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

Bradley, P., Gordon, N. C., Walker, T. M., Dunn, L., Heys, S., Huang, B., Earle, S. G., Pankhurst, L. J., Anson, L., de Cesare, M., Piazza, P., Votintseva, A. A., Golubchik, T., Wilson, D. J., Wyllie, D., Diel, R., Niemann, S., Feuerriegel, S., Kohl, T. A., Ismail, N., Omar, S. V., Smith, E. G., Buck, D., McVean, G., Walker, A. S., Peto, T. E. A., Crook, D. W. and Z. Iqbal (2015)
Nature Communications 6: 10063 (pdf).

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 sample and curated knowledge-base, for detection of species and minor resistant populations, and resistance-profile prediction. This is implemented in a package, Mykrobe predictor, for S. aureus and M. tuberculosis, and runs 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. Finally, we demonstrate feasibility of an emerging single-molecule sequencing technique.