Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.

Phelim Bradley ORCID logo; N Claire Gordon; Timothy M Walker; Laura Dunn; Simon Heys; Bill Huang; Sarah Earle; Louise J Pankhurst; Luke Anson; Mariateresa de Cesare; +18 more... Paolo Piazza; Antonina A Votintseva; Tanya Golubchik; Daniel J Wilson; David H Wyllie; Roland Diel; Stefan Niemann; Silke Feuerriegel; Thomas A Kohl; Nazir Ismail; Shaheed V Omar; E Grace Smith; David Buck; Gil McVean; A Sarah Walker; Tim EA Peto; Derrick W Crook; Zamin Iqbal ORCID logo; (2015) Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis. Nature communications, 6 (1). 10063-. ISSN 2041-1723 DOI: 10.1038/ncomms10063
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The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package ('Mykrobe predictor') that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S. aureus, the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n=470). For M. tuberculosis, our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n=1,609); sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.


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