Biology subjects

Walker, T. M.

Publications and source records attributed to Walker, T. M..

13 recordsLinked to original sources

bioRxiv2026

Long-read sequencing of Mycobacterial tuberculosis is comparable to short-read sequencing for antimicrobial resistance prediction and epidemiological studies.

BackgroundShort-read genetic sequencing technologies (mainly Illumina) have been extensively used for around a decade for Mycobacterium tuberculosis complex (MTBC) outbreak analysis and genomic drug susceptibility testing (gDST) with the result that Illumina has become the de facto gold standard. Long-read sequencing, as exemplified by Oxford Nanopore Technologies (ONT), offer the prospect of faster, simpler, and portable sequencing. In this work, we carry out the largest to date comparison of how well Illumina and ONT technologies sequence MTBC samples, making use of R10.4 ONT flowcells, updated basecalling models and deep-learning variant calling. MethodsA total of 508 samples were sequenced using both short and long-read platforms. All samples originated from South Africa or Vietnam and were over-selected for drug resistance and also included several local outbreaks and a range of lineages. The South African and Vietnamese samples had already been Illumina sequenced. Samples with [≥]50 read depth by Illumina were selected for sequencing by ONT using one of the GridION or PromethION platforms. Bioinformatics processing was done using a modified online cloud platform which included reference-based variant calling, catalogue-based gDST and identified related samples via SNP counting to inform outbreak detection. The lineages and gDST predictions obtained by short-and long-sequencing were compared for all samples as were all putative clusters identified via SNP counting. For convenience Illumina was used as the reference method. FindingsOf the 508 samples, 425 (83.7%) had sufficient read depths to permit comparison between the two sequencing technologies. The assigned lineages were identical for 407/425 (95.8%) samples and all discordances were due to mixed lineages being identified by one technology. Evidence of non-tuberculous mycobacterium (NTM) subpopulations were found in nine samples. Using Illumina as the reference method, the very major error (VME) rate of ONT for predicting resistance to all 15 drugs is 1.0% (0.6-1.5%) whilst the major error (ME) rate is 1.7% (1.3-2.2%) with an unclassified rate of 6.9% (6.3-7.5%). This is below the thresholds specified by the CLSI. Considering each of the 15 drugs individually they had VME and ME point estimates below [≤]3% in 29/30 cases; and most 25/30 below [≤]1.5%. Filtering out all samples containing mixtures left 382 isolates. By appropriate masking of the reference genome we were able to obtain a mean SNP distance between the two platforms of 0.13 (median of zero) for the same sample and for 376/382 samples (98.4%, CI:96.6-99.4%) the difference was [≤]1 SNPs. The high concordance in SNP identification ensured that few differences in the 43 putative clusters among 172 isolates were observed. InterpretationThe differences between the two sequencing platforms for the key clinical outputs is so small that it is now within the tolerances set by regulatory agencies. Provided the sequencing is of sufficient quality, we have therefore reached a threshold whereby sequencing data from long-and short-read platforms can be aggregated. This will enable large scale analyses by national and international public health agencies whilst allowing the MTBC community to take advantage of the portability and speed of long-read sequencing. FundingThe NIHR Health Protection Research Unit: Healthcare Associated Infections and Antimicrobial Resistance at University of Oxford (NIHR200915), a partnership between the UK Health Security Agency (UKHSA) and the University of Oxford, the National Institute for Health and Care Research Biomedical Research Centre: Oxford (BRC) and the Ellison Institute of Technology, Oxford Ltd. The CRyPTIC project was funded by Wellcome [214560/Z/18/Z], a Wellcome Trust/Newton Fund-MRC Collaborative Award (200205/Z/15/Z); and the Bill & Melinda Gates Foundation Trust (OPP1133541). Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe conducted a PubMed Central full text search for "tuberculosis" AND ("drug resistance prediction" OR "drug susceptibility prediction") AND ("genome" OR "genomic" OR "geno-typic") AND ("ont" OR "oxford nanopore") between 2022 and 2026 (conducted 1 April 2026). This returned 62 papers; of which, six used both Illumina and ONT sequencing. One of these, published in 2023, directly compared the performance of the two platforms on 151 M. tuberculosis isolates oversampled for resistance. The investigation yielded comparative results for the earlier generation ONT flow cell (R9{middle dot}4{middle dot}1) and base-caller (guppy version 5{middle dot}0{middle dot}16). Another, published in 2026, investigated a targeted next-generation sequencing panel of 20 amplicons using ONT sequencing on R10.4.1 flow cells with guppy 6{middle dot}4{middle dot}6. They compared the results on 71 isolates against phenotypic data and Illumina whole genome sequencing (for 53 isolates) but had low rates of resistance, with all drugs but isoniazid being limited to under five resistant isolates. Two other small studies (10 and 13 samples, respectively) conducted feasibility studies comparing ONT with Illumina, also using earlier generation flow cells and base-calling technology from ONT. Two further studies compared Illumina with ONT for direct sputum sequencing and did not investigate the comparative performance of the two platforms for variant call accuracy, resistance prediction, and outbreak detection. Illumina sequencing technology is widely used for genomic sequence analysis in research, and clinical and public health contexts. Consequently, it has become the de facto reference standard for generating whole genome sequence data. Whilst previous studies established the promise and limitations of long-read (ONT) sequencing as an alternative to short-read sequencing (mainly Illumina), the enhanced performance arising from newer flowcells (e.g. R10.4.1), V14 chemistry, and the latest basecallers (dorado v4.3.0/5.0.0) has not been analysed. Neither has any ONT analysis incorporating the new deep-learning variant callers been evaluated in a large-scale comparative study. Thus, it is currently unclear whether data generated by either platform can be used safely in aggregated analyses for research and clinical or public health service. Added value of this studyWe compared how well short-(Illumina) and long-read (ONT) sequencing platforms identify the genetic variants in M. tuberculosis, predict antituberculous drug resistance and recog-nise outbreaks. The long-reads were generated using the latest generation ONT R10.4.1 flows cells, V14 chemistry, super high accuracy basecalling (dorado v4.3.0/5.0.0) and a bioinformatics analysis pipeline built using the Clair3 deep-learning based variant caller. A total of 508 clinical samples were sequenced using both technologies, substantially more than previous studies. The sampling frame was much larger than previously investigations and included a large proportion of isolates with resistance to first-line and second-line antibiotics as well as bedaquiline. Thus, providing greater statistical power for resistance prediction than before. In particular, the inclusion of bedaquiline resistance provided evidence useful for predicting resistance to this newly deployed drug for treating multi-drug resistant (MDR) TB. We find that the differences between technologies are small meaning that either technology can be used alone safely, and services using both technologies can confidently aggregate the data for analysis. Implications of all the available evidenceThis will be a benefit to local, regional and international organisations, particularly public health agencies, which often have a mix of the two main sequencing technologies for characterising TB whole genome sequences. It also opens up the sequence based diagnostic market to greater competition, particularly if the observed performance can be replicated for other pathogen species.

microbiology↗

bioRxiv2026

Associations Between QuantiFERON-TB Gold Plus IFNγ Concentrations and Progression to Symptomatic Tuberculosis in Global High-Burden TB Settings

IntroductionPredictive biomarkers for symptomatic tuberculosis (TB) progression would transform targeted prevention efforts. Although interferon-gamma release assays (IGRAs), including QuantiFERON(R) TB-Gold Plus (QFT-Plus), have been studied for this purpose, systematic evaluation of the QFT-Plus TB1 and TB2 Interferon-Gamma (IFN{gamma}) concentrations remains limited, particularly in high-burden TB settings. MethodsBaseline TB1 and TB2 IFN{gamma} concentrations from 5,259 participants in TB-endemic regions were analyzed in relation to subsequent TB outcomes over a median of 525 days follow-up. Participants were categorized as controls (no TB), suspected TB (no microbiological confirmation), or laboratory-confirmed TB, including a subset meeting a stringent case definition ([≥]2 positive microbiologic tests). Associations between baseline IFN{gamma} concentrations and progression to symptomatic TB were assessed. ResultsIn the full cohort (IGRA+/-participants), baseline TB2 IFN{gamma} concentrations were significantly higher compared to controls among participants who developed suspected TB (p=0.01), laboratory-confirmed TB (p=0.01), or met the stringent case definition (p<0.0001). In IGRA+ participants, baseline TB2 concentrations were significantly higher than controls in suspected (p=0.01) and laboratory-confirmed (p=0.02) groups. Associations with baseline TB1 IFN{gamma} concentrations and TB progression were observed for participants meeting the stringent case definition within the full cohort (p=0.009). Among stringent definition cases, TB2 concentrations achieved an area under the Receiver Operating Characteristic curve of 0.84, with a sensitivity of 80% and specificity of 78%. ConclusionsQuantitative IFN{gamma} concentrations from QFT-Plus, particularly TB2, were associated with progression to symptomatic TB, met or exceeded WHO-recommended sensitivity and specificity thresholds for predictive biomarkers, and may support biomarker-based stratification in TB clinical research. SummaryIn an exploratory analysis from a global tuberculosis (TB) epidemiology study, higher QuantiFERON-TB Gold Plus IFN{gamma} concentrations were associated with progression to symptomatic TB and met WHO predictive biomarker thresholds, supporting their potential value for risk stratification in TB research.

immunology↗

bioRxiv2025

Rapidly and reproducibly building a comprehensive catalogueof resistance-associated variants for M. tuberculosis

BackgroundCatalogues of genetic variants associated with resistance underpin whole-genome sequencing (WGS)-based predictions of drug susceptibility in Mycobacterium tuberculosis, and are essential for molecular diagnostics and surveillance. The current gold standard catalogues are those released by the WHO but the underlying data are not fully released and they are difficult to interpret. Open and reproducible methods would help address these problems, extending the important work already done. MethodsWe have developed an automated method, catomatic, that uses a binomial test to associate informative isolates with resistance or susceptibility, and built a catalogue (catomatic-1) from the same 39,358 samples used to construct the first edition of the WHO catalogue (WHOv1). We performed a sensitivity analysis to optimise statistical and bioinformatic parameters for each drug, and benchmarked catomatic-1 against WHOv1 using an independent Validation Dataset of 14,380 isolates. FindingsBy using simpler statistics, catomatic-1 algorithmically classified 1,329 genetic variants, ranging from five for linezolid to 440 for pyrazinamide. WHOv1 included generalisable rules added by a panel of experts, increasing its predictive coverage, but at the cost of reproducibility. Despite not including such expert rules, catomatic-1 achieves comparable performance for all drugs, with sensitivities for first-line agents above 88% on the independent Validation Dataset. The automated process allowed us to efficiently explore parameter space; for instance, detecting resistant variants with low read support improved the sensitivity for all drugs. InterpretationPerformant resistance catalogues for M. tuberculosis can be built automatically using transparent and reproducible statistical methods. As more data are collected, catalogue content and performance will evolve, highlighting the need for proper versioning, machine/human readability, and open access. This approach demonstrates resistance catalogues used in surveillance and diagnostics can be rapidly and reproducibily updated. FundingThe National Institute for Health and Care Research (NIHR), Engineering and Physics Sciences Research Council (EPSRC) and ORACLE Corporation. Research in contextO_ST_ABSEvidence before this studyC_ST_ABSWe searched PubMed and preprint servers (bioRxiv, medRxiv), and publicly available mutation catalogues for studies linking Mycobacterium tuberculosis genomic variants with drug resistance using whole-genome or targeted sequencing and phenotypic drug-susceptibility testing (pDST). Search terms combined "Mycobacterium tuberculosis", "genome sequencing", "mutation catalogue", "mutation effects", "drug resistance", and individual drug names, with no language or date restriction. We included studies providing paired, clinical genomic and pDST or MIC data, excluding purely in-silico or case-only reports. This work directly builds on methodologies and data published by five prior studies, and makes primary comparisons with the First (WHOv1) and Second (WHOv2) Editions of the WHO Catalogue of mutations in Mycobacterium tuberculosis. Added value of this studyWe developed catomatic, a transparent, reproducible tool for building catalogues of resistance- and susceptibility-associated genetic variants. Trained on the same samples used to build WHOv1 and benchmarked on an independent Validation Dataset, catomatic achieves comparable sensitivity, specificity, and definitive prediction rates to WHOv1 without expert-rule augmentation and despite using simpler statistics. It optimises parameters per drug, produces machine-readable outputs (CSV/JSON), and demonstrates that adjusting read-support thresholds can improve detection of minor resistance subpopulations. Implications of all the available evidenceCatalogues of resistance-associated variants for M. tuberculosis can be rapidly and transparently constructed. Making catalogues available in human/machine-readable formats with uncertainty estimates will improve uptake of WGS for M. tuberculosis surveillance and diagnostics; using a reproducible process permits diagnostic test manufacturers, researchers, clinical and public health laboratories to select the level of statistical support necessitated by their specific use-case, Policymakers should balance the benefits of expert rules against loss of reproducibility. Future work will expand the size of the datasets used, integrate minimum inhibitory concentration data, and establish consensus workflows for routine, transparent catalogue updates.

microbiology↗

bioRxiv2025

Deep Learning-based Framework for Mycobacterium tuberculosis Bacterial Growth Detection for Antimicrobial Susceptibility Testing

Tuberculosis (TB) kills more people annually than any other pathogen. Resistance is an ever-increasing global problem, not least because diagnostics remain challenging and access limited. 96-well broth microdilution plates offer one approach to high-throughput phenotypic testing, but they can be challenging to read. Automated Mycobacterial Growth Detection Algorithm (AMyGDA) is a software package that uses image processing techniques to read plates, but struggles with plates that exhibit low growth or images of low quality. We developed a new framework, TMAS (TB Microbial Analysis System), which leverages state-of-the-art deep learning models to detect M. tuberculosis growth in images of 96-well microtiter plates. TMAS is designed to measure Minimum Inhibitory Concentrations (MICs) rapidly and accurately while differentiating between true bacterial growth and artefacts. Using 4,018 plate images from the CRyPTIC (Comprehensive Resistance Prediction for Tuberculosis: An International Consortium) dataset to train models and refine the framework, TMAS achieved an essential agreement of 98.8%. TMAS offers a reliable, automated and complementary evaluation to support expert interpretation, potentially improving accuracy and efficiency in tuberculosis drug susceptibility testing (DST). Author summaryTuberculosis (TB) is one of the worlds leading causes of death from infectious diseases, with drug resistance making treatment increasingly difficult. Accurate and timely drug susceptibility testing (DST) is essential to determine which antibiotics remain effective against a given TB strain. A widely used DST method involves 96-well broth microdilution plates, where bacteria are exposed to different antibiotic concentrations to determine the minimum inhibitory concentration (MIC), the lowest concentration that fully prevents bacterial growth. However, manually interpreting these plates is time-consuming and prone to human error, while existing automated methods often struggle with imaging artifacts such as condensation, shadows, and contamination. In this study, we developed the Tuberculosis Microbial Analysis System (TMAS), a machine learning-based tool designed to automate MIC determination from plate images. Training on advanced deep learning models, TMAS can learn to detect bacterial growth with high precision, distinguishing true growth from artefacts such as shadows, bubbles, sediment, condensation and contamination. When tested on a large dataset, TMAS outperformed existing automated methods in accuracy, reliability and efficiency. By reducing the reliance on manual interpretation, TMAS has the potential to streamline TB diagnostics, improve efficiency in laboratories, and improve access to high-quality DST, particularly in resource-limited settings.

microbiology↗

bioRxiv2022

Genomic Sequencing from Sputum for Tuberculosis Disease Diagnosis, Lineage Determination and Drug Susceptibility Prediction

BackgroundUniversal access to drug susceptibility testing for newly diagnosed tuberculosis patients is recommended. Access to culture-based diagnostics remains limited and targeted molecular assays are vulnerable to emerging resistance conferring mutations. Improved sample preparation protocols for direct-from-sputum sequencing of Mycobacterium tuberculosis would accelerate access to comprehensive drug susceptibility testing and molecular typing. MethodsWe assessed a thermo-protection buffer-based direct-from-sample M. tuberculosis whole-genome sequencing protocol. We prospectively processed and analyzed 60 acid-fast bacilli smear-positive sputum samples from tuberculosis patients in India and Madagascar. A diversity of semi-quantitative smear positivity level samples were included. Sequencing was performed using Illumina and MinION (monoplex and multiplex) technologies. We measured the impact of bacterial inoculum and sequencing platforms on M. tuberculosis genomic mean read depth, drug susceptibility prediction performance and typing accuracy. ResultsM. tuberculosis was identified from 88% (Illumina), 89% (MinION-monoplex) and 83% (MinION-multiplex) of samples for which sufficient DNA could be extracted. The fraction of M. tuberculosis reads from MinION sequencing was lower than from Illumina, but monoplexing grade 3+ sputum samples on MinION produced higher read depth than Illumina (p<0.05) and MinION multiplex (p<0.01). No significant difference in overall sensitivity and specificity of drug susceptibility predictions was seen across these sequencing modalities or within each sequencing technology when stratified by smear grade. Lineage typing agreement percentages between direct and culture-based sequencing were 85% (MinION-monoplex), 88% (Illumina) and 100% (MinION-multiplex) ConclusionsM. tuberculosis direct-from-sample whole-genome sequencing remains challenging. Improved and affordable sample treatment protocols are needed prior to clinical deployment.

microbiology↗

bioRxiv2022

Distribution and origins of Mycobacterium tuberculosis L4 in Southeast Asia

Molecular and genomic studies have revealed that Mycobacterium tuberculosis Lineage 4 (L4, Euro-American lineage) emerged in Europe before becoming distributed around the globe by trade routes, colonial migration, and other historical connections. Although L4 accounts for tens or hundreds of thousands of TB cases in multiple Southeast Asian countries, phylogeographic studies have either focussed on a single country or just included Southeast Asia as part of a global analysis. Therefore, we interrogated public genomic data to investigate the historical patterns underlying the distribution of L4 in Southeast Asia and surrounding countries. We downloaded 6037 genomes associated with 23 published studies, focusing on global analyses of L4 and Asian studies of M. tuberculosis. We identified 2256 L4 genomes including 968 from Asia. We show that 81% of L4 in Thailand, 51% of L4 in Vietnam and 9% of L4 in Indonesia belong to sub-clades of L4 that are rarely seen outside East and Southeast Asia. These sub-clades have been transmitted between East and Southeast Asian countries, with no recent European ancestor. Although there is considerable uncertainty about the exact direction and order of intra-Asian transmissions, due to differing sampling frames between countries, our analysis suggests that China may be the intermediate between Europe and Southeast Asia for two of the three predominantly East and Southeast Asian L4 sub-lineages. This new perspective on L4 in Southeast Asia raises the possibility of investigating host population specific evolution and highlights the need for more structured sampling from Southeast Asian countries to provide more certainty of the historical and current routes of transmission.

microbiology↗

bioRxiv2021

BashTheBug: a crowd of volunteers reproducibly and accurately measure the minimum inhibitory concentrations of 13 antitubercular drugs from photographs of 96-well broth microdilution plates.

Tuberculosis is a respiratory disease that is treatable with antibiotics. An increasing prevalence of resistance means that to ensure a good treatment outcome it is desirable to test the susceptibility of each infection to different antibiotics. Conventionally this is done by culturing a clinical sample and then exposing aliquots to a panel of antibiotics, thereby determining the minimum inhibitory concentration (MIC) of each drug. Using 96-well broth micro dilution plates with each well containing a lyophilised pre-determined amount of an antibiotic is a convenient and cost-effective way to measure the MICs of several drugs at once for a clinical sample. Although accurate, this is an expensive and slow process that requires highly-skilled and experienced laboratory scientists. Here we show that, through the BashTheBug project hosted on the Zooniverse citizen science platform, a crowd of volunteers can reproducibly and accurately determine the MICs for 13 drugs and that simply taking the median or mode of 11-17 independent classifications is sufficient. There is therefore a potential role for crowds to support (but not supplant) the role of experts in antibiotic susceptibility testing.

microbiology↗

bioRxiv2019

Using hospital network-based surveillance for antimicrobial resistance as a more robust alternative to self-reporting

Hospital performance is often measured using self-reported statistics, such as the incidence of hospital-transmitted micro-organisms or those exhibiting antimicrobial resistance (AMR), encouraging hospitals with high levels to improve their performance. However, hospitals that increase screening efforts will appear to have a higher incidence and perform poorly, undermining comparison between hospitals and disincentivising testing, thus hampering infection control. We propose a surveillance system in which hospitals test patients previously discharged from other hospitals and report observed cases. Using NHS Hospital Episode Statistics data, we analysed patient movements across England and assessed the number of hospitals required to participate in such a reporting scheme to deliver robust estimates of incidence. With over 1.2 million admissions to English hospitals previously discharged from other hospitals annually, even when only a fraction of hospitals (41/155) participate (each screening at least 1000 of these admissions), the proposed surveillance system can estimate incidence across all hospitals. By reporting on other hospitals, the reporting of incidence is separated from the task of improving own performance. Therefore the incentives for increasing performance can be aligned to increase (rather than decrease) screening efforts, thus delivering both more comparable figures on the AMR problems across hospitals and improving infection control efforts.

epidemiology↗

bioRxiv2019

Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches.

SynopsisO_ST_ABSBackgroundC_ST_ABSPyrazinamide is one of four first-line antibiotics used to treat tuberculosis, however antibiotic susceptibility testing for pyrazinamide is challenging. Resistance to pyrazinamide is primarily driven by genetic variation in pncA, an enzyme that converts pyrazinamide into its active form. MethodsWe curated a dataset of 664 non-redundant, missense amino acid mutations in pncA with associated high-confidence phenotypes from published studies and then trained three different machine learning models to predict pyrazinamide resistance. All models had access to a range of protein structural-, chemical- and sequence-based features. ResultsThe best model, a gradient-boosted decision tree, achieved a sensitivity of 80.2% and a specificity of 76.9% on the hold-out Test dataset. The clinical performance of the models was then estimated by predicting the binary pyrazinamide resistance phenotype of 4,027 samples harboring 367 unique missense mutations in pncA derived from 24,231 clinical isolates. ConclusionsThis work demonstrates how machine learning can enhance the sensitivity/specificity of pyrazinamide resistance prediction in genetics-based clinical microbiology workflows, highlights novel mutations for future biochemical investigation, and is a proof of concept for using this approach in other drugs.

microbiology↗

bioRxiv2018

Validation of a 14-drug microtitre plate that includes delamanid and bedaquiline for susceptibility testing of M. tuberculosis

UKMYC5 is a 96-well microtitre plate designed by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to enable the measurement of minimum inhibitory concentrations (MICs) of 14 different anti-TB compounds for >30,000 clinical tuberculosis isolates. Unlike the MYCOTB plate, on which UKMYC5 is based, the plate included two new (bedaquiline and delamanid) and two repurposed (clofazimine and linezolid) compounds. UKMYC5 plates were tested by seven laboratories on four continents using a panel of 19 external quality assessment (EQA) strains, including H37Rv. To assess the optimal combination of reading method and incubation time, MICs were measured from each plate by two readers using three methods (mirrored-box, microscope and Vizion Digital viewing system) after 7, 10, 14 and 21 days incubation. In addition, all EQA strains were whole-genome sequenced and phenotypically characterized by 7H10/7H11 agar proportion method (APM) and MGIT960. We conclude that the UKMYC5 plate is optimally read using the Vizion system after 14 days incubation, achieving an inter-reader agreement of 97.9% and intra- and inter-laboratory reproducibilities of 95.6% and 93.1%, respectively. The mirrored-box had similar reproducibilities. Strains classified as resistant by APM, MGIT960 or the presence of mutations known to confer resistance consistently record elevated MICs compared with those strains classified as susceptible. Finally, the UKMYC5 plate records intermediate MICs for one strain which the APM measured MICs close the applied critical concentration, providing early evidence that the UKMYC5 plate can quantitatively measure the magnitude of resistance to anti-TB compounds due to specific genetic variation.

microbiology↗

bioRxiv2017

Automated detection of Mycobacterial growth on 96-well plates for rapid and accurate Tuberculosis drug susceptibility testing

M. tuberculosis grows slowly and is challenging to work with experimentally compared with many other bacteria. Although microtitre plates have the potential to enable high-throughput phenotypic testing of M. tuberculosis, they can be difficult to read and interpret. Here we present a software package, the Automated Mycobacterial Growth Detection Algorithm (AMyGDA), that measures how much M. tuberculosis is growing in each well of a 96-well microtitre plate. The plate used here has serial dilutions of 14 anti-tuberculosis drugs, thereby permitting the minimum inhibitory concentrations (MICs) to be elucidated. The two participating laboratories each inoculated ten 96-well plates with the standard H37Rv reference strain and, after two weeks incubation, measured the MICs for all 14 drugs on each plate and took a photograph. By analysing the images, we demonstrate that AMyGDA is reproducible, and that the MICs measured are comparable to those measured by a laboratory scientist. AMyGDA software will be used by the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) to measure the drug susceptibility profile of a large number (> 30,000) of samples of M. tuberculosis from patients over the next few years.

microbiology↗

bioRxiv2017

A novel multi SNP based method for identifying subspecies and associated lineages and sub-lineages of the Mycobacterium tuberculosis complex by whole genome sequencing

The clinical phenotype of zoonotic tuberculosis, its contribution to the global burden of disease and prevalence are poorly understood and probably underestimated. This is partly because currently available laboratory and in silico tools have not been calibrated to accurately identify all subspecies of the Mycobacterium tuberculosis complex (Mtbc). We here present the first such tool, SNPs to Identify TB ( SNP-IT). Applying SNP-IT to a collection of clinical genomes from a UK reference laboratory, we demonstrate an unexpectedly high number of M. orygis isolates. These are seen at a similar rate to M. bovis which attracts much health protection resource and yet M. orygis cases have not been previously described in the UK. From an international perspective it is possible that M. orygis is an underestimated zoonosis. As whole genome sequencing is increasingly integrated into the clinical setting, accurate subspecies identification with SNP-IT will allow the clinical phenotype, host range and transmission mechanisms of subspecies of the Mtbc to be studied in greater detail.

microbiology↗

bioRxiv2016

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.\n\nWe 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 Illumina MiSeq/MiniSeq the workflow from patient sample to results can be completed in 44/16 hours at a cost of {pound}96/{pound}198 per sample.\n\nWe 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 the latest flowcell, the estimated turnaround time from patient to identification of BCG was 6 hours, with full susceptibility and surveillance results 2 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.

microbiology↗