Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing

Karel Břinda, Alanna Callendrello, Kevin C. Ma, Derek R. MacFadden, Themoula Charalampous, Robyn S. Lee, Lauren Cowley, Crista B. Wadsworth, Yonatan H. Grad, Gregory Kucherov, Justin O’Grady, Michael Baym, William P. Hanage

    Research output: Contribution to journalArticlepeer-review

    37 Citations (Scopus)

    Abstract

    Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called ‘genomic neighbour typing’ for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

    Original languageEnglish
    Pages (from-to)455-464
    Number of pages10
    JournalNature Microbiology
    Volume5
    Issue number3
    DOIs
    Publication statusPublished - 1 Mar 2020

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