Abstract

BackgroundGonorrhea, caused by the bacterium Neisseria gonorrhoeae, is a globally prevalent sexually transmitted infection. The dynamics of gonococcal population biology have been poorly defined due to a lack of resolution in strain typing methods. MethodsIn this study, we assess how the core genome can be used to improve our understanding of gonococcal population structure compared with current typing schemes.ResultsA total of 1668 loci were identified as core to the gonococcal genome. These were organized into a core genome multilocus sequence typing scheme (N gonorrhoeae cgMLST v1.0). A clustering algorithm using a threshold of 400 allelic differences between isolates resolved gonococci into discrete and stable core genome groups, some of which persisted for multiple decades. These groups were associated with antimicrobial genotypes and non-overlapping NG-STAR and NG-MAST sequence types. The MLST-STs were more widely distributed among core genome groups.ConclusionsClustering with cgMLST identified globally distributed, persistent, gonococcal lineages improving understanding of the population biology of gonococci and revealing its population structure. These findings have implications for the emergence of antimicrobial resistance in gonococci and how this is associated with lineages, some of which are more predisposed to developing antimicrobial resistance than others.

Highlights

  • Gonorrhea, caused by the bacterium Neisseria gonorrhoeae, is a globally prevalent sexually transmitted infection

  • Fastq reads were obtained from the European Nucleotide Archive (ENA) and assembled using the Velvet genome assembly program (v1.2.08) [26]

  • Resultant assemblies were deposited in the PubMLST Neisseria database, which uses the Bacterial Isolate Genome Sequence Database (BIGSdb) software [27]

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Summary

Methods

We assess how the core genome can be used to improve our understanding of gonococcal population structure compared with current typing schemes. Whole genome sequence data from 3750 N gonorrhoeae isolates, available on PubMLST (https://pubmlst.org/neisseria) [11], were included. This comprised published isolate collections and constituted a global dataset spanning 5 decades (1970 to 2018) [12,13,14,15,16,17,18,19,20,21,22,23,24,25]. Resultant assemblies were deposited in the PubMLST Neisseria database, which uses the Bacterial Isolate Genome Sequence Database (BIGSdb) software [27].

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