Abstract
Bacteria and Archaea are evolutionarily and biochemically distinct domains found together in many environments. Robust ‘universal’ PCR primer sets targeting both the bacterial 16S rRNA gene and the type I chaperonin gene have been established. However, ‘universal’ PCR primers for Archaea are currently limited to the 16S rRNA gene. We investigated the type II chaperonin (known as the thermosome, TF55, CCT or TCP-1) as a potential universal target (UT) for Archaea. Reproducible amplification of thermosome gene sequences from all major phyla tested was achieved through the application of a mixture or ‘cocktail’ of two forward and two reverse primers. Phylogenies based on the ∼750-bp thermosome UT were congruent with 16S rRNA gene phylogenies while exhibiting longer branch lengths, improving resolution of closely related taxa. ‘Universal’ thermosome primers were applied to profiling the archaeal community of dairy cow rumen and results compared with profiles based on the 16S rRNA gene and methyl co-enzyme M reductase (methanogen-specific) gene. Clone libraries generated from each target gene, as well as a pyrosequencing profile of one thermosome rumen library, revealed that all three targets consistently detected Methanobrevibacter smithii, Methanobrevibacter ruminantium and Methanosphaera stadtmanae as the dominant constituents; however, thermosome gene sequences were more diverse than either of the other targets providing a higher resolution description of the archaeal community. These findings demonstrate that a ‘universal’ thermosome PCR protocol is a powerful metagenomic tool for detecting and characterizing Archaea and archaeal communities.
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Introduction
The prokaryotic world is divided into the domains Bacteria and Archaea. Although these two groups are evolutionarily and biochemically distinct, both are important ecological contributors and are often found together in the same environments (Chaban et al., 2006; Martiny et al., 2006). Thus, a thorough understanding of the microbial component of an environment requires consideration of constituents from both prokaryotic domains. Limitations in culturing efficiencies (Kaeberlein et al., 2002) have lead microbiologists to rely on molecular techniques to investigate the composition of microbial communities. However, because of the genetic distinction between Bacteria and Archaea, molecular targeting of these microbial populations is necessarily domain specific.
No PCR primers can be absolutely universal, but the term ‘universal’ is commonly used to describe PCR primers designed to amplify phylogenetically informative gene sequences from virtually all members of a domain. Among these genes, the 16S rRNA gene is the most commonly targeted (Fox et al., 1980). The appeal of the 16S rRNA gene lies in its ‘universal’ nature, slow rate of evolution (allowing phylogenetic comparisons of distantly related organisms) and its sequence structure of alternating conserved and variable regions. These properties have facilitated the design of PCR primers that can amplify either the bacterial or archaeal 16S rRNA gene (Baker et al., 2003). As such, the phylogeny of the 16S rRNA gene has become the basis of prokaryotic molecular taxonomy (Woese and Fox, 1977; Woese et al., 1985). To complement the use of the 16S rRNA gene for phylogenetic and metagenomic studies, the Ribosomal Database Project was established (Cole et al., 2009).
Despite the utility of the 16S rRNA gene, there are well-documented shortcomings in assessing microbial diversity and for phylogenetic analysis. While the slow rate of evolution allows for broad taxonomic comparison, this same characteristic results in nearly identical sequences for closely related organisms, reducing the resolution of phylogenetic trees and making differentiation of these taxa difficult (Wang et al., 2007; Schellenberg et al., 2009; Weng et al., 2009). As well, the regular occurrence of insertions and deletions (indels) in rRNA genes has resulted in challenges for multiple sequence alignments, the first step in phylogenetic analysis (DeSantis et al., 2006). These challenges become exponentially more complex when large data sets, like those from microbial ecology studies, are considered. Finally, there will always be questions as to whether one gene can truly represent an organism's phylogeny, especially in light of horizontal gene transfer, from which the 16S rRNA gene is not exempt (Yap et al., 1999).
An alternative to the 16S rRNA gene has been the utilization of conserved protein-coding genes. These have advantages over 16S rRNA genes in that they are usually present in single copies in prokaryotic genomes, are subject to low rates of indel events and accumulate silent mutations due to codon degeneracy, resulting in better species resolution (Santos and Ochman, 2004). Genes such as recA (Weng et al., 2009), rpoB (Meintanis et al., 2008; Glazunova et al., 2009; Weng et al., 2009), recN (Zeigler, 2005; Arahal et al., 2008), gyrB (Wang et al., 2007; Glazunova et al., 2009) and cpn60 (Hill et al., 2006a; Glazunova et al., 2009) have been used when the 16S rRNA gene sequence could not provide species resolution. Of these, the type I chaperonin gene, cpn60 (also known as hsp60 or groEL) is the most developed alternative. It is the only target other than 16S rRNA gene that can be accessed with ‘universal’ PCR primers and a curated sequence database, cpnDB, is available (http://www.cpndb.ca; Hill et al., 2004, 2006b; Schellenberg et al., 2009). The cpn60 gene provides greater discriminating power than 16S rRNA gene for closely related taxa and the uniform size and sequence heterogeneity of the cpn60 ‘universal target’ (UT) simplify sequence comparisons and other bioinformatics tasks.
Unfortunately, ‘universal’ PCR protocols for the Archaea have been limited to the archaeal 16S rRNA gene. The major reason for this has been a lack of sequence data from the archaeal domain. With the wealth of complete archaeal genomes now available, it is possible to investigate and evaluate protein-coding genes as potential archaeal UTs.
A promising candidate archaeal UT is the type II chaperonin. Type II chaperonins, also known as thermosomes, TF55, CCT or TCP-1, are found in Archaea and the eukaryotic cytosol (Trent et al., 1991; Kubota et al., 1995; Large and Lund, 2009; Large et al., 2009). In Archaea, organisms possess one to three parologous thermosome genes, giving rise to α, β and γ subunits. The thermosome gene is an appealing target as its bacterial homologue, cpn60, has been demonstrated to be an excellent target for species detection, identification and quantification of individual bacterial species and strains as well as the metagenomic characterization of complex microbial communities (Hill et al., 2005; Dumonceaux et al., 2006b; Schellenberg et al., 2009). In addition, the infrastructure needed to translate the thermosome sequence into a useful identification tool is already in place since thermosome sequences have been included in the cpnDB database since its creation (Hill et al., 2004). In the current study, we set out to accomplish three goals: compare 16S rRNA gene-based and thermosome-based phylogenies of the Archaea; design a ‘universal’ PCR protocol to amplify the thermosome gene; and evaluate the thermosome ‘universal’ PCR protocol for its ability to detect and distinguish members of the archaeal community compared with established 16S rRNA gene and methanogen-specific (mcrA gene) PCR protocols. To accomplish the third objective, rumen samples from dairy cows fed two distinct diets were profiled using all three genetic targets by clone library analysis, and thermosome sequences were further analysed by pyrosequencing.
Materials and methods
Reference DNA sequences
Reference DNA sequences used in this study were taken from two sources: 16S rRNA genes and methyl co-enzyme M reductase α subunit (mcrA) genes were from NCBI Genomes (http://www.ncbi.nlm.nih.gov/genomes), while thermosome sequences were taken from cpnDB (http://www.cpndb.ca/; Supplementary Table 1). Eighty-four archaeal strains were identified for which both 16S rRNA gene and thermosome sequences were available. A single, representative 16S rRNA gene sequence was chosen from each strain as well as the thermosome genes from each subunit (from one to three parologues per genome, depending on the species). In addition, full-length mcrA genes from 25 methanogen strains (plus the homologous mrtA gene from Methanosphaera stadtmanae DSM 3091) were also included in the analysis.
Phylogenetic analysis
Multiple sequence alignments of 16S rRNA gene sequences were assembled at the Ribosomal Database Project website using the secondary structure alignment program Infernal (Nawrocki et al., 2009), while thermosome and mcrA gene sequences alignments were constructed with ClustalW (Thompson et al., 1997) with a gap opening penalty of 50 and gap extension penalty of 5. Alignments were manually inspected and minor edits were made when necessary with GeneDoc (Nicholas et al., 1997) before trees were constructed using PHYLIP (Felsenstein, 1989). DNA distance matrices were calculated based on the F84 maximum likelihood option and neighbour-joined trees were assembled as the consensus of 100 replicates. Final trees were visualized with TreeView (Page, 1996).
Archaeal genomic DNA
Archaeal genomic DNA from Methanococcus voltae PS, Methanococcus vannielii SB, Methanococcus maripaludis S2, Methanotorris igneus Kol 5, Sulfolobus solfataricus, Sulfolobus sp., Thermoplasma acidophilum and Halobacterium salinarum (formerly Halobacterium halobium) NRC 34008 was generously donated by Ken Jarrell, Queen's University at Kingston, Canada while Haloferax volcanii WR341 and Hf. volcanii WR536 were generously donated by Jerry Eichler, Ben-Gurion University of the Negev, Israel. Genomic DNA from Hb. salinarum (formerly Halobacterium cutirubrum) ATCC 33170, Hb. salinarum (formerly Hb. salinarium) ATCC 33171, Thermococcus gorgonarius ATCC 700654, Thermococcus pacificus ATCC 700653 and Thermococcus zilligii ATCC 700529 was acquired from the American Type Culture Collection (Manassas, VA, USA).
Rumen sample collection and processing
Rumen contents (both solid and liquor) were collected from fistulated dairy cows housed at the University of Saskatchewan dairy barn. Six cows on a ‘regular’ high milk production dairy cow diet and two cows on a modified ‘dry’ diet were sampled ∼2 h after feeding and DNA was extracted immediately. Samples were thoroughly mixed before a 300-μl volume of solid and liquor was removed for processing. Total DNA extraction from the samples was accomplished as previously described (Dumonceaux et al., 2006a). Final DNA extracts were suspended in Tris-EDTA buffer, pH 8.0 and stored at −20 °C.
PCR primers and conditions
Thermosome-specific archaeal primers were designed from a multiple sequence alignment of 166 thermosome sequences (Table 1; Figure 1). The thermosome primer pair, JH0175 and JH0178, is theoretically appropriate for the entire archaeal domain and performed best on low to mid-range GC content organisms. An alternative version of these primers, JH0268 and JH0269, contains no degeneracies and was engineered to target high GC organisms.
Standard thermosome PCR consisted of 1 × PCR reaction buffer (20 mM Tris-HCl, pH 8.4, 50 mM KCl), 2.5 mM MgCl2, 200 μM dNTP, 400 nM of each forward and reverse primer, 2.5 U Platinum Taq DNA Polymerase (Invitrogen, Burlington, Canada) and 1.0 ng template DNA, carried out in a final volume of 50 μl. A Mastercycler thermocycler (Eppendorf, Mississauga, Canada) or a MyiQ thermocycler (Bio-Rad, Mississauga, Canada) was used with initial denaturing at 98 °C for 3 min, followed by 40 cycles of 30 s at 98 °C, 1 min at 54 °C and 1 min at 72 °C, followed by a final extension at 72 °C for 10 min. Annealing temperature gradients (45–66 °C) were tested with both thermosome primer sets (individually and in combination) with several archaeal genomic DNAs (spanning the range of GC contents from 28% to 66%) and 54 °C was determined to be the optimal annealing temperature (data not shown). For the generation of JH0175/JH0178 PCR products from rumen samples, a pool of PCR products was generated from four PCRs with annealing temperatures of 56.7 °C, 53.6 °C, 49.2 °C and 46.3 °C, respectively. For the generation of thermosome PCR products from regular diet rumen samples using a 7:1 molar ratio cocktail of JH0175/JH0178:JH0268/JH0269, the standard thermosome PCR program (annealing temperature of 54 °C only) was used. The primer cocktail experiments conducted to determine that a 7:1 molar ratio mix of JH0175/JH0178:JH0268/JH0269 was the optimal ratio to allow for amplification of a broad spectrum of GC content sequences in a single PCR are detailed in Supplementary materials.
The 16S rRNA gene primer set and appropriate PCR protocol were taken from Baker et al. (2003), while the methyl co-enzyme M reductase α subunit (mcrA) gene-specific primer set and appropriate PCR protocol were taken from Mihajlovski et al. (2008) (Table 1). The Mihajlovski et al. mcrA gene primers are a slight modification of the original mcrA gene primer set proposed by Luton et al. (2002).
Clone library construction and sequencing
For comparison of the archaeal communities in dairy cow rumen, PCR products from each target gene (thermosome, 16S rRNA, mcrA) and each diet (regular and dry) were cloned into the vector pGEM-T Easy (Invitrogen) as previously described (Hill et al., 2005). A total of 96 white colonies were picked randomly from each library (192 colonies for the 7:1 tcp regular library) and sequenced.
454 pyrosequencing
In addition to the clone library, the thermosome PCR product pool generated from the dry diet rumen samples (tcpdry) was sequenced on a 454 GS FLX Titanium instrument (454 Life Sciences, Branford, CT, USA). PCR products were sequenced directly as an untagged sample within a larger multiplexed run. Sequences were identified as originating from the tcpdry pool if 12–24 bp of either the forward or the reverse primer (from the 5′-end) was detected at the beginning of the sequence.
Clone library and pyrosequencing data processing
Raw sequence data were trimmed for quality and PCR primer/vector sequences and assembled into clusters of identical sequences using APED (http://sourceforge.net/projects/aped). A combination of BLAST and Smith–Waterman alignments (watered-BLAST; Schellenberg et al., 2009) was used to identify the best match (nearest neighbour) for each individual sequence read from an appropriate reference sequence collection (Supplementary Table 1). Results were filtered to remove matches shorter than 100 bp to ensure reliable identifications. The distribution of nearest neighbour percent identities within the clone library data sets showed a clear distinction between target sequences (99–75% for all three targets) and non-target sequences (49% and lower; better non-target identities were obtained from the GenBank nr database). The thermosome pyrosequencing data set was filtered for nearest neighbour percent identities >50% (based on the thermosome clone library analysis), with sequences having between 50% and 75% identity to a thermosome nearest neighbour confirmed as thermosome by Blastx (Altschul et al., 1990) search with the GenBank nr database. Sequences generated in this study have been deposited in GenBank under the accession numbers HQ268028–HQ268244 and JF717634–JF717664.
Statistical analysis
Statistical analyses were done using SPSS software (SPSS Inc., Chicago, IL, USA). Differences between dairy cow archaeal communities from different diets as determined by each target gene were analysed for significance using the Wilcoxon signed ranks test, while the differences between the archaeal communities determined from different target genes for the same diet were analysed for significance using the Friedman's test.
Results
Design of ‘universal’ PCR primers for the archaeal thermosome
Full-length thermosome sequences (n=166, including α, β and γ subunits) were compiled and aligned to identify a universally amplifiable region of the thermosome gene. Degenerate ‘universal’ PCR primers JH0175 and JH0178 (Figure 1) were designed corresponding to positions 145–168 and 895–917 of the Nanoarchaeum equitans Kin4-M thermosome gene sequence, respectively. Primers were predicted to amplify a UT product of 752–803 bp, depending on the template species.
Thermosome phylogeny
Thermosome sequences were trimmed to the UT region and used to construct a phylogeny (Figure 2a; Supplementary Figure 1B). The phylogeny of the thermosome UT was compared with the thermosome full-length gene phylogeny and the resulting trees were virtually identical (Supplementary Figure 1A and B). The phylogeny derived from the thermosome UT region shares the same overall tree topology when compared with the full-length 16S rRNA gene phylogeny at the order level (Figure 2b; Supplementary Figure 1C). Only the Methanobacteriales order separates into two distinct clusters by thermosome phylogeny, with the species Methanothermobacter thermoautotrophicus Delta H clustering separately from the rest of the order (visible as two Methanobacteriales (α and β) modes in Figure 2a and expanded in Supplementary Figure 1A and B). Pairwise identities for full-length 16S rRNA gene sequences (1316–1539 bp) ranged from 54% to 99% (median 73%; Supplementary Figure 2). Pairwise identities for thermosome UT sequences (705–756 bp; amplification primers were removed for analysis) ranged from 35% to 100% (median 57%; Supplementary Figure 2). The multiple subunit nature of the thermosome genes resulted in distinct clustering of α, β and γ subunits within the phylogenetic tree. This phenomenon, illustrating the recurrent paralogy of the thermosome gene, has been discussed by Archibald et al. (1999) and the clustering of subunits seen in this analysis is consistent with distributions found in the previous, smaller study.
‘Universal’ PCR amplification of thermosome sequences from individual archaeal species
The JH0175/JH0178 primer set was tested on a range of purified archaeal genomic DNA extracts (Figure 3a). The predicted PCR product was amplified from most templates, with the notable exception of the halophiles (Hb. salinarum and Hf. volcanii strains), which are 66% GC (Figure 3a). To improve amplification of thermosome sequences from high GC organisms, the primers were modified to remove degenerate positions in favour of nucleotides most common in halophile sequences. When tested on the panel of genomic DNA, this high GC primer set, JH0268/JH0269, amplified thermosome sequences from the halophiles, and also members of the Thermoplasma and Thermococcus genera (Figure 3b). These results suggested that a mixture of ‘universal’ and ‘high GC’ thermosome primers would give optimal amplification across the domain. A range of primer cocktails were tested to determine what ratio of JH0175/JH0178 primers to JH0268/JH0269 primers for use in thermosome PCR most faithfully amplified a complex community (Supplementary material; Supplementary Figure 3). The 7:1 molar ratio of JH0175/JH0178:JH0268/JH0269 (350 nM per reaction of each JH0175 and JH0178 and 50 nM per reaction of each JH0268 and JH0269) gave the best overall representation (Figure 3c; Supplementary Figure 3).
Amplification of thermosome, 16S rRNA gene and mcrA gene sequences from rumen contents
To evaluate the performance of the thermosome PCR primers on complex samples, rumen contents from dairy cows on two different diets were obtained, total DNA was isolated and the archaeal communities present were determined using thermosome, 16S rRNA gene and mcrA ‘universal’ primers. Although only the thermosome and 16S rRNA primer sets are designed to be domain-wide, the archaeal community of the rumen is known to be methanogen dominated (Shin et al., 2004; Wright et al., 2007; Zhou et al., 2009), making the methanogen-specific, protein-encoding mcrA gene a worthwhile target for comparison. In addition, the mcrA gene is a common target for archaeal rumen research (Tatsuoka et al., 2004; Denman et al., 2007). Clone libraries were made for each gene target from each of the diets, resulting in six libraries. A regular diet clone library was also generated using the 7:1 thermosome primer cocktail. Clones were selected randomly from each library and sequenced. Thermosome PCR amplicons (JH0175/JH0178 only) from the dry food diet were also subjected to pyrosequencing. Sequences obtained were processed for quality and the nearest neighbour was determined for each sequence.
Table 2 reports the compositions of the various libraries by nearest neighbour. Regardless of the target gene used, Methanobrevibacter smithii, Methanobrevibacter ruminantium and Ms. stadtmanae were the dominant species detected. Within the clone library data (based on the six libraries where the same primers were used for both diets), there were no statistically significant differences between the archaeal populations detected in the two diets examined, regardless of the gene targeted (thermosome, P=0.593; 16S rRNA, P=0.715; mcrA, P=0.610). As well, the target used had no significant effect on the archaeal population detected within a diet (regular diet, P=0.513; dry diet, P=0.405).
Phylogenetic trees of clone library sequences from JH0175/JH0178 only, thermosome 7:1 primer cocktail, 16S rRNA gene and mcrA are shown in Supplementary Figure 4A–D, respectively. Both α and β subunit sequences were detected and are clearly distinguishable for Mb. smithii and Mb. ruminantium in the thermosome clone libraries (Supplementary Figure 4A and B).
When the thermosome PCR products from the dry diet were sequenced to greater depth by 454 pyrosequencing, a comparable archaeal community profile was observed (Table 2). As expected, the increase in community sampling revealed taxa not seen in the clone libraries. Interestingly, among the additional sequences, three non-identical thermosome sequences were detected that appear to be non-methanogen in origin (closest neighbour sequence was Desulfurococcales species Staphylothermus hellenicus, 67.3%, 63.2% and 63.2% identical; Table 2).
Comparison of intraspecies resolution of the three gene targets
Phylogenetic trees of Mb. smithii-like sequences from all three gene targets were constructed to evaluate the intraspecies resolution of each target (Figure 4). The 16S rRNA gene sequence target (trimmed to 617–620 bp common to all reads) generated 16 unique sequences from 81 sequence reads (Figure 4a). Pairwise sequence identities ranged from 93% to 99% (median 98%; Supplementary Figure 5). Thirty-three unique Mb. smithii mcrA sequences (436–442 bp) were detected, representing 32 unique subunit 1 types (from 46 reads) and 1 subunit 2 type (from a single read) (Figure 4b). Pairwise identities within mcrA α subunits were 84–99% (median 95%), while the single β subunit was 78% identical to the Mb. smithii mcrA type 2 subunit. Between subunits, type 1 and type 2 shared an average of 67% identity, with a range of 65–70% identity (Supplementary Figure 5). The longest branch lengths were observed in the thermosome tree (based on alignment of 707–711 bp), with 23 unique α subunit sequences (from 43 reads) and 14 unique β subunit sequences (from 16 reads) (Figure 4c). Pairwise DNA sequence identities were 75–99% (median 82%) for α subunits, 82–99% (median 85%) for β subunits and 59–67% (median 62%) between α and β subunits (Supplementary Figure 5).
Discussion
The objective of this study was to develop a ‘universal’ archaeal PCR protocol, based on the conserved archaeal thermosome (type II chaperonin) gene, for application in archaeal species identification, metagenomic and phylogenetic studies. The strength of the thermosome gene as a target is that it combines universality (like the 16S rRNA gene) with the greater sequence diversity associated with a protein-encoding gene (like the mcrA gene). The benefits of targeting a protein-encoding gene have been seen in bacteria, where the cpn60 (type I chaperonin) UT has advantages over 16S rRNA gene in terms of discrimination of closely related taxa and bioinformatics (Schellenberg et al., 2009).
PCR primers were designed with the intent to amplify at least one thermosome subunit sequence from each known archaeal species. However, given the conservation across α, β and γ subunits at the primer annealing sites, the thermosome primers presented here are expected to amplify most or all subunits from most of the species examined. Experimentation revealed that while the JH0175/JH0178 primer pair could amplify thermosome sequence from all Archaea tested (Supplementary Figure 3), amplification of high GC sequences was relatively inefficient (Figure 3a). Difficulty with PCR amplification of high GC content templates has been reported with both specific primers (Varadaraj and Skinner, 1994) and degenerate primers containing inosine (Hill et al., 2006b). This problem was encountered in the cpn60 UT PCR protocol and was overcome by the application of a ‘cocktail’ of inosine containing primers with primers specifically designed to favour high GC sequences (Hill et al., 2006b). We applied a similar approach and designed primers JH0268 and JH0269 to enhance amplification of high GC organisms (Figure 3b). A primer cocktail containing a 7:1 molar ratio of primers JH0175/JH0178 and JH0268/JH0269 resulted in balanced amplification of the complete spectrum of organisms tested (Figure 3c; Supplementary Figure 3).
The thermosome UT region proved to be a useful phylogenetic target. With approximately half the sequence length of the full 16S rRNA gene, the thermosome-based phylogenetic tree featured longer branch lengths and clearer separation of most species compared with the 16S rRNA gene-based tree (Figure 2; Supplementary Figure 1). For example, the three C strains of Mc. maripaludis (C5, C6 and C7) that were virtually identical by 16S rRNA gene sequence (99%) are distinguishable by the shorter thermosome sequence (96–97%; Supplementary Figure 1). The only instance where there was a difference distinguishable by 16S rRNA gene and not thermosome UT was between the two strains of S. solfataricus (98/2 and P2), where they had 99% identity over the 1497-bp of 16S rRNA gene sequence and 100% identity within each class of thermosome subunit over 726 bp.
When applied to the complex microbial community of the rumen, the thermosome PCR detected the same major species as the 16S rRNA gene and mcrA PCR protocols: Mb. smithii, Mb. ruminantium and Ms. stadtmanae (Table 2). The species of methanogens detected in this study were expected, with many of them previously reported from ruminants (Tatsuoka et al., 2004; Wright et al., 2004, 2007; Denman et al., 2007). Interestingly, the pyrosequencing library contained three apparently non-methanogen thermosome sequences (Table 2). These sequences were too short for inclusion in the phylogenetic analysis; however, their observation is consistent with reports that non-methanogenic Archaea exist as part of the rumen community (Shin et al., 2004; Wright et al., 2007). Examination of the sequences classified as Mb. smithii by each target gene illustrated differing degrees of strain variation within this species (Figure 4). There was an average of 98% pairwise identity between Mb. smithii-like 16S rRNA gene sequences. Conversely, thermosome α and β subunits shared only 82% and 85% pairwise identity within each of the α and β subunits, respectively, with further subgroupings within subunits apparent in the phylogenetic tree (Figure 4c). Whether these subgroups represent ecotypes, as has been shown for cpn60 ecotypes (Vermette et al., 2010), is an area for future study.
Our results demonstrate that (1) the thermosome gene and portions thereof can be taxonomically informative and are more diverse than 16S rRNA gene sequences; (2) a ‘universal’ PCR protocol could be designed to target the thermosome gene; (3) thermosome ‘universal’ PCR performed comparably to established 16S rRNA gene and mcrA gene ‘universal’ PCR protocols for the detection of archaeal members within rumen communities, while yielding greater sequence diversity. The advantage of the thermosome system is in its combined broad domain specificity and its superior discriminating power. The relatively homogenous length of the thermosome UT simplified bioinformatic analyses and the availability of a curated collection of thermosome sequences (in cpnDB) facilitates the application of thermosome sequences to metagenomic and phylogenetic studies. The occurrence of multiple subunits results in more complex profiles from community samples than those obtained with 16S rRNA gene or mcrA, which is potentially an advantage since these more complex profiles may make for more robust diagnostics. Currently, the ability to identify thermosome paralogues in communities containing poorly characterized, uncultured Archaea is limited by the lack of complete genome sequences for reference. However, given the rapid pace of accumulation of genomic and metagenomic sequence data from environmental Archaea, this limitation will likely be short lived. Meanwhile, the method described here can be implemented for the identification and phylogenetic placement of archaeal isolates, the development of species-specific detection and quantification methods and the characterization of archaeal communities.
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References
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Acknowledgements
We thank Ken Jarrell (Queen's University, Canada) and Jerry Eichler (Ben-Gurion University of the Negev, Israel) for generously providing archaeal DNA and Steve Hendrick for collecting rumen samples. We are grateful to Champika Fernando for statistic assistance and Matthew Links for advice and support in pyrosequencing data processing. BC was supported by a Saskatchewan Health Research Foundation postdoctoral fellowship.
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Chaban, B., Hill, J. A ‘universal’ type II chaperonin PCR detection system for the investigation of Archaea in complex microbial communities. ISME J 6, 430–439 (2012). https://doi.org/10.1038/ismej.2011.96
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DOI: https://doi.org/10.1038/ismej.2011.96
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