Neisseria gonorrhoeae clustering to reveal major European whole-genome-sequencing-based genogroups in association with antimicrobial resistance

Abstract

Neisseria gonorrhoeae, the bacterium responsible for the sexually transmitted disease gonorrhoea, has shown an extraordinary ability to develop antimicrobial resistance (AMR) to multiple classes of antimicrobials. With no available vaccine, managing N. gonorrhoeae infections demands effective preventive measures, antibiotic treatment and epidemiological surveillance. The latter two are progressively being supported by the generation of whole-genome sequencing (WGS) data on behalf of national and international surveillance programmes. In this context, this study aims to perform N. gonorrhoeae clustering into genogroups based on WGS data, for enhanced prospective laboratory surveillance. Particularly, it aims to identify the major circulating WGS-genogroups in Europe and to establish a relationship between these and AMR. Ultimately, it enriches public databases by contributing with WGS data from Portuguese isolates spanning 15 years of surveillance. A total of 3791 carefully inspected N. gonorrhoeae genomes from isolates collected across Europe were analysed using a gene-by-gene approach (i.e. using cgMLST). Analysis of cluster composition and stability allowed the classification of isolates into a two-step hierarchical genogroup level determined by two allelic distance thresholds revealing cluster stability. Genogroup clustering in general agreed with available N. gonorrhoeae typing methods [i.e. MLST (multilocus sequence typing), NG-MAST (N. gonorrhoeae multi-antigen sequence typing) and PubMLST core-genome groups], highlighting the predominant genogroups circulating in Europe, and revealed that the vast majority of the genogroups present a dominant AMR profile. Additionally, a non-static gene-by-gene approach combined with a more discriminatory threshold for potential epidemiological linkage enabled us to match data with previous reports on outbreaks or transmission chains. In conclusion, this genogroup assignment allows a comprehensive analysis of N. gonorrhoeae genetic diversity and the identification of the WGS-based genogroups circulating in Europe, while facilitating the assessment (and continuous monitoring) of their frequency, geographical dispersion and potential association with specific AMR signatures. This strategy may benefit public-health actions through the prioritization of genogroups to be controlled, the identification of emerging resistance carriage, and the potential facilitation of data sharing and communication.

Keywords: Neisseria gonorrhoeae; antimicrobial resistance; molecular epidemiology; whole-genome sequencing.

Fig. 1.

Comparison of WGS-based genogroups, defined at two levels, and both traditional typing methods for N. gonorrhoeae . The MSTs enrol all 3791 N . gonorrhoeae isolates based on the MScgMLST scheme (822 loci). Nodes, corresponding to a unique allelic profile, are coloured according to their corresponding (a) high-level WGS-based genogroup, (b) low-level WGS-based genogroup, (c) ST of the traditional seven loci MLST scheme and (d) ST of the two loci NG-MAST scheme. Numbers in parenthesis refer to the number of isolates comprising each genogroup or ST. The MSTs were generated using GrapeTree v1.5.0 software [56].

Fig. 2.

Phylogeny of 3791 N . gonorrhoeae isolates from Europe, based on a gene-by-gene approach using the MScgMLST scheme. The MST was constructed based on allelic diversity found among the 822 genes shared by 100 % of the isolates. All nodes (which represent a unique allelic profile) presenting an allelic distance below 40, corresponding to the low-level genogroup threshold, have been collapsed for visualization purposes. Nodes are coloured according to different countries of origin. Straight and dotted lines reflect nodes linked with the allelic distances below and above the threshold applied for high-level WGS-based genogroup definition (allelic distance 79), respectively. Low-level WGS-based genogroups comprised more than ten isolates are highlighted by thicker black circles. The MST was generated using GrapeTree v1.5.0 software [56].

Fig. 3.

Heatmap distribution and occurrence of the genetic determinants involved in AMR by high-level (a) and low-level (b) WGS-based genogroups. Genetic determinants are ordered by the affected antimicrobial drug class/antibiotic, with resistance or decreased susceptibility effect described at the bottom. Heatmap colour range correlates with the percentage of isolates carrying each genetic determinant within a given WGS-based genogroup. The numbers of isolates within each genogroup are presented on the right of each panel. The contextual neighbour-joining phylogenetic tree at the left side of each panel was generated based on the MScgMLST allelic profiles using GrapeTree v1.5.0 software [56]. The asterisk indicates that the combination of these three mutations has been proposed as potentially inducing resistance to cephalosporins [8]. AZM, Azithromycin; CFM, cefixime; CIP, ciprofloxacin; CRO, ceftriaxone; PEN, penicillins; RI, rifampicin; SPT, spectinomycin, SUL, sulphonamides; TET, tetracycline.

Fig. 4.

Major predicted AMR profiles observed within each low-level genogroup. Yellow circles indicate genogroups where the dominant AMR profile exhibits less genetic determinants associated with resistance than the second observed profile, for multiple classes of antimicrobials. Red circles indicate genogroups where the dominant AMR profile exhibits more genetic determinants associated with resistance for multiple classes of antimicrobials. White circles indicate genogroups with a unique AMR profile. AZM, Azithromycin; CEF, cephalosporins; CIP, ciprofloxacin; NA, not applicable; PEN, penicillins; RI, rifampicin; SUL, sulphonamides; TET, tetracycline. dXXX indicates decreased susceptibility to the named antibiotic, while rXXX indicates resistance to the named antibiotic.

Fig. 5.

Analysis of N. gonorrhoeae WGS-based genetic clusters at low-resolution level potentially concordant with epidemiological link. (a) Genetic cluster isolates’ distribution by collection date, with detailed data of each cluster presented on the right. Each isolate colour refers to a specific study, and black lines link the earliest and latest isolate detected. Numbers in parentheses in the figure key refer to the study reference. (b) MST (also described in Fig. 2) of all isolates analysed in the present study highlighting WGS genetic clusters identified at a conservative threshold of 1.5 % AD. Nodes (which represent a unique allelic profile) presenting an allelic distance below 40, corresponding to the low-level genogroup threshold, have been collapsed for visualization purposes. The letters within (b) represent the following: a, WGSCL0025 Sheffield outbreak described in [34]; b, WGSCL0021 including cluster ST2400 described in [39] and the ST2400 MSM-associated isolates described in [6]; c. WGSCL0028 North-East England outbreak described in [39]; d, WGSCL0024 clade 2 described in [35]; e, WGSCL0023 Leeds outbreak [20] plus clade 1 and 3 described in [35]; f, WGSCL0011 large cluster ST2992 described in [39]; g, WGSCL0034 cluster ST26 described in [39]; h, WGSCL0029 cluster ST292 described in [39]; i and j, WGSCL0019 and WGSCL0020 London outbreaks described in [34]; k, WGSCL0041 small cluster ST2992 described in [39]; l, WGSCL0003 cluster ST1407, which includes linked isolates from Brighton and London described in [39], and isolates described as cephalosporin resistant in [6]; m, WGSCL0032, the ST4995 MSM-associated isolates described in [6].