Journal
NATURE MICROBIOLOGY
Volume 5, Issue 3, Pages 455-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41564-019-0656-6
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Funding
- Bill & Melinda Gates Foundation [GCGH GCE OPP1151010]
- NIH-National Institute of Allergy and Infectious Diseases [R01 AI106786-05]
- Canadian Institutes of Health Research [MFE 152448]
- NSF (GRFP)
- David and Lucile Packard Foundation
- National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme [RP-PG-0514-20018]
- UK Antimicrobial Resistance Cross Council Initiative [MR/N013956/1]
- Rosetrees Trust [A749]
- Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Microbes in the Food Chain [BB/R012504/1, BBS/E/F/000PR10348, BBS/E/F/000PR10349]
- NIH [R01 AI46645]
- University of East Anglia
- Oxford Nanopore Technologies
- Research Computing Group at Harvard Medical School
- Research Computing Group at Harvard Faculty of Arts and Sciences
- BBSRC [BBS/E/F/000PR10348] Funding Source: UKRI
- MRC [MR/N013956/1] Funding Source: UKRI
- National Institutes of Health Research (NIHR) [RP-PG-0514-20018] Funding Source: National Institutes of Health Research (NIHR)
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Genomic neighbour typing can be used to infer the antimicrobial susceptibility and resistance of a bacterial sample based on the genomes of closest relatives. Combined with MinION sequencing, it can rapidly determine microbial resistance for clinical samples within 4 h. 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.
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