RESEARCH ARTICLE
Archaeal rRNA diversity and methane production in deep boreal peat ¨ a¨ 1 Anuliina Putkinen1, Heli Juottonen1, Sari Juutinen2, Eeva-Stiina Tuittila2, Hannu Fritze3 & Kim Yrjal 1
Department of Biological and Environmental Sciences, General Microbiology, University of Helsinki, Helsinki, Finland; 2Department of Forest Ecology, University of Helsinki, Helsinki, Finland; and 3Finnish Forest Research Institute, Vantaa Research Unit, Vantaa, Finland
¨ a, ¨ Department of Correspondence: Kim Yrjal Biological and Environmental Sciences, General Microbiology, University of Helsinki, PO Box 56, 00014 Helsinki, Finland. Tel.: 1358 9 19 159 220; fax: 1358 9 19 159 262; e-mail:
[email protected] Received 5 March 2009; revised 12 June 2009; accepted 14 June 2009. Final version published online 27 July 2009. DOI:10.1111/j.1574-6941.2009.00738.x
MICROBIOLOGY ECOLOGY
Editor: Alfons Stams Keywords peat; sediment; archaea; methane; 16S rRNA; T-RFLP.
Abstract Northern peatlands play a major role in the global carbon cycle as sinks for CO2 and as sources of CH4. These diverse ecosystems develop through accumulation of partially decomposed plant material as peat. With increasing depth, peat becomes more and more recalcitrant due to its longer exposure to decomposing processes. Compared with surface peat, deeper peat sediments remain microbiologically poorly described. We detected active archaeal communities even in the deep bottom layers ( 220/ 280 cm) of two Finnish fen-type peatlands by 16S rRNAbased terminal restriction fragment length polymorphism analysis. In the sediments of the northern study site, all detected archaea were methanogens with Rice Cluster II (RC-II) and Methanosaetaceae as major groups. In southern peatland, Crenarchaeota of a rare unidentified cluster were present together with mainly RC-II methanogens. RNA profiles showed a larger archaeal diversity than DNA-based community profiles, suggesting that small but active populations were better visualized with rRNA. In addition, potential methane production measurements indicated methanogenic activity throughout the vertical peat profiles.
Introduction Peatlands are areas where degradation of organic matter is lower than its production, mainly because of a high water table level. In the water-saturated layer, lack of oxygen significantly impedes the degradation, which causes plant residues to slowly accumulate as peat. During several millennia, accumulating residues form sediments that can be several metres deep. The sediment structure is layered, each layer reflecting plant assemblages and environmental conditions during its formation. Although generally sinks for carbon dioxide, peatlands are also major sources of another 21–24 times more powerful greenhouse gas, methane (CH4), which is produced microbiologically in the final stage of the anaerobic degradation of organic matter (Whalen, 2005). Over one-third of global terrestrial carbon is stored in peatland formations (Gorham, 1991). With growing concerns over climatic change, the fate of these massive carbon pools is of great scientific interest. Peatland carbon cycle, including turnover of CH4, has been analysed from various perspectives, but biologists have literally only scratched the FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
surface: studies have primarily focused on the biologically most active peat layers close to the water table level, leaving deeper peat poorly characterized. The deepest peat layers have been considered to have very low activity because, already in the upper anoxic peat conditions for microbial activity are demanding with a low temperature, acidic pH and poor nutrient status. With depth, peat becomes increasingly decomposed, providing hardly any substrates for cell functions. Transport of nutrients from surface to deeper layers is restricted by the high bulk density of peat, which may limit the vertical movement of water. Limited water flow also leads to accumulation of CH4 and dissolved inorganic matter, further making degradation processes thermodynamically unfavourable (Beer et al., 2008). Still stable isotopic signatures and high CH4 concentrations in pore water at the depths of several metres suggest active methanogenesis (Siegel et al., 2001; Beer & Blodau, 2007). Furthermore, changes in peatland hydrology, caused by, for example, climate change, have been proposed to potentially increase degradation in the deep peat layers (Siegel et al., 1995; Beer et al., 2008). Considering the vast carbon pool in peat sediments, possible microbiological activity and 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
88
A. Putkinen et al.
especially CH4 production in deeper peat may be of considerable importance. The few reported microbial analyses of deep peat layers (41 m depth) have detected considerable amounts of microbial cells and methanogenic activity, but have used only culture-dependent and physicochemical methods (Williams & Crawford, 1983, 1984; Kravchenko & Sirin, 2007). With modern molecular tools, methanogenic communities have already been investigated in the surface layers of many northern peatlands (Galand et al., 2002, 2003, 2005; Basiliko et al., 2003; Juottonen et al., 2005; Metje & Frenzel, 2005; Cadillo-Quiroz et al., 2006; Meril¨a et al., 2006). To gain a comprehensive understanding of peatland microbiology and carbon cycle, peat needs to be analysed with molecular methods and these data linked to CH4 production. In anoxic, low-temperature environments, such as deep peat, a large fraction of microbial cells may be dormant or dead, and extracellular DNA may survive several millennia (Coolen & Overmann, 2007; Corinaldesi et al., 2008). RNA, though, is degraded much faster. Moreover, the ribosome per cell ratio is considered roughly proportional to the growth rate of bacteria and thus 16S rRNA as an indicator of active bacteria (Wagner, 1994). The RNA pool, in contrast to DNA, thereby contains fresh nucleic acids reflecting the more or less active populations of microorganisms. We studied two boreal peatlands, located in southern and northern Finland, focusing on the role of deep sediment layers in CH4 production and their microbial composition. By studying RNA in the community analysis, we wanted to highlight the active microbial fraction. Because CH4 production activity was of main interest, we analysed archaeal communities including both methanogens and nonmethanogenic archaea such as Crenarchaeota. Communities were
characterized by terminal restriction fragment length polymorphism (T-RFLP) and sequence analysis of 16S rRNA gene fragments. In addition to molecular analysis, we assessed methanogenic activity by measuring potential CH4 production rates. Because the northern peatland site had evolved from the terrestrialization of the adjacent lake, we examined the archaeal communities of the lake sediment for comparison.
Materials and methods Study sites and sampling Samples were taken from two Finnish fen-type peatlands in August 2006. The southern site Siikaneva (SN) was located in south-west Finland (61150 0 N 24112 0 E), and the northern site Kiposuo (KS) was located in northern Finnish Lapland (69111 0 N 27117 0 E). Both peatlands started to develop after the last ice age and their ages were between 8000 and 10 000 years. At KS, the peatland is slowly spreading over the adjacent lake. Thus, the bottom of the peatland has previously been the bottom of a lake and, respectively, in the future, the lake sediment will be covered by peat. Because of the location next to an esker and the strong spring flooding after the snow melt in the north, water flow is stronger in KS compared with SN and there are thin layers of water-drifted mineral soil in the KS peat profile, leading to a high variation in the organic matter content (Table 1). The temperature fluctuates in surface peat according to air temperatures, but remains constant in deeper peat (data not shown). From SN, samples were taken from the wettest point of the study site and from KS from two points along a peatland–lake gradient. Three replicate peat cores were taken from each point. Sampling depths were 10, 20, 50
Table 1. Properties of studied peatlands Water level from the peat surface (cm)
Peat temperature ( 1C)
Sedges (Carex rostrata, C. lasiocarpa), Sphagnum mosses
22
5 cm: 14.6 20 cm: 13.5 35 cm: 12.4 50 cm: 11.7
Sedges (Carex rostrata, C. lasiocarpa), brown mosses (Scorpidium scorpioides, Warnstorfia exannulata)
37
5 cm: 13.1 20 cm: 10.7
Peatland
Peatland type
Vegetation
SN
Aapamire, lawn
KS
Aapamire, lawn
Organic content (% dw)
pH 10 cm: 4.6 0.1 20 cm: 4.7 0.1 50 cm: 4.5 0.1 150 cm: 4.5 0.1 280 cm: 4.9 0.01 10 cm: 5.5 0.3 20 cm: 5.7 0.2 50 cm: 5.8 0.3 150 cm: 6.3 0.3 220 cm: 6.2 0.1 Lake sediment 10 cm: 6.0 0.1
ND (estimated to be close to 100%)
10 cm: 80 20 cm: 56 50 cm: 69 150 cm: 19 220 cm: 53
All data gathered during August 2006 except pH for Siikaneva, which was measured in May 2006. Mean SD (n = 3 for SN, n = 6 for KS).
2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
FEMS Microbiol Ecol 70 (2009) 87–98
89
Archaeal rRNA in deep boreal peat
and 150 cm and the bottom of the peat, which in SN was 280 cm and in KS 220 cm below the soil surface. Samples from depths between 10 and 50 cm were taken using a box corer (8 8 90 cm) and the deeper samples with a Russian corer (diameter 5 cm, length 50 cm). In KS, lake sediment from depths of 10 and 20 cm was sampled using an HTHgravity corer (HTH-Teknik, Lule˚a, Sweden). The actual study samples contained the peat/lake sediment 2 cm above and below the sampling depth (4 cm in total). Samples were temporarily (o 24 h) stored at 14 1C before samples for the molecular analysis were transferred to 70 1C.
Measurement of potential CH4 production Potential CH4 production at 15 1C was measured in anoxic laboratory incubations using GC as described in Juottonen et al. (2008). During the 4-day measurement, a 1-day lag phase occurred in many samples, and the methanogenesis rate was thus calculated from the following linear or closest to linear increase in CH4 concentration.
Extraction of nucleic acids, purification and reverse transcription Total nucleic acids were extracted using a slight modification of a protocol presented in Korkama-Rajala et al. (2008). Cells in a 0.4-g (fresh weight) soil sample were lysed with 400 mL of buffer (75 mM Tris-HCl, pH 7.4; 25 mM EDTA, pH 8.0; 4.5% sodium dodecyl sulfate; 1.5% b-mercaptoethanol) together with sterile quartz sand in a FastPreps beater (FP120A-model, Qbiogene, Illkirch, France) for 30 s at a setting of 4.5 m s1. Lysis was continued by incubating the samples at 65 1C for 30 min. Purification was started by adding 400 mL of phenol : chloroform : isoamyl alcohol (50 : 49 : 1), vortexing (1 min, half speed) and centrifugation (2 min, 16 000 g). The upper phase was transferred to a clean tube and 400 mL of chloroform : isoamyl alcohol (24 : 1) was added. Samples were vortexed (2 min, half speed) and centrifuged (3 min, 16 000 g). The upper phase was centrifuged through a polyvinylpolypyrrolidone column (3 min, 12 000 g). Nucleic acids were precipitated by adding 0.6 volumes of 20% (w/v) polyethylene glycol–2.5 M NaCl at a PEG : sample ratio of 6 : 10 and incubating on ice for 20 min. After centrifugation (20 min, 16 000 g, 14 1C), the pellet was washed with 800 mL of 70% ethanol, centrifuged (5 min, 16 000 g) and air dried at room temperature. The pellet was suspended in 50 mL of TE buffer at 50 1C for 1 h. To obtain a separate RNA fraction, DNA was removed by DNAse treatment (RQ1-DNAse, Promega, Madison, WI) according to the manufacturer’s instructions. Reverse transcription was performed using archaea-specific 16S rRNA gene primer Ar912rt (Lueders & Friedrich, 2002) and RevertAidTMM-Mulv Reverse Transcriptase (Fermentas, FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
Vilnius, Lithuania) according to the manufacturer’s instructions.
T-RFLP of archaeal communities For T-RFLP profiling, partial archaeal 16S rRNA gene fragments were amplified using primers Ar109f (Grosskopf et al., 1998a) and Ar912rt. The reverse primer was fluorescently labelled with FAM (carboxyfluorescein). PCR reactions (50 mL) contained 8 pmol of both primers, 0.2 mM dNTP mix, 1 U DNA polymerase (Biotools, Madrid, Spain), 1 Biotools buffer and 2 mL bovine serum albumin (BSA) (10 mg mL1). The cDNA template amounts were optimized separately for every sample. PCR was performed using a touchdown protocol, consisting of a denaturation step (94 1C, 2 min), 20 cycles of 94 1C for 1 min, 62 1C (decreasing by 0.5 1C per cycle) for 45 s, 72 1C for 1 min 20 s, followed by 10 cycles of 94 1C for 1 min, 53 1C for 45 s and 72 1C for 1 min 20 s, and a final elongation step of 72 1C for 7 min. Products were visualized on 1% agarose gels with ethidium bromide staining. PCR products were digested with TaqI at 65 1C for 5 h and ethanol was precipitated. Dried samples were suspended in a mixture of 15 mL of Hi-Di formamide (Applied Biosystems, Foster City, CA) and 0.4 mL of an internal size standard (GeneScans-500 TAMRA, Applied Biosystems). Analysis was performed twice for every sample using the ABI PRISM 310 DNA sequencer (Applied Biosystems). The electropherograms were analysed using GENESCAN ANALYSIS software v3.7 (Applied Biosystems). The threshold for peak heights was 100 fluorescence units. Peaks with a relative area o 2.5% and peaks not appearing in both electropherograms of the same sample were discarded as background noise. Community compositions were compared by cluster analysis carried out with PAST software v.1.82b (Hammer et al., 2001) using Bray–Curtis distances and relative proportions of T-RFs based on peak areas. T-RFLP data were also evaluated through ordination analysis: first, detrended correspondence analysis was used to reveal the gradient length. As the length of the first axis was o 2.5 SD units, linear methods were chosen instead of unimodal methods (ter Braak & Prentice, 1988). Redundancy analysis (RDA) was then used to test the difference between sites, between RNA and DNA (SN data) and between KS peat and lake sediment (with depth as a covariate). The effect of depth was tested separately on both sites. Finally, the effect of pH and CH4 production potential on the presence of different T-RFs was tested. The significance of the factors and the constraint axis were tested using Monte Carlo permutations (499 permutations). All the analyses were carried out with CANOCO for Windows 4.52 (ter Braak & Sˇmilauer, 2002). The level of significance in the statistical analyses was P 0.05. 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
90
Cloning, RFLP and sequencing To identify T-RFLP peaks, i.e. T-RFs, six clone libraries were created from RNA extracted from deeper peat layers ( 50, 150 and 280 cm) and the lake sediment (three libraries per study site). PCR conditions and reaction mixtures were as in T-RFLP analysis, except without the FAM label in the reverse primer. Products were cloned using the pGEMs-T Vector System kit (Promega) according to the manufacturer’s instructions. Forty clones per library were screened by RFLP analysis using TaqI as a restriction enzyme. Clones not cut by TaqI were also digested with HhaI. For the screening, clones were PCR amplified as above, but in 25-mL reactions and without BSA. Clone colonies suspended in water were used as a template. PCR started with 3-min denaturation at 94 1C, followed by 35 cycles of 94 1C for 45 s, 52 1C for 1 min and 72 1C for 1 min 30 s, and a final elongation step at 72 1C for 7 min. One to three clones from every RFLP group were sequenced. Clones were amplified with a vector-specific UP-RP primer pair. PCR reactions (50 mL) contained 20 pmol of both primers, 0.2 mM dNTP-mix, 1 U DNA polymerase (Biotools), 1 Biotools buffer and 1 mL of a clone colony suspension as a template. PCR conditions were 94 1C for 2 min, 35 cycles of 94 1C for 15 s, 55 1C for 30 s, 68 1C for 30 s; and 68 1C for 7 min. Products were sequenced with T7 primer at the Institute of Biotechnology, Helsinki, Finland. Coverage of clone libraries was determined as in Galand et al. (2003).
Phylogenetic analysis Phylogenetic analyses were performed separately for Euryarchaeota and Crenarchaeota. Sequences were compared with those in the GenBank database with BLASTN program (http://blast.ncbi.nlm.nih.gov/Blast.cgi). Possible chimeric sequences were identified with BELLEROPHON (Huber et al., 2004; http://foo.maths.uq.edu.au/huber/ bellerophon.pl) and one sequence was discarded as a probable chimera. Clone and reference sequences were aligned with CLUSTALW (European Bioinformatics Institute, http:// www.ebi.ac.uk/Tools/clustalw2/index.html) and alignments were manually checked using BIOEDIT 7.0.5.3 (Hall, 1999). Phylogenetic trees based on maximum likelihood were constructed with PHYML v2.4.4 software (Guindon & Gascuel, 2003; Guindon et al., 2005) using GTR as an evolutionary model combined with a g distribution parameter. Sequence lengths in trees were 747 bp for Euryarchaeota and 674 bp for Crenarchaeota. Bootstrapping was performed 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
with 100 replicates. Parallel phylogenetic trees were constructed with the neighbour-joining method using the PHYLIP 3.5-package (Felsenstein, 1993). Evolutionary distances were calculated using the Jukes–Cantor algorithm. Sequences have been deposited in the EMBL nucleotide sequence database with accession numbers AM905951– AM905991.
Results Potential CH4 production The CH4 production potential of peat layers from northern and southern sites was estimated in 1-week incubations. The CH4 production potential of deep peat sediments was clearly lower than the potential of surface peat (Fig. 1). In the 10-cm layer, KS showed clearly higher rates than SN where production was practically zero (mean values 23 and 0.004 nmol g1 dw h1, respectively). A large difference between the sites was also detected in 20-cm (KS, 8.3 nmol g1 dw h1; SN, 1.5 nmol g1 dw h1) and 50-cm layers (KS, 3.0 nmol g1 dw h1; SN, 0.59 nmol g1 dw h1). In the deeper layers ( 150 and 220/ 280 cm), the production rates were low at both sites (o 1 nmol g1 dw h1). The production of KS lake sediments was comparable to the deepest peat layers.
T-RFLP of archaeal communities in whole vertical peat profiles The structures of archaeal communities from five SN and KS peat layers and also from KS lake sediment were analysed by 16S rRNA gene-based T-RFLP. In addition, the same SN peat layers were profiled using 16S rDNA. Communities differed between study sites (P = 0.002 in RDA) (Figs 2–4). Peat depth had an impact on the community composition (P-values 0.014 and 0.002 for SN and KS, respectively), seen
–10 –20 Depth (cm)
T-RFs were identified by digesting clone sequences in silico with TaqI. Identification was verified by T-RFLP analysis of clones.
A. Putkinen et al.
–50 –150 –220 –280
Kiposuo (KS)
Lake –10
Siikaneva (SN)
Lake –20 0
5
10
25
30
Methane production (nmol–1 g dw–1 h–1) Fig. 1. Methane production potential (mean1SE, n = 3/6) of peat and lake sediment. Samples from 220 cm and lake sediment were only from KS, and samples from 280 cm were only from SN.
FEMS Microbiol Ecol 70 (2009) 87–98
91
Archaeal rRNA in deep boreal peat
Fig. 2. Percentages of T-RFs from SN. Averages of two parallel cores are presented. Identifications (i.e. archaeal groups) of T-RFs are based on rRNA clone library data from SN (Table 2.), except for Mst and LDS, which are derived from KS clone libraries, and library data from Juottonen et al. (2008). Unknown includes unidentified T-RFs (100, 113, 144, 160, 434 and 456 bp). Cren, Crenarchaeota; RC-II, Rice Cluster II; Mm, Methanomicrobiales; Mst, Methanosaetaceae; Mb, Methanobacteriales; LDS, Lake Dagow Sediment cluster.
Fig. 3. Percentages of T-RFs from KS. Averages of two parallel cores from two sampling points are presented (KS-150 and KS-220 from one sampling point). Identifications (i.e. archaeal groups) of T-RFs are based on clone library data (Table 2.). Unknown includes unidentified T-RFs (69, 78, 100, 144 and 434 bp). MBD, marine benthic group D; Cren, Crenarchaeota; RC-II, Rice Cluster II; Mm, Methanomicrobiales; Mst, Methanosaetaceae; Msr, Methanosarcinaceae; Mb, Methanobacteriales.
both in the presence/absence of T-RFs and their abundances. Cluster analysis showed a clear separation between the surface and the deep layers. RNA- and DNA-based communities formed their own subgroups inside surface and deep layer clusters, but the difference between RNA and DNA was not statistically significant (P = 0.280). The KS lake sediment did not differ from KS peat (P = 0.1240). Communities of SN deeper layers clearly differed from those at the surface. In the deep layers ( 50 to 280 cm), the major components of rRNA profiles were T-RFs 494 and 4 700 bp (Fig. 2). These T-RFs were rare or absent in the surface layers ( 10 and 20 cm). In surface peat, T-RF 393 bp constituted over 60% of the total T-RF peak area. DNA-based communities of the same SN samples showed fewer T-RFs than rRNA communities, although they shared many T-RFs. The main difference between DNA and RNA profiles was the presence of T-RFs 92 and 284 bp only in the FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
RNA. The T-RF 84 bp was absent from DNA profiles of deep peat layers ( 50 to 280 cm), although present in RNA. The only T-RF found exclusively in DNA was the 494-bp fragment in the 10-cm layer. In KS peat, three main T-RFs (494, 393 and 284 bp) were present in all layers, but with differing abundances (Fig. 3). Deeper layers differed from surface layers ( 10 and 20 cm) by the absence of T-RFs 92, 186 and 84 bp. KS lake sediment had similar T-RF composition as deep peat layers, differing only by the presence of T-RF 92 bp and the greater abundance of T-RF 4 700 bp.
Identification of T-RFs and phylogenetic analysis of sequenced clones T-RFs were identified by in silico and T-RFLP analysis of clones. KS communities consisted almost completely of 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
92
A. Putkinen et al.
Similarity 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 SN–20 DNA SN–10 DNA SN–20 RNA SN–10 RNA KS–10 RNA KS–50 RNA KS–20 RNA KS–220 RNA KS–lake-10 RNA KS–150 RNA SN–280 DNA SN–150 DNA SN–50 DNA SN–280 RNA SN–150 RNA SN–50 RNA Fig. 4. Dendrogram based on cluster analysis of archaeal communities from different depths of SN (RNA and DNA) and KS (RNA). Analysis was performed using Bray–Curtis distances and average percentages of T-RFs. Deep peat layers are designated in bold and DNA samples in italics.
Table 2. T-RF lengths, phylogenetic affiliations of T-RFs and percentages of phylogenetic groups in study sites based on RFLP screening of archaeal clone libraries
Phylogenetic lineage Crenarchaeota 1.1c Methanobacteriales Methanosarcinaceae Crenarchaeota 1.1c Crenarchaeota RC-II Methanosaetaceae Methanomicrobiales E1/E2 RC-II RC-II Crenarchaeota DP Crenarchaeota 1.3 MBD
%Clones T-RF %Clones in %Clones in in KS lake KS peatw sedimentz length (bp) SN peat 84 92 186 186 186 258 284 393
12 – – 1 2 – – –
– –
1 29 10
– 24 – – – – 29 12
393 494 4 700 4 700 4 700
4 42 37 2 –
17 31 1 1 7
9 15 – – 12
3 – –
105 clones from three libraries. w
72 clones from two libraries. 34 clones from one library.
E1 methanogens (Galand et al., 2002; Cadillo-Quiroz et al., 2006). All Methanobacteriales clones were found in the lake sediment library, and their sequences were 98% similar to Methanobacterium beijingense strains 4-1 (AY552778) isolated from an anaerobic digester. In SN, all methanogen clones were closely related to uncultured RC-II group methanogens. Nonmethanogenic euryarchaeal clones, found only in KS libraries, were related to marine benthic group D (MBD) (Fig. 5). Within the crenarchaeal clones, the largest phylogenetic group consisted of previously rarely encountered sequences found almost exclusively in SN (37% of the clones in SN libraries). They could not be linked to any identified phylogenetic cluster and were hence named the deep peat (DP) group based on their source location (Fig. 6). The closest match to the DP group was a sequence from a hot spring in Thailand (AY555826), which showed 99% similarity (only over c. 70% of the sequence length). Japanese shallow Holocene sediment ITKA sequences (AB198105, AB198135) showed 94–97% similarity and Arc.201 from the deep subsurface sediment showed 90–94% similarity. Recently, sequences with 96–98% similarity were recovered from an acidic fen in Germany (AM270541, AM270561). Other abundant crenarchaeal clones, detected exclusively in SN, belonged to the 1.1c group. The majority of these sequences showed the highest similarity (96%) to sequence ARCP2-5 (AF523944) from a forested wetland. Three individual clones, two from SN and one from KS, clustered with sequences of the crenarchaeal 1.3 group.
Effect of environmental variables on community composition We used RDA to test the effect of pH and CH4 production potential on T-RFLP community compositions. Especially pH (P = 0.002) and also CH4 production (P = 0.048) significantly affected the community compositions (Fig. 7). T-RF 284 bp identified as Methanosaetaceae correlated well with the increasing pH. Crenarchaeal groups (T-RFs 84, 186 and 4 700 bp) correlated with decreasing pH. Groups associated with high CH4 production were RC-II (T-RF 494 bp) and Methanobacteriales (T-RF 93 bp).
z
Discussion methanogens, but formed the majority in SN Crenarchaeota (Table 2). Coverage of the clone libraries was 88–97%. Methanogens in KS belonged to three different orders (Methanosarcinales, Methanomicrobiales and Methanobacteriales) and to the Rice Cluster II (RC-II) group (Fig. 5). Methanosarcinales clones included archaea from both Methanosaetaceae and Methanosarcinaceae families. Methanomicrobiales clones were related to the E2/Fen cluster and 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
We showed methanogenic and other archaeal activity throughout the investigated peat profiles. To the best of our knowledge, our study was the first to analyse the microbial communities of deep peatland sediments with molecular methods. Previous studies have already indicated microbial activity in deep peat sediments. Kravchenko & Sirin (2007) detected considerable amounts of microbial cells and evidence of methanogenic activity at peat depths of several metres. In another peatland, high potential CH4 production FEMS Microbiol Ecol 70 (2009) 87–98
93
Archaeal rRNA in deep boreal peat
MH1492_2E (EU155947)-Minerotrophic fen Ks150-27 (AM905977) Baqar.Sedi.Arch.1 (AB355106) -Lake sediment Kj10-10 (AM905961) Methanosaeta concilii (M59146) Ks150-18 (AM905969) Soyang1Af-1100Ar (AF056363) -Lake sediment Methano LH11 ( AY177805) -Antarctic sediment saetaceae MHLsu47_4B (EU155900) -Minerotrophic fen T-RF 284 bp Ks150-14 (AM905965) 2C5 (AJ937876) -Lake CBd-366G (DQ301880) -Acidic peatland Ks50-5 (AM905956) MH1492_2G (EU155954) -Minerotrophic fen Kj10-6 (AM905957) Methanosaeta thermophila (NC_008553) SibT-59 (DQ869368) -Acidic subarctic permafrost peat Methano Ks50-4 (AM905955) sarcinaceae FenE1-16S (AJ548944) -Oligotrophic fen Methanosarcina lacustris (DQ058823) T-RF 186 bp Methanosarcina barkeri (AF028692) Clone2 (AY856366) -Sphagnum peat fraction with particle size < 0.2 µm Sn50-21 (AM905971) MB-05 (AY175392) -Northern peatland SnRA1 (AM905410) -Siikaneva -20 cm peat FSAr23 (AJ576202) -Food soil of Pachnoda ephippiata larva CBd-464H (DQ301896) -Acidic peatland Kj10- 1 (AM905952) CBd-366D (DQ301898) -Acidic peatland Ks50-24 (AM905974) ARR18 (AJ227931) -Rice roots pTN-2 (AB182752) -Rice paddy soil Ks50-13 (AM905963) FenK-16S (AJ548952) -Oligotrophic fen E2/ Methanoregula boonei (DQ282124) Kj10-8 (AM905959) Fen cluster St_D_31 (AY531737) -Lake sediment Ks150-7 ( AM905958) Fuku08 (AF481343) -Lake sediment E1 CBd-472G (DQ301909) -Acidic peatland Methanospirillum hungatei (AY196683) Methanobacterium aarhusense (DQ649334) SnRF3 (AM905399) -Siikaneva -20 cm peat MP104-1109-a25 (DQ088782) -Crustal biotome Kj10-3 (AM905954) Methanobacterium beijingense (AY552778) vadinDC06 (UAU81775) -Anaerobic digestor D64AR30R8 (AM778307) -Rice paddy soil MH1100_C3E (EU155985) -Minerotrophic fen Ks150-26 (AM905976) Baqar.Sedi.Arch.16. (AB355121) -Lake sediment Kj10-25 (AM905975) TA1e6 (AF134389) -Marine sediment MKCST -A7 ( DQ363830) -Mangrove soil Nitrosopumilus maritimus (DQ085097)
Methano sarcinales
RC-II T-RF 393, 494 bp
Methano microbiales T-RF 393 bp
Methano bacteriales T-RF 92 bp
Marine benthic group D T-RF >700 bp
0.1
Fig. 5. Maximum likelihood tree of euryarchaeal 16S rRNA gene sequences from deep peat sediments ( 50, 150 and 220/ 280 cm) of Siikaneva (Sn) and Kiposuo (Ks), and Kiposuo lake sediment (Kj) (in bold) together with related sequences of environmental clones and cultured methanogens. The tree was rooted using Nitrosopumilus maritimus as an outgroup. Nodes with 4 90% support value in bootstrapping analysis are marked with a filled circle and nodes with 4 70% support value are marked with an unfilled circle. The scale bar represents a 0.1 change per nucleotide position. The corresponding T-RF sizes are indicated.
and great numbers of total bacteria (3.5–5.4 108) and methanogens (1.0 104) were detected at the depth of 210 cm (Williams & Crawford, 1983, 1984). Our study provides a much-needed complement to these studies by FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
linking physicochemical analysis of methanogenic activity to modern molecular data of microbial communities. Methanogens were revealed in the deepest layers of both the northern KS site and the southern SN site, but the 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
94
A. Putkinen et al.
HrhA34 (AJ878954) -Rice rhizosphere Pav-Arc-010 (DQ785306) -Anoxic zone of a meromictic lake GR-WP33-A4 (AJ583413) -Uranium mill tailings GRU17 (AY278084) -Freshwater lake FenO-16S (AJ548934) -Oligotrophic fen Ks150-38 (AM905988) ASC25 (AB161328) -Petroleum-contaminated soil VAL81 (AJ131315) -Freshwater lake Sn150-41 (AM905991) Sn50-37 (AM905987) VAL18 (AJ131321) -Freshwater lake ASC43 (AB161341) -Petroleum-contaminated soil Str6_A4 (AM055701) -Sulphidic springs in a marsh Kazan-2A-13 /BC19-2A-13 (AY591991) -Marine sediment HAuD-LA27 (AB113631) - Subsurface geothermal water OHKA4.47 (AB094540) -Subseafloor sediment Sn50-40 (AM905990) YS16As18 (AB329790) -Hydrothermal deposit FJQOTA5 (AM039529) -Subsurface thermal spring Sn50-35 (AM905985) Sn150-34 (AM905984) Sn50-39 (AM905989) Ks150-33 (AM905983) Sn150-32 (AM905982) ITKA-155 (AB198135) -Shallow Holocene sediment ITKA-048 (AB198105) -Shallow Holocene sediment Arc.201 (AF005766) -Deep subsurface sediment JG36-GR-130 (AJ535134) -Uranium mill tailings ARCP2-5 (Af523944) -Forested wetland Sn280-29 (AM905979) Sn150-31 (AM905981) SnRA5 (AM905414) -Siikaneva -20 cm peat A109-24 (AM291989) -Forest soil GRU11 ( AY278095) -Freshwater lake SnDF5 (AM905394) -Siikaneva -20 cm peat Sn150-28 (AM905978) RotF-135iia (DQ278152) -Soil FHMa9 (AJ428031) -Forest soil FFSB3 (X96690) -Forest soil FFSA1 (Y08984) -Forest soil SCA1145 (U62811) -Soil OdenC-100iia (DQ278128) -Soil Nitrosopumilus maritimus (DQ085097) Cenarchaeum symbiosum (CSU51469) Sulfolobus acidocaldarius (D14053) Desulfurococcus mobilis (M36474) Methanococcus maripaludis (MMU38941)
Group 1.3 T-RF>700
DP group T-RF>700
Group 1.1c T-RF 84, 186
1.1b 1.1a Hyperthermophiles
0.1
Fig. 6. Maximum likelihood tree of crenarchaeal 16S rRNA gene sequences from deep sediments ( 50, 150 and 280 cm) of Siikaneva (Sn) and Kiposuo (Ks) (in bold) together with related sequences of environmental clones and cultured members of groups 1.1a and 1.1b. The tree was rooted using Methanococcus maripaludis as an outgroup. Nodes with 4 90% support value in bootstrapping analysis are marked with a filled circle, and nodes with 4 70% support value are marked with an open circle. The scale bar represents a 0.1 change per nucleotide position. The corresponding TRF sizes are indicated.
communities differed. KS displayed several methanogenic groups, whereas SN showed practically only RC-II archaea, which are presumed to be methanogens based on the phylogenetic position between Methanosarcinales and Methanomicrobiales (Grosskopf et al., 1998b). Further, in SN, the bottom layers clearly differed from the surface, whereas in KS, deep layers clustered more closely with surface peat. These site- and depth-related differences likely 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
are a consequence of the distinct hydrological conditions of the study sites. Because deeper subsurface peat is largely recalcitrant and a poor source of carbon substrates, the main sources of new substrates in deeper peat are exudates of deep-rooting sedges and, particularly in the rootless bottom layers, new dissolved organic carbon provided by water movement (Chanton et al., 1995; Chasar et al., 2000). KS had stronger surface- and groundwater flows, which may FEMS Microbiol Ecol 70 (2009) 87–98
95
Archaeal rRNA in deep boreal peat
1.0
RDA axis 2
CH4
0.0
–0.4 –1.0
0.0 RDA axis 1
1.0
Fig. 7. Ordination diagram from RDA of the effect of pH and CH4 production potential on the presence of different T-RFs. Together, pH and CH4 production explained 26.9% of the observed variation in T-RFLP data. Identifications (i.e. archaeal groups) of T-RFs are based on rRNA clone library data (Table 2.) and library data from Juottonen et al. (2008). MBD, marine benthic group D; Cren, Crenarchaeota; RC-II, Rice Cluster II; Mm, Methanomicrobiales; Mst, Methanosaetaceae; Msr, Methanosarcinaceae; Mb, Methanobacteriales; LDS, Lake Dagow sediment cluster.
provide acetate even to the bottom layers, explaining the observation that obligate acetoclastic methanogens (Methanosaetaceae) formed a large proportion of the archaeal population, especially in the bottom layers. Acetoclastic methanogenesis is generally linked to the availability of fresh organic matter. In recalcitrant deep peat, hydrogenotrophic i.e. H2/CO2-based methanogenesis is considered to be the main pathway (Hornibrook et al., 1997; Chasar et al., 2000). Hydrogenotrophs of the order Methanomicrobiales were present in KS deep layers. The pathway of the RC-II archaea, the main methanogenic group in SN deep peat, remains undetermined. All methanogen groups detected in SN and KS deep peat occur commonly in the surface layers of northern peatlands (e.g. Basiliko et al., 2003; Kotsyurbenko et al., 2004; Juottonen et al., 2005; Cadillo-Quiroz et al., 2006). Except for the deepest layers, CH4 production potentials were clearly higher in KS. Exceptionally dry summer conditions with a low water table level decreased CH4 production in the top layer of SN. The difference between sites was clear, however, even in the 50-cm layer, which indicates better substrate availability in KS. Moreover, most methanogens thrive at a near-neutral pH (Garcia et al., 2000), and the pH of KS peat was closer to this than the pH of SN. Acetoclastic methanogenesis is especially considered sensitive to acidic pH (van Kessel & Russell, 1996). Accordingly, our results showed a correlation between the occurrence of Methanosaeta populations and increasing pH. In deep layers, CH4 production potentials were low, but detectable in both study FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
sites. In fact, the CH4 production potential in SN bottom layers was higher than in the uppermost SN peat, highlighting that microbial activity is not restricted to the surface layers. Another indication of methanogenic activity in the deep layers was the detection of methanogen rRNA. The direct correlation between rRNA and viability is debatable, but the larger diversity found in our rRNA communities suggests that RNA may have highlighted cells that are not numerically very abundant, but have a relatively high metabolic activity. Methanobacteriales and Methanosaetaceae were only visualized in rRNA analysis, indicating the presence of small, but active, populations. Methodological differences between RNA and DNA analysis may have influenced this result, but similar detection of Methanosaetaceae particularly in RNA analysis has been reported for SN surface peat (Juottonen et al., 2008) and also for freshwater lake sediment (Koizumi et al., 2004). In addition to methanogens, the deep peat layers revealed interesting crenarchaeal populations. The DP group typical to SN sediments had only a few similar sequences in the databases. The presence of the DP group only in the deepest and thus the oldest layers and its close relation to archaeal sequences from another kind of Holocene sediment (ITKA sequences AB198105, AB198135) suggest that this group might be a living remnant of archaeal populations dominating thousands of years ago at the beginning of peatland development. Group 1.1c Crenarchaeota were detected in all layers of SN, but not in KS. This is most likely due to the lower pH of SN peat, because this group has been suggested to have an upper pH limit of around 5 (Nicol et al., 2005; Hansel et al., 2008). As far as we know, 1.1c Crenarchaeota have not been detected in other pristine peatlands, but very often in boreal forest soils (Jurgens et al., 1997; Yrj¨al¨a et al., 2004; Bomberg & Timonen, 2007). Both SN and KS deep peat contained group 1.3 Crenarchaeota, found previously in upper layers of several different peatlands (Utsumi et al., 2003; Høj et al., 2006; Rooney-Varga et al., 2007). Knowledge of crenarchaeal functions in peatlands is very scarce. Some crenarchaea have proven to be ammonia oxidizers in marine and soil environments (Prosser & Nicol, 2008), but no evidence exists about their involvement in the nitrogen cycle in peatlands. The possible role in carbon turnover is still unresolved as well. Thus, one can only speculate on the function of DP-group-like archaea in deep peat sediments. They are quite certainly anaerobic, because plant roots are not likely to reach the bottom of the peat to provide oxygen. As for the methanogens, the most probable carbon source of deep peat crenarchaea is CO2, which is also used by crenarchaea in deep sea (DeLong et al., 2006). The KS site offered a chance to compare two different but closely related sediments. KS peatland has developed through a still ongoing process of lake terrestrialization, 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
96
i.e. the peatland is slowly spreading over the adjacent lake. The deep peat sediment layers are thus lake sediments that have been covered by peat. This connection became evident in the highly similar archaeal community compositions detected in KS deep peat and lake sediments. The only lake sediment group absent in deep peat was Methanobacteriales, which occurred instead in surface peat ( 10 and 20 cm). Another archaeal group mainly detected in lake sediment, MBD, belongs to Euryarchaeota but is most likely not methanogenic. This group has been proposed to favour saline and alkaline environments (Jiang et al., 2008). Interestingly, we found MBD in a slightly acidic (pH 6) freshwater environment. Deep peat sediments are often studied by palaeoecologists using methods such as macrofossil analysis to describe, for example, past climate changes (Booth et al., 2004; Mitchell et al., 2008). In lake and marine sediments, DNA has also been exploited in palaeoecological reconstructions (Coolen & Overmann, 2007; Manske et al., 2008). Nucleic acids could be used to analyse past microbial communities in a peat environment as well. Comparison of RNA and DNA could provide a chance to evaluate whether there are signs of old, no longer prevailing communities in DNA. From our study of archaeal communities, no such conclusions can be made because no unique phylogenetic groups were present only in DNA. If there were ‘old’ DNA preserved in peat layers, methods of higher resolution would be required to recover it among the DNA of present-day organisms. Old DNA is also most likely highly fragmented and thus needs to be examined through amplification of shorter fragments than in our study (Hebsgaard et al., 2005). In conclusion, our study expands the knowledge of the complex peatland ecosystems by showing methanogenic activity and diverse archaeal communities in deep peat layers. If the conditions in deep peat change, this activity may become even more important in the future. If, for example, climate change increases precipitation as predicted in some scenarios (Carter et al., 2005), changed hydrology may increase the substrate supply to deeper layers, stimulating methanogenic activity. Because of the large volume of the deep layers, the increased activity could influence the CH4 budget of peatlands.
Acknowledgements We thank Marjo Nissinen for technical assistance, Krista Peltoniemi and Mirva Sandberg for advice on RNA methods, Janne Levula and Virpi Kuutti for help with sampling and Janne Rinne and Sami Haapanala for SN field temperature data. Funding was provided by the Academy of Finland (project 109816) and the Research Foundation of the University of Helsinki. 2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
A. Putkinen et al.
References Basiliko N, Yavitt JB, Dees PM & Merkel SM (2003) Methane biogeochemistry and methanogen communities in two northern peatland ecosystems, New York State. Geomicrobiol J 20: 563–577. Beer J & Blodau C (2007) Transport and thermodynamics constrain belowground carbon turnover in a northern peatland. Geochim Cosmochim Ac 71: 2989–3002. Beer J, Lee K, Whiticar M & Blodau C (2008) Geochemical controls on anaerobic organic matter decomposition in a northern peatland. 53: 1393–1407. Bomberg M & Timonen S (2007) Distribution of Cren- and Euryarchaeota in Scots pine mycorrhizospheres and boreal forest humus. Microb Ecol 54: 406–416. Booth RK, Jackson ST & Gray CED (2004) Paleoecology and high-resolution paleohydrology of a kettle peatland in upper Michigan. Quaternary Res 61: 1–13. Cadillo-Quiroz H, Brauer S, Yashiro E, Sun C, Yavitt J & Zinder S (2006) Vertical profiles of methanogenesis and methanogens in two contrasting acidic peatlands in central New York State, USA. Environ Microbiol 8: 1428–1440. Carter TR, Jylh¨a K, Perrels A, Fronzek S & Kankaanp¨aa¨ S (2005) FINADAPT scenarios for the 21st century: alternative futures for considering adaptation to climate change in Finland. FINADAPT Working Paper 2, Finnish Environment Institute Mimeographs 332, Helsinki, 42pp. Chanton JP, Bauer JE, Glaser PA, Siegel DI, Kelley CA, Tyler SC, Romanowicz EH & Lazrus A (1995) Radiocarbon evidence for the substrates supporting methane formation within northern Minnesota peatlands. Geochim Cosmochim Ac 59: 3663–3668. Chasar LS, Chanton JP, Glaser PH, Seigel DI & Rivers JS (2000) Radiocarbon and stable carbon isotopic evidence for transport and transformation of dissolved organic carbon, dissolved inorganic carbon and CH4 in a northern Minnesota peatland. Global Biogeochem Cy 14: 1095–1108. Coolen MJL & Overmann J (2007) 217 000-year-old DNA sequences of green sulfur bacteria in Mediterranean sapropels and their implications for the reconstruction of the paleoenvironment. Environ Microbiol 9: 238–249. Corinaldesi C, Beolchini F & Dell’Anno A (2008) Damage and degradation rates of extracellular DNA in marine sediments: implications for the preservation of gene sequences. Mol Ecol 17: 3939–3951. DeLong EF, Preston CM, Mincer T et al. (2006) Community genomics among stratified microbial assemblages in the ocean’s interior. Science 311: 496–503. Felsenstein J (1993) PHYLIP (Phylogeny Inference Package), Version 3.5 c. Department of Genetics, University of Washington, Seattle. Galand P, Juottonen H, Fritze H & Yrj¨al¨a K (2005) Methanogen communities in a drained bog: effect of ash fertilization. Microb Ecol 49: 209–217. Galand PE, Saarnio S, Fritze H & Yrj¨al¨a K (2002) Depth related diversity of methanogen Archaea in Finnish oligotrophic fen. FEMS Microbiol Ecol 42: 441–449.
FEMS Microbiol Ecol 70 (2009) 87–98
97
Archaeal rRNA in deep boreal peat
Galand PE, Fritze H & Yrj¨al¨a K (2003) Microsite-dependent changes in methanogenic populations in a boreal oligotrophic fen. Environ Microbiol 5: 1133–1143. Garcia J, Patel BKC & Ollivier B (2000) Taxonomic, phylogenetic, and ecological diversity of methanogenic Archaea. Anaerobe 6: 205–226. Gorham E (1991) Northern peatlands: role in the carbon cycle and probable responses to climatic warming. Ecol Appl 1: 182–195. Grosskopf R, Janssen P & Liesack W (1998a) Diversity and structure of the methanogenic community in anoxic rice paddy soil microcosms as examined by cultivation and direct 16S rRNA gene sequence retrieval. Appl Environ Microb 64: 960–969. Grosskopf R, Stubner S & Liesack W (1998b) Novel euryarchaeotal lineages detected on rice roots and in the anoxic bulk soil of flooded rice microcosms. Appl Environ Microb 64: 4983–4989. Guindon S & Gascuel O (2003) A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst Biol 52: 696–704. Guindon S, Lethiec F, Duroux P & Gascuel O (2005) PHYML: online – a web server for fast maximum likelihood-based phylogenetic inference. Nucleic Acids Res 33: W557–W559. Hall TA (1999) BioEdit: a user-friendly biological sequence alignment editor and analysis program for Windows 95/98/ NT. Nucl Acids Symp Ser 41: 95–98. Hammer Q, Harper DAT & Ryan PD (2001) PAST: paleontological statistics software package for education and data analysis. Palaeontol Electron 4: 1–9. Hansel CM, Fendorf S, Jardine PM & Francis CA (2008) Changes in bacterial and archaeal community structure and functional diversity along a geochemically variable soil profile. Appl Environ Microb 74: 1620–1633. Hebsgaard MB, Phillips MJ & Willerslev E (2005) Geologically ancient DNA: fact or artefact? Trends Microbiol 13: 212–220. Høj L, Rusten M, Haugen LE, Olsen RA & Torsvik VL (2006) Effects of water regime on archaeal community composition in Arctic soils. Environ Microbiol 8: 984–996. Hornibrook ERC, Longstaffe FJ & Fyfe WS (1997) Spatial distribution of microbial methane production pathways in temperate zone wetland soils: stable carbon and hydrogen isotope evidence. Geochim Cosmochim Ac 61: 745–753. Huber T, Faulkner G & Hugenholtz P (2004) Bellerophon: a program to detect chimeric sequences in multiple sequence alignments. Bioinformatics 20: 2317–2319. Jiang H, Dong H, Yu B, Ye Q, Shen J, Rowe H & Zhang C (2008) Dominance of putative marine benthic Archaea in Qinghai Lake, north-western China. Environ Microbiol 10: 2355–2367. Juottonen H, Galand P, Tuittila E, Laine J, Fritze H & Yrj¨al¨a K (2005) Methanogen communities and Bacteria along an ecohydrological gradient in a northern raised bog complex. Environ Microbiol 7: 1547–1557. Juottonen H, Tuittila ES, Juutinen S, Fritze H & Yrj¨al¨a K (2008) Seasonality of rDNA- and rRNA-derived archaeal
FEMS Microbiol Ecol 70 (2009) 87–98
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
communities and methanogenic potential in a boreal mire. ISME J 2: 1157–1168. Jurgens G, Lindstr¨om K & Saano A (1997) Novel group within the kingdom Crenarchaeota from boreal forest soil. Appl Environ Microb 63: 803–805. Koizumi Y, Takii S & Fukui M (2004) Depth-related change in archaeal community structure in a freshwater lake sediment as determined with denaturing gradient gel electrophoresis of amplified 16S rRNA genes and reversely transcribed rRNA fragments. FEMS Microbiol Ecol 48: 285–292. Korkama-Rajala T, Muller MM & Pennanen T (2008) Decomposition and fungi of needle litter from slow- and fastgrowing Norway spruce (Picea abies) clones. Microb Ecol 56: 76–89. Kotsyurbenko OR, Chin KJ, Glagolev MV, Stubner S, Simankova MV, Nozhevnikova AN & Conrad R (2004) Acetoclastic and hydrogenotrophic methane production and methanogenic populations in an acidic West-Siberian peat bog. Environ Microbiol 6: 1159–1173. Kravchenko IK & Sirin AA (2007) Activity and metabolic regulation of methane production in deep peat profiles of boreal bogs. Microbiology 76: 888–895. Lueders T & Friedrich M (2002) Effects of amendment with ferrihydrite and gypsum on the structure and activity of methanogenic populations in rice field soil. Appl Environ Microb 68: 2484–2494. Manske AK, Henssge U, Glaeser J & Overmann J (2008) Subfossil 16S rRNA gene sequences of green sulfur bacteria in the Black Sea and their implications for past photic zone anoxia. Appl Environ Microb 74: 624–632. Meril¨a P, Galand PE, Fritze H, Tuittila ES, Kukko-oja K, Laine J & Yrj¨al¨a K (2006) Methanogen communities along a primary succession transect of mire ecosystems. FEMS Microbiol Ecol 55: 221–229. Metje M & Frenzel P (2005) Effect of temperature on anaerobic ethanol oxidation and methanogenesis in acidic peat from a northern wetland. Appl Environ Microb 71: 8191–8200. Mitchell EAD, Payne RJ & Lamentowicz M (2008) Potential implications of differential preservation of testate amoeba shells for paleoenvironmental reconstruction in peatlands. J Paleolimnol 40: 603–618. Nicol GW, Tscherko D, Embley TM & Prosser JI (2005) Primary succession of soil Crenarchaeota across a receding glacier foreland. Environ Microbiol 7: 337–347. Prosser JI & Nicol GW (2008) Relative contributions of archaea and bacteria to aerobic ammonia oxidation in the environment. Environ Microbiol 10: 2931–2941. Rooney-Varga JN, Giewat MW, Duddleston KN, Chanton JP & Hines ME (2007) Links between archaeal community structure, vegetation type and methanogenic pathway in Alaskan peatlands. FEMS Microbiol Ecol 60: 240–251. Siegel DI, Reeve AS, Glaser PH & Romanowicz EA (1995) Climate-driven flushing of pore water in peatlands. Nature 374: 531–533.
2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
98
Siegel DI, Chanton J, Glaser P, Chasar L & Rosenberry D (2001) Estimating methane production rates in bogs and landfills by deuterium enrichment of pore water. Global Biogeochem Cy 15: 967–976. ter Braak CJF & Prentice IC (1988) A theory of gradient analysis. Adv Ecol Res 18: 271–317. ter Braak CJF & Sˇmilauer P (2002) CANOCO Reference Manual and CanoDraw for Windows User’s Guide: Software for Canonical Community Ordination (Version 4.5). Microcomputer Power, Ithaca, NY. Utsumi M, Belova SE, King GM & Uchiyama H (2003) Phylogenetic comparison of methanogen diversity in different wetland soils. J Gen Appl Microbiol 49: 75–83.
2009 Federation of European Microbiological Societies Published by Blackwell Publishing Ltd. All rights reserved
c
Downloaded from https://academic.oup.com/femsec/article-abstract/70/1/87/466665 by guest on 07 March 2018
A. Putkinen et al.
van Kessel JAS & Russell JB (1996) The effect of pH on ruminal methanogenesis. FEMS Microbiol Ecol 20: 205–210. Wagner R (1994) The regulation of ribosomal RNA synthesis and bacterial cell growth. Arch Microbiol 161: 100–109. Whalen SC (2005) Biogeochemistry of methane exchange between natural wetlands and the atmosphere. Environ Eng Sci 22: 73–94. Williams R & Crawford R (1983) Microbial diversity of Minnesota peatlands. Microb Ecol 9: 201–214. Williams R & Crawford R (1984) Methane production in Minnesota peatlands. Appl Environ Microb 47: 1266–1271. Yrj¨al¨a K, Katainen R, Jurgens G, Saarela U, Saano A, Romantschuk M & Fritze H (2004) Wood ash fertilization alters the forest humus Archaea community. Soil Biol Biochem 36: 199–201.
FEMS Microbiol Ecol 70 (2009) 87–98
