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1 Cooperative Research Centre for Diagnostics, Queensland University of Technology, GPO Box 2434, Brisbane, Queensland 4001, Australia
2 Queensland Health Pathology Services, Princess Alexandra Hospital, Brisbane, Australia
3 Infection Management Services, Princess Alexandra Hospital and District Health Service, Brisbane, Australia
Correspondence
Philip M. Giffard
p.giffard{at}qut.edu.au
Received 17 May 2005
Accepted 21 September 2005
Abbreviations: CA-MRSA, community-acquired MRSA; D, Simpson's index of diversity; MLST, multilocus sequence typing; MRSA, methicillin-resistant Staphylococcus aureus; SLV, single locus variant; SNP, single nucleotide polymorphism; ST, sequence type.
A complete description of the MRSA isolates used in this study is available in Supplementary Table S1 with the online version of this paper.
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INTRODUCTION |
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 TOP INTRODUCTION METHODSRESULTS AND DISCUSSIONREFERENCES |
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Recent years have seen a rapid increase in the understanding of the comparative genomics and population biology of S. aureus, which is leading to an equally rapid increase in the understanding of the relationship between genotype and phenotype (Baba et al., 2002; Robinson & Enright, 2004a). This in turn is facilitating the development of genotyping procedures based upon the interrogation of known genetic polymorphisms (Francois et al., 2004; Robertson et al., 2004; van Leeuwen et al., 2003).
It is now known that the S. aureus genome consists of a ‘backbone’ that is composed of genes that are essentially common to all variants within the species, and which evolves primarily by mutation, punctuated by rare recombination events of unknown mechanism (Baba et al., 2002; Feil et al., 2003; Holden et al., 2004; Kuroda et al., 2001; Robinson & Enright, 2004b). Superimposed upon this is a complement of mobile genes that give rise to binary gene variation between and within lineages (Baba et al., 2002). The staphylococcal cassette chromosome mec (SCCmec) is an example of an accessory element whose diversity is able to demonstrate the multiple insertion events that have occurred in the evolution of MRSA (Ito et al., 2001; Katayama et al., 2000).
Our overarching aim is to develop microbial genotyping methods based upon small sets of polymorphisms selected from known genomic diversity on the basis of their optimal combinatorial informative power. Such methods would be suitable for low-density array, or real-time PCR technology platforms, and would facilitate routine surveillance, infection control, and rapid and cost-effective identification of clones of interest. We have previously reported a computerized method for identifying sets of single nucleotide polymorphisms (SNPs) in mutilocus sequence typing (MLST) databases that provide high Simpson's index of diversity (D) values with respect to the databases, and the application of this to S. aureus (Robertson et al., 2004). A set of seven SNPs was identified that provides a D of 0·95 with respect to the S. aureus MLST database, and a real-time PCR method for interrogating these SNPs was reduced to practice. Robertson and co-workers did not determine the concordance between SNP profiles and the S. aureus population structure, i.e. whether the SNP-based genotyping clusters closely related isolates and discriminates more distantly related ones (Robertson et al., 2004). Accordingly, one of the principal aims of this study was to address this question as it relates to MRSA, and also to apply the SNP-based genotyping method to a larger collection of MRSA isolates.
An ideal molecular typing method would indicate the position of the isolate within the species population structure, and also economically and conveniently serve as a high-resolution comparative method. The ‘progressive hierarchical resolving assays using nucleic acids' (PHRANA) concept articulated by Keim et al. (2004) is very helpful in devising such typing methods. The underlying principle of PHRANA is that slowly evolving polymorphic sites do not provide high resolution, while rapidly evolving markers are unreliable at indicating the position of the isolate within the population structure and are subject to homoplasy if used by themselves. However, rapidly and slowly evolving markers in combination provide a usefully complete picture. Therefore, the other principal aim of this study was to identify a small set of binary markers that adds resolving power to the SNP set. The markers identified are four virulence-associated genes and three SCCmec-associated integrated plasmids. Polymorphisms in SCCmec and the agr locus have previously been used in combination with MLST to define MRSA clones (Oliveira et al., 2001), so we have also assessed the informative powers of SCCmec and agr typing in this context. The genotypes defined by the combination of SNPs and binary markers are very easy to obtain and digitize, consistent with previous studies, and provide easily understood genetic fingerprints for the well-known MRSA clones.
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METHODS |
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TOPINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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Cultivation and DNA template extraction. Each isolate was grown on brain heart infusion (BHI) agar plates overnight at 37 °C. A single colony was selected and grown in 5 ml BHI broth overnight in an incubating shaker at 37 °C. The DNA template was extracted using the QIAGEN DNA mini kit, as recommended by the manufacturer, with 200 µg lysostaphin ml–1 (Sigma). The DNA template was quantified using UV spectrophotometry before use in PCR.
MLST clonal group determination using SNP-based genotyping. Allele-specific real-time PCR and the clustering algorithm eBURST were used to group uncharacterized isolates into MLST clonal complexes. The SNP-based genotyping methodology has been previously described (Robertson et al., 2004); however, the total reaction volume was reduced from 25 to 20 µl.
Binary gene selection, primer design and PCR amplification.
Virulence genes that were candidates for intra-MLST clonal complex variation were selected based on previous studies (Moore & Lindsay, 2001; Peacock et al., 2002). Real-time PCR primers for the genes fnbA, cna, icaA, sdrE, bbp, hlg, pvl and tst (Table 1
) were designed using the Primer Express software package (Applied Biosystems). Primer sequences and PCR conditions for amplifying the plasmids pT181, pUB110 and pI258 have been published previously (Huygens et al., 2002). Virulence gene amplification was conducted on an ABI7000 real-time PCR device with the following PCR cycling conditions: stage 1, 50 °C for 2 : 00 (MM : SS); stage 2, 95 °C for 10 : 00; stage 3 step 1, 95 °C for 00 : 15; stage 3 step 2, 60 °C for 01 : 00; repeat stage 3 30 times. Each 20 µl reaction was conducted in duplicate and contained 10 µl 2x SYBR green PCR master mix (Applied Biosystems), 1 µl of a 2·5 µM solution of each forward and reverse primer, and 8 µl DNA template containing approximately 10 ng DNA.
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SCCmec typing. SCCmec element type was determined by PCR identification of the ccr (cassette chromosome recombinase) allele and the mec complex class, as described elsewhere (Okuma et al., 2002). The recently characterized ccrC from SCCmec V was identified using primers described by Ito et al. (2004). Isolates previously characterized for SCCmec are described by Coombs et al. (2004).
agr genotyping. The agr locus alleles were amplified using the multiplex PCR procedure described by Lina et al. (2003).
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RESULTS AND DISCUSSION |
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TOPINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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It is desirable that a genotyping method provides outputs that are consistent with the population structure of the species in question (i.e. similarity of genotypes is correlated with relationship of isolates), as this facilitates the inference of dissemination and evolutionary events. Accordingly, the consistency between the MRSA population structure and the SNP profiles was determined (Fig. 1). The population structure has been depicted using an eBURST analysis of MRSA sequence types present in the S. aureus MLST database as at January 25 2005. eBURST was used because conventional phylogenetic analyses of MLST databases may be confounded by recombination events. The eBURST algorithm identifies clonal complexes using MLST allele data and attempts to identify clonal complex progenitors (Feil et al., 2004). This analysis showed that seven SNPs resolve known human-derived MRSA sequence types (STs) into 29 SNP profiles. The major hospital-acquired and community-acquired clones are discriminated, with the exception of the unrelated STs 59 and 93, which represent the North American SF25 : P (Diep et al., 2004) and Queensland clones (Munckhof et al., 2003) of CA-MRSA, respectively. Even though the complete set of STs includes many that were not known at the time that the SNP set was defined, the SNP genotypes are concordant with the population structure.
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, it can be seen that there are two related but different phenomena. Firstly, there are instances in which the SNPs fail to discriminate unrelated singletons or clonal complexes. We have found that increasing the size of the SNP set from seven to 14 confers the ability to resolve the great majority of unrelated clonal complexes and singletons (data not shown). It is reasonable to state that the optimum size of an SNP set is a function of the need to discriminate singletons and small clonal complexes. We have chosen to use seven SNPs, as this discriminates the major clones and also indicates whether or not an isolate belongs to a more unusual lineage. The addition of another seven SNPs would reveal which minor clonal complex or singleton an unusual isolate belonged to. Secondly, the SNP set provides very little power to discriminate between members of clonal complexes. This is expected, as S. aureus is a clonal organism with low diversity, and the great majority of single locus variants (SLVs) of clonal complex progenitors have arisen by mutation (Feil et al., 2003). For a given SLV, the SNP that discriminates it from the clonal complex progenitor is not polymorphic anywhere else in the species, and so will not be readily identified in a computerized search for high D SNP sets. An SNP-based method for resolving within S. aureus clonal complexes as defined by MLST would need to individually resolve each clonal complex member from the clonal complex progenitor, and so would be most inefficient. Consistent with this, in the majority of the small number of instances in which the SNP profile of an SLV differs from the SNP profile of the clonal complex progenitor, the SLV has arisen by recombination rather than mutation. For example, the alleles that discriminate STs 221, 194 and 148 from ST5 are found elsewhere in the MLST database, while the corresponding alleles for the other SLVs of ST5 are unique. ST221 is particularly interesting in that it has acquired an allele that is characteristic of ST1, and ST221 shares four of the seven alleles with ST1, as well as an SNP profile. In this instance, the analysis of the SNP profiles has revealed a recombination event.
It is unclear how informative intra-clonal complex population structures as revealed by eBURST analysis are, particularly with clonal bacterial species. SLVs derived by mutation are extremely closely related to the clonal complex progenitor, and the highly stochastic nature of this process suggests that the population structure within clonal complexes, as revealed by eBURST analysis, would be unreliable. For example, it is doubtful that the same isolates would be identified as clonal complex progenitors and SLVs if a different set of genes happened to be chosen for MLST analysis. Evidence supporting this has been reported by Robinson et al. (2005), who used two separate MLST schemes to analyse a set of S. aureus clonal complex 30 isolates. The two MLST methods identified different isolates as SLVs. These considerations are in large part the rationale for our search for sets of binary markers that efficiently add resolution to the SNP set.
SNP-based genotyping of Australian MRSA
In order to test the resolving power of the high D SNP set with actual isolates, and also the robustness of our kinetic PCR method for interrogating these SNPs (Robertson et al., 2004), a selection of MRSA isolates of Australian origin were subject to SNP typing. Firstly, the SNP interrogation method was validated using 32 MRSA isolates of known ST. The kinetic PCR was completely successful at calling the bases at the SNPs (see Supplementary Table S1). For every polymorph (allelic state of an SNP), the reaction with the perfectly matched primer reached the threshold in fewer cycles than the reaction with the mismatched primer, i.e. the 
CT was always in the expected orientation. It was concluded that the kinetic PCR approach to interrogating these SNPs is robust. The 11 STs (1, 8, 22, 30, 45, 73, 78, 88, 93, 128 and 239) were resolved into nine SNP profiles. The unresolved STs (ST239–ST128 and ST78–ST88) are very closely related to each other (Supplementary Table S1, Fig. 1).
The SNP typing was then carried out on 76 MRSA isolates from South-East Queensland and elsewhere in Australia that had not been subject to full ST determination (Table 2). Nine SNP profiles were obtained, and these were consistent with ST1, ST73 (ST5), ST8, ST22, ST30, ST45, ST78–ST88, ST93 and ST239 (ST73 was found in the fully sequence-typed isolates, and is an SLV of ST5 which is a very well known, and common, clonal complex progenitor). The SNP profiles showed consistency with the PFGE profiles and origins of the isolates. The ST30/pulsotype A isolates are members of the Western Samoan Phage Pattern (WSPP)/South West Pacific (SWP) clone that is strongly associated with community-acquired infections in Oceania (Nimmo et al., 2000). As expected, PFGE provided higher resolution than the SNPs, and on no occasion were different SNP profiles associated with the same pulsotype. The STs included in this study encompass much of the diversity of Australian MRSA (Coombs et al., 2004). It was concluded that the high D SNPs are useful for identifying the major Australian MRSA clones.
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In order to identify the gene variation within our collection, real-time PCR assays were designed and optimized. The PCR amplicon length for each reaction was verified using agarose gel electrophoresis. The results from the PCR assays indicated that the pvl, cna, sdrE/bbp and tst genes displayed binary variability within the collection, and that the icaA, fnbA and hlg genes were present in all isolates (Table 3). The distributions of the variable virulence genes are consistent with previous reports (Moore & Lindsay, 2001; Peacock et al., 2002). The pvl and tst genes are known to be mobile, the sdrE locus is polymorphic, and cna has been reported not to be consistently carried throughout the species, although the basis for its mobility is not understood. The three genes common to all isolates are chromosomally located and do not have any obvious means of mobility, although variation of these genes has been demonstrated elsewhere (Peacock et al., 2002). The tst gene was found in only a very small number of isolates, so in the context of this study was shown to have limited usefulness. Therefore, the informative power of pvl, cna and sdrE/bbp in combination with the high D SNPs was assessed.
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With respect to the nosocomial isolates, the ST239 group consisted of 24 isolates, but was only divided into two subtypes based on virulence gene variation; a single isolate tested negative to both sdrE and bbp. The two isolates of the ST5 SNP profile were found to differ at the cna locus, the ST5 isolate was negative and the ST73 isolate was positive. The three ST8 and three ST78 SNP group isolates were also divided through the lack of the cna gene in two isolates from each group. Variation of the virulence gene targets within the SNP groups of ST22 and 45 was not identified, most likely due to the small sizes of these groups.
SCCmec-associated plasmids add resolving power
It is well known that one mode of variation in the SCCmec element is the plasmids integrated at the 3' end (Oliveira et al., 2000). Our group has previously shown that these plasmids can be used for binary typing (Huygens et al., 2002). Here, we have determined the resolving power of these integrated plasmids in combination with the high D SNPs and the variable virulence genes.
The entire collection was tested for these plasmids. Variation was identified in the ST1, ST30 and ST239 SNP profiles. An additional subtype was identified from the CA-MRSA-associated ST1 SNP profile, with five pvl-negative isolates testing positive for pT181. The ST36 isolate sharing the ST30 SNP profile was further differentiated from the ST30 strain by testing positive for pUB110. Most variation was in the ST239 SNP profile isolates, among which four plasmid profiles were found. The majority carried pT181 and pI258, with the remaining isolates carrying either pI258, pUB110 or no plasmids. It was concluded that these plasmids efficiently increased genotyping resolution, particularly with nosocomial isolates, which characteristically harbour the large forms of the SCCmec element.
Comparison of combinatorial genotyping with SCCmec and agr typing
One combinatorial genotyping method that has become well accepted is SCCmec typing in combination with ST determination. mec typing is a binary system based upon variation in the ccr gene complex and the upstream mecA regulatory region (Okuma et al., 2002). It was therefore of interest to compare the informative power of mec typing with that of the other binary markers.
SCCmec characterization was undertaken on 83 isolates. Each of the five major structural types was successfully identified (SCCmec I–V), along with at least four novel variants. The five ST1 SNP profile isolates that tested negative for pvl and positive for pT181 were found to carry a novel SCCmec element composed of ccrA1 with mec class C. Other novel structural variants were identified in two unrelated isolates, which tested positive for mec class B and negative for each ccr allele. Surprisingly, 10 of the 29 ST30 SCCmec type IV isolates tested negative for subtype a, as did the ST22 SNP group. These isolates were further analysed and proved negative for subtypes b and c. The ST239 SNP profile isolates tested in this study were each found to carry SCCmec type III, but as stated in the previous section, these elements vary with respect to integrated plasmids that define the subgroups. There was also a single ST1 SNP isolate (FH53) that carried SCCmec type I. In conclusion, SNP genotyping used in combination with SCCmec types, including the novel variants, defined 17 genotypes. This is fewer than the 21 genotypes defined by the SNPs+binary virulence genes+integrated plasmids, although 24 genotypes were defined when all loci were interrogated. SCCmec subtyping could define more genotypes, but the subtypes have not been fully defined, with some isolates remaining un-subtypable. Also, the SCCmec elements are very diverse and appear to evolve very rapidly, thus making it difficult to design a generally applicable and easily interpretable typing method. It may be that the SCCmec element will prove most valuable for providing higher genotyping resolution on occasions when this is required.
The agr locus is a polymorphic region of the genome that modulates expression of a set of virulence-related genes (Ji et al., 1995). To determine if variation at this locus is useful for adding resolution, the collection was assayed for each of the four agr types. A single instance of intra-SNP profile variation at the agr locus was identified. The pvl-negative ST59 isolate that shares an SNP profile with the ST93 SNP genotype was found to have agr type I, compared to the ST93 isolates that carry agr type III. These results indicate that with this collection of isolates, binary variation is concordant with the genotypes defined by the SNPs and the other binary markers, so interrogation of the agr locus adds little informative power in this context.
The genotypes obtained in this study are collated in Table 4.
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ACKNOWLEDGEMENTS |
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REFERENCES |
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TOPINTRODUCTIONMETHODSRESULTS AND DISCUSSIONREFERENCES |
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