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

Publication

Date: 15th February 2016 | Source: Nature Communications

Authors: 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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