Equations To Predict Antimicrobial MICs in Neisseria gonorrhoeae Using Molecular Antimicrobial Resistance Determinants

Abstract

The emergence of Neisseria gonorrhoeae strains that are resistant to azithromycin and extended-spectrum cephalosporins represents a public health threat, that of untreatable gonorrhea infections. Multivariate regression modeling was used to determine the contributions of molecular antimicrobial resistance determinants to the overall antimicrobial MICs for ceftriaxone, cefixime, azithromycin, tetracycline, ciprofloxacin, and penicillin. A training data set consisting of 1,280 N. gonorrhoeae strains was used to generate regression equations which were then applied to validation data sets of Canadian (n = 1,095) and international (n = 431) strains. The predicted MICs for extended-spectrum cephalosporins (ceftriaxone and cefixime) were fully explained by 5 amino acid substitutions in PenA, A311V, A501P/T/V, N513Y, A517G, and G543S; the presence of a disrupted mtrR promoter; and the PorB G120 and PonA L421P mutations. The correlation of predicted MICs within one doubling dilution to phenotypically determined MICs of the Canadian validation data set was 95.0% for ceftriaxone, 95.6% for cefixime, 91.4% for azithromycin, 98.2% for tetracycline, 90.4% for ciprofloxacin, and 92.3% for penicillin, with an overall sensitivity of 99.9% and specificity of 97.1%. The correlations of predicted MIC values to the phenotypically determined MICs were similar to those from phenotype MIC-only comparison studies. The ability to acquire detailed antimicrobial resistance information directly from molecular data will facilitate the transition to whole-genome sequencing analysis from phenotypic testing and can fill the surveillance gap in an era of increased reliance on nucleic acid assay testing (NAAT) diagnostics to better monitor the dynamics of N. gonorrhoeae.

Adaptation to the cervical environment is associated with increased antibiotic susceptibility in Neisseria gonorrhoeae

“…1, Supplementary Table 1). We modeled log-transformed MICs using multiple regression on a panel of experimentally characterized resistance markers for the three most clinically relevant antibiotics 5,11,12 (Supplementary Data 1). This enabled us to make quantitative predictions of MIC based on known genotypic markers and to assess how well these markers predicted true MIC values.…”

“…MICs were log 2transformed for use as continuous outcome variables in linear modeling and GWAS. We modeled transformed MICs using a panel of known resistance markers 11,12 and included the recently characterized mosaic mtrCDE alleles 5 and rplD G70D substitution 78 To visualize the continuous MICs using thresholds as on Fig. 1, we binarized MICs using the CLSI resistance breakpoint for ciprofloxacin, the CLSI nonsusceptibility breakpoint for azithromycin, and the CDC GISP surveillance breakpoint for ceftriaxone.…”

Molecular characterization of a ceftriaxone-resistant Neisseria gonorrhoeae strain found in Switzerland

“…Penicillin and extended-spectrum cephalosporin resistance has been associated with mutations and recombination within the penA, porinB, and ponA genes and the presence of bla and involves a complex interaction [12].…”

Molecular Typing of Neisseria gonorrhoeae Clinical Isolates in Russia, 2018–2019: A Link Between penA Alleles and NG-MAST Types

“…Linear regression analysis was used to study the influence of penA allele type and other molecular determinants of resistance on the MIC cro and MIC pen values in detail. The application of this method to predict the MIC values of various antimicrobial drugs against N. gonorrhoeae has been described previously [ 30 , 31 , 32 ]. The main determinants affecting MIC cro have been shown to be five amino acid substitutions in PBP2 Ala311Val, Ala501Pro/Thr/Val, Asn512Tyr, Ala516Gly, and Gly542Ser (numeration of amino acids Asn513Tyr, Ala517Gly, and Gly543Ser is given in the paper), mutation of the mtrR promoter, substitutions of the Gly120 residue in the PorB protein, and the Leu421Pro substitution in PBP1 [ 32 ].…”

“…The application of this method to predict the MIC values of various antimicrobial drugs against N. gonorrhoeae has been described previously [ 30 , 31 , 32 ]. The main determinants affecting MIC cro have been shown to be five amino acid substitutions in PBP2 Ala311Val, Ala501Pro/Thr/Val, Asn512Tyr, Ala516Gly, and Gly542Ser (numeration of amino acids Asn513Tyr, Ala517Gly, and Gly543Ser is given in the paper), mutation of the mtrR promoter, substitutions of the Gly120 residue in the PorB protein, and the Leu421Pro substitution in PBP1 [ 32 ]. Analysis of 182 Russian isolates showed that the presence of the mosaic type XXXIV penA allele and the Gly120Lys substitution in the PorB protein made the greatest contributions to the increase in the MIC cro .…”