The global distribution and spread of the mobilized colistin resistance gene mcr-1.

Colistin represents one of the few available drugs for treating infections caused by carbapenem-resistant Enterobacteriaceae. As such, the recent plasmid-mediated spread of the colistin resistance gene mcr-1 poses a significant public health threat, requiring global monitoring and surveillance. Here, we characterize the global distribution of mcr-1 using a data set of 457 mcr-1-positive sequenced isolates. We find mcr-1 in various plasmid types but identify an immediate background common to all mcr-1 sequences. Our analyses establish that all mcr-1 elements in circulation descend from the same initial mobilization of mcr-1 by an ISApl1 transposon in the mid 2000s (2002-2008; 95% highest posterior density), followed by a marked demographic expansion, which led to its current global distribution. Our results provide the first systematic phylogenetic analysis of the origin and spread of mcr-1, and emphasize the importance of understanding the movement of antibiotic resistance genes across multiple levels of genomic organization

Same-day diagnostic and surveillance data for tuberculosis via whole genome sequencing of direct respiratory samples

Routine full characterization of Mycobacterium tuberculosis (TB) is culture-based, taking many weeks. Whole-genome sequencing (WGS) can generate antibiotic susceptibility profiles to inform treatment, augmented with strain information for global surveillance; such data could be transformative if provided at or near point of care. We demonstrate a low-cost DNA extraction method for TB WGS direct from patient samples. We initially evaluated the method using the Illumina MiSeq sequencer (40 smear-positive respiratory samples, obtained after routine clinical testing, and 27 matched liquid cultures). M. tuberculosis was identified in all 39 samples from which DNA was successfully extracted. Sufficient data for antibiotic susceptibility prediction was obtained from 24 (62%) samples; all results were concordant with reference laboratory phenotypes. Phylogenetic placement was concordant between direct and cultured samples. Using an 70 Illumina MiSeq/MiniSeq the workflow from patient sample to results can be completed in 44/16 hours at a reagent cost of £96/£198 per sample. We then employed a non-specific PCR-based library preparation method for sequencing on an Oxford Nanopore Technologies MinION sequencer. We applied this to cultured Mycobacterium bovis BCG strain (BCG), and to combined culture negative sputum DNA and BCG DNA. For flowcell version R9.4, the estimated turnaround time from patient to identification of BCG, detection of pyrazinamide resistance, and phylogenetic placement was 7.5 hours, with full susceptibility results 5 hours later. Antibiotic susceptibility predictions were fully concordant. A critical advantage of the MinION is the ability to continue sequencing until sufficient coverage is obtained, providing a potential solution to the problem of variable amounts of M. tuberculosis in direct samples

Accuracy of Different Bioinformatics Methods in Detecting Antibiotic Resistance and Virulence Factors from Staphylococcus aureus Whole-Genome Sequences.

In principle, whole-genome sequencing (WGS) can predict phenotypic resistance directly from a genotype, replacing laboratory-based tests. However, the contribution of different bioinformatics methods to genotype-phenotype discrepancies has not been systematically explored to date. We compared three WGS-based bioinformatics methods (Genefinder [read based], Mykrobe [de Bruijn graph based], and Typewriter [BLAST based]) for predicting the presence/absence of 83 different resistance determinants and virulence genes and overall antimicrobial susceptibility in 1,379 Staphylococcus aureus isolates previously characterized by standard laboratory methods (disc diffusion, broth and/or agar dilution, and PCR). In total, 99.5% (113,830/114,457) of individual resistance-determinant/virulence gene predictions were identical between all three methods, with only 627 (0.5%) discordant predictions, demonstrating high overall agreement (Fleiss' kappa = 0.98, P < 0.0001). Discrepancies when identified were in only one of the three methods for all genes except the cassette recombinase, ccrC(b). The genotypic antimicrobial susceptibility prediction matched the laboratory phenotype in 98.3% (14,224/14,464) of cases (2,720 [18.8%] resistant, 11,504 [79.5%] susceptible). There was greater disagreement between the laboratory phenotypes and the combined genotypic predictions (97 [0.7%] phenotypically susceptible, but all bioinformatic methods reported resistance; 89 [0.6%] phenotypically resistant, but all bioinformatics methods reported susceptible) than within the three bioinformatics methods (54 [0.4%] cases, 16 phenotypically resistant, 38 phenotypically susceptible). However, in 36/54 (67%) cases, the consensus genotype matched the laboratory phenotype. In this study, the choice between these three specific bioinformatic methods to identify resistance determinants or other genes in S. aureus did not prove critical, with all demonstrating high concordance with each other and phenotypic/molecular methods. However, each has some limitations; therefore, consensus methods provide some assurance.This research was supported by the National Institute for Health Research Health Protection Research Unit (NIHR HPRU) in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with Public Health England ([PHE] grant HPRU-2012-10041) and the NIHR Oxford Biomedical Research Centre; D.C. and T.P. are NIHR senior investigators

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 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

Whole-genome sequencing for prediction of Mycobacterium tuberculosis drug susceptibility and resistance : a retrospective cohort study

BACKGROUND : Diagnosing drug-resistance remains an obstacle to the elimination of tuberculosis. Phenotypic drugsusceptibility testing is slow and expensive, and commercial genotypic assays screen only common resistancedetermining mutations. We used whole-genome sequencing to characterise common and rare mutations predicting drug resistance, or consistency with susceptibility, for all fi rst-line and second-line drugs for tuberculosis. METHODS : Between Sept 1, 2010, and Dec 1, 2013, we sequenced a training set of 2099 Mycobacterium tuberculosis genomes. For 23 candidate genes identifi ed from the drug-resistance scientifi c literature, we algorithmically characterised genetic mutations as not conferring resistance (benign), resistance determinants, or uncharacterised. We then assessed the ability of these characterisations to predict phenotypic drug-susceptibility testing for an independent validation set of 1552 genomes. We sought mutations under similar selection pressure to those characterised as resistance determinants outside candidate genes to account for residual phenotypic resistance. FINDINGS : We characterised 120 training-set mutations as resistance determining, and 772 as benign. With these mutations, we could predict 89·2% of the validation-set phenotypes with a mean 92·3% sensitivity (95% CI 90·7–93·7) and 98·4% specifi city (98·1–98·7). 10·8% of validation-set phenotypes could not be predicted because uncharacterised mutations were present. With an in-silico comparison, characterised resistance determinants had higher sensitivity than the mutations from three line-probe assays (85·1% vs 81·6%). No additional resistance determinants were identifi ed among mutations under selection pressure in non-candidate genes. INTERPRETATION : A broad catalogue of genetic mutations enable data from whole-genome sequencing to be used clinically to predict drug resistance, drug susceptibility, or to identify drug phenotypes that cannot yet be genetically predicted. This approach could be integrated into routine diagnostic workfl ows, phasing out phenotypic drugsusceptibility testing while reporting drug resistance early.Wellcome Trust, National Institute of Health Research, Medical Research Council, and the European Union.http://www.thelancet.com/infectionhb201

Efficient analysis of microbial whole-genome sequence data using de Bruijn graphs

Antimicrobial resistance (AMR) is a persistent and growing threat to global health. Whole genome sequencing (WGS) has the potential to dramatically improve our ability to detect, understand, and monitor AMR. However, microbial diversity and complexity means that the analysis and interpretation of their genomes is challenging. In this thesis, I explore applications of de Bruijn graphs (DBGs) to the analysis of these data. First, I present a tool, Mykrobe predictor, that uses DBGs to rapidly identify species and AMR from WGS data. I show that it is accurate, flexible, and efficient. Next, I explore an extension of Mykrobe predictor to long read sequencing of direct clinical samples of M. tuberculosis. In doing so, I show that one could reduce the turn-around time for susceptibility testing of an M. tuberculosis isolate from 2 weeks to 12 hours. Finally, I explore the challenges of DNA search in very large collections (millions) of microbial data sets. In particular, I address the super-linear scaling of existing k-mer indexing tools and present a novel representation and implementation of a probabilistic coloured de Bruijn graph, “Coloured Bloom Graph" (CBG). I demonstrate its scalability by building a CBG of all publicly accessible microbial WGS data (almost half a million samples) and use it to run millisecond searches in these data.</p

Efficient analysis of microbial whole-genome sequence data using de Bruijn graphs

Antimicrobial resistance (AMR) is a persistent and growing threat to global health. Whole genome sequencing (WGS) has the potential to dramatically improve our ability to detect, understand, and monitor AMR. However, microbial diversity and complexity means that the analysis and interpretation of their genomes is challenging. In this thesis, I explore applications of de Bruijn graphs (DBGs) to the analysis of these data. First, I present a tool, Mykrobe predictor, that uses DBGs to rapidly identify species and AMR from WGS data. I show that it is accurate, flexible, and efficient. Next, I explore an extension of Mykrobe predictor to long read sequencing of direct clinical samples of M. tuberculosis. In doing so, I show that one could reduce the turn-around time for susceptibility testing of an M. tuberculosis isolate from 2 weeks to 12 hours. Finally, I explore the challenges of DNA search in very large collections (millions) of microbial data sets. In particular, I address the super-linear scaling of existing k-mer indexing tools and present a novel representation and implementation of a probabilistic coloured de Bruijn graph, âColoured Bloom Graph" (CBG). I demonstrate its scalability by building a CBG of all publicly accessible microbial WGS data (almost half a million samples) and use it to run millisecond searches in these data.</p

Efficient analysis of microbial whole-genome sequence data using de Bruijn graphs

Antimicrobial resistance (AMR) is a persistent and growing threat to global health. Whole genome sequencing (WGS) has the potential to dramatically improve our ability to detect, understand, and monitor AMR. However, microbial diversity and complexity means that the analysis and interpretation of their genomes is challenging. In this thesis, I explore applications of de Bruijn graphs (DBGs) to the analysis of these data. First, I present a tool, Mykrobe predictor, that uses DBGs to rapidly identify species and AMR from WGS data. I show that it is accurate, flexible, and efficient. Next, I explore an extension of Mykrobe predictor to long read sequencing of direct clinical samples of M. tuberculosis. In doing so, I show that one could reduce the turn-around time for susceptibility testing of an M. tuberculosis isolate from 2 weeks to 12 hours. Finally, I explore the challenges of DNA search in very large collections (millions) of microbial data sets. In particular, I address the super-linear scaling of existing k-mer indexing tools and present a novel representation and implementation of a probabilistic coloured de Bruijn graph, “Coloured Bloom Graph" (CBG). I demonstrate its scalability by building a CBG of all publicly accessible microbial WGS data (almost half a million samples) and use it to run millisecond searches in these data