TY - JOUR
T1 - Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing
AU - Břinda, Karel
AU - Callendrello, Alanna
AU - Ma, Kevin C.
AU - MacFadden, Derek R.
AU - Charalampous, Themoula
AU - Lee, Robyn S.
AU - Cowley, Lauren
AU - Wadsworth, Crista B.
AU - Grad, Yonatan H.
AU - Kucherov, Gregory
AU - O’Grady, Justin
AU - Baym, Michael
AU - Hanage, William P.
PY - 2020/3/31
Y1 - 2020/3/31
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85079454338&partnerID=8YFLogxK
U2 - 10.1038/s41564-019-0656-6
DO - 10.1038/s41564-019-0656-6
M3 - Article
C2 - 32042129
AN - SCOPUS:85079454338
VL - 5
SP - 455
EP - 464
JO - Nature Microbiology
JF - Nature Microbiology
SN - 2058-5276
IS - 3
ER -