterial community at station 176. When we comparedthe two libraries using UniFrac, the P value was less than0.05 which indicates the two libraries were significantlydifferent. Many of the bacterial lineages detected in station188 were not detected in station 176 (Fig 4 and Supple-mentary Fig. 4). Only two phylotypes—B176T-4 (gamma-Proteobacteria) (11 clones) and B176T-2 (Flavobacteria)(7 clones)—were commonly detected in both stations(Supplementary Fig. 4). The sequence of the clones ofB176T-4 and B176T-2 was found to be most similar tothose of SC1-44 and PV2-28, respectively, which wereisolated from coastal regions [51, 52]. The gamma-

Table 2 Estimation of sequence diversity and phylotype coverage of16S rRNA gene sequence libraries

Sampling station Archaeaa Bacteriaa

176 188 176 188

Phylotypes 11 12 33 42

Singletons 4 5 21 17

No. of clones 55 47 81 99

Shannon's Index for diversity 1.50 2.24 3.07 3.46

Simpson's Index for diversity 0.60 0.85 0.93 0.96

Chao1 estimated richness 13 16 75 51.07

Chao1 standard deviation 2.89 2.59 26.02 5.9

Good's coverage, % 80.0 74.5 59.3 57.6

Diversity indices and richness estimators were calculated using theESTIMATES softwarea Diversity was estimated using operational taxonomic unit anddefined as groups with ≥97% sequence similarity

Figure 3 Abundance of archaea from marine sediment. Relativeabundance (percent contribution) of archaeal groups of arctic marinesediments is shown in the pie chart

Figure 4 Abundance of bacteria from marine sediment. Relativeabundance (percent contribution) of bacterial groups of arctic marinesediments is shown in the pie chart

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Proteobacteria were the dominant occupants of bothstations, accounting for 31.3% and 45.7% of the popula-tions therein, respectively (Fig. 4). Other bacterial groupsdetected in the stations were as follows: Flavobacteria(7.4% and 36.4%), delta-Proteobacteria (32.1% and 3.0%),alpha-Proteobacteria (0% and 21.2%), Planctomycetacia(6.2% and 0%), Verruccomicrobia (0% and 4.0%), Actino-bacteria (0% and 3.0%), Sphingobacteria (1.0% and 1.1%),epsilon-Proteobacteria (1.2% and 0%), Deferribacteres(0% and 1.2%), and unclassified bacteria (4.9% and 0%)(stations 176 and 188, respectively) (Fig. 4).

One significant difference between stations 176 and 188 inFig. 4 was the abundance of delta-Proteobacteria, which wasdistributed throughout various marine sediments [53–55]. Adominant delta-Proteobacteria (10.5%) at station 176 wasrelated to the sulfate-reducing bacteria (SRB) of the ordersDesulfobacterales and Desulfuromonadales (SupplementaryFig. 4b). By way of contrast, no SRB-related clones weredetected at station 188. Many clones of SRB detected atstation 176 were related to previous Svalbard sedimentclones [56]. The most dominant SRB clones (10 clones)represented as 176T-45 were closely related to Desulfobulbusmediterraneus (92.1% 16S rRNA gene similarity) [57] andSvalbard sediment clones (Sva0103 and Sva1041) (98.0%16S rRNA gene similarity) [56]. The other clones (B176T-65)were found to be related to uncultured Desulfuromonadaceaesuch as Sva1033, Sva1034, and Sva0566, all of which werefound in the Svalbard sediment (96.2–98.6% 16S rRNA genesequence similarity) [56].

Alpha-Proteobacteria, which are commonly detected inplanktonic microbial communities [58], were also abundant(21.2%) in station 188. This demonstrates that the coastalsediment at station 188 differs substantially from typicalmarine sediments. The majority of phylotypes of the alpha-Proteobacteria were not closely related to any culturedrelatives, and only one phylotype (B188-34) was related tothe heterotrophic bacterium Hoeflea marina [59].

The Flavobacteria cluster comprised a significantproportion of the community of station 188 (36%). Theproportion of the Flavobacteria cluster varies considerablydepending on the properties of the sediment, although thephylum is a common member of microbial communities inmarine sediment [58, 60, 61].

The gamma-Proteobacteria were the predominant speciesin both examined stations (188 and 176). The predominanceof the gamma-Proteobacteria was observed broadly intypical marine sediments, such as Arctic Ocean sediments[56, 62] and marine sediments in Washington, USA, [63]and Japan [64]. Many clones of the gamma-Proteobacteriaspecies acquired in this study were related to sulfur-oxidizingbacteria such as Thiohalpophilus (B188-32, B176-58 andB176-4), unclassified Ectothiorhodospira, and Lucinomaendosymbionts in the gut (B188-193 and B188-28).

Discussion

Influxes of meltwater runoff from terrestrial deglaciation tocoastal environments are known to alter significantly theproperties of seawater and sediments during the summerseason, as is shown in Table 1. The decreasing copynumber of 16S rRNA gene and extraction yields ofgenomic DNA from stations 176 to 188 demonstrate thatecological habitats were generally deteriorated with in-creasing proximity to the glacier front and abundance ofmicrobial populations are profoundly influenced by degla-ciation runoff. The dominance of foraminiferous speciesthat thrive in cold, turbid waters and nutrient-deficientconditions was previously noted in the inner parts of thefjord [65, 66] and has been generally attributed toenvironmental stresses, resulting from the profound impactof meltwater runoff. This is suggested by the observedreduction in TN, coupled to an increase in the C/N ratiosand TIC percentages (Table 1) and a decreasing number ofbenthic foraminifera in the glacier fronts [67]. Accordingly,the microbial communities of coastal sediment in vicinity tothe melting glacier front were distinct from those distantfrom the glacier front. The results of our UPGMA analysisof the PCR–DGGE profiles of the 16S rRNA genedemonstrated that the archaeal and bacterial communitieswere distinct between station 176 and the other stations.Results of both CCA analyses and Monte Carlo tests ofDGGE banding profiles also support the notion that thedifferences in microbial communities could be correlatedstrongly with seawater and sediment properties (such asTC, calcium carbonate, and chlorophyll a (bottom water)),reflecting the influence of meltwater runoff (see Table 1).

Despite the diversity of archaeal sequences in our clonelibraries, we were able to observe only simple DGGEbanding profiles at each site. As previously noted by Banoet al. [68], there were significant mismatches on the PCRprimers for the archaeal 16S rRNA genes. In our clonelibrary, 70% of sequences (40% of all phylotypes)evidenced more than one mismatch with the 340F primer,and all of the sequences exhibited more than one mismatchwith the 517R primer (Supplementary data Table 2). Thisimplies that the universal primers used routinely for thePCR–DGGE profiling of the 16S rRNA gene sequences ofthe archaeal community should be revised on the basis ofupdated archaeal rRNA gene databases.

Fresh sediments piled up with soils in deglaciationrunoff can create unique microbial niches. In fact, asignificant fraction of the bacterial community in station188 was not related to any currently known taxa, but onlyto uncultured organisms. When we compared individualclones of station 188 and 176 to sediments of the Svalbard[61] and Pacific Arctic Ocean areas [61, 62], the bacterialclones obtained from station 176 evidenced 2–3 times

Prokaryotic Diversity Influenced by Deglaciation 545

higher matches than those of station 188 based on a cutoffvalue of 97% or 95% 16S rRNA gene sequence similarity(data not shown). PCA results based on the communitycomposition on the level of phylum and/or class demon-strated that only the bacterial communities of station 176 wereclustered together with those of the Svalbard, Antarctic, andPacific Arctic sediments (Supplementary data Fig. 1). Thisanalysis demonstrates that the microbial community ofstation 188 is distinct from microbial communities of typicalmarine sediment, such as station 176.

The low TOC and high TIC, coupled with high levels ofsuspended particles, demonstrate that the majority of thesediment soils in the proximal basin are derived from theturbid water drained from Tunabreen and von Postbreen(Fig. 1). Owing to the influx of deglaciation runoff,allochthonous microorganisms were expected to bedetected in station 188. However, the detected microbialcommunity seemed to be autochthonous. Despite the factthat the majority of soil particles of the marine sedimentswere delivered recently from melting glaciers, only negli-gible portions of clones were possibly related to terrestrialmicroorganisms. None of the clones related to cyanobac-teria and CG I.1b was observed in our libraries. Further-more, only minor portions of clones associated with theterrestrial environment were detected in stations 188 (5.1%)and 176 (1.2%). These phylotypes (B188-71, B188-95,B188-49, and 176T-63) exhibited sequence similarities of92.5% to 98.8% to phylogenetic genes retrieved from soilenvironments [69–71].

One of the most interesting differences in bacterialcommunities is that we were unable to detect candidateclones of the SRB at station 188, although these clonesare found quite routinely in station 176 and other marinesediments [53–55]. In coastal marine sediments, an anoxicenvironment develops beneath the surface layer, allowingfor the SRB-mediated production of reduced sulfurcompounds, which support the growth of SOB in thesurface layer. In station 188, the microbial biomass wasquite low compared to typical coastal marine sedimentssuch as those of station 176, and the major bacterialconstituents were putatively aerobic heterotrophs. Thechlorophyll a content profile supports the notion that thestratification observed in the typical seawater column asobserved in station 176 was not observed in station 188[72]. Thus, the photosynthetic activity might not be sufficientto supply organic matter to the seafloor microbial community,rendering less likely the development of a typical coastalmarine sediment ecosystem.

In conclusion, the results of this study demonstrate thatthe sediment properties such as TC and calcium carbonateand chlorophyll a in bottom water in the stations close toglacier front were significantly affected by the influx ofdeglaciation runoffs. Thus, the sediment is considered to

provide microbial habitats for distinct archaeal and bacterialcommunities. Therefore, future study about microbialactivities is required to address the function of abundantphylogenetic groups in the microbial communities.

Acknowledgments This work was supported by Ministry of Scienceand Technology grant (R01-2007-000-20806-0), Basic Science Re-search Program (2009–0087901), and Mid-career Research Program(2010–0014384) through the National Research Foundation of MEST(Ministry of Education, Science and Technology), South Korea.

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