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Bacillus Genetic Stock Center, Department of Biochemistry, The Ohio State University, Columbus, OH 43210, USA
Correspondence
Daniel R. Zeigler
zeigler.1{at}osu.edu
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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The GenBank/EMBL/DDBJ accession number for the 16S rRNA gene sequences described in this study are AY297092 and AY608927–AY608993, inclusive; those for the recN sequences are AY434725 and AY608994–AY609060, inclusive.
A table giving details for the bacterial strains used in this study and a figure showing the locations of primers used for the recN sequencing project are available as supplementary material in IJSEM Online.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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). For measuring genome similarity, DNA–DNA hybridization studies have been regarded as the ‘gold standard’ (Wayne et al., 1987), but it can be difficult to reproduce hybridization values between laboratories with the necessary precision. Furthermore, hybridization methods often require specialized equipment or the use of radioactive labels. Consequently, systematists have come to rely increasingly on comparison of DNA sequences, especially 16S rRNA gene sequences, as a supplementary or alternative approach (Stackebrandt & Goebel, 1994). High-throughput facilities have made DNA sequencing rapid, reproducible and inexpensive. Public databases and sophisticated analysis software are freely available. Recently, an ad hoc committee for re-evaluating the species definition called on systematists to determine whether sequencing a set of genes could yield results congruent with DNA hybridization methods, with a view towards identifying even single genes that could be useful for assigning isolates to species (Stackebrandt et al., 2002).
Recently, Zeigler (2003) identified over 30 genes that met specific criteria: (1) wide distribution among bacteria; (2) uniqueness within each genome; (3) phylogenetically informative size; and (4) sequence divergence that mirrors whole genome divergence among related species. One strong candidate as a genome similarity predictor was recN. For the species studied, genome identity scores predicted by recN analysis differed from those measured directly in genomic alignments by an average of only 4·4 %.
In the present study, sequences of both the 16S rRNA gene and recN were determined for a group of 68 isolates from the genus Geobacillus (Nazina et al., 2001). The striking congruence of phylogenetic trees constructed with these sequences suggests that the two genes have experienced similar histories within the genus, and that horizontal gene transfer has not disrupted their relationship. For grouping closely related organisms, recN was clearly superior to the 16S rRNA gene, with nearly an order of magnitude greater resolving power at the species–subspecies level. Thus, recN seems to satisfy the requirements of Stackebrandt et al. (2002) as a useful tool for assigning isolates to species within this genus.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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DNA sequencing.
Each isolate was grown overnight at 60 °C with vigorous aeration in 1 litre shake flasks containing 50 ml liquid medium – Luria broth, brain heart infusion or TBAB-B (10·0 g tryptose, 3·0 g beef extract and 5·0 g NaCl per litre of water). Genomic DNA was isolated from the culture by using the Qiagen Genomic-tip 500/G kit according to the manufacturer's instructions, except that the cleared lysate was vortexed at high speed for 30 s prior to loading on the binding column. DNA sequences were obtained directly from genomic DNA samples, without amplification or subcloning, using custom primers designed for recN or the 16S rRNA gene. For 16S rRNA gene sequencing, primers were pA and pD(R) (Edwards et al., 1989), 765r and 1495r (Lu et al., 2001), 16F358, 16F926 and 16R1093 (Coenye et al., 1999), and 16F1074, which is the reverse complement of 16R1093. A complete list of the primers used for recN sequencing is available with the Supplementary Figure in IJSEM Online. DNA sequences were determined on an automated 3730 DNA Analyser (Applied Biosystems), using BigDye terminator cycle sequencing, following the manufacturer's specifications for genomic DNA.
Sequence analysis.
DNA sequences were assembled with SeqManII (DNASTAR, Madison, WI, USA). Multiple alignments and distance matrices were constructed by using CLUSTAL W (Thompson et al., 1994). DNA alignments were hand-corrected to ensure that they were consistent with predicted amino acid alignments for each gene product. Phylogenetic trees were constructed by using the NEIGHBOR application of the PHYLIP software package (Felsenstein, 1989) and visualized by using TreeView (http://taxonomy.zoology.gla.ac.uk/rod/treeview.html). Unrooted parsimony analysis was conducted on CLUSTAL W-generated multiple alignments with the PHYLIP DNAPARS application. Statistical analysis was performed with Sigma Plot.
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RESULTS AND DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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proposed that recN sequence comparisons could accurately measure genome similarities for a wide range of bacterial taxa. Members of the Gram-positive thermophilic genus Geobacillus (Nazina et al., 2001) were chosen as a test set for validating recN analysis as a taxonomic tool. A total of 48 Geobacillus isolates was obtained from public culture collections in Germany, Japan and the United States, as well as from individual researchers in Australia, Italy, Turkey, Japan and the United States. Bacteria in this collection had been isolated from a wide variety of sources, including geothermal waters and soils, spoiled foods, composted organic matter and temperate soils. Included in the collection were the type strains or other representatives of each of the Geobacillus species that had validly published names at the beginning of the study. The collection was supplemented with 20 novel isolates obtained from soil and other environmental samples collected from two locations with mesic temperature regimes: one an arid, rural region of the southwestern United States and the other a well-watered, urban area in the mid-western United States. Altogether, the strains in this collection should include a broad sampling of the genus Geobacillus, presenting the kind of taxonomic challenges typically faced with bacterial isolates assembled during a moderately sized discovery programme. The strains in the collection, together with GenBank/EMBL/DDBJ accession numbers for sequence data, are listed in the Supplementary Table in IJSEM Online.
For each of the 68 strains, high-quality, full-length DNA sequences were determined for both recN and the 16S rRNA gene. Because 16S rRNA gene sequencing has become a standard procedure in characterizing a new bacterial isolate, selection of suitable primers was a simple matter. Sequencing the recN gene, which is much less highly conserved than the 16S rRNA gene (Zeigler, 2003), was a considerable challenge. Primer selection was greatly aided by aligning genomic DNA sequences for several members of the family Bacillaceae (not shown) with the unfinished genome sequence of Geobacillus stearothermophilus strain 10 (=BGSC 9A21) [B. Roe, Bacillus (Geobacillus) stearothermophilus Genome Sequencing Project, http://www.genome.ou.edu/bstearo.html]. The gene order spoIVB–recN–ahrC is very highly conserved among the Bacillaceae and Clostridiaceae; the order recN–ahrC is conserved even more widely within the phylum Firmicutes (unpublished data). Because the sequences of spoIVB and ahrC are more highly conserved than recN, it was possible to design primers flanking recN that allowed for direct sequencing of genomic DNA in the Geobacillus isolates. Partial recN sequences provided enough data to allow for a ‘primer walking’ strategy to sequence the remainder of the gene in each strain in the collection. The recN sequencing primers are detailed in the Supplementary Figure in IJSEM Online.
Comparison of recN and 16S rRNA gene phylogenies for Geobacillus
Phylogenies constructed with the 16S rRNA and recN gene sequences are remarkably similar for the strains in the Geobacillus collection (Fig. 1). Each phylogram clusters the 68 strains into the same sequence similarity groups. The main difference between the phylograms is in branch length. In particular, the branches separating the nine sequence similarity groups are especially elongated in the recN tree relative to the 16S rRNA tree. Bootstrap support is strong for the groups in both trees, but is nearly unanimous for the recN phylogram.
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). It is obvious from inspection that sequence identity scores for the two genes cluster tightly in the plot. The relationship between the identity scores fits a cubic equation with a high coefficient of determination (R2=95 %), low standard error (S=2·2 %) and narrow 95 % confidence intervals. The tight confidence intervals suggest that horizontal gene transfer has not disrupted the vertical co-transfer of the recN and 16S rRNA genes within this collection of Geobacillus strains. The exact parameters of this regression curve are unlikely to be of any special biological significance, but the higher-order equation (in this case, cubic) probably is significant. Within the genus Geobacillus – and probably among all bacteria that have retained the recN gene (Zeigler, 2003) – recN sequences have diverged much more rapidly than have 16S rRNA gene sequences. The linear portion of the plot in Fig. 2, where recN sequence identity is greater than about 85 % and 16S rRNA sequence identity is greater than about 98·5 %, has a slope of approximately 7·5. Among closely related Geobacillus strains, then, mutations are becoming fixed in recN at a rate almost an order of magnitude greater than they are in the 16S rRNA gene. It is well established, however, that as sequences continue to diverge, the variable positions become saturated with mutations (Gribaldo & Philippe, 2002). As a result, Fig. 2 reveals an inflection point where recN gene sequences become increasingly less reliable phylogenetic markers than 16S rRNA gene sequences. This comparison suggests that recN probably has a significantly greater resolving power than the 16S rRNA gene for assigning strains to taxa at the genus, species or subspecies level, but that at higher taxa recN might have considerably lower power.
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), is instructive. For lower order taxa (genus, species and subspecies), recN has superior resolving power to the 16S rRNA gene, as reflected in the greater branch lengths separating the nodes that join organisms related at this level. In contrast, recN seems unsuitable for analysing higher order taxa (family, order and class); nodes joining organisms related at those levels are poorly separated with short branch lengths. This poor resolving power is probably related to mutational saturation in the rapidly diverging recN gene. For analysis of higher taxa, the more slowly diverging 16S rRNA gene appears to have a higher resolving power (Fig. 3).
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). From a set of aligned sequences, DNAPARS generates a tree based on the minimum number of mutational ‘steps' required to account for the observed sequence differences for a proposed phylogeny. DNAPARS tallies the number of steps contributed by each individual residue position in the alignment. For a given alignment, a particular residue may be perfectly conserved, contributing zero steps and generating no phylogenetic signal. If a residue contributes one to five steps to the maximum-parsimony tree, it is generating a usable signal for constructing phylogenies. If it contributes a greater number of steps, it is generating noise, which becomes increasingly random as the position becomes saturated with mutations, confounding attempts to infer phylogenies (Gribaldo & Philippe, 2002). For this analysis, recN and 16S rRNA gene sequence alignments were produced for three strain sets of different phylogenetic depth. The ‘Geobacillus thermoglucosidasius set’ contained all organisms falling in sequence identity groups 4A, 4B and 5 in Fig. 1
, a level of similarity suggestive of closely related species or subspecies. The ‘Geobacillus set’, containing a sample sequence from each of the nine similarity groups in Fig. 1, has a moderate phylogenetic depth, typical of a bacterial genus. The ‘Firmicutes set’, containing the same species analysed in Fig. 2 (with E. coli omitted), has much more depth, typical of a bacterial phylum. Results of this analysis are presented in Table 1.
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confirms that recN is superior to the 16S rRNA gene at resolving lower taxa (genus and below), but that the 16S rRNA gene is much more useful for resolving higher taxa. With sets of closely related organisms, represented by the G. thermoglucosidasius and Geobacillus sets, neither gene generated significant noise. At the species–subspecies level, recN produced a phylogenetic signal roughly six times stronger than the 16S rRNA gene, with a correspondingly higher percentage of residues showing sequence variation. At the genus level, over 40 % of the recN residues generated a usable phylogenetic signal, as opposed to only 6 % of the 16S rRNA gene residues. Total signal strength was almost an order of magnitude greater for recN than for the 16S rRNA gene. At a phylum level, however, the 16S rRNA gene was clearly superior to recN. Although both genes began to show phylogenetically noisy residues at this level, 70 % of the signal produced by the 16S rRNA gene was still usable. In contrast, almost half of the recN residues were too highly saturated with mutations to generate usable signal, and 77 % of the total signal was of poor quality due to low signal-to-noise ratio. For the genus Geobacillus – and perhaps for other bacterial taxa as well – recN analysis should prove to be a powerful tool for organizing strains into lower taxa. The 16S rRNA gene does contribute enough useful phylogenetic signal even at this level, however, that alignments of concatenated recN and 16S rRNA gene sequences should further enhance the precision and accuracy of species and subspecies assignments.
Prediction of whole genome similarity by recN analysis
Zeigler (2003) suggested that recN sequence identity scores could predict, with a high degree of accuracy, the whole genome sequence identity shared by two organisms. From a survey of 44 complete genome sequences representing 16 bacterial genera, Zeigler (2003) developed the following model to relate SIgenome, the predicted DNA sequence identity shared by the genomes, and SIrecN, the sequence identity shared by their recN orthologues: SIgenome=–1·30+2·25(SIrecN).
The data from the current study could potentially allow an evaluation of this model for applicability to the genus Geobacillus. One useful comparison would be the predicted genome identity, as calculated from recN identity scores, with the percentage genome identity measured by DNA–DNA hybridization studies. Although the genus was only recently described, several references in the research literature do report DNA–DNA hybridization data for some of the strains in the present Geobacillus collection (Ahmad et al., 2000; Caccamo et al., 2000; Manachini et al., 2000; Nazina et al., 2001; Sung et al., 2002; Sunna et al., 1997; White et al., 1993). Table 2 compares predicted with measured genome identity scores for these strains.
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shows that, in general, the Zeigler (2003) model predicts a somewhat higher genome identity than has been estimated from hybridization studies. The predicted and estimated values tend to be more similar towards their middle ranges and more dissimilar when comparing strains with very high or very low relatedness. It is difficult to assess whether these differences are due to inaccuracies in the recN prediction model, in the hybridization methodologies, or both. Table 2 does highlight a weakness of DNA–DNA hybridization studies; namely, the difficulty in reproducing similarity estimates obtained in different laboratories using different methods. Published estimates of genome similarity between Geobacillus kaustophilus and ‘Bacillus caldotenax’, for example, range from 32 to 85 %, yielding conflicting answers to questions regarding the species identity of these bacteria. Nevertheless, it is entirely possible that the recN prediction model will require some recalibration for Geobacillus. In particular, the proposed species boundary of 89 % recN sequence identity (Zeigler, 2003) may be too conservative for this genus. At this point, it is not possible to use recN identity scores as the sole basis for assigning isolates to species within Geobacillus. It is clear, however, that recN comparisons could be a powerful tool for the preliminary organization of isolates into possible taxa that could be validated by additional data from complementary methods.
Implications for Geobacillus taxonomy
During the brief period since its description, the genus Geobacillus (Nazina et al., 2001) and its members have become a significant research focus. As Gram-positive thermophiles, these organisms have considerable potential for applications in biotechnology and bioremediation (Obojska et al., 2002; Peng et al., 2003). Their roles in natural and artificial thermal biotypes as well as in temperate soil environments are also of interest (Marchant et al., 2002; McMullan et al., 2004). The original genus description included eight species (Nazina et al., 2001). The taxonomy of the group is in a rapid state of flux, however. On the one hand, the distinctiveness of several of these species has already been questioned (Sunna et al., 1997). On the other hand, Geobacillus discovery programmes are uncovering novel isolates and spawning novel species proposals at a rapid rate (Banat et al., 2004; Kuisiene et al., 2004; Nazina et al., 2004; Schäffer et al., 2004). Analysis of recN gene sequences, in combination with 16S rRNA sequence analysis, could provide a powerful, high-throughput tool for validating and maintaining the taxonomy of this genus.
Both phylogenetic analyses represented in Fig. 1 cluster this set of thermophilic Geobacillus strains into nine similarity groups, all enjoying strong bootstrap support. Depending on where one chooses to draw the boundary demarcating inter- from intraspecific clusters, these homology groups could plausibly comprise from six to nine species. Groups 1A, 2, 4A, 5 and 6A appear to correspond unambiguously to the species Geobacillus thermodenitrificans, G. stearothermophilus, G. thermoglucosidasius, Geobacillus toebii and Geobacillus caldoxylosilyticus, respectively. Identification of the other four similarity groups with currently recognized species is somewhat more difficult, however.
Group 4B contains a single member, the proposed type strain of Bacillus thermantarcticus (Nicolaus et al., 1996) (BGSC 20A1T). The validity of this species has been questioned on technical grounds because, at the time of publication, the type strain was not deposited in two publicly accessible service collections in different countries (Euzéby & Tindall, 2004). An inspection of the original publication also suggests that the novel species was proposed on slender evidence. The authors reported no DNA–DNA hybridization data to test for genome similarity between B. thermantarcticus and related species. Their sole basis for distinguishing their novel isolate from the type strain of what is now termed G. thermoglucosidasius, which it closely resembled based on partial 16S rRNA gene sequence data, was a difference in G+C content (Nicolaus et al., 1996). However, more recent measurements (Nazina et al., 2001) show that the G+C content for G. thermoglucosidasius, as well as nearly every type strain in the genus Geobacillus, is virtually identical with those Nicolaus et al. (1996) reported for B. thermantarcticus. The recN and 16S rRNA gene sequence comparisons reported in this study form a sound basis for transferring this organism to the genus Geobacillus, either as a novel species or as a subspecies of G. thermoglucosidasius. Further analysis should readily distinguish between these possibilities.
Group 6B includes NUB3621 (=BGSC 9A5), doubtless the most well-characterized Geobacillus strain from a genetic standpoint. Systems for plasmid transformation (Wu & Welker, 1989), generalized transduction (Welker, 1988) and protoplast fusion (Chen et al., 1986) have been described for this strain, and data generated from those studies have revealed a circular genetic map (Vallier & Welker, 1990). Although the research literature describes NUB3621 as G. stearothermophilus, the recN and 16S rRNA gene sequence analysis presented in Fig. 1 suggests that it is much more closely related to G. caldoxylosilyticus. It is probable that further analysis of Group 6B will result in the proposal of a novel subspecies of G. caldoxylosilyticus or of a new Geobacillus species.
The clustering of strains in similarity Group 3 raises significant questions for the taxonomy of the genus. Based on DNA–DNA hybridization data, Sunna et al. (1997) have suggested that the species described as G. kaustophilus and G. thermocatenulatus actually belong to G. thermoleovorans. In confirmation of their proposal, Group 3 includes the type strains of all three species (BGSC 90A1T, BGSC 93A1T and BGSC 96A1T, respectively). Furthermore, the group also includes the type strains of Bacillus vulcani (Caccamo et al., 2000) (BGSC 97A1T) and of a recently proposed novel species, Geobacillus lituanicus (Kuisiene et al., 2004) (BGSC W9A89T). Clearly, a careful analysis of these species is required to clarify their relationships. It is interesting that all three of the Geobacillus strains currently the focus of genomic sequencing efforts – G. stearothermophilus strain 10 (equal to the BGSC 9A21 in Group 3), G. kaustophilus HTA426 and G. thermoleovorans T80 (McMullan et al., 2004) – may be closely related. If the data analysed in Fig. 1 are representative of other members of these species, then one would predict that these three genome sequences will be found to differ only in detail.
Group 1B likewise presents a taxonomic puzzle. Its position on the recN and 16S rRNA gene phylograms (Fig. 1) suggests that this group either corresponds to a subspecies of G. thermodenitrificans (Group 1A) or composes a separate but closely related Geobacillus species. Indeed, the group contains the type strain of Geobacillus subterraneus (BGSC 91A1T) along with two other isolates also described as belonging to that species (Nazina et al., 2001). Yet the group also contains a strain described as Geobacillus uzenensis X (=BGSC 92A2) (Nazina et al., 2001). The full-length 16S rRNA gene sequence determined for this strain in the present study (GenBank/EMBL/DDBJ accession no. AY608959) is only 98 % identical to the partial 16S rRNA gene sequence that served as the basis for its inclusion in G. uzenensis (GenBank/EMBL/DDBJ accession no. AF276305). It is not clear whether sequencing errors in one or both GenBank entries account for the differences, or whether BGSC 92A2 is not in fact equivalent to strain X of Nazina et al. (2001). Although the type strain of G. uzenensis was not included in this study, its 16S rRNA gene sequence determined in our hands differs in only two to three positions from each of the sequences composing Group 3 in the present report (unpublished data). These data highlight the need for further analysis to confirm the taxonomic identity of G. uzenensis strains.
The study demonstrates the power of a highly variable but widely distributed sequence, such as recN, for organizing and maintaining the taxonomy of a bacterial genus. Further work should better calibrate recN as a molecular chronometer for Geobacillus and its relatives, allowing a more certain correlation between sequence identity scores and taxonomic relatedness. It appears that recN is a promising candidate for inclusion in a ‘species prediction gene set’ (Zeigler, 2003).
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ACKNOWLEDGEMENTS |
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REFERENCES |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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