Correction
26 Apr 2012: McMillan DJ, Kaul SY, Bramhachari PV, Smeesters PR, Vu T, et al. (2012) Correction: Recombination Drives Genetic Diversification of Streptococcus dysgalactiae Subspecies equisimilis in a Region of Streptococcal Endemicity. PLOS ONE 7(4): 10.1371/annotation/fbdb6697-5b22-4ac5-b38a-b55fc6f86beb. https://doi.org/10.1371/annotation/fbdb6697-5b22-4ac5-b38a-b55fc6f86beb View correction
Figures
Abstract
Infection of the skin or throat by Streptococcus dysgalactiae subspecies equisimilis (SDSE) may result in a number of human diseases. To understand mechanisms that give rise to new genetic variants in this species, we used multi-locus sequence typing (MLST) to characterise relationships in the SDSE population from India, a country where streptococcal disease is endemic. The study revealed Indian SDSE isolates have sequence types (STs) predominantly different to those reported from other regions of the world. Emm-ST combinations in India are also largely unique. Split decomposition analysis, the presence of emm-types in unrelated clonal complexes, and analysis of phylogenetic trees based on concatenated sequences all reveal an extensive history of recombination within the population. The ratio of recombination to mutation (r/m) events (11∶1) and per site r/m ratio (41∶1) in this population is twice as high as reported for SDSE from non-endemic regions. Recombination involving the emm-gene is also more frequent than recombination involving housekeeping genes, consistent with diversification of M proteins offering selective advantages to the pathogen. Our data demonstrate that genetic recombination in endemic regions is more frequent than non-endemic regions, and gives rise to novel local SDSE variants, some of which may have increased fitness or pathogenic potential.
Citation: McMillan DJ, Kaul SY, Bramhachari PV, Smeesters PR, Vu T, Karmarkar MG, et al. (2011) Recombination Drives Genetic Diversification of Streptococcus dysgalactiae Subspecies equisimilis in a Region of Streptococcal Endemicity. PLoS ONE 6(8): e21346. https://doi.org/10.1371/journal.pone.0021346
Editor: Frank R. DeLeo, National Institute of Allergy and Infectious Diseases, National Institutes of Health, United States of America
Received: January 26, 2011; Accepted: May 29, 2011; Published: August 3, 2011
Copyright: © 2011 McMillan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Funding: This work was supported by an Australian Government National Health and Medical Research Council, National and International Alliance Program of Queensland, Australia, and India-Australia Strategic Research Fund and Department of Biotechnology, India. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
Streptococcus dysgalactiae subspecies equisimilis (SDSE) is a β-haemolytic Gram-positive bacterium that typically colonises the oropharynx and skin of humans. The species is closely related to S. pyogenes, a significant human pathogen [1]. Although generally a less common cause of human infection, SDSE causes many of the same diseases as S. pyogenes, including pharyngitis, pyoderma, post-glomerulonephritis, bacteraemia and other invasive diseases [2], [3]. The incidence of SDSE disease is also reported to be increasing, and in some studies exceeds that of S. pyogenes disease [4], [5], [6].
SDSE and S. pyogenes share in common many virulence factors that contribute to virulence, including the M-protein [7], [8], [9]. The M-protein protects bacteria from opsonophagocytosis by blocking deposition of complement on the bacterial surface. Nucleotide variation in emm, the gene encoding the M-protein, is used to type both SDSE and S. pyogenes at the subspecies level. Currently more than 50 SDSE emm-genes are present in the Centre for Disease Control emm-gene database (http://www.cdc.gov/ncidod/biotech/strep/types_emm103-124.htm). Other typing methods such as vir-typing [10], [11] and emm-pattern typing [12] also target the locus encoding the emm-gene. However several studies have reported the emm-gene, and surrounding loci to be subject to lateral gene transfer (LGT) [13], [14].
Recovery of SDSE from the throats has been reported to exceed that of S. pyogenes in regions where streptococcal disease is endemic [15], [16]. The diversity of circulating SDSE and S. pyogenes emm-types in endemic regions is high. Multilocus sequence typing (MLST) is a nucleotide based method for characterising genetic relationships amongst isolates of the same bacterial species. Unlike emm-typing, MLST utilises multiple housekeeping genes considered to be selectively neutral that are located in different parts of the genome. MLST therefore provides a better tool for determination evolutionary relationships within the SDSE population than typing using emm gene, which is under strong diversifying selective pressure. Recent MLST studies of SDSE isolates from Australia, Portugal and USA reported a high degree of genetic diversity in these populations, and revealed LGT of housekeeping alleles was occurring [14], [17]. In the current study we have used MLST to assess the genetic diversity of SDSE recovered from India, a country where streptococcal disease is endemic [18], [19] and emm-type diversity is high [15], [20]. Our results demonstrate this geographically confined collection to contain predominantly novel sequence types (STs). The ratio of recombination to mutation in house-keeping alleles in this endemic region surpasses that reported for non-endemic regions [17]. The data additionally suggests that LGT between SDSE and other streptococcal species occur. Our findings suggest an evolutionary process in which novel genetic variants of SDSE, possibly with altered fitness or pathogenic potential, are more likely to arise in endemic regions than non-endemic regions.
Results
Allelic variation
Details of the ST and emm-type of the 181 SDSE isolates from India are provided in Table S1. A summary of nucleotide variation in the seven loci used for MLST in SDSE is provided in Table 1. The total number of alleles present at each locus varied from six for gtr to sixteen for xpt. Ten of the new SDSE MLST alleles identified in the study were identical to alleles from GAS (Table S2). Another allele, recP22, was identical to a nucleotide sequence from S. agalactiae, and shares greater than 99% identity with the same sequence in two other S. agalactiae genomes. As recP22 is less than 90% identical to other so far known SDSE and S. pyogenes recP alleles, the allele was most likely acquired from S. agalactiae. Although evidence for mobile genetic element (MGE) mediated LGT between SDSE and S. agalactiae has been reported [21], to our knowledge this is the first evidence suggesting lateral transfer of an S. agalactiae housekeeping gene to SDSE.
When MLST alleles predicted to have been acquired by SDSE from non-SDSE sources through recombination (defined below) were disregarded, nucleotide diversity ranged from 0.004 for gtr to 0.042 for atoB. With the exception of gtr and atoB, the dn/ds ratio was less than 0.4 for all loci, indicating that the variation observed in these alleles is likely constrained by purifying selection. Although the dn/ds for gtr was relatively high compared to the other MLST alleles, nucleotide variation only occurred at four sites. The number of variable sites for the other MLST alleles ranged from 6 to 47. Phylogenetic analysis of the SDSE atoB and its orthologue in GAS (yqiL), showed two SDSE alleles, atoB16 and atoB17, which accounted for the majority of non-synonomous mutations, lie at a node midway between the SDSE and GAS clusters (Figure S1).
Clonal relationships in the Indian SDSE population
MLST resolved the 181 isolates into 52 STs. The most common ST, ST84, was found in 43 isolates, all of which were emm-type stg4831. The five next most abundant STs (ST44, ST15, ST89, ST81 and ST107) each individually constitute 5 to 10% of the total collection, and together with ST84 account for 53% of isolates. In contrast, thirty two STs (62%) are represented by a single isolate. The overall diversity of emm-type (D = 0.898, 95% CI: 0.872–0.924) and ST (D = 0.916, 95% CI: 0.890–0.942) in the population is similar. The eBURST analysis segregated the 52 STs into seven single locus variant clonal complexes (CCslvs), and twenty singletons (Figure 1). CCslv44 contained the greatest number of STs (n = 13). CCslv84 contained the greatest number of isolates (n = 43), and together with CCslv44 (n = 41), account for 58% of all isolates. No other CCslv contained more than 15 isolates. When clustered at the DLV (CCdlv) level, seven complexes, and six singletons were defined. Together CCdlv44 and CCdlv107 account for 70% of isolates. Overall, while a large proportion of SLV defined singletons became a part of CCdlvs, the CCslvs themselves remained largely discrete.
The 52 STs clustered into seven SLV-defined CCs. STs part of the same SLV defined CC are joined by black lines. When grouped at the less stringent DLV level seven CCs are defined (light shaded circles).
Relationships between STs were examined at the nucleotide level by constructing Minimum Evolution (ME) spanning trees using concatenated sequences of all seven MLST loci. In general, there is a good congruence between relationships of STs within CCslvs, and the clustering of STs within individual branches of the ME tree (Figure 2). The branches of the ME tree were subsequently partitioned at nodes separating STs belonging to different CCslvs The grouping of STs in these partitions were then compared to the grouping of STs by eBURST at the CCslv level using the Wallace co-efficient [17], [22]. The correlation between eBURST derived partition and ME derived partitions was calculated to be 0.630 (CI95% 0.388–0.872). There were five instances in the tree where STs did not cluster with the CCslv as predicted by eBURST. As examples, ST117 and ST123, both of which belong to CCslv44, possess gki15 and gki16. These alleles are identical to the GAS MLST alleles gki67 and gki38, respectively. ST86, another ST from CCslv44 possesses the divergent ato17 (Figure S1). Genetic relationships consistent with recombination were also apparent in STs that did not cluster as predicted from eBURST analysis.
The tree was constructed using concatenated sequences of 52 SDSE STs. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. Only bootstrap values greater than 80% are shown. Clonal complexes at both the SLV and DLV level, and singletons (s), as determined using eBURST, are also depicted.
Recombination and mutation in MLST loci
Estimates of recombination and mutation in SLV pairs were determined using previously described methods [22], [23], [24]. Of the 25 SLV pairs 22 are predicted to have arisen through recombination (Table S3); three recombination events involved non-SDSE derived alleles. Only two SLV pairs are predicted to have arisen through point mutation. The r/m ratio in the population was 11, and per site r/m ratio 164. When alleles predicted to be derived from non-SDSE species were excluded the per site r/m ratio fell to 41. The standardised Index of Association (IA) across the population was 0.28, also suggestive of a high rate of recombination.
Recombination events can distort or conceal true evolutionary relationships that exist between STs. In these instances, standard phylogenetic trees, which only display single relationships between isolates or clones, do not provide the equal representation for all possible evolutionary relationships. Split decomposition analysis was therefore used to visualise alternative phylogenetic relationships between STs (Figure 3). The reticulated phylogenetic structure of this figure is indicative of extensive recombination of loci [25] providing additional support for the high estimates of recombination and low IA. The majority of STs were found in the same groupings as determined by eBURST. However STs belonging to DLV107 segregated into two separate groups.
Shaded circles indicate DLV defined CCs. Solid lines enclose SLV defined CCs. STs associated with stC36, stg245 and stg480 are also indicated.
Relationship between emm-type and ST
emm-typing is the most common method of typing SDSE. Discrepancies between emm-type and ST have previously been reported [17] and offers evidence that LGT of emm-genes occurs in nature [26]. The presence of multiple emm-types within a single ST that are identical to, or have close similarity to emm-genes found in distant STs is suggestive of LGT of this gene [17], [27]. Alternatively, diversifying selection pressure on the emm-gene may also give rise to STs that harbour multiple emm-types. In this study, eleven STs were associated with more than one emm-type (Table 2). Two of these, ST44 and ST15, were each associated with five emm-types. Together nineteen instances of multiple emm-genes present in individual STs was observed. A complementary method for inference of LGT is the identification of the same emm-gene in distantly related or unrelated STs. In total eleven emm-genes were found in two or more CCdlvs (Table S4). Four emm genes, stc36, stg485, stg480 and stg866 were present in 4, 4, 3 and 3 CCdlvs respectively (Figure 3). Seventeen instances of emm-gene LGT were inferred using this method. In contrast to the frequency of recombination of the emm-gene, the predicted number of recombination events for individual housekeeping alleles used in MLST ranged from one for xpt to six for murI based on SLV relationships.
To further investigate the relationships between emm-type and ST, we constructed a phylogenetic tree using the 150 nucleotides of the emm-gene used to determine emm-type (Figure 4). The emm-gene sequences were aligned using ClustalW prior to construction of the ME tree. When MLST data was overlaid onto this tree, CCslvs and CCdlvs were scattered throughout the tree. When emm-types were mapped onto the SplitsTree phylogram (which accommodates effects of recombination), a much clearer association between emm-type and MLST data became apparent, with the majority of isolates of the emm-types falling into the same cluster (Figure 3). These clusters are therefore the likely progenitors of specific emm-genes.
The tree was constructed using the ME algorithm. Bootstrap values in which associated taxa are clustered in greater than 60% of cases are shown next to branches. CCslvs and CCdlvs associated with emm-types are also shown.
Indian isolates possess unique STs
Eighty STs were described in a previous study of 178 SDSE isolates from non-endemic regions [17]. Only six of these STs (ST15 ST29 ST34 ST44 ST66 ST70) were found amongst the Indian strains. Of the 46 new STs in this study, nineteen possessed previously reported alleles at all seven loci, but in combinations not previously found. Twenty-seven STs possessed at least one new allelelic sequence. When all known SDSE STs were compared using eBURST, seven CCdlvs were defined (Figure S2). One of these complexes was considerably larger than all others with 87 STs. With the exception of four STs (ST97, ST108, ST100 and ST104), all Indian STs were found in this large complex. In contrast only 43 of the 74 non-Indian STs were found within this complex. Further comparisons using split decomposition (Figure S3) and ME analysis (Figure S4) also segregated Indian isolates into subclusters separate from other SDSE isolates.
Association between disease and ST
Chi-squared goodness-of-fit tests were used to determine if any ST or CC was over-represented in strains recovered from individuals with throat infection,(i.e. presenting with pharyngitis or tonsillitis) when compared to strains recovered from individuals without symptoms of throat infection [15]. The latter control group included all SDSE throat carriage isolates, and SDSE recovered from the skin. Three STs were found to have statistically different distributions between throat-infection and control isolates. ST89 and ST111 were found to be overrepresented in the throat infection isolates (relative risk of 2.61 and 2.85 respectively). The third ST, ST107 was underrepresented (relative risk of 1.63 for non-throat infection). No CC had an increased association with throat infection.
Discussion
Streptococcal infection is considered to be endemic in India. As a consequence co-infection with multiple SDSE strains, or SDSE and other streptococcal species, is more likely to occur here than in countries where streptococcal disease is non-endemic. An outcome of increased inter-strain contact is increased opportunity for LGT, resulting in an increase in overall strain diversity. The ratio of recombination to mutation reported here is double that reported for SDSE from non-endemic areas, supporting this scenario [17]. Further the inter-relatedness of Indian isolates and presence of only a few global STs, support a model whereby ongoing recombination between isolates in a single geographic location result in strains that over time become more related to each other than to strains from outside of the population.
In our study, recombination involving the emm-gene was more frequent than recombination involving individual housekeeping alleles. The emm-gene is part of an ancient pathogenicitiy island that is mobilisable in vitro [13]. An active mechanism for LGT involving the emm-gene therefore exists, and may account for the increased LGT of this gene. The acquisition of new emm-genes that assist SDSE in evasion of host immune responses, giving recipients a selective advantage, may also increase the frequency in which LGT involving the emm-gene is observed when compared to selectively neutral house-keeping genes. In spite of this, we observed more S. pyogenes housekeeping alleles than S. pyogenes emm-genes among our SDSE isolates. This suggests restricted compatibility for the S. pyogenes emm genes in SDSE, and may explain distinct evolutionary clades for the genes in the two species. The amino-termini of the mature M-proteins are the major target of type-specific anti-GAS and anti-SDSE antibodies. While population and virulence studies of S. pyogenes report associations between emm-types and specific disease, associations between emm-type and SDSE-mediated disease are not obvious [28], [29], [30]. One outcome from our study that could account for this observation is that the greater recombination occurring among SDSE isolates in regions where streptococcal infection is endemic results in the weakening of emm-typing as a useful tool for assigning genetic relationships over a meaningful time frame amongst geographically separated SDSE isolates. Of the six STs shared between India and the rest of the world, only five emm-STs were common. Twelve emm-STs were only found in India, and another nine emm-STs found in non-Indian isolates.
One emm-type, stg480, has been associated with SDSE infection in several studies [5], [31]. This emm-type was also one of the most commonly recovered emm-types in this study, but was not recovered more frequently from individuals with pharyngitis. The apparent increased association with diseases reported in other studies may reflect the relative abundance of this emm-type within the population, rather than an increased virulence potential. stg480 has now been associated with five STs in this and previous studies [14], [17], suggesting that this emm-locus has propensity to participate in LGT frequently, which indeed further clouds epidemiological findings based on emm-type. Larger prospective studies that include characterisation of both the emm-gene and ST are required to determine the pathogenic potential of individual SDSE lineages.
In contrast to stg480, the majority of stg4831 isolates were predominantly associated with ST84. All ST84 isolates also possessed the stg4831 emm-gene. As all ST84 and stG4831 isolates were only recovered in Mumbai, it is likely that during the collection period, an outbreak of stg4831-ST84 was occurring. The general lack of variation in ST-emm combinations suggests this outbreak is relatively new, leaving little time for mutation or recombination with existing strains. Nevertheless, the presence of three stG4831 clones with different ST (ST87, ST85 and ST102), all recovered from Mumbai, once again suggests that recombination was occurring at a local level. ST87 is an SLV of ST84, predicted to have arisen via recombination. ST85, a DLV variant of ST84 is more closely related to ST84 than any other ST. Two of the three alleles that differ between ST102 and ST84 are found in ST89. In this instance, the data suggest recombination between an ST84 clone and isolate from CCslv89 has occurred.
Frequent recombination is emerging as a paradigm for the β-hemolytic streptococci. Taken together our findings suggest that SDSE ST diversity is high in regions where streptococcal disease is considered endemic, and is driven mainly by recombination. In endemic regions, the opportunity for different streptococcal isolates to come into contact, and share genes is greater than in regions where streptococcal infection is less prevalent. SDSE, S. pyogenes and S. agalactiae all possess multiple MGEs, and MGE-related interspecies transfer of virulence genes has been reported by several groups [26], [32], [33], The transfer of non-MGE housekeeping genes may occur as bystander event during transfer of MGEs [34]. It is noteworthy that transfer of allelic variants of virulence genes not associated with mobile genetic elements is also occurring, possibly having a greater impact on the fitness or virulence of some SDSE isolates. The observed increase in the rate of recovery of new variants in endemic regions suggests rapid emergence of more fit or virulent clones are more likely to occur in these regions.
Materials and Methods
Ethics statement
Ethical approval for swabbing of individuals in the study was granted by the Seth G. S. Medical School and KEM Hospital Ethic Committee, India (EC/Gov/-4/2006). Written informed consent for the swabbing of children was obtained from the guardians of all children included in the study.
Bacterial Strains
A total of 181 SDSE isolates collected as part of community and school surveys carried out in Mumbai [15] and Chennai [35] were used in the study. Eighty-five isolates were collected from individuals presenting with pharyngitis (n = 66) or tonsillitis (n = 10), and were classified as ‘throat-infection’ isolates. Another 102 isolates were recovered from the throats of individuals lacking clinical signs of streptococcal infection. Nine SDSE isolates were collected from the skin of individuals presenting with pyoderma. All isolates were classified as SDSE on the basis of β-hemolytic activity, expression of the group C or G carbohydrate, and possession of characteristic emm-type and molecular markers characteristic of SDSE [10]. Emm-types for these isolates were previously reported [15], [35] or determined using standard protocols [20], [36]. Full details of strains used in this study are provided in Table S1.
MLST
The SDSE MLST scheme based on the following seven gene targets (glucose kinase (gki), glutamine transport protein (gtr), glutamate racemase (murI), DNA mismatch repair protein (mutS), transketolase (recP), xanthine phosphoribosyl transferase (xpt) and acetoacetyl-coathioloase (atoB)) has been described previously [17]. With the exception of atoB these alleles are the same as used in the GAS MLST scheme [27]. DNA was extracted using the QIAGEN DNAeasy kit (QIAGEN, Australia), and 450–500 base-pair internal fragments of these genes were amplified under the following conditions; 2 min denaturation at 95°C, followed by 35 cycles of 95°C (45 s), 50°C (45 s) and 72°C (60 s). PCR products were purified using ExoSAPIT (USB Corp, USA), and sequenced in the forward and reverse directions by Macrogen (Korea) or in-house. Sequencher (Genecodes, USA) was used for initial analysis and trimming of sequences to match reference sequences. All sequences were compared to existing SDSE MLST alleles to determine specific allele number at a given locus. Unique allelic sequences identified in this study were assigned a new allele number. The combination of seven allele numbers was then used to determine the sequence type (ST). goeBURST (http://goeburst.phyloviz.net/) was used to identify related STs [37], [38]. In this study Clonal Complex (CCs) were defined as a group of STs that are related to each other at the Single Locus Variant (CCslv) or Double Locus Variant (CCdlv) level.
Recombination and mutation
Rates of recombination and mutation that give rise to SLV pairs were estimated as previously described [22], [23], [24]. Briefly, SLV pairs that contain greater than one nucleotide difference in the variant alleles were classified as arising through recombination. SLV pairs in which one ST contains a unique single nucleotide polymorphism not found in other STs were classified as a point mutation event. Single nucleotide changes in SLVs pairs giving rise to alleles already present in unrelated STs in different CCslvs were classified as recombination events. Both the ratio of recombination/mutation events (r/m) and per nucleotide site ratio of recombination/mutation are reported.
Phylogenetic Analysis
Nt diversity (π), nonsynonymous (dn) and synonymous substitution rates (ds) were calculated using DnaSP (version 5) [39]. Linkage equilibrium, expressed as standardised Index of Association (IA) was calculated using LIAN 3.5 [40]. Distance matrices for phylogenetic analysis were calculated using START [41]. Phylogenetic networks were constructed using Splitstree 4 [25]. Minimum evolution (ME) spanning trees using concatenated nucleotide sequences from all seven MLST loci were constructed in Mega4 [42], with support for branches provided by bootstrapping (n = 1000). Phylogenetic analysis of the 180 nt variable region of the emm-gene used for emm-typing was also performed using Mega4. The sequences were aligned using ClustalW, using default parameters, prior to construction of the ME tree. The Wallace co-efficient [23], [24] was calculated using www.comparingpartitions.info.
Statistical analysis
Statistically significant associations between ST, Clonal Complex and other epidemiological factors were assessed using the Chi-squared goodness of fit test (p<0.05). Confidence intervals for Simpson index of diversity (D) were calculated as previously described [43].
Supporting Information
Figure S1.
Phylogenetic analysis of SDSE atoB and S. pyogenes yqiL alleles. The relationship between alleles was inferred using the Minimum Evolution method, and support for branches provided by bootstrapping (n = 1000). Bootstrap values are only provided for branches with greater than 50% support.
https://doi.org/10.1371/journal.pone.0021346.s001
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Figure S2.
eBURST analysis of all known SDSE STs. Blue circles represent STs only found in India. Red circles represent STs found outside of India (McMillan et al., 2010). Green circles represent STs found in both collections. Dark connecting lines join SLV related pairs. Grey lines connect DLV related pairs.
https://doi.org/10.1371/journal.pone.0021346.s002
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Figure S3.
Split decomposition of all SDSE STs. STs found in India are circled.
https://doi.org/10.1371/journal.pone.0021346.s003
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Figure S4.
ME tree of all SDSE STs. The tree is drawn to scale, with branch lengths in the same units as those of the evolutionary distances used to infer the phylogenetic tree. Only bootstap values greater than 50% are shown. Blue circles represent STs only found in India. Red circles represent STs found outside of India (McMillan et al., 2010). Green circles represent STs found in both collections.
https://doi.org/10.1371/journal.pone.0021346.s004
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Table S1.
Details of SDSE isolates used in this study.
https://doi.org/10.1371/journal.pone.0021346.s005
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Table S2.
Novel MLST alleles in the Indian SDSE population.
https://doi.org/10.1371/journal.pone.0021346.s006
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Table S3.
Recombination and mutation in SDSE.
https://doi.org/10.1371/journal.pone.0021346.s007
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Table S4.
Emm -types associated with multiple Sequence Types.
https://doi.org/10.1371/journal.pone.0021346.s008
(DOC)
Author Contributions
Conceived and designed the experiments: DJM MGK MSS KSS. Performed the experiments: DJM SYK PVB TV. Analyzed the data: DJM SYK PVB PRS. Contributed reagents/materials/analysis tools: DJM MSS KSS. Wrote the paper: DJM PRS KSS.
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