wgs-guided
s.pyogenes outbreak control 859
figure 3. Epidemiological curve of the outbreak. All samples collected and tested as part of the outbreak investigation. emm typing, which involves sequencing of the hypervariable
region at the 5’ end of the emm gene, is the most commonly used S. pyogenes typing method in outbreak investigations.16,23 Sanger-based emm typing was performed in the laboratory at Tan Tock Seng Hospital to corroborate the WGS-derived emm types (see Supplementary Results). We concluded that the cases in cluster C1 constituted an outbreak of a single clone of S. pyogenes, which is supported by the strong epidemiological connection (nearly all from ward A), identical emm types, sequence types, antibiotic resistance profiles, and very close genome sequences (pairwise SNP distances of 0–5). Similar data (concordant epidemiology and pairwise SNP differences of <14 SNPs) have been used previously to support the conclusion of clonal outbreaks of S. pyogenes causing lethal puerperal sepsis,24 direct transmission between 2 closely related care homes,25 and emm59 invasive disease.26 The strains in clusters C2–C3 also had identical emm types
(except for C3 wherein there was 1 strain with different emm type), sequence types, and antibiotic resistance patterns, but they had higher pairwise SNP differences: 21–45 for C2 and 26–58 for C3, respectively. Moreover, for cluster C3, there was little epidemiological connection between the patients. Higher pairwise SNP differences along with epidemiological data allowed us to preclude recent transmissions in clusters C2 and C3. Indeed, the genomic mutation rate of S. pyogenes has been estimated at 1.3–2.1 SNPs per strain per year,27–29 suggesting that clusters with SNP differences in the range of cluster C2 and C3 may be independent non-outbreak infections by closely related (and probably locally circulating) S. pyogenes strains. Importantly, the use of SNP counts in isolation as a proxy to
help delineate clonal spread should be treated with caution. We believe that confounding variables pose difficulties in imposing a simple threshold for the number of SNPs
between isolates to decide whether they are part of a recent transmission event. Koser et al30 described the presence of a hypermutator phenotype in an MRSA isolate from an out- break in a neonatal unit which resulted in that isolate having a higher number of SNPs than the other outbreak isolates. Other factors that may affect SNP differences include differing rates of accumulation of genome polymorphisms among S. pyogenes strains over time and organism population size.31 We also relied on a reference-based analysis, explicitly excluding annotated mobile genetic elements, to calculate SNP distances. This analysis, therefore, did not capture differences in these mobile genetic elements or other sequences not present in the reference used, which may have caused higher SNP differ- ences.32 Furthermore, the effect of environmental conditions, tissue site, or presence of other organisms (eg, S. aureus) on the mutation rate is unknown. In future outbreaks of S. pyogenes, WGS should be con-
sidered the primary typing method to guide the outbreak management if rapid access to bioinformatics expertise to set up pipelines for assembly, alignment, and analysis of WGS is available. However, if this expertise is not immediately avail- able, a 2-step approach with an initial traditional emm typing followed by WGS to further discriminate closely related isolates would be a reasonable strategy.
acknowledgments
We would especially like to thank the GIS platform for its support during the project in the areas of scientific and research computing (led by Chih Chuan Shih) and infection prevention and control unit of Institute of Mental Health of Singapore for their support in the outbreak investigation. Financial support: The outbreak investigation was supported by Institute of
Mental Health. Potential conflicts of interest: K.M. is a consultant for bioMérieux for Global Point Prevalence Survey
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