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

doi:10.1038/s41564-019-0656-6

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

Department of Life Sciences

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67 Citations (SciVal)

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 language	English
Pages (from-to)	455-464
Number of pages	10
Journal	Nature Microbiology
Volume	5
Issue number	3
Early online date	10 Feb 2020
DOIs	https://doi.org/10.1038/s41564-019-0656-6
Publication status	Published - 31 Mar 2020

Funding

This work was supported by the Bill & Melinda Gates Foundation (GCGH GCE OPP1151010 to K.B. and W.P.H.), the NIH—National Institute of Allergy and Infectious Diseases (R01 AI106786-05 to K.B.), the Canadian Institutes of Health Research (MFE 152448, a fellowship grant, to R.S.L. and D.R.M.), the NSF (GRFP to K.C.M.), and the David and Lucile Packard Foundation (to M.B.). This paper presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (reference no. RP-PG-0514-20018 to J.O.), the UK Antimicrobial Resistance Cross Council Initiative (MR/N013956/1 to J.O.), the Rosetrees Trust (A749 to J.O.), the Biotechnology and Biological Sciences Research Council (BBSRC) Institute Strategic Programme Microbes in the Food Chain (BB/R012504/1 and its constituent projects BBS/E/F/000PR10348 and BBS/E/ F/000PR10349 to J.O.), the University of East Anglia (to J.O. and T.C.), and Oxford Nanopore Technologies (to J.O. and T.C.). Portions of this research were conducted on the O2 and Odyssey high-performance compute clusters, supported by the Research Computing Groups at Harvard Medical School and at the Harvard Faculty of Arts and Sciences, respectively. We thank J. Metlay for providing the test isolates for experiments SP03–SP06, which were collected as part of a population-wide surveillance study done in the Philadelphia region, supported by the NIH (R01 AI46645), and to B. J. Arnold, T. Azarian and C. M. Herren for useful comments during various stages of this project. This paper is dedicated to the memory of Nicholas Greenfield. J.O. received financial support for attending ONT and other conferences and a honorarium for speaking at ONT headquarters. J.O. received funding and consumable support from ONT for T.C.’s PhD studentship. T.C. received financial support from ONT for attending an international conference. All other authors have no competing interests.

ASJC Scopus subject areas

Microbiology
Immunology
Applied Microbiology and Biotechnology
Genetics
Microbiology (medical)
Cell Biology

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Cite this

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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 {\textquoteleft}genomic neighbour typing{\textquoteright} 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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AU - Grad, Yonatan H.

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AU - Baym, Michael

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Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing

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Lauren Cowley

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Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing

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Lauren Cowley

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