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