Centre for Applied Microbiology and Research, Porton Down, Salisbury, Wiltshire SP4 0JG, UK1
Author for correspondence: Paul W. Riley. Tel: +44 1980 612315. Fax: +44 1980 611320. e-mail: paul.riley{at}camr.org.uk
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ABSTRACT |
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Keywords: methanogenic Archaea, methyl coenzyme-M reductase, detection, identification
Abbreviations: MCR, methyl coenzyme-M reductase; OTU, operational taxonomic unit
b The GenBank accession numbers for the mcrA sequences reported in this paper are AF414034AF414051 (see Fig. 2) and AF414007AF414033 (environmental isolates in Fig. 3).
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INTRODUCTION |
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Our chosen alternative was to develop PCR-based techniques to investigate naturally occurring methanogen populations. Whilst many groups have used the amplification and analysis of 16S rRNA genes to unravel the complexities of microbial populations in environmental samples, our approach was to simplify the analysis by using PCR to generate methanogen-specific DNA fragments. The DNA sequence of each fragment could then be established and these data used to construct phylogenetic trees to enable comparisons with 16S rRNA phylogenetic trees, for which there is far more sequence data available.
The target for this methanogen-specific PCR was the terminal enzyme complex in the methane generation pathway, methyl coenzyme-M reductase (MCR), which catalyses the reduction of a methyl group bound to coenzyme-M, with the concomitant release of methane. This enzyme complex is thought to be unique to, and ubiquitous in, methanogens (Thauer, 1998 ), making it a suitable tool for their specific detection. Targeting an enzyme also offers the potential for the development of activity-based detection methods based on the analysis of messenger RNA. The MCR operon exists in two forms, MCRI and MCRII. The MCRI form is thought to be present in all methanogens, whilst the MCRII form has only been demonstrated to be present in members of the orders Methanobacteriales and Methanococcales. One peptide of the MCRI complex, encoded by the mcrA gene, was selected as a suitable candidate for the development of PCR-based detection of methanogens. Others have demonstrated the suitability of this marker for the analysis of one of the five recognized orders, the Methanosarcinales (Springer et al., 1995
).
Further studies have highlighted the use of the mcrA gene as the target for the detection of methanogens in a wide range of environments. These have included rice paddies (Lueders et al., 2001 ), peat bogs (Hales et al., 1996
; Lloyd et al., 1998
; Nercessian et al., 1999
), the guts of termites (Ohkuma et al., 1995
) and marine environments (Bidle et al., 1999
). None of these studies have confirmed, however, that the primers will detect all known orders of methanogen. Lueders et al. (2001)
successfully detected methanogens from a range of orders, but failed to detect members of the Methanomicrobiales, an order previously found in endosymbionts isolated from the same environment (Grosskopf et al., 1998
). They suggested that their mcrA primers used for PCR were not of sufficient utility to detect all the orders of the methanogens.
In this paper we describe the development and evaluation of the methanogen-specific PCR approach to the detection of all known orders of methanogen. We confirm that mcrA and small subunit rRNA gene phylogenies are remarkably similar, validating this approach. Subsequently, we describe the application of this approach to the phylogenetic analyses of two different landfill methanogen populations.
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METHODS |
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Landfill sample collection and processsing.
Landfill material was recovered from two landfills, the Cory Environmental Waste Management site at Mucking, Essex, and the Wyvern Waste Management site at Odcombe, Somerset. The Mucking material was new landfill, less than 3 years old, recovered from a depth of 110 m using an excavator. The Odcombe material was obtained during drilling operations to install gas abstraction wells. It was obtained from a depth of 520 m, it was 25 years old and appeared well digested. The landfill material was packed into double-lined gas impermeable plastic sacks and sealed to exclude as much air as possible. Both samples of landfill material were hand-sorted to remove recalcitrant material and placed into model systems incubated at 37 °C. The models were flushed with oxygen-free nitrogen for 10 min to accelerate the establishment of anaerobic conditions. Sterile distilled water was added to the top of these model systems and leachate was collected from a sample port located at the bottom. To maximize the numbers of methanogens, the samples were incubated in the models until methane production had peaked, when it was believed the methanogen population would be at its greatest. Leachate samples were then collected. Cells were recovered immediately from the Odcombe leachate sample. Leachate (50 ml) was centrifuged at 7000 g for 10 min at room temperature. The pellet was resuspended in 0·6 ml TE (10 mM Tris/HCl, 1 mM EDTA, pH 8·0) and the DNA was extracted using the RiboLyser technique described below. Leachate from the Mucking sample was inoculated into a methanogen growth medium (DSM 119) and grown for a further 2 weeks prior to processing as described above, for the Odcombe leachate sample.
DNA extraction and recovery.
The RiboLyser (Hybaid), a high-speed reciprocal shaker which retains samples in screw-capped tubes containing ceramic and silica beads, was used for DNA isolation. The sample (0·5 ml) was added, together with 0·5 ml Tris/HCl-equilibrated phenol, pH 8·0, and 0·1 ml chloroform/isoamyl alcohol solution (25:1, v/v), to a blue RiboLyser tube and placed into the RiboLyser. The tube was treated for 30 s at 6 m s-1 to maximize the release of DNA, and cooled on ice for 10 s. The phases were separated by centrifugation (10000 g, 10 min, 4 °C) and the aqueous phase was recovered and washed with 0·3 ml chloroform/isoamyl alcohol solution. The aqueous phase was recovered as before and the DNA was precipitated with an equal volume of 2-propanol, followed by an ethanol wash, brief drying of the pellet in a vacuum desiccator and resuspension in 0·1 ml TE.
Oligonucleotide primers.
Degenerate primers were designed based on the alignment of five published mcrA gene sequences (Allmansberger et al., 1986 ; Bokranz et al., 1988
; Bokranz & Klein, 1987
; Cram et al., 1987
; Weil et al., 1988
) and the identification of two regions with a high degree of homology flanking a more variable region (Fig. 1
). The two oligonucleotide primers were a forward primer, 5'-GGTGGTGTMGGATTCACACARTAYGCWACAGC-3', and a reverse primer, 5'-TTCATTGCRTAGTTWGGRTAGTT-3'. These oligonucleotides were obtained from the Structural Sciences Department, CAMR, and synthesized on an Applied Biosystems 380B DNA synthesizer.
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Cloning.
PCR products were purified using GeneClean (Bio 101), ligated into the pCR2.1 plasmid vector of the TA cloning kit (Invitrogen) and transformed into the E. coli INV-F' cells provided, following the manufacturers instructions. Plasmid DNA was recovered from individual colonies using GeneReleaser (Bioventures) and used directly in PCR reactions to generate material for restriction fragment analysis and DNA sequencing.
Restriction fragment analysis.
PCR product amplified from single clones (10 µl) was digested in PCR reaction buffer by the addition of 10 U TaqI and incubation at 65 °C for 1 h. Digested products were run through a 4% agarose gel and visualized by ethidium bromide staining.
DNA sequencing.
PCR products were sequenced using one of two methods.
Using the TAQuence cycle sequencing kit (USB).
Fresh PCR products were purified using GeneClean and resuspended in a total volume of 40 µl TE. A sample (6 µl) of this DNA was used in the master reaction mix of the sequencing reaction along with 2 pmol 32P end-labelled PCR primer and other reagents as per the manufacturers instructions. Twenty cycles of the reaction were found to give the clearest bands when the reaction mixes were run on polyacrylamide gradient sequencing gels (Sambrook et al., 1989 ).
Using an ABI377 automated fluorescence sequencer.
PCR products were reamplified from single clones using chimeric primers, which consisted of the methanogen mcrA forward and reverse primers, 3' to the M13 forward (-21) and M13 reverse primers respectively. The primer sequences were mcrA/M13F, 5'-TGTAAAACGACGGCCAGTGGTGGTGTMGGATTCACACARTAYGCWACAGC-3', and mcrA/M13R, 5'-CAGGAAACAGCTATGACCTTCATTGCRTAGTTWGGRTAGTT-3'. The resultant PCR products were then used directly with M13 forward (-21) and M13 reverse primers in automated sequencing using the fluoresence sequencer.
Phylogenetic analysis.
Gene sequences and inferred protein sequences of the mcrA gene obtained in this study, and 16S rDNA sequences from GenBank, were aligned using PILEUP in the GCG suite of programs (Wisconsin package version 10.1; Genetics Computer Group), using the Blosum 62 matrix, with gap creation and gap extension penalties both set to 1. Alignments were scrutinized and altered manually where appropriate. Phylogenetic trees were constructed by analysing the data with one of the following algorithms: DNADIST or PROTDIST followed by FITCH or NEIGHBOR; DNAPARS or PROTPARS followed by CONSENSE; all found in PHYLIP (Phylogeny Inference Package version 3.57c; Felsenstein, 1995 ). Trees were drawn using TREEVIEW, version 1.6.1 (Page, 1996
). Bootstrap analysis was carried out using SEQBOOT to generate 100 random combinations of the alignments followed by the algorithms stated previously to generate the trees and CONSENSE to produce a consensus tree and bootstrap values. SEQBOOT is also found in PHYLIP. Bootstrap values were then transposed onto the phylogenetic trees.
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RESULTS |
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DNA was successfully extracted from all 23 species of methanogen using the RiboLyser. Once the DNA had been purified, the PCR methodology was evaluated. A single major PCR product of the expected size (464491 bp) was generated for all 23 methanogen species using the primers described. These primers were those found to give the most consistent results, although other primer pairs were evaluated (data not shown).
To confirm the identity of PCR products, the DNA sequence of each was determined. These sequences were verified as the predicted part of the mcrA gene by comparison with the published sequences of the mcrA gene. Those evaluated included the five previously published DNA sequences for the mcrA gene used in the alignment for the design of the PCR primers.
Eight DNAs from non-methanogen species, seven eubacterial and one archaeal DNA (Pyrococcus furiosus) were also evaluated to see if they generated a PCR product with the same primers. Under the conditions outlined for the PCR, none of these DNAs generated a PCR product (data not shown).
Restriction enzyme analysis
Once purified DNA had been recovered from the model reactors, the mixed mcrA gene fragments were PCR-amplified and the population of fragments separated by cloning and transformation. A total of 63 Mucking clones and 102 Odcombe clones were screened for the presence of the mcrA gene fragment by restriction digestion with TaqI. In total six restriction patterns were obtained from the Mucking site and 17 from the Odcombe site, four of which were found in both (Table 1). Screening by restriction analysis allowed the clones to be grouped based on TaqI RFLP patterns, reducing the total number of clones that needed to be sequenced. However, several members of each pattern were sequenced to confirm the reliability of this pre-screening. In total, 20 of the 63 Mucking clones and 53 of the 102 Odcombe clones were sequenced.
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Phylogenetic trees based on DNA and amino acid sequences of the mcrA gene were compared to see if there where any major differences in the phylogenetic trees generated. No major differences were apparent between DNA and amino acid sequences, nor between the different algorithms used. The possibility of chimeric sequences were also checked by separate phylogenetic analyses on the N and C termini of the McrA proteins. No evidence for chimeras were seen (data not shown). We have chosen to present trees generated from DNA sequence data when comparing mcrA and 16S rRNA sequences from known species (Fig. 2), and inferred amino acid sequences when examining environmental samples (Fig. 3
), all using DNADIST or PROTDIST followed by FITCH. The use of inferred amino acid sequences reduces the variation seen in the DNA sequence, principally in the third base of the codon, and simplifies the trees by grouping together those with identical amino acid sequence. This is suitable for our purpose, but does mean that some of the finer genetic variation enabling differentiation at the subspecies level is lost. It has been suggested, however, that the third codon position can introduce major bias into a phylogenetic analysis, particularly when the G+C content is similar in unrelated species (Gupta, 1998
). The use of amino acid sequences avoids this potential source of error and is more appropriate as we are analysing a functional gene.
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Four major clusters of sequences grouped within the Methanomicrobiales and each showed strong similarity to a recognized species: 92·5% to Methanoculleus bourgensis, 93·8% to Methanospirillum hungatei, 92·496% to Methanocorpusculum bavaricum and 99·3% to Methanocorpusculum parvum. All the sequences grouping within the Methanocorpusculaceae were isolated from the Odcombe site. A single clone, OS77, branching at a deeper point in the tree, showed 74·7% similarity to its closest recognized species, Methanomicrobium mobile. Two sequences grouping close to Methanosarcina barkeri showed 92·493·1% similarity. Another two sequences, OS18 and OS55, both from the Odcombe sample, formed a deep-branching cluster with only 60·7% similarity to their closest recognized species, Methanosaeta concilii.
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DISCUSSION |
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To minimize bias in DNA recovery, physical shear techniques were developed. The RiboLyser was successfully used in recovering DNA from the 23 pure cultures of methanogens covering the broad range of known methanogen phylogeny used in this study. On application to environmental samples, DNA could also be recovered from solid landfill and leachate. Additionally, slight modifications to the protocol enables the recovery of RNA using the RiboLyser, which can be used in studies of activity within methanogen populations.
Springer et al. (1995) previously reported that the phylogenetic trees of 16S rRNA, and part of the mcrA gene analysed, for the methanogen family Methanosarcinaceae, were broadly similar. The far more comprehensive analysis, in terms of the breadth of methanogen phylogeny, examined in this study (Fig. 2B
), supports this view, with the five proposed orders of methanogens clearly distinguishable. Indeed, closer comparison at the genus and species level within orders reveals that there is a strong correlation between 16S rRNA and mcrA even at this level. This strongly supports the view that we can use the analysis of mcrA gene sequences to describe the community present in environmental samples without the need to recover these isolates in culture.
However, there are some anomalies. Whilst the reorganization of the taxonomy of the methanogens as suggested by Boone et al. (1993) is broadly supported by our data, some of the more specific taxonomic relationships are not confirmed. An example is the position of Methanosphaera stadtmanae, which is considered to be a member of the order Methanobacteriales, grouping within this order by 16S rRNA analysis, but not by mcrA analysis (Fig. 2
). Methanosphaera stadtmanae is an unusual member of the Methanobacteriales, having a coccoid shape, unlike all the rest which are rods, and growth is limited to the reduction of methanol by H2. The mcrA sequence we obtained is more closely related to the mrtA sequences of other Methanobacteriales (Fig. 3
). It is therefore possible that either our primers failed to amplify the Methanosphaera stadtmanae mcrA gene fragment, or it is no longer present, as we were only able to detect this mrtA-like sequence. The rest of the Methanobacteriales fall readily into the taxonomic organization proposed by Boone et al. (1993)
and confirmed by Wasserfallen et al. (2000)
. Similarly, the reorganization of the Methanococcales into two families and four genera is supported.
The taxonomy of the Methanomicrobiales is more complex with not all of the nine established genera represented on our mcrA tree, making overall comparisons difficult. However, the Methanocorpusculaceae clearly group together in a coherent group and the recognition of Methanocorpusculum aggregans and Methanocorpusculum parvum as synonymous (Boone et al., 1993 ) is supported. The application of the mcrA gene in the analysis of the Methanosarcinales has been considered before (Springer et al., 1995
). An additional observation, supported by our data, is the division of the Methanosarcinales into two families, the Methanosaetaceae, which is represented by only one species, Methanosaeta concilii, in our mcrA study, and the Methanosarcinaceae, which includes a large number of species.
A potential pitfall in using the mcrA gene for detection is the high degree of DNA sequence conservation between mcrA and mrtA, the equivalent gene in the MCRII complex, a complex found in a limited number of methanogen species. This means that detection based on primers for PCR designed to conserved regions in mcrA is also likely to detect mrtA sequences, complicating the analysis. Fortunately, the mrtA gene sequences form a coherent group close to the Methanococcales (Fig. 3) and therefore are readily distinguishable from mcrA gene sequences. This close proximity has been reported previously and led to the suggestion that the mrtA gene has arisen by lateral transfer from the Methanococcales rather than gene duplication (Reeve et al., 1997
). There were, however, noticably more mrtA sequences than mcrA Methanobacteriales sequences, suggesting overrepresentation of this sequence type in the analysis. This is likely to be due to PCR bias, amplifying the mrtA sequences preferentially, rather than a wider distribution of mrtA genes in the methanogen community, as the mrtA gene sequences group closely with a single Methanobacteriales mrtA sequence from Methanobacterium formicicum.
The principal aim of this study was not to compare 16S rRNA and mcrA as rival phylogenetic approaches, but to confirm the validity of using the mcrA gene as an alternative phylogenetic tool in the specific detection and identification of methanogens. This was achieved successfully, a comparison of the phylogeny of the mcrA and 16S rRNA genes from the five recognized orders of methanogens clearly indicating a strong similarity (Fig. 2). Additionally, unlike other studies, where all the expected orders of methanogens have not been detected in environmental samples (Lueders et al., 2001
), our primers were able to amplify the mcrA gene from all the known orders using DNA extracted from type species (Fig. 2B
), and all the expected orders using DNA extracted from environmental samples (Fig. 3
).
The two landfill sites examined both had complex methanogen communities, reflected by a wide distribution of sequences, with a far greater diversity than had previously been described in the literature (Fielding et al., 1988 ; Maule et al., 1994
). This was despite the extensive sample treatment undergone by the solid landfill material collected, which was necessary to convert it into artificial leachate. The reason for using leachate is that it is likely to yield a more representative sample of the microbial community compared to extracting DNA from a small, single solid sample (Barlaz, 1997
). Additionally, the artificial leachate from the Mucking sample was then inoculated into a methanogen growth medium in an attempt to boost numbers further. It is therefore unlikely that the populations we examined in this study are accurate representations of the overall methanogen population in the original solid landfill samples, but they will be subsets of this population. No attempts have therefore been made to compare populations as we were seeking only to exemplify the methods developed. Additionally, taking single samples is not statistically valid for the purpose of making comparisons between populations.
Methanobacterium formicicum has been detected previously in landfill, as an endosymbiont of the ciliate protozoan Metopus palaeformis (Finlay & Fenchel, 1991 ) and by oligonucleotide probe analysis to the mcrA gene on leachate samples (Maule et al., 1994
). Micro-organisms antigenically related to Methanobrevibacter arboriphilicus have been detected in an anaerobic sludge blanket reactor (Grotenhuis et al., 1991
), but there are no reports of previous detection in landfill.
Of the four Methanomicrobiales group sequences, two have been detected before in landfill, Methanoculleus bourgensis and Methanospirillum hungatei, using species-specific probes against the mcrA gene (Luton, 1996 ). Fielding et al. (1988)
isolated a coccoid methanogen from landfill that was thought to be a Methanogenium sp. or a Methanocorpusculum sp. Mori et al. (2000)
also reported the isolation of Methanocalculus pumilus, a member of the Methanocorpusculaceae family, from landfill. The inclusion of the mcrA gene sequence from this recently isolated methanogen may have further resolved the large number of clones in these Methanocorpusculum clusters. The unclustered sequence, OS77, could represent a novel Methanomicrobiales lineage or could simply be a representative of an as yet unsequenced mcrA gene from a recognized species. No previous studies have described this level of diversity among the Methanomicrobiales in landfill. Additionally, a recent report by Lueders et al. (2001)
using detection of the mcrA gene in rice field soil failed to detect any Methanomicrobiales.
Analysis revealed a low number of clones from both samples grouping with the acetoclastic methanogen family, the Methanosarcinaceae. In the Mucking site, 2 of the 66 clones examined aligned with the acetoclastic methanogen Methanosarcina barkeri; in the Odcombe sample no acetoclastic methanogen clones were detected. However, these landfill samples were introduced into laboratory model systems which is likely to have had some effect on methanogen population composition. The Mucking sample, which was subsequently inoculated into a Methanobacterium medium, did yield some acetoclastic methanogen sequences as well as a range of Methanomicrobiales sequences. The medium therefore was not totally selective for Methanobacteriales. Reports of low numbers of acetoclastic methanogens as compared with hydrogenotrophic methanogens in landfill have been reported previously (Fielding & Archer, 1986 ). Alternatively, the low numbers of Methanosarcinaceae could be explained by bias in the PCR where some sequences are amplified in preference to others. Bias in PCR has been observed when amplifying 16S rRNA genes (Reysenbach et al., 1992
), when very low template concentration is used (Chandler et al., 1997
) or when using degenerate primers (Polz & Cavanaugh, 1998
).
Further examination of the OS18 and OS55 clones, which formed a deep-branching cluster separate from the other five orders of methanogens, revealed amino acid sequence similarity of between 55 and 61% with these orders. An additional sequence, uncultured archaeon 85A (GenBank accession no. AF268654), isolated from an anaerobic digestor and deposited in GenBank, showed 84% amino acid sequence similarity and also fell into this cluster when included in a phylogenetic analysis. The low values of sequence identity with other methanogenic orders support the conclusion that this cluster of sequences is distinct from any described species of methanogen and may represent an uncultured novel methanogenic lineage. The independent recovery of sequences within this cluster by two groups strengthens the validity of this observation.
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ACKNOWLEDGEMENTS |
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Received 8 April 2002;
revised 28 June 2002;
accepted 12 July 2002.