Presented at London Calling 2016

Included in antibiotic_resistance, tuberculosis themes

Research area: genomics

Rapid antibiotic resistance predictions from genome sequence data for S. aureus and M. tuberculosis.

Created on 26th April 2015

Phelim Bradley; N Claire Gordon; Timothy M Walker; Laura Dunn; Simon Heys; Bill Huang; Sarah Earle; Louise J Pankhurst; Luke Anson; Mariateresa de Cesare; 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 Peto; Derrick Crook; Zamin Iqbal;

Rapid and accurate detection of antibiotic resistance in pathogens is an urgent need, affecting both patient care and population-scale control. Microbial genome sequencing promises much, but many barriers exist to its routine deployment. Here, we address these challenges, using a de Bruijn graph comparison of clinical isolate and curated knowledge-base to identify species and predict resistance profile, including minor populations. This is implemented in a package, Mykrobe predictor, for S. aureus and M. tuberculosis, running in under three minutes on a laptop from raw data. For S. aureus, we train and validate in 495/471 samples respectively, finding error rates comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.3%/99.5% across 12 drugs. For M. tuberculosis, we identify species and predict resistance with specificity of 98.5% (training/validating on 1920/1609 samples). Sensitivity of 82.6% is limited by current understanding of genetic mechanisms. We also show that analysis of minor populations increases power to detect phenotypic resistance in second-line drugs without appreciable loss of specificity. Finally, we demonstrate feasibility of an emerging single-molecule sequencing technique.

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