Genomic Analysis Reveals Antibiotic-Susceptible Clones and Emerging Resistance in Neisseria gonorrhoeae in Saskatchewan, Canada

Whole-Genome Sequencing to Predict Antimicrobial Susceptibility Profiles in Neisseria gonorrhoeae

BACKGROUND

Neisseria gonorrhoeae is a major public health problem due to increasing incidence and antimicrobial resistance. Genetic markers of reduced susceptibility have been identified; the extent to which those are representative of global antimicrobial resistance is unknown. We evaluated the performance of whole-genome sequencing (WGS) used to predict susceptibility to ciprofloxacin and other antimicrobials using a global collection of N. gonorrhoeae isolates.

METHODS

Susceptibility testing of common antimicrobials and the recently developed zolifodacin was performed using agar dilution to determine minimum inhibitory concentrations (MICs). We identified resistance alleles at loci known to contribute to antimicrobial resistance in N. gonorrhoeae from WGS data. We tested the ability of each locus to predict antimicrobial susceptibility.

RESULTS

A total of 481 N. gonorrhoeae isolates, collected between 2004 and 2019 and making up 457 unique genomes, were sourced from 5 countries. All isolates with demonstrated susceptibility to ciprofloxacin (MIC ≤0.06 μg/mL) had a wild-type gyrA codon 91. Multilocus approaches were needed to predict susceptibility to other antimicrobials. All isolates were susceptible to zoliflodacin, defined by an MIC ≤0.25 μg/mL.

CONCLUSIONS

Single marker prediction can be used to inform ciprofloxacin treatment of N. gonorrhoeae infection. A combination of molecular markers may be needed to determine susceptibility for other antimicrobials.

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.

Molecular characterization of Neisseria gonorrhoeae isolates collected through a national surveillance programme in Japan, 2013: evidence of the emergence of a ceftriaxone-resistant strain from a ceftriaxone-susceptible lineage

OBJECTIVES

To investigate the spread of ceftriaxone-resistant Neisseria gonorrhoeae lineages similar to strains H041 (2009) and FC428 (2015), we characterized 55 strains collected in 2013 from hospitals across Japan.

METHODS

Susceptibility testing and whole-genome sequencing.

RESULTS

Susceptibility rates were 58% for cefixime and 98% for ceftriaxone. The 55 strains were whole-genome sequenced and classified into nine MLST-STs. MLST-ST1901 was the most prevalent (n = 19) followed by MLST-ST7363 (n = 12) and MLST-ST7359 (n = 11). The most prevalent penA [encoding penicillin binding protein 2 (PBP2)] mosaic types, based on the N. gonorrhoeae sequence typing for antimicrobial resistance (NG-STAR) scheme, were 10.001 (n = 20) followed by 34.001 (n = 13). The H041 and FC428 strains were not detected; however, a single ceftriaxone-resistant strain (TUM15748) with a MIC of 0.5 mg/L ceftriaxone was identified. The TUM15748 strain belonged to MLST-ST7359 and N. gonorrhoeae multiantigen sequence typing-ST6771, and had a novel PBP2 (PBP2TUM15748, penA type 169.001). The amino acid sequence of PBP2TUM15748 showed partial similarity to that of PBP2 from N. gonorrhoeae GU140106 and commensal Neisseria perflava and Neisseria cinerea. Natural transformation and recombination experiments using full-length TUM15748 penA showed that the ceftriaxone MICs of transformants increased 16-fold or more compared with the parental ceftriaxone-susceptible recipient strain (NG9807, belonging to MLST-ST7363). No ceftriaxone-resistant MLST-ST7359 strains have previously been reported.

CONCLUSIONS

We showed here that a ceftriaxone-susceptible lineage acquired a mutant PBP2 mosaic type, integrating partial PBP2 sequences from commensal Neisseria species, resulting in the emergence of ceftriaxone-resistant strains.

Bioinformatics tools used for whole-genome sequencing analysis of Neisseria gonorrhoeae: a literature review

Whole-genome sequencing (WGS) data are well established for the investigation of gonococcal transmission, antimicrobial resistance prediction, population structure determination and population dynamics. A variety of bioinformatics tools, repositories, services and platforms have been applied to manage and analyze Neisseria gonorrhoeae WGS datasets. This review provides an overview of the various bioinformatics approaches and resources used in 105 published studies (as of 30 April 2021). The challenges in the analysis of N. gonorrhoeae WGS datasets, as well as future bioinformatics requirements, are also discussed.