2-AIN-506 a 2-AIN-252: Seminár z bioinformatiky (2) a (4)
Leto 2021

Karel Brinda, 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. Rapid inference of antibiotic resistance and susceptibility by genomic neighbour typing. Nat Microbiol, 5(3):455-464. 2020.

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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.