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

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 c...

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Bibliographic Details
Published in:Nature communications Vol. 6; no. 1; p. 10063
Main Authors: Bradley, Phelim, Gordon, N. Claire, Walker, Timothy M., Dunn, Laura, Heys, Simon, Huang, Bill, Earle, Sarah, Pankhurst, Louise J., Anson, Luke, de Cesare, Mariateresa, Piazza, Paolo, Votintseva, Antonina A., Golubchik, Tanya, Wilson, Daniel J., Wyllie, David H., Diel, Roland, Niemann, Stefan, Feuerriegel, Silke, Kohl, Thomas A., Ismail, Nazir, Omar, Shaheed V., Smith, E. Grace, Buck, David, McVean, Gil, Walker, A. Sarah, Peto, Tim E. A., Crook, Derrick W., Iqbal, Zamin
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 21.12.2015
Nature Publishing Group
Subjects:
631/114/2785
631/208/325/1506
631/208/514
631/326/41/1969/2038
Anti-Bacterial Agents - pharmacology
Antibiotic resistance
Antibiotics
Bacteria
Bacterial infections
Biomedical research
Correspondence
Drug resistance
Drug Resistance, Multiple, Bacterial
Genome, Bacterial
Genomes
Genotype & phenotype
Graph representations
Humanities and Social Sciences
Humans
Methods
Microbial Sensitivity Tests
multidisciplinary
Mutation
Mycobacterium tuberculosis - drug effects
Mycobacterium tuberculosis - genetics
Public health
Research centers
Science
Science (multidisciplinary)
Staphylococcal Infections - microbiology
Staphylococcus aureus - drug effects
Staphylococcus aureus - genetics
Surveillance
Tuberculosis
Tuberculosis - microbiology
United Kingdom > UK
South Africa
Germany
ISSN:2041-1723, 2041-1723
Online Access:Get full text
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Summary: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. The clinical application of new sequencing techniques is expected to accelerate pathogen identification. Here, Bradley et al . present a clinician-friendly software package that uses sequencing data for quick and accurate prediction of antibiotic resistance profiles for S. aureus and M. tuberculosis .
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ISSN:2041-1723
2041-1723
DOI:10.1038/ncomms10063