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.
Bibliographical noteFunding Information:
We would like to thank Michel Doumith and Angela Kearns for helpful discussions, Sebastian Gagneux and Andreas Steiner for help running KvarQ and Dag Harmsen for help running SeqSphere. We are also grateful to the MinION Access Program from Oxford Nanopore Technologies, which enabled us to trial their new sequencer. We acknowledge funding from UK Clinical Research Collaboration (Wellcome Trust (grant 087646/Z/08/Z), Medical Research Council, National Institute for Health Research (NIHR grant G0800778)), NIHR Oxford Biomedical Research Centre, NIHR Oxford Health Protection Research Unit on Healthcare Associated Infection and Anti-microbial Resistance, EU FP7 Patho-Ngen-Trace (FP7-278864-2) and Wellcome Trust Core Award Grant Number 090532/Z/09/Z. Z.I. and D.J.W. were funded by two Wellcome Trust/Royal Society Sir Henry Dale Fellowships (grants 102541/Z/13/Z and 101237/Z/13/Z, respectively). P.B. was funded by a Wellcome Trust PhD studentship, and S.E. was funded by an MRC funded prize studentship to the Nuffield Department of Medicine, University of Oxford. D.W.C. and T.E.A.P. acknowledge NIHR funding their Senior Investigators awards. G.M. was funded by grant 100956/Z/13/Z from the Wellcome Trust.
1Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK. 2Nuffield Department of Medicine, University of Oxford, Oxford OX1 1NF, UK. 3Institute for Epidemiology, University Medical Hospital Schleswig-Holstein, Niemannsweg 11, 24105 Kiel, Germany. 4Molecular and Experimental Mycobacteriology, Research Centre Borstel, Parkallee 1, 23845 Borstel, Germany. 5German Centre for Infection Research, Partner Site Borstel, Parkallee 1, 23845 Borstel, Germany. 6Centre for Tuberculosis, National Institute for Communicable Diseases, Private Bag X4 Sandringham, Johannesburg 2131, South Africa. 7Department of Medical Microbiology, University of Pretoria, PO Box 667, Pretoria 0001, South Africa. 8Regional Centre for Mycobacteriology, PHE Public Health Laboratory Birmingham. Heartlands Hospital, Bordesley Green East, Birmingham B9 5SS, UK. 9Biomedical Research Centre, NIHR (National Institutes of Health Research) Oxford Biomedical Research Centre, Oxford OX3 7LE, UK. 10National Infection Service, Public Health England, Wellington House, 133-155 Waterloo Road, London SE1 8UG, UK. Correspondence and requests for materials should be addressed to Z.I. (email: firstname.lastname@example.org).