Abstract
AbstractSurveillance 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.
Funder
U.S. Department of Health & Human Services | NIH | National Institute of Allergy and Infectious Diseases
Bill & Melinda Gates Foundation
NSF
Gouvernement du Canada | Canadian Institutes of Health Research
DH | National Institute for Health Research
David and Lucile Packard Foundation
Bill and Melinda Gates Foundation
Publisher
Springer Science and Business Media LLC
Subject
Cell Biology,Microbiology (medical),Genetics,Applied Microbiology and Biotechnology,Immunology,Microbiology
Cited by
81 articles.
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