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Department of Biology and Biochemistry, University of Bath, Claverton Down, Bath BA2 7AY, UK
Correspondence
Edward J. Feil
e.feil{at}bath.ac.uk
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ABSTRACT |
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 TOP ABSTRACT INTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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17·8 kb of sequence was reconstructed and the goodness of fit of each individual gene tree was computed. No strong association was noted between gene function per se and phylogenetic reliability, but it is suggested that candidate loci should possess at least the average degree of nucleotide diversity for all genes in the genome. In the case of S. aureus this threshold is >1 % mean pairwise diversity.
The GenBank/EMBL/DDBJ accession numbers for the sequences reported in this paper are DQ413277–DQ414234.
Two supplementary tables and two supplementary figures are available with the online version of this paper.
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INTRODUCTION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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). In particular, recent years have seen the rise of multilocus sequence typing (MLST) for epidemiological or population studies on single named species. These studies commonly involve the characterization of hundreds of isolates at a small number of gene loci, assumed to be a representative sample of the ‘core’ genome (‘housekeeping’ genes). Homologous recombination, the replacement of a gene with an orthologue from an unrelated lineage, may confound attempts at intra-species phylogenetic reconstruction or accurate typing (Feil et al., 1999, 2000; Jolley et al., 2000). The use of multiple (typically seven) loci in MLST is necessary to ‘buffer’ against this effect in single genes (Hanage et al., 2005), and the employment of housekeeping genes is presumed to provide added insurance as there is no a priori reason to expect recombination to confer a selective advantage at such genes (Maiden et al., 1998; Spratt & Maiden, 1999).
Whilst practical considerations dictate that candidate markers should be ubiquitous throughout the population under consideration and present in single copy, other desirable criteria are not so clear-cut. For example, it is typically not possible to gauge which genes most closely reflect the underlying organismal phylogeny, or even if such a phylogeny exists (Bapteste et al., 2005). Although genes encoding essential housekeeping functions are commonly viewed as the most reliable markers, the precise importance of gene function in predicting the utility of intra-species markers has not been systematically studied. Similarly, the optimal window of variation remains poorly defined, although it is clear that too little variation will result in poor resolution whereas too much will separate isolates that are very closely related.
It might be expected that genes encoding proteins which interact with the host or the external environment will be highly variable owing to strong diversifying selection, and as such be poor reflections of the underlying phylogeny. Two recent reports have compared the phylogenetic signal of MLST (housekeeping) genes in Staphylococcus aureus with those of highly variable genes encoding proteins putatively associated with the cell wall (Robinson et al., 2005) or adhesins implicated to play a central role in host colonization and/or virulence (Kuhn et al., 2006). Contrary to expectations, both investigations noted that highly variable genes were at least as informative for phylogenetic reconstruction as the slowly evolving housekeeping genes. These observations suggest that strong diversifying selection may not significantly confound the phylogenetic signal within the S. aureus genome in general.
Here we expand on these observations using S. aureus as a model population and a range of unlinked loci from all functional classes. The use of S. aureus has several advantages, as follows. (i) Extensive information on the population structure of this species is available through the generation of MLST data. (ii) Although recombination does occur (Robinson & Enright, 2004), S. aureus is basically clonal, which allows the reconstruction of a reasonably robust tree. This then facilitates comparisons between individual gene trees and a consensus tree. (iii) The data will provide a valuable phylogenetic framework for this important human pathogen.
We present sequence data from 33 unlinked gene loci representing a range of functions for 30 diverse S. aureus isolates. Supplementing these sequences with existing MLST data we reconstruct a phylogeny based on 17·8 kb of concatenated sequence and compare each individual gene tree against a consensus phylogeny. We note no strong evidence that gene function, dS/dN ratio, G+C content or codon bias are strong predictors of phylogenetic reliability. This analysis does, however, provide a convenient rule of thumb that candidate phylogenetic markers should possess at least the average degree of sequence divergence (expressed as mean pairwise diversity, 
) for all genes in the genome.
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METHODS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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Gene loci.
We supplemented the MLST data already available for this strain collection (based on seven housekeeping genes) with a further 33 gene loci representing various functional categories to give a total dataset encompassing 40 loci, including 16S rRNA. These loci represent a range of functions, and are widely distributed across the chromosome (Fig. 1, Table 1). Genes were grouped into three functional classes, following Kuroda et al. (2001), adopted from the study of Kunst et al. (1997): informational pathways (IP; DNA replication and processing, regulators; n=9), housekeeping (HK; central and intermediary metabolism; n=13), and cell envelope and cellular processes (CE; n=5). We also characterized conserved genes of unknown function (UF; n=7) and orphans (OR; unknown function, no similarity to other genes in the database; n=6). Genes of unknown function are referred to throughout using the SA ORF numbers proposed by Kuroda et al. (2001), except SA2439, which has subsequently been renamed sasF (Robinson & Enright, 2004).
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Computation of sequence parameters.
dS/dN ratios were calculated using the method of Nei & Gojobori (1986) as implemented in MEGA version 3.1 (Kumar et al., 2004). Nucleotide diversity (; the mean percentage of polymorphic sites over all pairwise comparisons) and G+C content were calculated using MEGA version 3.1. The codon adaptation index (CAI) (Sharp & Li, 1987) was calculated by reference to the codon usage in ribosomal proteins using EMBOSS (Rice et al., 2000).
Phylogenetic analysis.
Of the 33 gene sequences generated, 30 were used for the phylogenetic analysis (two genes were not present in all strains, and the 16S rRNA fragment was invariant). These 30 genes were supplemented with the existing MLST data and a consensus Bayesian phylogeny was reconstructed from the concatenated sequences of all 37 genes representing 17 814 bp using MrBayes version 3.1 (Huelsenbeck & Ronquist, 2001; Ronquist & Huelsenbeck, 2003). This procedure uses a simulation technique, Markov chain Monte Carlo (MCMC), to approximate the posterior probabilities of alternative trees conditioned on the input data. As well as being very computationally efficient, the approach enables the sampling of a wide range of ‘tree-space’ rather than just locally optimum trees as in hill-climbing algorithms (for more details see http://mrbayes.csit.fsu.edu/manual.php). Four MCMC chains were run for 1 000 000 generations. The optimal trees were sampled every 100 generations (with the first 2000 trees discarded as ‘burn-in’). A 50 % majority rule consensus tree was then calculated using PAUP* version 4.0b10 (Swofford, 2000) with the posterior probabilities indicating the percentage of optimal trees supporting each node.
Fit to the consensus tree.
As very variable genes make a larger contribution, in terms of informative sites, to the consensus tree than very uniform genes, variable genes are more likely to show a closer fit. In order to draw independent comparisons between individual gene trees and the consensus, we constructed a further 37 consensus trees, in each case excluding a single gene. We then compared each of these consensus trees in turn with the gene tree corresponding to the excluded gene. We used the Shimodaira–Hasegawa (S-H) test (Shimodaira, 2002) in order to rank each gene with respect to the differences in likelihood values between individual gene trees and the corresponding consensus tree (using the concatenated data as the reference). The S-H test was implemented in PAUP* version 4.0b10 (Swofford, 2000); a lower likelihood difference (S-H score) reflects a closer fit to consensus tree (FCT).
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RESULTS |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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gives the mean pairwise percentage nucleotide diversity (), the mol% G+C content, the codon adaptation index (CAI) and the dS/dN ratios of all the gene loci employed in this study. The genes are ranked according to the likelihood differences between individual gene trees and the consensus tree (FCT). The value of for all genes was 1·28 %. Five of the six most uniform genes were classified as IP genes (16S rRNA, 0·0 %; sarA, 0·02 %; tufA, 0·2 %; serS, 0·3 %; and sigB, 0·4 %). At the other extreme, three genes appeared unusually diverse [agrC (IP), 5·5 %; aapA (CE), 5·3 %; and SA1619 (OR), 4·0 %]. Although the dS/dN ratio varies substantially both within and between gene classes, none of the genes showed evidence of positive selection. The orphans tended to exhibit low dS/dN ratios (mean 3·1, median 2·8), suggesting a low level of functional constraint and rapid evolution. This is consistent with the non-essentiality of these genes, as indicated by their absence from the sequenced genomes of the closely related species S. epidermidis.
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17·8 kb for each of the 30 strains, and used to produce the unrooted Baysian tree presented in Fig. 2. This tree is broadly consistent with one previously published based on the concatenated sequences of the seven MLST genes, but is more robust and contains no unresolved branches. The tree confirms the division into two main groups, as reported previously (Feil et al., 2003; Holden et al., 2004; Robinson et al., 2005) with Group 1 being further subdivided in to Groups 1a and 1b. ST55 is an exceptional genotype, previously being classified as Group 1 but appearing to fall at an intermediate position between the two main groups from the current data.
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; Oliveira et al., 2002), as well as the common MSSA clone ST30 from which ST36 is thought to have evolved (Enright et al., 2000). Interestingly, and in contrast to the MLST tree, these data suggest that ST45 and ST30 share a common ancestor. Group 1b contains no major nosocomial lineages; although ST59 was found to be relatively common amongst intravenous drug users in Brighton, UK (Monk et al., 2004), and is an important community-acquired MRSA from the USA (Pan et al., 2005; Vandenesch et al., 2003). Group 2 contains the related CC8 clones (EMRSA-1, 2, 4, 5, 6, 11 and 17), which includes the first MRSA lineage to be described (Crisostomo et al., 2001), ST5 (EMRSA-3; the New York/Japan clone) (Oliveira et al., 2002) and ST1, which is the genotype of sequenced strains MSSA476 (Holden et al., 2004) and MW2 (Kuroda et al., 2001). Group 2 also contains a relatively high number of sporadic or asymptomatic genotypes (e.g. ST20, ST9, ST13, ST101, ST7, ST97) and exhibits shorter branch lengths and lower clade credibility values than Group 1a.
Relationship between gene function and fit to the consensus tree
We ranked each gene tree with respect to its fit to a consensus tree (FCT) reconstructed excluding the gene under examination using the S-H test, as described in Methods (Table 2
). All the genes showed significantly lower likelihood scores (P<0·001) against the consensus tree (compared with the concatenated data) using the S-H test. The gene showing the closest FCT (i.e. the smallest likelihood difference) was sasF (SA2439) which is of unknown function but likely to encode a surface-associated protein as it contains an LPXTG motif (Roche et al., 2003); it was one of several putative cell-wall-associated genes used for a fine-scale study of the micro-evolution of MRSA clonal lineages by Robinson & Enright (2003). This result is surprising, as cell-wall-associated genes might be expected to be subject to diversifying selection pressure from the host immune response, and hence are likely candidates for frequent recombination. sasF exhibits reasonably high nucleotide diversity (=1·7 %) and the lowest dS/dN ratio of all the genes examined (1·1). The high degree of congruence of this gene to the consensus tree suggests that diversifying selection has not compromised the phylogenetic signal of the gene. The next highest scoring gene was pbpB, which encodes the bifunctional protein PBP2 (Pinho et al., 2001). Although fulfilling an essential housekeeping function, PBP2 is an important target for 
-lactam resistance (Leski & Tomasz, 2005) and vancomycin-intermediate glycopeptide resistance (Sieradzki & Tomasz, 1999). The third highest scoring gene in the S-H analysis is SA1619. This is an orphan of unknown function, although clearly one which has a stable and long-term association with S. aureus and is not prone to frequent transfer. Thus there are reasons for which each of the three top-scoring genes might have been avoided under classical MLST criteria.
With the exception of the very uniform informational genes, with their poor fit to the consensus tree, there is no obvious relationship between gene function and FCT. The housekeeping genes rank between 4th and 35th (Table 2) and in general score no better than cellular envelope genes or ORFans. It is noteworthy that three of the MLST genes, gmk, glpF and arcC, rank 30th–32nd respectively and only outrank those genes which are extremely uniform. This analysis also confirms a previous suggestion (Feil et al., 2003) that arcC in particular possesses an atypical phylogenetic signal.
Relationship between nucleotide diversity and fit to the consensus tree
To examine the role of other sequence parameters we plotted the FCT of each gene against , G+C content, dS/dN ratio and codon bias. Owing to very low levels of diversity, sarA was excluded from these analyses. Plotting against FCT confirms that more diverse genes tend to show a closer fit to the consensus (linear plot: R2=0·111, P=0·047; Fig. 3a; Spearman's rank correlation coefficient=–0·508, P=0·002). The use of a quadratic plot increases the R2 to 0·329 (P=0·001; not shown), suggesting that the relationship between and FCT is not linear. If the pairwise diversities are ranked, which provides a closer fit of the residuals to a normal distribution (by controlling for the effect of extreme values), a quadratic plot gives an R2 of 0·441 (P<0·0001; Fig. 3b). This plot demonstrates that the relationship between diversity and FCT only holds for the more uniform genes.
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and plotted the rank in diversity against FCT as a linear trend for each group. Examining the most uniform 18 genes separately reveals a linear correlation of increasing FCT with increasing (R2=0·342, P=0·011), whereas the 18 most diverse genes did not reveal a significant trend (R2=0·024, P=0·54; plots not shown). Thus for genes which fall below a threshold level of (in this case approximately 1 %), pairwise nucleotide diversity is a strong predictor of phylogenetic reliability. For genes above 1 % diversity there is no obvious relationship, but the observation that two of the three very diverse genes show a modest FCT (Fig. 3a) suggests that there is also an upper threshold of with respect to FCT. The most diverse gene, agrC, is ranked 17th in terms of FCT, which confirms the discrepancies between agr groups and S. aureus phylogeny discussed elsewhere (Robinson et al., 2005). We also examined the correlation between FCT and G+C content, dS/dN ratio and codon bias. We noted no evidence of a correlation with dS/dN ratio or codon bias (data not shown), but a weak correlation with G+C content (R2=12·7 %, P=0·033; see supplementary Fig. S1). The significance of this association with G+C content is unclear and requires further analysis.
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DISCUSSION |
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TOPABSTRACTINTRODUCTIONMETHODSRESULTSDISCUSSIONREFERENCES |
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17·8 kb of concatenated sequence which provides hypotheses concerning the relatedness between the major MRSA lineages. Although this is an improvement on the existing tree, the branching order cannot be reconstructed with complete confidence in some parts of the tree. Would the tree be improved by the addition of yet more data? Rokas et al. (2003) examined phylogenetic congruence in eight yeast species and concluded that the concatenated data of a minimum of 20 genes are required to produce a robust tree. Although in terms of nucleotide sites our dataset of 38 gene fragments is of a similar size to the 20 genes of Rokas et al. (2003), the use of a higher number of independently evolving genes should increase the performance of the data. Therefore we feel that the intra-species tree we present here would not be greatly improved by the addition of yet more data. The broad consistency of this phylogeny with the basic groupings previously inferred from MLST genes (Feil et al., 2003), sas genes (Robinson et al., 2005), adhesin genes (Kuhn et al., 2006), AFLP clustering (Melles et al., 2004), PFGE (Grundmann et al., 2002) and microarray analysis (Lindsay et al., 2006) provides additional support.
The topology within Group 2 remains relatively poorly supported, and contrasts with the much longer branches evident in Group 1a. This difference between these groups has also been noted in an analysis of MLST and sas genes (Robinson et al., 2005). One possibility is that the globally disseminated Group 1a clones (clonal complexes –‘CCs' – 30, 45 and 22) may be particularly efficient at out-competing close relatives and that the longer branch lengths in Group 1a reflect a higher rate of stochastic extinction than in Group 2. The relatively poor clade credibility scores in Group 2 are also consistent with a higher rate of recombination in Group 2 strains, although comparisons of the two groups using various tests for recombination did not produce strong evidence to support this view (data not shown).
Our data also suggest that Group 1 strains can be further subdivided into Group 1a and Group 1b, a division not recognized in previous phylogenetic studies. Although there is generally little association between phylogenetic distribution and epidemiological source, it is noteworthy that Group 1b contains no major nosocomial lineages, whereas Group 1a contains the two major MRSA clones currently circulating in the UK (STs 36 and 22), as well as the Berlin clone (ST45). Future studies aimed at identifying the genetic factors underlying the ability to rapidly disseminate might therefore focus on comparisons between Group 1a and Group 1b strains. An interesting observation in this context is the high degree of divergence between Group 1a and Groups 1b and 2 at aapA (see supplementary Fig. S2); an examination of the region surrounding this gene might therefore shed some light on the epidemiological differences between the two groups.
Although the phylogenetic emphasis of this study was on the relationships between the major clonal lineages, we included duplicates of four STs (5, 22, 36 and 121). In each case, these duplicates differed at five or fewer positions in the concatenated sequence of 17 814 sites (<0·0004 %). This is consistent with a comparative genome analysis of two ST1 isolates (MSSA476 and MW2) which only revealed 285 single base changes in all orthologous gene pairs (1 in 10,000 sites) (Holden et al., 2004). These results confirm the high degree of genetic relatedness between isolates sharing identical STs. However, a more extensive investigation of intra-clonal differences has proved successful in providing detailed hypotheses concerning the emergence of closely related MRSA clones (Robinson & Enright, 2003). This study utilized the highly variable sas genes, and our current results suggest that these genes might also be highly informative for reconstructing deeper relationships within the S. aureus population. A second study, utilizing variable adhesin genes, provided some evidence that recombination is more common within, rather than between, clonal complexes (Kuhn et al., 2006).
Gene function, diversity and informative trees
These data are not only relevant for studies on S. aureus, but also provide clues as to the extent to which the current criteria for choosing gene loci for phylogenetic, systematics or epidemiological studies can be justified or relaxed. Here we find little evidence to justify the current emphasis on housekeeping genes, at least on an intra-species level, and indeed our results for S. aureus suggest that the MLST genes for this species rate amongst the poorest phylogenetic markers. In contrast, the three genes which score highly against the consensus tree are putatively associated with the cell wall (sasF), modified in antibiotic-resistant strains (pbpB) or an orphan (SA1619), all of which would have been avoided under classical MLST criteria.
We suggest that the emphasis on gene choice for intra-species phylogenetic markers should be shifted to the more tangible parameter of nucleotide diversity, with gene function being regarded as secondary. Clearly, gene function and diversity are not always independent; ‘informational pathway’ genes (in particular 16S rRNA) should generally be avoided due to the extremely low levels of diversity of these genes. It is not clear what determines the point at which extra variation ceases to improve the tree, and more specifically why variation in reasonable excess of 1 % generally does not result in a closer fit to the consensus phylogeny. Nevertheless, this analysis provides a convenient ‘rule of thumb’ for identifying genes which are likely to contain sufficient diversity, i.e. those containing at least the average for all genes. Our results also raise the possibility of a correlation between G+C content and closeness of fit to a consensus tree. Given the large number of potential candidate loci for each gene it may therefore also be sensible to avoid those with extreme G+C contents.
We emphasize that we do not advocate changes to any established MLST scheme. The current MLST scheme for S. aureus has proved extremely successful in understanding the population structure of this species and for assigning isolates to particular lineages. In a highly clonal organism, almost any gene will typically provide the same basic lineage assignments – in the case of S. aureus this is clear from the broad consistency of different genes as well as pan-genome techniques such as PFGE (Grundmann et al., 2002) and microarrary analysis (Lindsay et al., 2006). However, individual genes may vary in their utility to reconstruct the relationships between these lineages, and we find no evidence to suggest that MLST genes can be considered the most reliable in this regard.
Concluding remarks
We present the most robust tree to date of the natural S. aureus population, and identify three distinct groups within the population. We propose an emphasis on gene diversity, rather than gene function, when identifying suitable phylogenetic markers. Although this may necessitate preliminary work on candidate loci before final genes are chosen, we argue that this represents a sensible investment of resources. Finally, our analysis differs from studies on more deep-rooted phylogenies (i.e. those between genera or orders) (Zeigler, 2003). In this case, the presence of sufficient diversity is not likely to be problematic and the use of ‘core’ genes may well be justified. At an intra-species level, however, given the choice of many candidate ubiquitous genes, we argue that the presence of sufficient diversity should be considered first and foremost, and other considerations relating to gene function should be secondary.
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ACKNOWLEDGEMENTS |
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Received 21 October 2005;
revised 21 January 2006;
accepted 30 January 2006.
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M. J. Noto, B. N. Kreiswirth, A. B. Monk, and G. L. Archer Gene Acquisition at the Insertion Site for SCCmec, the Genomic Island Conferring Methicillin Resistance in Staphylococcus aureus J. Bacteriol., February 15, 2008; 190(4): 1276 - 1283. [Abstract] [Full Text] [PDF]
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M. Miragaia, J. C. Thomas, I. Couto, M. C. Enright, and H. de Lencastre Inferring a Population Structure for Staphylococcus epidermidis from Multilocus Sequence Typing Data J. Bacteriol., March 15, 2007; 189(6): 2540 - 2552. [Abstract] [Full Text] [PDF]
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