RESEARCH ARTICLE

Bacterial community composition and diversity of five different permafrost-affected soils of Northeast Greenland Lars Ganzert1, Felizitas Bajerski1 & Dirk Wagner2 1

Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Potsdam, Germany; and 2GFZ German Research Centre for Geosciences, Section 4.5 Geomicrobiology, Potsdam, Germany

Correspondence: Dirk Wagner, GFZ German Research Centre for Geosciences, Section 4.5 Geomicrobiology, Telegrafenberg C-425, 14473 Potsdam, Germany. Tel.: +49 331 288 28800; fax: +49 331 288 28802; e-mail: [email protected]

MICROBIOLOGY ECOLOGY

Present addresses: Lars Ganzert, Finnish Forest Research Institute, Rovaniemi, Finland Felizitas Bajerski, Leibniz Institute DSMZ – German Collection of Microorganisms and Cell Cultures, Inhoffenstr. 7-8, 38124, Braunschweig, Germany Received 29 November 2013; revised 5 March 2014; accepted 7 May 2014. Final version published online 9 June 2014. DOI: 10.1111/1574-6941.12352

Abstract Greenland is one of the regions of interest with respect to climate change and global warming in the Northern Hemisphere. Little is known about the structure and diversity of the terrestrial bacterial communities in ice-free areas in northern Greenland. These soils are generally poorly developed and usually carbon- and nitrogen-limited. Our goal was to provide the first insights into the soil bacterial communities from five different sites in Northeast Greenland using culture-independent and culture-dependent methods. The comparison of environmental and biological data showed that the soil bacterial communities are diverse and significantly pH-dependent. The most frequently detected OTUs belonged to the phyla Acidobacteria, Bacteroidetes and (Alpha-, Beta-, Delta-) Proteobacteria. Low pH together with higher nitrogen and carbon concentrations seemed to support the occurrence of (Alpha-, Beta-, Delta-) Proteobacteria (at the expense of Acidobacteria), whereas Bacteroidetes were predominant at higher values of soil pH. Our study indicates that pH is the main factor for shaping bacterial community, but carbon and nitrogen concentrations as well may become important, especially for selecting oligotrophic microorganisms.

Editor: John Priscu Keywords Northeast Greenland; Arctic; active layer soil; T-RFLP; bacterial community.

Introduction Scientific and public interest in Greenland is rapidly increasing as it is one of the key regions in the Arctic which might be severely affected by the climate change (Howat & Eddy, 2011; Normand et al., 2013). As the average temperature in northern high latitudes has increased twice over the past 100 years compared with the global average rate (Bernstein et al., 2007), these ecosystems are likely to be more susceptible by global warming than other regions, and changes have already been documented (reviewed in Serreze et al., 2000). Warmer temperatures could cause not only a further retreat of inland ice, but also a succession of plants and thereby a shift in the bacterial community as shown to occur in alpine glacier forefields (Miniaci et al., 2007). Plants have a direct influence on the structure of the soil microbial ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

communities as they provide organic matter in form of litter or low-molecular root exudates which can be used by the soil microbiota as a carbon source (Rovira, 1969; van Veen et al., 1989; Griffiths et al., 1998; Str€ om et al., 2003). The response of the microbial communities to these changes and its impact on the ecosystem development is therefore of high relevance for the understanding of these polar soil habitats (Graham et al., 2012). Over the last 20 years, microbiological investigations using different molecular methods showed a high bacterial diversity in different cold terrestrial environments such as the Arctic region (Hansen et al., 2007; Steven et al., 2007; Liebner et al., 2008; Sch€ utte et al., 2009; Frank-Fahle et al., 2014), the Antarctic continent (Aislabie et al., 2006; Smith et al., 2006; Yergeau et al., 2007; Niederberger et al., 2008; Ganzert et al., 2011a; Bajerski & Wagner, 2013) or high alpine zones (Sigler et al., 2002; Bai et al.,

FEMS Microbiol Ecol 89 (2014) 426–441

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Bacterial communities in Northeast Greenland soils

2006; Costello & Schmidt, 2006; Schmidt et al., 2009; Zinger et al., 2009). The main groups that could be identified belonged to the phyla Proteobacteria, Actinobacteria, Acidobacteria, Bacteroidetes, Planctomycetes, Chloroflexi and Verrucomicrobia. Using culture-dependent approaches, mainly Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes, were isolated from high Arctic soils (Hansen et al., 2007; Steven et al., 2007), with similar results reported for high alpine (Bai et al., 2006; Zhang et al., 2007a) and Antarctic regions (Shivaji et al., 2004; Aislabie et al., 2006). These isolation strategies contributed to the description of several novel psychrotolerant species (e.g. Bajerski et al., 2011; Ganzert et al., 2011b; Mykytczuk et al., 2012; Wagner et al., 2013). Microbial cell counts revealed up to 109 cells g
1 of dry weight (DW) in Siberian permafrost (Vorobyova et al., 1997; Kobabe et al., 2004), Spitsbergen (Hansen et al., 2007) and other cold habitats like Antarctic mineral soils (B€ olter, 1995; Cowan et al., 2002) or glacier forefields (Sigler et al., 2002), which is comparable with cell numbers from a temperate forest soil (Ekelund et al., 2001). The current knowledge of the bacterial community and diversity of soils of Greenland is little and limited to thermophilic and methanotrophic bacteria (Boyd et al., 1990; Roslev & Iversen, 1999; Barcena et al., 2011). Most investigations have targeted the microbial communities in marine habitats (Glud et al., 2004; Schmidt et al., 2006), ice cores (Sheridan et al., 2003; Miteva et al., 2004), cryoconite holes (Cameron et al., 2012) and snow and freshwater (Møller et al., 2013). The aim of this study was to provide a first insight into the general bacterial communities of five different Arctic soils from Northeast Greenland using culture-dependent and culture-independent methods. We used terminal restriction length polymorphism (T-RFLP) and clone libraries to determine bacterial community composition and diversity. Numbers of heterotrophic bacteria were determined using plate counts and total numbers of microorganisms were counted using epifluorescence microscopy. We also performed culturing to unveil part of the cultivable heterotrophic bacterial diversity and thus to expand the information of bacterial diversity in permafrost-affected soils of Northeast Greenland.

Materials and methods Study area

The islands of Store Koldewey (SK) (N75°550 –76°450 / W18°270 –19°100 ) and Geographical Society Ø (GSØ) (N72°400 –73°040 /W21°52–24°350 ) are situated at the coast of Northeast Greenland (Fig. 1; Supporting Information, Table S1). SK is c. 80 km long with a maximum width of FEMS Microbiol Ecol 89 (2014) 426–441

10.5 km and consists of Precambrian metamorphic bedrocks (mainly gneiss) older than 1800 Ma and Jurassic and Cretaceous marine sediments that can be found in the eastern parts of the island (Henriksen, 1997; Henriksen & Higgins, 2009; Bennike et al., 2010). Along the western side of the island flat-topped mountain ranges of 500– 900 m elevation a.s.l. are present, whereas the eastern part is characterized by plains of 100–200 m a.s.l. Perennial snow fields as well as a few ice caps occur especially at higher elevations; however, most areas of the island are snow-free during the summer season. Temperatures are low with an average of
24 to
10 °C during winter season and of
4 to 7 °C during summer (Hansen, 2001). Annual precipitation is low with about 150 mm (Cappelen et al., 2001). Vegetation is mostly sparse and dominated by dwarf shrubs (Salix arctica, Dryas octopetala, Cassiope tetragona), various herbs, sedges, grasses, bryophytes and lichens. Due to the cold temperature and the low precipitation, soil formation is limited. The soils can be mostly classified as permafrost-affected polar desert soils (gelic gleysols, regosols) with only a thin humus layer (Jakobsen, 2001; Bennike et al., 2004). The topology of GSØ is characterized by mountains that reach up to 1700 m a.s.l. in the western part and a hilly terrain in the southern an eastern part of the island. Climatic conditions are similar to SK, but the average annual precipitation is higher, 300 mm (Reeh, 1989), and temperature warmer, which allows the growth of Betula nana. Soil sampling

During the summer period in 2003, soil samples from SK and GSØ were collected. Five soil profiles were excavated using a shovel and each soil layer was randomly subsampled to reduce sample heterogeneity. Altogether, 21 samples from defined layers were used for soil chemical, soil physical and soil microbiological analyses (Table 1). Four of the soils (SK1, SK2, GSØ1, GSØ2), situated near the shores of different small lakes, were classified as Gleysols whereas one soil (SK3) could be classified as a Regosol (FAO, 1998). Samples for soil physicochemical analyses were transferred into sealable plastic bags and stored at 4 °C until analysed in the laboratory. Samples for microbiological analyses were placed in 125-mL sterile Nalgene boxes thatwere immediately frozen and then transferred at
20 °C to Germany with the research vessel ‘RV Polarstern’. Soil physicochemical and microbiological analyses

Grain size distribution was determined on freeze-dried, homogenized and sieved soil samples (< 2 mm) of ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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L. Ganzert et al.

Fig. 1. Investigation areas located in Northeast Greenland.

about 15 g (Schlichting et al., 1995). Water content was determined after freeze-drying the soil samples. Total carbon (TC) and total nitrogen (TN) contents were measured in duplicate on a milled sample using an automatic element analyser (Elementar Vario EL III). Conductivity and pH were measured on a 1 : 2.5 soil : de-ionized water slurry that was shaken for 1 h in the dark using a MultiLab 540 (WTW, Germany). Numbers of cultivable heterotrophic bacteria were determined in triplicate by plating serial soil dilutions (down to 10
5) on modified BR agar plates (Ganzert et al., 2011b) with incubation at 10 °C for 14 days. These plates were also used for the isolation of pure cultures. Cells from randomly chosen single colonies were transferred to fresh BR agar plates by using a sterile inoculation loop. After several repeats, using the respective inoculated plates as source, pure cultures could be established. For direct total microbial counts, soil dilutions were prepared in 0.9% NaCl and 10 lL were pipetted onto each of 10 individual fields of a coated microscopy slide (Erie Scientific Company, Portsmouth). Cells were stained with 40 ,6-diamidino-2-phenylindole (DAPI) and counted using epifluorescence microscopy with 10009 magnification. For each sample, 30 separate squares integrated in the ocular were counted and the mean value was calculated. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

DNA extraction, PCR amplification and sequencing of 16S rRNA genes

DNA was extracted in triplicates from 0.5 g of the soil layers from each soil profile using the PowerSoil Extraction Kit (MoBio Laboratories, Inc.) according to the manufacturer’s protocol. DNA from the three replicates were pooled together and 16S rRNA gene fragments were generated in duplicate reactions using primers 27F (30 AGAGTTTGATCMTGGCTCAG-50 ; Lane, 1991) and 907R Muyzer et al., (30 -CCGTCAATTCCTTTRAGTTT-50 ; 1995). A 25 lL PCR contained 12.5 lL Sybr Green Polymerase Mix (Qiagen), 0.5 lL of each primer (20 lM) and 3 lL 10-fold diluted DNA extract, brought to final volume with PCR grade water. PCR amplification was carried out with a BioRad iCycler system (BioRad, Munich, Germany) with 94 °C for 5 min followed by 30 cycles of 94 °C, 60 s; 55 °C, 45 s; and 72 °C, 60 s and a final elongation step of 15 min at 72 °C. To get a general overview of the soil bacterial community, PCR products originated from each sampling site were combined. The purified PCR product (1 lL) was ligated in a pGEM-TEasy vector system (Promega, Mannheim, Germany) according to the manufacturer’s protocol with incubation at 4 °C overnight. After transformation and overnight growth at 37 °C, 288 clones were picked from each FEMS Microbiol Ecol 89 (2014) 426–441

FEMS Microbiol Ecol 89 (2014) 426–441

0–1 1–6 6–12 12+ 0–2 2–4 4–6 6–29 29+ 0–4 4–35 35–44 44+ 0–2 2–4 4–6 6–9 9–22 0–2 2–9 9–16

SK1

34.8 19.1 21.5 20.6 70.3 53.8 25.4 30.2 42.1 13.4 15.8 17.5 10.9 35.7 24.3 29.1 22.0 16.9 64.6 19.5 13.6

Moisture (%) 78.3 62.6 58.6 63.8 75.5 76.3 79.1 78.0 64.1 69.7 77.4 50.9 30.6 78.5 76.5 75.7 88.8 79.2 66.4 66.0 69.1

Sand* (%)

n.d., no data. *Part of the grain size fraction < 2 mm. † Standard deviation in brackets.

GSØ2

GSØ1

SK3

SK2

Depth (cm)

Site 11.1 28.8 26.0 22.8 15.6 16.3 15.9 19.3 31.7 21.6 17.1 28.1 34.3 16.4 17.5 17.8 6.4 11.9 26.5 30.7 23.5

Silt* (%) 10.6 8.7 15.3 13.4 8.9 7.5 5.1 2.7 4.2 8.8 5.5 21.0 35.1 5.1 6.0 6.5 4.8 9.0 7.0 3.2 7.4

Clay* (%) 0.42 0.55 0.63 0.58 7.36 2.42 1.97 0.83 0.29 0.72 0.47 0.49 2.98 0.81 0.74 1.03 0.21 0.17 0.85 0.43 0.35

( 0.0019) ( 0.0038) ( 0.0026) (0.0152) ( 0.2729) ( 0.0357) ( 0.0045) ( 0.0020) ( 0.0099) ( 0.0004) ( 0.0032) ( 0.0074) ( 0.0057) ( 0.0124) ( 0.0059) ( 0.0048) ( 0.0063) ( 0.0039) ( 0.0097) ( 0.0068) ( 0.0046)

Total C (%)† 6.80 6.43 6.25 5.84 5.69 5.37 5.08 4.66 6.27 6.39 7.29 7.56 7.65 6.15 4.90 5.20 6.66 7.17 7.27 8.30 8.00

0.0001) 0.0006) 0.0099) 0.0013) 0.0134) 0.0007) 0.0014) 0.0308) 0.0047) 0.0026) 0.0001) 0.0032) 0.0056) 0.0013) 0.0039) 0.0046) 0.0030) 0.0017) 0.0058) 0.0003) 0.0003)

< 0.10 0.10 0.11 0.10 0.51 0.24 0.21 0.15 < 0.10 0.12 < 0.10 < 0.10 0.27 0.11 0.10 0.12 < 0.10 < 0.10 0.13 < 0.10 < 0.10 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (

pH

Total N (%)†

Table 1. Soil physicochemical and microbiological parameters of five permafrost-affected soils from Northeast Greenland

1883 60 80 77 1219 241 144 202 59 197 71 72 66 239 227 272 65 44 199 128 191

EC (lS cm
1) 1.9 6.5 6.0 5.5 1.2 2.8 2.6 6.7 9.2 1.3 1.2 2.7 3.2 1.2 1.2 5.8 3.5 2.5 6.7 1.7 6.7

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 105 105 103 105 106 105 104 102 103 106 106 105 104 105 104 103 104 104 105 105 103 ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( ( (

5.2 3.0 3.1 7.2 4.8 7.8 2.5 2.7 4.3 3.1 2.7 5.9 4.6 2.8 5.4 2.9 2.3 4.1 9.4 3.5 5.0

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9

104) 105) 103) 104) 104) 104) 103) 102) 103) 105) 105) 104) 103) 104) 103) 103) 103) 103) 104) 104) 102)

Number of culturable heterotrophs (g
1 dry soil)†

6.4 4.4 2.5 1.8 1.4 2.4 8.1 1.0 5.7 4.2 2.0 2.8 4.3 1.1 2.4 3.3 3.5 1.0 3.0 3.7

9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9 9

107 107 107 107 108 108 107 108 107 107 107 106 105 107 106 106 106 106 107 106 n.d.

Total microbial counts (g
1 dry soil)

Bacterial communities in Northeast Greenland soils

429

ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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430

library. Re-amplification of cloned sequences was carried out in a direct whole-cell PCR with forward and reverse primer M13 recognizing the vector region, Mango Polymerase Mix (Bioline) and 1 lL template. Sequencing was performed at GATC (Konstanz, Germany). DNA from pure cultures was isolated with an UltraClean Microbial DNA Isolation Kit (MoBio Laboratories, Inc.) according to the manufacturer’s protocol. 16S rRNA gene fragments were amplified using the PCR conditions as previously described with primers 27F and 1492R (ACCTTGTTACGACTT; Lane, 1991). Partial sequences were obtained by GATC using only the 27F forward primer for sequencing. All soil clone and isolate sequences obtained in this study have been deposited under the GenBank Accession Numbers KF973488–KF974271 and KF974272–KF974324, respectively. Terminal restriction length polymorphism (TRFLP), data processing and statistics

A semi-nested PCR was used to amplify 16S rRNA genes as described elsewhere (Bajerski & Wagner, 2013). For all samples, the 20 lL digestion reaction contained about 150 ng of purified PCR product, 10 U restriction enzyme HaeIII (New England Biolabs, Frankfurt a. M.) and 2 lL corresponding buffer. Amplicons were digested in duplicates at 37 °C for 2 h, and the reaction was stopped by incubating at 80 °C for 20 min (UnoCylcer, VWR). Pooled and cleaned duplicate digestions were run on an ABI 3730xl DNA Analyser (Applied Biosystems, Darmstadt, Germany) at GATC Biotech (Konstanz, Germany) using GeneScanTM LIZ 500 (Applied Biosystems) as an internal size standard and POP7 as the running polymer. Raw data were analysed with PEAK SCANNER Software 1.0 (Applied Biosystems) and output T-RF profiles were examined according to the five-step procedure described by Dunbar et al. (2001). Within one sample, triplicate peaks within the range of 0.5 bp were aligned, but duplicate peaks were allowed and processed further as well. Only peaks with fluorescence threshold ≥ 25 the fluorescence units were taken into account. Unfortunately, the digestion of sample SK1/0-1 was not successful, most probably due to low PCR product; therefore, it was excluded from further processing. Primer v6 (Primer-E Ltd, Lutton, UK) was used to carry out statistical analyses of the bacterial community composition. A hierarchal cluster analysis was performed on presence/absence data of the relative abundances of all T-RFs using Bray–Curtis similarity index. Further statistics were based on the concept of redundancy analysis (RDA) as implemented in the R package vegan v2.1-20 (Oksanen et al., 2013; R Core Team, 2013) comparing biological (T-RFs) and ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

environmental data (grain size, TN, TC, moisture, pH, conductivity). Abundance count data were Hellingertransformed to produce valid results in the RDA. This transformation downweights highly abundant T-RFs and is a good compromise between linearity and resolution (Legendre & Gallagher, 2001). The most appropriate statistical model for RDA was determined via bidirectional (combined forward/backward) selection based on both adjusted R2 and AIC-like statistics implemented in vegan’s ordistep () function. Significance of individual effects within the selected model were determined by customized permutation tests modelled after vegan’s anova.cca () function with restricted random permutations (n = 999). In the RDA plot, environmental vectors were fitted onto the RDA using the vegan function nfit (). Phylogenetic analyses and statistics

Gene sequence chromatographs were first checked manually with Sequencher v4.7 (Gene Codes, Ann Arbor, MI). Sequences were saved as a multifasta file and checked for chimeras using Uchime (Edgar et al., 2011). QIIME (Caporaso et al., 2010) was used to determine operational taxonomic units with Uclust (Edgar, 2010) and to calculate the Shannon H’ (Shannon & Weaver, 1949) and Simpson D (Simpson, 1949) diversity indices, Chao1 and ACE richness estimators (Chao, 1984; Chao & Lee, 1992), Good’s coverage C (Good, 1953) and Shannon’s evenness EH using the following equation: EH = H/Hmax = H/lnS, where H is Shannon’s diversity index and S the total number of species in the community. Bray–Curtis dissimilarity values were calculated to compare the different samples based on the bacterial community composition (Bray & Curtis, 1957). Unweighted Pair group Method with Arithmetic mean (UPGMA) was used to build a cluster tree based on the Bray–Curtis dissimilarity distance matrix for ≥ 97% sequence similarity. Beta diversity calculations are based on a rarefied sample set of 95 sequences to avoid sample heterogeneity. Classification was performed using the SINA alignment tool (Pruesse et al., 2012) integrated in the Silva website (www.arb-silva.de).

Results Soil characteristics

For five soils from Northeast Greenland, the following characteristics were analysed: moisture, grain size distribution, TC and nitrogen (TN) content, pH, conductivity (EC), number of culturable heterotrophic bacteria and total microbial counts (Table 1). The soil texture is mainly sandy for all investigated soils, with a shift to silty-clayey grain sizes in the FEMS Microbiol Ecol 89 (2014) 426–441

431

Bacterial communities in Northeast Greenland soils

lowermost layer for SK3. The carbon and nitrogen contents were in general low, with values < 1% for TC and around 0.1% for TN. SK2 soil profile showed higher carbon and nitrogen values in the upper parts (between 2% and 7% for TC and 0.2–0.5% for TN) that decreased significantly with increasing depth (to 0.3% TC and < 0.1% TN). The pH ranged from acidic (pH 4.7) to slightly alkaline (pH 8.3) conditions. Electrical conductivity (EC) was generally low with values between 44 and 272 lS cm
1, but two exceptions. The uppermost layers of profiles SK1 and SK2 showed a much higher EC value (1883 and 1219 lS cm
1, respectively) compared with the other parts of the profiles. The numbers of culturable heterotrophic bacteria ranged from 6.7 9 102 to 1.3 9 106 cells g
1 dry soil. Total microbial counts were one to five orders higher than the cell numbers of culturable heterotrophic bacteria ranging from 4.3 9 105 to 2.4 9 108 cells g
1 dry soil. Culturable heterotrophic bacteria

A total of 213 pure cultures could be obtained reflecting 65 OTUs at a sequence similarity level of 98.5%. The different OTUs were mostly assigned (55 of 65) to the bacterial phyla Actinobacteria and Proteobacteria, but strains related to the phyla of the Bacteroidetes and Firmicutes were also isolated (Table S2). Most of the Actinobacteria belonged to the genera Arthrobacter and Cryobacterium. A number of isolates were related to the order Rhizobiales (Alphaproteobacteria) with the closest match to the genera Rhizobium, Mesorhizobium and Bradyrhizobium. The pure cultures were closely related to other isolated strains with sequence similarities ranging from 96.9% to 100% (Table S2). More than 70% (47 of 65) of the closest matches obtained by a FASTA Prokaryote search (http:// www.ebi.ac.uk) belonged to sequences from other cold environments such as permafrost soil, high alpine regions, glaciers or the Antarctic continent. T-RFLP profiling

Within the set threshold of 0.5 bp, a total of 30 different terminal restriction fragments (T-RFs) were identified for both study sites. Eight T-RFs of 19 were unique to the SK location, 11 of 21 T-RFs resembled the GSØ location and 11 peaks were found at both locations (Fig. 2). No peaks could be determined for sample SK1/0-1. Some samples were equally composed of some major T-RFs (e.g. SK1/ 12+ SK2/0-2, SK2/2-4, SK2/4-6, SK2/6-29), while others were dominated by a specific T-RF (e.g. GSØ1/0-2, GSØ1/ 9-22, GSØ2/9-16). The most abundant and dominant T-RFs of this study were T-RF 26, 28, 33 and 74, FEMS Microbiol Ecol 89 (2014) 426–441

respectively, being present in at least 10 of 20 samples, which represents more than 10% of the total peak fluorescence each. Comparing all sites, the profiles of SK1 and GSØ2 differ substantially from the profiles of SK2, SK3 and GSØ1. The T-RFs 71 (c. 50% in SK1/1-6 and SK1/612), 72 (36% in SK1/6-12) and 245 (10% in SK1/3-6) were unique to the profile SK1. The deepest sample of profile SK1 (12+) was equally composed of the T-RFs 26, 28 and 33, sharing a higher similarity with profile SK2 that was further complemented by the T-RFs 35 and 53. The surface sample SK2/0-2 additionally contains T-RF 27, which could be determined in all measurable surface samples of the profiles. T-RF 74 was detected with high percentage (22–94%) in almost all samples of the profiles SK3, GSØ1 and GSØ2 (except GSØ1/9-22), whereas the samples GSØ1/9-22, GSØ2/0-2 and GSØ2/2-9 were distinctively dominated by the T-RF 73. A cluster analysis based on the abundance of the different T-RFs shows the similarities and differences of the samples (Fig. 3a). The cluster analysis revealed two main clusters formed by the T-RFs of the profile SK1 (1–12 cm) and the T-RFs present in all other samples clustering together with at least 20% similarity. This second main cluster is further divided in three subclusters. Here, the T-RFs of the GSØ2 profile and GSØ1/9-22 clearly differentiate from the other two subclusters sharing more than 50% similarity. The biggest cluster with 60% intersample similarity is mainly formed by the T-RFs of the profiles SK3 and GSØ1. The T-RFs of the third cluster showed a high similarity (> 90%) to the profile SK2 including the deepest layer of the SK1 profile (SK1/12+) with 70% similarity. Using step forward and backward selection, only the pH significantly influenced the T-RF distribution (P ≤ 0.01; Table S3) and the RDA explained 7.2% of the variance of the data (R2 = 0.07). The RDA showed that the T-RF distribution, and hence the microbial community structure, was highly pH-dependent (Fig. 4). In the first step of the forward selection, TN appeared to have a significant influence (P ≤ 0.05), but in the second forward step, none of the other parameters significantly influenced the data variance. The T-RF patterns of the samples SK3/0-4/4-35/, GSØ1/0-2/9-22 and GSØ2 correlated positively to the pH, whereas SK1/12+ and the SK2 cluster (depth 0–29 cm) showed a negative correlation to the pH. Combining all environmental parameters in the RDA, the cluster SK2 indicated a positive relation to TN and TC. Phylogenetic analysis of the dominant bacterial clones

To gain an insight into the terrestrial bacterial community of Northeast Greenland 1440 clones in total were ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

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432

Depth (cm)

SK1 0–2

1–6

0–4

2–4

6–12

4–35

4–6

35–44

6–29

12+

44+

29+ 0

20

40

60

80

100

0

20

40

60

80

% of peak fluorescence

% of peak fluorescence

GSØ1

GSØ2

0–2

Depth (cm)

SK3

SK2

100

6–9

9–16

9–22 0

20

40

60

80

100

% of peak fluorescence

40

25 26 27 28 33 35 36 37 53 71

2–9

4–6

20

0

20

40

60

80

60

80

100

% of peak fluorescence

0–2

2–4

0

100

72 73 74 100 233 239 243 245 246 249

253 257 379 397 463 558 560 585 587 596

Average peaks size (T-RFs)

% of peak fluorescence

Fig. 2. T-RF-based bacterial community composition. (first number is the sampling site, following numbers indicate the depth where the sample was taken from).

picked (288 per sampling site) and, after quality and chimera check, 784 sequences were used for further analysis. They could be assigned to 25 different (sub-)phyla, mainly belonging to Acidobacteria, Actinobacteria, Bacteroidetes, Chloroflexi, Verrucomicrobia, (Alpha-, Beta-, Delta-) Proteobacteria (Fig. 5). At the phyla-level, sites SK1 to SK3 showed a higher diversity than GSØ1 or GSØ2, with 18–21 different phyla compared to 16 and 10, respectively. The phyla Acidobacteria, Bacteroidetes, Chloroflexi, Firmicutes, Planctomycetes, candidate division OP11, (Alpha-, Beta-, Delta-) Proteobacteria were detected in every site, although often only in low abundances. Verrucomicrobia were present in all the SK sites, whereas they were absent in the clone libraries of the GSØ sites. In the GSØ sites, the main fraction of the sequences (50–60%) consisted of Acidobacteria and Bacteroidetes. Sequences belonging to Cyanobacteria were found in low abundance only in SK3. Other detected phyla were Chlorobi, Elusimicrobia, Fibrobacteres, Gemmatimonadetes, Nitrospirae, candidate divisions OD1, TM7, WS3, WS6, Gammaproteobacteria, Epsilonproteobacteria and unclassified bacteria, but they were present only in certain sites and mainly in low abundances. Of the 784 sequences, 370 OTUs (97% sequence similarity) were obtained (Table S4). Acidobacteria were the most diverse group with 72 different OTUs with about 40% of them related to Subgroup 4. Other diverse groups ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

were Bacteroidetes and Actinobacteria with 64 and 29 OTUs, respectively, as well as Chloroflexi (22 OTUs), Alphaproteobacteria (25 OTUs), Betaproteobacteria (22 OTUs), Deltaproteobacteria (21 OTUs) and Verrucomicrobia (21 OTUs). Interestingly, most unclassified Bacteroidetes were found in the locations GSØ1 and GSØ2, whereas OTUs that could be classified at a further level (order, family and genus) were mainly observed in the SK locations. Candidate divisions were generally low in abundance, except for SK2 and GSØ1, where phyla OD1 (13.1%) and OP11 (12.0%), respectively, represented a substantial part of the bacterial community. Alpha diversity values were calculated for different sequence similarity levels (Table S5). Good’s coverage (estimates the sampling completeness) was not very high at 97% sequence similarity, especially for sample SK3 where diversity seems to be much higher than in for example GSØ2. This was supported by Chao1 and ACE richness estimators where the GSØ locations showed much lower diversity compared with SK locations. With decreasing sequence similarity levels numbers of unique OTUs and estimated richness were approaching, reaching the same or almost the same number of observed OTUs at 70% sequence similarity. The coverage followed the same trend, indicating a sampling completeness at 70% sequence similarity. OTU distribution (97% sequence similarity) in the SK locations showed a high evenness, FEMS Microbiol Ecol 89 (2014) 426–441

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Bacterial communities in Northeast Greenland soils

Fig. 4. RDA ordination plot of the investigated soil samples, showing weights and orientation of samples and soil parameters.

(a)

evenness decreased, indicating a shift to the dominance of certain OTUs. Bray–Curtis dissimilarity values show differences in the community composition among the samples (Table 2). In Table 2, the closer the number is to 1, the higher is the dissimilarity between the bacterial communities. Especially, at a higher level of sequence similarity (e.g. species or genus), there were greater differences among the samples analysed. As previously observed, samples from the SK locations were more similar in bacterial community composition compared with samples from GSØ (see UPGMA tree in Fig. 3b).

Discussion

(b) Fig. 3. (a) Cluster analysis of T-RFLP pattern based on Bray–Curtis similarity values. Sample labels indicate the different soil profiles with the respective depth in cm. (b) Cluster analysis (UPGMA) of the clone libraries based on Bray–Curtis dissimilarity values. Sequence similarity level used in the distance matrix was 97%.

with values between 0.966 and 0.979, whereas the communities in the GSØ locations were less evenly distributed, with values of 0.920 and 0.937. Evenness is a parameter that shows whether the community is dominated by a minority or if OTUs are distributed evenly, with a value of 1 indicating a completely even distributed community. With decreasing sequence similarity levels FEMS Microbiol Ecol 89 (2014) 426–441

We investigated the bacterial community structure and diversity in five different soils of Northeast Greenland. Despite being influenced by harsh environmental conditions Arctic permafrost-affected soils are characterized by high bacterial diversity (Neufeld & Mohn, 2005; Liebner et al., 2008; Chu et al., 2010; Frank-Fahle et al., 2014). No analyses of the bacterial community in soils of Northern Greenland have been performed so far. Therefore, the results of this study provide the first insights into the terrestrial bacterial community structure of this part of the Arctic. The investigated soils were in general poorly developed. Although carbon content was slightly higher compared with other extreme cold habitats such as the maritime Antarctic (Ganzert et al., 2011a) or the Larsemann Hills, East Antarctica (Bajerski & Wagner, 2013), it was much lower than in organic-rich tundra soils in the Siberian or ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

L. Ganzert et al.

434

Fig. 5. Relative abundance of sequences in 16S rRNA gene clone libraries of combined soil samples from Northeast Greenland. Colour classification starts at 0° clockwise. Number of sequences: SK1 = 238, SK2 = 191, SK3 = 110, GSØ1 = 150, GSØ2 = 95. Table 2. Comparison of the bacterial clone libraries using Bray–Curtis dissimilarity for different sequence similarity cutoffs (lower numbers indicate higher similarities between the samples). Order of the numbers indicate the following sequence similarities: (a) ≥ 97%; ≥ 95%; ≥ 90; (b) ≥ 85%; ≥ 80%; ≥ 70%

(a) SK1 SK2 SK3 GSØ1 GSØ2 (b) SK1 SK2 SK3 GSØ1 GSØ2

SK1

SK2

SK3

GSØ1

GSØ2

0 0.92/0.89/0.80 0.97/0.90/0.81 0.96/0.93/0.88 0.97/0.96/0.92

0 0.97/0.92/0.87 0.94/0.94/0.90 0.98/0.98/0.94

0 0.92/0.88/0.82 0.94/0.90/0.86

0 0.88/0.77/0.70

0

0 0.64/0.52/0.26 0.70/0.58/0.37 0.84/0.75/0.32 0.97/0.84/0.46

0 0.69/0.57/0.32 0.83/0.74/0.41 0.87/0.79/0.48

0 0.74/0.65/0.31 0.80/0.79/0.32

0 0.63/0.49/0.25

0

Canadian Arctic (Wagner et al., 2005; Barbier et al., 2012). Nitrogen content was close to the detection limit and seems to be one of the limiting nutrient factors in ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

these soil ecosystems. The climate in Northeast Greenland is dry-cold and could be described as a polar semi-desert or desert. Low temperatures and low water availability are FEMS Microbiol Ecol 89 (2014) 426–441

Bacterial communities in Northeast Greenland soils

the main factors limiting the growth of plants. The investigated sites were characterized by sparse vegetation. Together with animals, plants are an important carbon and nitrogen source for soil microorganisms. Similarly, important in such nutrient-poor systems are autotrophic and diazotrophic microorganisms that are capable of fixing carbon dioxide and molecular nitrogen (Falkowski, 1997; Postgate, 1998; Yoon et al., 2000). Our results showed that total microbial cell numbers are in the range of 107–108 cells g
1 DW for the site with higher carbon content (SK2), whereas the low carbon sites were characterized by lower cell numbers. This is comparable with Arctic terrestrial sites from Spitsbergen (Hansen et al., 2007), the Lena Delta (Liebner et al., 2008) and the Canadian Arctic (Steven et al., 2008; Barbier et al., 2012). One reason for the lower total cell numbers could be the limited plant coverage and plant production in these High Arctic regions, which hampers the soil formation as carbon and nitrogen mineralization processes are reduced by the temperature regime with short cold summers and long cold winters. When comparing the five soil profiles, direct cell counts were several orders of magnitude higher than the counts by plate methods, which can be explained by the inability to culture all microorganisms (Torsvik & Øvre as, 2002) and also the selectivity of the medium used. Furthermore, the difference in numbers of culturable heterotrophic bacteria and the total cell numbers determined by epifluorescence microscopy could be attributed to the anaerobic conditions that are prevalent especially in the deeper parts of the soil profiles SK1, SK2, GSØ1 and GSØ2. These soils were influenced by the proximity of a lake and the consequent wet conditions finally lead to anoxic conditions. Here, the numbers of culturable aerobic bacteria decreased with increasing depth, whereas total cell counts did not change or decreased only to a minor extent. Although the majority of the sites were poor in carbon and nitrogen, an overall diverse bacterial community was observed. Many different phylogenetic lineages such as Actinobacteria, Bacteroidetes, Firmicutes, Chloroflexi, Chlorobi, Cyanobacteria and Proteobacteria include nitrogen-fixing organisms (Boyd & Peters, 2013), with representatives of these groups that could be detected in the investigated soils as well. Together with CO2-fixing bacteria from the phyla Chloroflexi, Chlorobi, Cyanobacteria, Firmicutes and (Alpha-, Beta-, Gamma-) Proteobacteria (Tabita et al., 2007), these autotrophic microorganisms might play a keyrole in establishing a functional microbial food web in cold, nutrient-depleted environments such as the high Arctic and Antarctica. The T-RFLP results showed differences as well as similarities in the bacterial community composition of the study sites. The profiles of SK1 and GSØ2 in particular FEMS Microbiol Ecol 89 (2014) 426–441

435

differed from the other profiles in appearance and abundance of the T-RF peaks. The T-RFLP analysis also revealed a change in community composition between upper and lower soil profiles. Moreover, some soil samples showed a differentiated community with the dominance of one or two groups (e.g. SK1/1-6 or GSØ2/9-16), whereas other sites had a heterogeneous and diverse composition, indicating a less specialized community (e.g. SK2/0-2, SK3/35-44). Integrating environmental and biological data, it became apparent that the bacterial community as determined by the T-RFLP-results was mainly shaped by the pH values of the profiles. Low pH values, together with higher nutrient concentrations, support the formation of a special uniform community cluster throughout the soil profile SK2. Acidobacteria, Bacteroidetes and Proteobacteria were the most frequently detected OTUs and could be found in each of the soil profiles. Higher carbon and nitrogen values in SK2 seem to reduce the abundance of Acidobacteria and lead to a predominance of (Alpha-, Beta-, Delta-) Proteobacteria. Although pH is low in SK2, and should be therefore more supportive for Acidobacteria (Lauber et al., 2009), the high nutrient content might be a limiting factor as members of this phylum are known to have an oligotrophic lifestyle (Ward et al., 2009). Environmental studies in different polar habitats showed a distribution of Acidobacteria in relation to low nutrient availability (Ganzert et al., 2011b) or acidic pH (M€annist€ o et al., 2007), indicating that the interaction of both parameters play an important role for the Acidobacteria community. Yergeau et al. (2012) further reported that a higher nutrient availability due to global warming could determine an increase in the Alphaproteobacteria-to-Acidobacteria ratio in Antarctic soils. Most OTUs (42%) of the Acidobacteria belonged to subgroup 4 that was shown to have a higher abundance in low carbon soils (Eichorst et al., 2011). The first isolated strain of this subgroup has been described recently (Foesel et al., 2013). Differently than isolates from other Acidobacteria subgroups it was not able to utilize low-molecular compounds but more complex substrates. This implies that members of Acidobacteria subgroup 4 could play a significant role in the degradation of highmolecular substances in such cold terrestrial habitats. Representatives of the phylum Bacteroidetes are also known to be capable of degrading high-molecular compounds and were shown to express genes involved in polysaccharide degradation in high Arctic peat soils (Tveit et al., 2013) together with Actinobacteria and Verrucomicrobia. Bacteroidetes were observed in all of the investigated sites, forming two main groups. About half of the OTUs belonged to the class Sphingobacteriales which were mainly abundant in sites of SK. They were also found in other cold habitats and are likely involved in the ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

436

degradation of biopolymers such as chitin or plant organic polymers (Yergeau et al., 2007; Wagner et al., 2009; Ganzert et al., 2011a). The other half of the Bacteroidetes OTUs could only be assigned as unclassified Bacteroidetes and were abundant mainly in the GSØ sites (see Table S4). They were distantly related to the families Cryomorphaceae, Cytophagaceae and Flavobacteriaceae which include also psychrotolerant species (e.g. van Trappen et al., 2005; Holmes et al., 2007). The dominance of Bacteroidetes sequences in sample GSØ2 could be due to the higher pH. This is in line with the results of Lauber et al. (2009) who found that the abundance of Bacteroidetes correlated positively with increasing pH. They also suggested that the same could happen for Actinobacteria but this was not the case for sample GSØ2 as members of this phylum were not found in our sequences although they are usually very common in soils (Janssen, 2006). However, we were able to isolate Actinobacteria from site GSØ2. It is known that some Actinobacteria (e.g. Arthrobacter) can be isolated using a wide range of media and substrates, whereas members of other phyla such as Acidobacteria and Verrucomicrobia are more difficult to culture, although they constitutes a large fraction of the bacterial community (Janssen, 2006). Thus, changes of the pH regimes influence microbial community composition in general, but certain phyla such as Acidobacteria are also affected by nutrient concentrations (Fierer et al., 2007). Interestingly, two candidate divisions, OD1 (13.1% in sample SK2) and OP11 (12.0% in sample GSØ1) represented a significant fraction of the bacterial community and might be therefore important in the nutrient turnover in certain soils of Northeast Greenland. Members of OD1 and OP11 were detected mainly in anoxic habitats (Dojka et al., 1998; Teske et al., 2002; Harris et al., 2004; Elshahed et al., 2005, 2008; Abulencia et al., 2006; Briee et al., 2007; Barberan & Casamayor, 2011; Peura et al., 2012). So far, no cultured representative is known from these groups and physiological characteristics are based on metagenomics only. Recently, Wrighton et al. (2012) described the potential physiological capabilities from 21 to 19 partial genomes belonging to the groups OD1 and OP11, respectively, which were recovered from an aquifer sample. Metagenomic features suggest a strictly anaerobic fermentation-based lifestyle and the involvement in hydrogen production and sulphur cycling. Furthermore, as the abundance of candidate division OD1 was found to correlate positively with methane concentration, OD1 might play a role in the anaerobic methane oxidation (Peura et al., 2012). Both groups were also found in a Siberian permafrost-affected soil (Wagner et al., 2009), indicating a possible relevance of these groups in carbon and nitrogen turnover in cold habitats. ª 2014 Federation of European Microbiological Societies. Published by John Wiley & Sons Ltd. All rights reserved

L. Ganzert et al.

The cultured strains from this study were taxonomically related to the phyla (Alpha-, Beta-, Gamma-) Proteobacteria, Actinobacteria, Bacteroidetes and Firmicutes which can be readily isolated from cold habitats (e.g. Shivaji et al., 2004; Bai et al., 2006; Møller et al., 2013). Representatives of these phyla are known to be involved in degradation and fermentation processes (Tveit et al., 2013). Some of the isolated bacteria in our study belonged to the order Rhizobiales (Alphaproteobacteria) and could be assigned to the genera Rhizobium, Mesorhizobium and Bradyrhizobium. They are known for their ability to fix nitrogen and therefore likely play an important role in the nitrogen cycle of Northeast Greenland soils (Martinez-Romero, 2006; Willems, 2006). About 70% of our isolates showed highest 16S rRNA gene sequence similarity (up to 100%) to strains isolated from other cold environments such as Antarctica, an Alaskan glacier surface, permafrost, sediment from Spitsbergen or high altitudinal regions of the Tibet Plateau and the Tianshan Mountains (see Table S2). This suggests that similar climatic conditions that prevail in different cold habitats, such as Arctic, Antarctic or high alpine areas, can have a significant effect in shaping the microbial community so that these regions are inhabited by the same or closely related species. Some of the strains we obtained are closely related to isolates that have not been described yet, while some others are highly similar to type strain organisms, for example: strain G03 79-8 and Mesorhizobium gobiense from an inner-asian desert (Han et al., 2008) (99.9% similarity); strain GL 112-12 and Cryobacterium psychrotolerans (Zhang et al., 2007b) (100%); or strain G03 45-14 and Paenibacillus glacialis (Kishore et al., 2010) from high central-asian mountains (99.6%). Dispersal over large distances can occur actively or passively. Active dispersal includes animal migration, most likely by birds, whereas passive dispersal includes transportation of dust particles by wind (Griffin et al., 2002; Wainwright et al., 2003). Both ways imply a high stress tolerance to several environmental factors like low temperature and nutrient concentrations, limited water availability or high UV radiation to survive the transportation conditions (Perfumo & Marchant, 2010; Harding et al., 2011). On the other hand, the local environmental conditions are of fundamental importance to select the colonizing organisms. Our findings are in accordance with Hansen et al. (2007) who also suggest the existence of cosmopolitan species in cold-temperature habitats. Briefly, the numbers of sequences that could not be assigned to any known phylum were low (≤ 4%) compared with other similar studies of cold environments which often exceeded 20% (e.g. Yergeau et al., 2007; Niederberger et al., 2008; Bajerski & Wagner, 2013). This is possibly due to a better taxonomical assignment as the FEMS Microbiol Ecol 89 (2014) 426–441

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training sets and alignment tools used for classification have developed substantially (Werner et al., 2012). Therefore, it is of great importance to use these new improved programs and reference databases, besides quality- and chimera-checked sequencing reads, to reduce the number of sequences that could not be assigned to a known group or to obtain a deeper taxonomy characterization.

Conclusion In this study, we showed that soils of Northeast Greenland are diverse and variable with regards to the bacterial community. pH is the main parameter shaping the community structure in the investigated ice-free habitats. Furthermore, soils from Northeast Greenland seem to be inhabited by cold-adapted bacteria that can be found also in other terrestrial low-temperature habitats such as high alpine or Antarctic soils.

Acknowledgements The fieldwork was conducted as part of the ARK XIX/4 expedition with RV Polarstern organized by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research. We thank the crew and captain of RV Polarstern, the crew of the helicopters, the expedition leader Wilfried Jokat and our fieldwork companions, particularly Bernd Wagner (University of Cologne) for organization of the land campaign and Svenja Kobabe (Alfred Wegener Institute) for soil sampling. We are grateful for the help and technical assistance of Ute Bastian and Friederike Bruns. We also thank Andre Lipski (University of Osnabrueck) for his support in classifying the isolated strains and Henrik Knecht (St€adtisches Krankenhaus Kiel, Hematology) for providing the R-based statistics. We thank Amedea Perfumo (GFZ German Research Centre for Geosciences Potsdam) for helping with the manuscript. This study was supported by the Deutsche Forschungsgemeinschaft (DFG) in the framework of the priority program ‘Antarctic Research with Comparative Investigations in Arctic Ice Areas’ by a grant to D.W. (WA 1554/4). The authors declare no conflict of interest.

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Supporting Information Additional Supporting Information may be found in the online version of this article: Table S1. Coordinates of the sampling sites. Table S2. Phylogenetic classification of the cultured isolates. Table S3. Overview of the RDA statistic. Table S4. Closest phylogenetic affiliation and abundance of each OTU (based on a sequence similarity of ≥ 97%). Table S5. Alpha diversity characteristics (diversity indices, coverage, evenness, species richness) of bacterial 16S rRNA gene clone libraries of samples from Northeast Greenland.

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