Objectives: Within-host genetic diversity has been used to establish single nucleotide polymorphism (SNP) thresholds for defining transmission of pathogens between individuals. Here, we aimed to characterise the genetic diversity of Neisseria gonorrhoeae, within and between anatomical sites and compare bioinformatics methods for defining SNP thresholds.
Methods: In this study, two approaches were used to explore genetic diversity. First, we examined a collection of stored, clinical N. gonorrhoeae isolates sourced from multiple anatomical sites of single individuals attending a sexual health clinic in Melbourne from 2011-2019. Second, we obtained multiple colony picks from primary clinical samples from individuals attending this sexual health clinic from 2019-2020. Whole genome sequencing (WGS) and a variety of bioinformatics approaches were used to determine within-host and within-sample genetic diversity.
Results: Thirty-seven individuals were identified that had cultured N. gonorrhoeae from two or more anatomical sites (urogenital, anorectal, or oropharyngeal), with a final dataset of 105 isolates. In 35/37 (94.6%) individuals, infections were highly similar at the genetic level, with identical Multi-locus Sequence Type (MLST) and Multi-antigen Sequence Type (NG-MAST) profiles. Comparisons of isolates within each individual indicated that the maximum within-host pairwise SNP distance was 13 SNPs (median = 1, IQR: 0-3). Notably, four distinct multi-individual phylogenetic clusters were identified, where the maximum pairwise SNP distance was 19 SNPs (median = 6, IQR = 2-11). Similarly, comparisons of isolates within each primary sample indicated that the maximum pairwise SNP distance was 8 SNPs (median = 2, IQR:1-3).
Conclusions: This study suggests that in most cases of multi-site infection, the same strain of N. gonorrhoeae causes the infection at each anatomical site. However, WGS data alone cannot differentiate between the same infecting strain or (re)infections from the same transmission network. These data guide recommendations regarding optimal bioinformatic approaches to infer genetic relatedness of N. gonorrhoeae and will help inform future studies of gonorrhoea transmission.