Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium 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 E. A. Peto, Derrick W. Crook and Zamin Iqbal ()
Additional contact information
Phelim Bradley: Wellcome Trust Centre for Human Genetics, University of Oxford
N. Claire Gordon: University of Oxford
Timothy M. Walker: University of Oxford
Laura Dunn: University of Oxford
Simon Heys: Wellcome Trust Centre for Human Genetics, University of Oxford
Bill Huang: Wellcome Trust Centre for Human Genetics, University of Oxford
Sarah Earle: University of Oxford
Louise J. Pankhurst: University of Oxford
Luke Anson: University of Oxford
Mariateresa de Cesare: Wellcome Trust Centre for Human Genetics, University of Oxford
Paolo Piazza: Wellcome Trust Centre for Human Genetics, University of Oxford
Antonina A. Votintseva: University of Oxford
Tanya Golubchik: University of Oxford
Daniel J. Wilson: Wellcome Trust Centre for Human Genetics, University of Oxford
David H. Wyllie: University of Oxford
Roland Diel: Institute for Epidemiology, University Medical Hospital Schleswig-Holstein
Stefan Niemann: Molecular and Experimental Mycobacteriology, Research Centre Borstel
Silke Feuerriegel: Molecular and Experimental Mycobacteriology, Research Centre Borstel
Thomas A. Kohl: Molecular and Experimental Mycobacteriology, Research Centre Borstel
Nazir Ismail: Centre for Tuberculosis, National Institute for Communicable Diseases, Private Bag X4 Sandringham
Shaheed V. Omar: Centre for Tuberculosis, National Institute for Communicable Diseases, Private Bag X4 Sandringham
E. Grace Smith: Regional Centre for Mycobacteriology, PHE Public Health Laboratory Birmingham. Heartlands Hospital, Bordesley Green East
David Buck: Wellcome Trust Centre for Human Genetics, University of Oxford
Gil McVean: Wellcome Trust Centre for Human Genetics, University of Oxford
A. Sarah Walker: University of Oxford
Tim E. A. Peto: University of Oxford
Derrick W. Crook: University of Oxford
Zamin Iqbal: Wellcome Trust Centre for Human Genetics, University of Oxford

Nature Communications, 2015, vol. 6, issue 1, 1-15

Abstract: Abstract 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.

Date: 2015
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DOI: 10.1038/ncomms10063

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