Fifteen years of a nationwide culture collection of Neisseria gonorrhoeae antimicrobial resistance in Portugal

Trends of Antimicrobial Susceptibility of Neisseria gonorrhoeae Isolates Between 2012 and 2023: Results From an Open Italian Cohort

BACKGROUND

Neisseria gonorrhoeae (Ng) is a public health priority because of the rapid evolution of antimicrobial resistance, the emergence of antibiotic resistance, and the absence of a vaccine against Ng. The aim of this study was to investigate trends in the minimum inhibitory concentration and resistance (R) or reduced susceptibility (DS) of Ng cases to ceftriaxone (CRO), azithromycin (AZM), tetracycline (TET), benzylpenicillin (PenG), and ciprofloxacin (CIP) during a 10-year period.

METHODS

We conducted a retrospective analysis on an open cohort of Ng cases diagnosed on rectal, urethral, and pharyngeal samples at San Raffaele Scientific Institute, between September 2012 and February 2023. Minimum inhibitory concentrations of antibiotics were determined by gradient-test strips. Bivariate linear regression models were applied on logarithmic minimum inhibitory concentrations values; Cochran-Armitage test was used to determine a linear trend in the proportions of resistant strains.

RESULTS

A total of 436 Ng isolates from 352 individuals were analyzed. Minimum inhibitory concentrations of CRO and PenG reduced over time ( P < 0.001, P = 0.030), AZM increased ( P = 0.001), and CIP and TET did not change ( P = 0.473, P = 0.272). The percentages of resistant strains were as follows: PenG, 89.9%; TET, 90.8%; CIP, 48.2%; AZM, and 4.4%. CRO-DS strains were 8.7%, and only 1 case of CRO-R was identified. The proportion of resistant strains increased over time for AZM ( P = 0.007), TET ( P = 0.001), and CIP ( P < 0.001), whereas it decreased for PenG ( P < 0.001) and CRO-DS/R strains ( P < 0.001).

CONCLUSIONS

Ng strains showed high susceptibility to CRO, although we identified cases of DS/R and observed high levels of susceptibility to AZM. Overall, the recommended primary regimen for Ng treatment was confirmed to be effective.

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

Neisseria gonorrhoeae is an urgent public health threat due to the emergence of antibiotic resistance. As most isolates in the United States are susceptible to at least one antibiotic, rapid molecular antimicrobial susceptibility tests (ASTs) would offer the opportunity to tailor antibiotic therapy, thereby expanding treatment options. With genome sequence and antibiotic resistance phenotype data for nearly 20,000 clinical N. gonorrhoeae isolates now available, there is an opportunity to use statistical methods to develop sequence-based diagnostics that predict antibiotic susceptibility from genotype. N. gonorrhoeae, therefore, provides a useful example illustrating how to apply machine learning models to aid in the design of sequence-based ASTs. We present an overview of this framework, which begins with establishing the assay technology, the performance criteria, the population in which the diagnostic will be used, and the clinical goals, and extends to the choices that must be made to arrive at a set of features with the desired properties for predicting susceptibility phenotype from genotype. While we focus on the example of N. gonorrhoeae, the framework generalizes to other organisms for which large-scale genotype and antibiotic resistance data can be combined to aid in diagnostics development.

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

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.