WGS to predict antibiotic MICs for Neisseria gonorrhoeae.

ABSTRACT:

Background

Tracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes.

Objectives

We investigate whether WGS and simultaneous analysis of multiple resistance determinants can be used to predict antimicrobial susceptibilities to the level of MICs in N. gonorrhoeae.

Methods

WGS was used to identify previously reported potential resistance determinants in 681 N. gonorrhoeae isolates, from England, the USA and Canada, with phenotypes for cefixime, penicillin, azithromycin, ciprofloxacin and tetracycline determined as part of national surveillance programmes. Multivariate linear regression models were used to identify genetic predictors of MIC. Model performance was assessed using leave-one-out cross-valida

SUBMITTER: Eyre DW 

PROVIDER: S-EPMC5890716 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature