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Two billion people are infected with Mycobacterium tuberculosis, leading to 10 million new cases of active tuberculosis and 1.5 million deaths annually. Universal access to drug susceptibility testing (DST) has become a World Health Organization priority. We previously developed a software tool, Mykrobe predictor, which provided offline species identification and drug resistance predictions for M. tuberculosis from whole genome sequencing (WGS) data. Performance was insufficient to support the use of WGS as an alternative to conventional phenotype-based DST, due to mutation catalogue limitations. Here we present a new tool, Mykrobe, which provides the same functionality based on a new software implementation. Improvements include i) an updated mutation catalogue giving greater sensitivity to detect pyrazinamide resistance, ii) support for user-defined resistance catalogues, iii) improved identification of non-tuberculous mycobacterial species, and iv) an updated statistical model for Oxford Nanopore Technologies sequencing data. Mykrobe is released under MIT license at https://github.com/mykrobe-tools/mykrobe. We incorporate mutation catalogues from the CRyPTIC consortium et al. (2018) and from Walker et al. (2015), and make improvements based on performance on an initial set of 3206 and an independent set of 5845 M. tuberculosis Illumina sequences. To give estimates of error rates, we use a prospectively collected dataset of 4362 M. tuberculosis isolates. Using culture based DST as the reference, we estimate Mykrobe to be 100%, 95%, 82%, 99% sensitive and 99%, 100%, 99%, 99% specific for rifampicin, isoniazid, pyrazinamide and ethambutol resistance prediction respectively. We benchmark against four other tools on 10207 (=5845+4362) samples, and also show that Mykrobe gives concordant results with nanopore data. We measure the ability of Mykrobe-based DST to guide personalized therapeutic regimen design in the context of complex drug susceptibility profiles, showing 94% concordance of implied regimen with that driven by phenotypic DST, higher than all other benchmarked tools.

Purpose Speed of bloodstream infection diagnosis is vital to reduce morbidity and mortality. Whole genome sequencing (WGS) performed directly from liquid blood culture could provide single-assay species and antibiotic susceptibility prediction; however, high inhibitor and human cell/DNA concentrations limit pathogen recovery. We develop a method for the preparation of bacterial DNA for WGS-based diagnostics direct from liquid blood culture. Methodology We evaluate three commercial DNA extraction kits: BiOstic Bacteraemia, Amplex Hyplex and MolYsis Plus. Differential centrifugation, filtration, selective lysis and solid-phase reversible immobilization bead clean-up are tested to improve human cells/DNA and inhibitor removal. Using WGS (Illumina/MinION), we assess human DNA removal, pathogen recovery, and predict species and antibiotic susceptibility inpositive blood cultures of 44 Gram-negative and 54 Staphylococcus species. Results/Key findings BiOstic kit extractions yield the greatest mean DNA concentration, 94–301 ng µl−1, versus 0–2.5 ng µl−1 using Amplex and MolYsis kits. However, we note higher levels of inhibition (260/280 ratio 0.9–2.1) and human DNA (0.0–4.4×106 copies) in BiOstic extracts. Differential centrifugation (2000 g, 1 min) prior to BiOstic extraction reduces human DNA by 63–89 % with selective lysis minimizing by a further 62 %. Post-extraction bead clean-up lowers inhibition. Overall, 67 % of sequenced samples (Illumina MiSeq) contain 93 % concordance between WGS-based species and susceptibility predictions and clinical diagnosis. If >60 % of sequencing reads are human (7/98 samples) susceptibility prediction becomes compromised. Novel MinION-based WGS (n=9) currently gives rapid species identification but not susceptibility prediction. Conclusion Our method for DNA preparation allows WGS-based diagnosis direct from blood culture bottles, providing species and antibiotic susceptibility prediction in a single assay.

Trabajo presentado en el Applied Bioinformatics and Public Health Microbiology Cambridge, UK, 5-7 June 2019, M. tuberculosis, causal agent of the disease tuberculosis (TB) causes over a million deaths annually, and is responsible for as much as 1/3 of drug resistant infections globally. Genomics offers a route to rapid and comprehensive diagnostics, and if we could enable default (or at least common) data-sharing, there would be huge global benefits for public health epidemiology and drug discovery. However, the transition from research to routine public health usage is challenging: high and low burden countries have different needs, there is no directly useful way to share, and the act of sharing currently offers no direct benefit to the person doing it. We set out to build a publicly available service to address this. Mykrobe Atlas provides rapid offline diagnostics for illumina or nanopore data, that run on a laptop. When a network connection is available, the raw sequence data is synched to the EBI, decontaminated of human and HIV data, and deposited in the ENA under embargo. Rapid turnaround (minutes) gives comparison with a live updated database of all TB ever deposited in NCBI/EBI, including identification of potential outbreak clusters. The user is able to filter and visualise the global set by genetic, geographical, or metadata criteria (e.g. phenotype). The global library of TB uses the BIGSI search index (Bradley et al, 2019), which scales to millions of samples, distributed across multiple disks (low RAM usage). On a slower timescale (within an hour) the data is run through the full variation analysis pipeline that is being evaluated at Public Health England. Of particular note is the system that allows countries to participate while maintaining control of their data, by supporting centralised data with access control *and* distributed databases controlled by the data owners. I will discuss this concretely by looking at a real data from an ongoing (1993-2018) outbreak from the Canary Islands in the context of our current database of over 38,250 M. tuberculosis isolates. I will show how we find both the known outbreak samples from Valencia and Canary Islands, and also related samples from across Europe and Africa. I will finish by detailing future plans, including public availability, incorporation of 100k genomes including thousands with phenotypes, upload of lossy compressed data when internet bandwidth is low, and integration with Nextstrain.

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.

This is the report for our entry in the Bioinformatics Challenge in the American Society of Microbiology "2015 Rapid NGS Bioinformatic Pipelines for Enhanced Molecular Epidemiologic Investigation of Pathogens" on 26 September 2015

Poster for MSDTC symposia at the University of Oxford

Mixed populations of susceptible and resistant to antimicrobials can occur in \aureus infections. This can delay drug susceptibility testing of the samples as clones have to be re-cultured and separated. It can also have clinical implications as a minor populations of resistant bacteria can dramatically expand to dominate infections as antibiotic treatment removes their susceptible competition.It has previously been shown that it is possible to predict drug resistance from WGS genotype information in single-clone samples . Since whole genome bacterial sequencing involves considerable oversampling, it is possible to detect minor population genotypes, provided they occur at a frequency that is distinguishable from sequencing errors. This would potentially enable detection of the presence and frequency of minor resistant populations in mixed bacterial infections from WGS data.

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.