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.
Highlights
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
The marked increase in antibiotic use in health care and agriculture since the 1940s has driven a rise in frequency of drug-resistant bacterial strains, which present a global threat to public health
Microbial genome sequencing has the potential to substantially increase the speed of antibiotic resistance detection for many pathogens[2] and in addition provides valuable information on relatedness that could contribute to surveillance
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. The key biological constraint is the extent of our understanding of the genotype-to-phenotype correspondence—that is, the genotype needs to be sufficiently predictive of resistance This correspondence is high for many bacterium/drug combinations. We set out to develop methods applicable to standard clinical samples, and solve the multiple computational challenges that act as barrier to routine and rapid deployment of such a system in clinical practice These challenges include the need to determine species and predict resistance, and developing a framework extensible to many species, and ensuring accessibility of the tool to a user base who may be unskilled in bioinformatics. There may be a species associated with high mortality (for example, S. aureus) that can occur in samples mixed with other species (for example, coagulase-negative staphylococci (CoNS), which are common contaminants of blood cultures, being present on the skin through which the blood was taken), and that may even share the same resistance genes and confound inference
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