Emergence of a cephalosporin reduced susceptible Neisseria gonorrhoeae clone between 2014-2019 in Amsterdam, the Netherlands, revealed by a genomic population analysis

Abstract

Background Emerging resistance to cephalosporins in Neisseria gonorrhoeae (Ng) is a major public health threat, since these are considered antibiotics of last resort. Continuous surveillance is needed to monitor the circulation of reduced susceptible and resistant strains. Aim For the purpose of epidemiological surveillance, a genomic population analysis was performed on Ng isolates from Amsterdam with a focus on ceftriaxone reduced susceptible isolates. Methods Whole genome sequences were obtained from 318 isolates from 2014-2019 from Amsterdam, the Netherlands. Isolates were typed according to the Multi-Locus Sequence Typing (MLST) scheme and resistance markers were identified. Phylogenetic trees were created to identify genetic clusters and to compare Dutch- and global MLST7827 isolates. Results The majority of ceftriaxone reduced susceptible isolates belonged to MLST7363, MLST1901 and MLST7827. MLST7363 and MLST1901 were dominant reduced susceptible strains from 2014-2016 although MLST7827 emerged and became dominant during 2017-2019. MLST7827 reduced susceptible isolates carried a non-mosaic penA gene with A501V mutation and porB G121K/A121D mutations, which were lacking in susceptible MLST7827 isolates. Phylogenetic analysis of all publicly available MLST7827 isolates showed strong genetic clustering of Dutch- and other European MLST7827 isolates. Conclusion Ceftriaxone reduced susceptible MLST7827 isolates emerged during recent years in Amsterdam. Co-occurrence of penA A501V and porB G121K/A121D mutations was strongly associated with ceftriaxone reduced susceptibility. Genetic clustering of Dutch- and other European MLST7827 isolates indicates extensive circulation of this strain in Europe. Therefore, close monitoring of the spread of this strain with an alarming susceptibility profile is needed.

Machine learning models for Neisseria gonorrhoeae antimicrobial susceptibility tests

“…Machine learning models that predict resistance phenotype from genotype rely on datasets that provide both. Regional gonococcal surveillance programs (such as GISP, Euro GASP, WHO‐GASP, and ASGAP) and smaller epidemiological studies have reported genome sequences and AMR profiles from over 18,000 clinical isolates of N. gonorrhoeae ; 16,17,23,49,52–55,57–70,101,102 many of these have been curated and aggregated in databases, such as Pathogenwatch 66 . But the sampling strategies differ among surveillance programs, as can the quality of the sequencing or phenotypic data.…”

“…Much work has been done to determine the genetic modulators of AMR in N. gonorrhoeae , 34–48 with a focus on the antibiotics for which antimicrobial susceptibility test (AST) phenotypes are available—thus, most data are for antibiotics in recent use (ceftriaxone, cefixime, azithromycin, and ciprofloxacin) as well as others for which surveillance programs often collect data (penicillin and tetracycline) 16,17,23,25,49–72 . Most of the loci that account for a large extent of resistance in circulating populations in areas of high surveillance have been well characterized, 66,73 with several recent additions to the list of genetic modulators being described, 18,74–77 including some of increasing prevalence, such as the mosaic mtr 78,79 (where mosaicism refers to interspecies recombination between N. gonorrhoeae and another Neisseria species).…”