Public health surveillance of multidrug-resistant clones of Neisseria gonorrhoeae in Europe: a genomic survey

Abstract

Background: Traditional methods for molecular epidemiology of Neisseria gonorrhoeae are suboptimal. Whole-genome sequencing (WGS) offers ideal resolution to describe population dynamics and to predict and infer transmission of antimicrobial resistance, and can enhance infection control through linkage with epidemiological data. We used WGS, in conjunction with linked epidemiological and phenotypic data, to describe the gonococcal population in 20 European countries. We aimed to detail changes in phenotypic antimicrobial resistance levels (and the reasons for these changes) and strain distribution (with a focus on antimicrobial resistance strains in risk groups), and to predict antimicrobial resistance from WGS data.

Methods: We carried out an observational study, in which we sequenced isolates taken from patients with gonorrhoea from the European Gonococcal Antimicrobial Surveillance Programme in 20 countries from September to November, 2013. We also developed a web platform that we used for automated antimicrobial resistance prediction, molecular typing (N gonorrhoeae multi-antigen sequence typing [NG-MAST] and multilocus sequence typing), and phylogenetic clustering in conjunction with epidemiological and phenotypic data.

Findings: The multidrug-resistant NG-MAST genogroup G1407 was predominant and accounted for the most cephalosporin resistance, but the prevalence of this genogroup decreased from 248 (23%) of 1066 isolates in a previous study from 2009-10 to 174 (17%) of 1054 isolates in this survey in 2013. This genogroup previously showed an association with men who have sex with men, but changed to an association with heterosexual people (odds ratio=4·29). WGS provided substantially improved resolution and accuracy over NG-MAST and multilocus sequence typing, predicted antimicrobial resistance relatively well, and identified discrepant isolates, mixed infections or contaminants, and multidrug-resistant clades linked to risk groups.

Interpretation: To our knowledge, we provide the first use of joint analysis of WGS and epidemiological data in an international programme for regional surveillance of sexually transmitted infections. WGS provided enhanced understanding of the distribution of antimicrobial resistance clones, including replacement with clones that were more susceptible to antimicrobials, in several risk groups nationally and regionally. We provide a framework for genomic surveillance of gonococci through standardised sampling, use of WGS, and a shared information architecture for interpretation and dissemination by use of open access software.

Funding: The European Centre for Disease Prevention and Control, The Centre for Genomic Pathogen Surveillance, Örebro University Hospital, and Wellcome.

Figure 1

Violin plots of observed minimum inhibitory concentrations for combinations of known genotypic antimicrobial resistance determinants or without any of these mutations Minimum inhibitory concentrations are on the y-axis. Combinations of resistance determinants are on the x-axis; different colours indicate different mutation combinations. Data were recorded after re-testing. Dashed horizontal lines indicate breakpoints from the European Committee on Antimicrobial Susceptibility Testing.

Figure 2

Comparison of whole-genome sequencing, NG-MAST genogrouping, and multilocus sequence typing Data are the phylogenetic tree from the whole-genome sequence analysis from Euro-GASP, 2013. Columns indicate the location of isolates in the eight most prevalent Neisseria gonorrhoeae multiantigen sequence typing genogroups, the five most prevalent multilocus sequence types, whole-genome sequence clades M1 and M2 (defined from the phylogenetic tree), and the SIR data for cefixime, azithromycin, and ciprofloxacin. NG-MAST=Neisseria gonorrhoeae multi-antigen sequence typing. SIR=susceptible, intermediate, resistant. *Secondary clades of NG-MAST genogroup 1407 and multilocus sequence type 7363 isolates. Figure produced with Phandango.

Figure 3

Histogram of pairwise phylogenetic distance of isolates on the Whole Genome Sequence Analysis tree Data are split into four geographical categories. White lines indicate the splits of geographical categories for all isolates. Phylogenetic distance data are presented up to 100 single-nucleotide polymorphisms.

Figure 4

Whole Genome Sequence Analysis screenshot of genomic epidemiology of two minor clusters of multi-drug resistant Neisseria gonorrhoeae Data in the top left box are the phylogenetic reconstruction of the relationships of isolates in part of the Euro-GASP tree, generated by the Whole Genome Sequence Analysis web application. Red circles are isolates belonging to the two clusters with predicted resistance to azithromycin, based on the presence of known genetic determinants. Branch two-letter labels represent the country of origin of the isolates. The scale bar relates to horizontal branch lengths and indicates the number of single nucleotide polymorphisms that are proposed to have occurred on the branches. Data in the top right panel are the geographical distribution of isolates in the collection. The red pie charts at each location indicate predicted resistance to azithromycin. The larger pie chart is the total predicted azithromycin resistance in the entire collection. Data in the lower panel are the predicted resistance profiles of the six isolates that are highlighted in red in the top left panel. Red circles indicate predicted resistance and orange circles indicate where known determinants of decreased susceptibility are present, although these determinants do not necessarily lead to resistance.