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

By Phelim Bradley, N. C. Gordon, T. Walker, L. Dunn, S. Heys, Bill Huang, S. Earle, L. Pankhurst, Luke W. Anson, M. de Cesare and 18 more. Nature Communications, 2015

509 citations recorded by the source corpus.

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.

Subfields: Clinical Biochemistry, Epidemiology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/4495d423a3a1ccfccc56848a58323dbd4a913836

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Citations in the corpus

Multidrug evolutionary strategies to reverse antibiotic resistance (2016, unknown): “Genomic analysis can potentially detect both the resistance profile of the bug and the specific resistance genes involved (89), allowing more targeted use of approaches that inhibit or select against the specific resistance mechanism.”
Oxford Nanopore MinION Sequencing and Genome Assembly (2016, background): “…also been reported, including prediction of antibiotic resistance by sequencing the genomes of Staphylococcus aureus and Mycobacterium tuberculosis [62]; real-time strain typing and analysis of antibiotic resistance [63]; and aneuploidy detection in prenatal and miscarriage samples with sequencing…”
ARIBA: rapid antimicrobial resistance genotyping directly from sequencing reads (2017, background): “Mykrobe predictor [4] is an extremely fast tool that matches k-mers in reads to a reference graph, and although it can identify variants, it is currently limited to Staphylococcus aureus and Mycobacterium tuberculosis, and it is not possible for users to”