WGS to predict antibiotic MICs for Neisseria gonorrhoeae

David W. Eyre*, Dilrini De Silva, Kevin Cole, Joanna Peters, Michelle Cole, Yonatan H. Grad, Walter Demczuk, Irene Martin, Michael R. Mulvey, Derrick W. Crook, A. Sarah Walker, Tim E.A. Peto, John Paul

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

83 Citations (Scopus)


Background: Tracking the spread of antimicrobial-resistant Neisseria gonorrhoeae is a major priority for national surveillance programmes. Objectives: We investigate whetherWGS and simultaneous analysis ofmultiple 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 crossvalidation. Results: Overall 1785/3380 (53%) MIC values were predicted to the nearest doubling dilution and 3147 (93%) within ±1 doubling dilution and 3314 (98%) within ±2 doubling dilutions. MIC prediction performance was similar across the five antimicrobials tested. Prediction models included the majority of previously reported resistance determinants. Applying EUCAST breakpoints to MIC predictions, the overall very major error (VME; phenotypically resistant, WGS-prediction susceptible) rate was 21/1577 (1.3%, 95% CI 0.8%-2.0%) and the major error (ME; phenotypically susceptible, WGS-prediction resistant) rate was 20/1186 (1.7%, 1.0%-2.6%). VME rates met regulatory thresholds for all antimicrobials except cefixime and ME rates for all antimicrobials except tetracycline. Country of testing was a strongly significant predictor of MIC for all five antimicrobials. Conclusions: We demonstrate a WGS-based MIC prediction approach that allows reliable MIC prediction for five gonorrhoea antimicrobials. Our approach should allow reasonably precise prediction of MICs for a range of bacterial species.

Original languageEnglish
Article numberdkx067
Pages (from-to)1937-1947
Number of pages11
JournalJournal of Antimicrobial Chemotherapy
Issue number7
Publication statusPublished - 1 Jul 2017

Bibliographical note

Funding Information:
We acknowledge Gwenda Hughes and Cathy Ison (from PHE) for their assistance. The research was funded by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford in partnership with Public Health England (PHE) (HPRU-2012-10041), the NIHR Oxford Biomedical Research Centre and the Health Innovation Challenge Fund [a parallel funding partnership between the Wellcome Trust (grant WT098615/Z/12/Z) and the Department of Health (grants WT098615 and HICF-T5-358)]. D. W. E. is an NIHR clinical lecturer. D. W. C. and T. E. A. P. are NIHR senior investigators.

Publisher Copyright:
© The Author 2017. Published by Oxford University Press on behalf of the British Society for Antimicrobial Chemotherapy. All rights reserved.


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