Biology subjects

Omar, S. V.

Publications and source records attributed to Omar, S. V..

7 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↗

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

Unpacking bedaquiline hetero-resistance: the importance of intermediate profiles for phenotypic drug-susceptibility testing

Phenotypic drug susceptibility testing (pDST) remains the gold standard for Mycobacterium tuberculosis complex drug resistance determination. Next generation sequencing technologies can identify heteroresistant populations at low frequencies, but little is known about the impact of heteroresistance on bedaquiline (BDQ) pDST results. We simulated heteroresistance using in vitro generated MmpR5 mutants mixed with the progenitor strain at various percentages (1-20%) and did pDST using MGIT960 culture (1 and 2 {micro}g/mL BDQ concentrations). Targeted Next Generation Sequencing (tNGS) was used to quantify the mutant sub-population in growth control tubes, which were expected to maintain the mutant: wild type proportion throughout the assay. Growth units of these growth control tubes were also comparable with minor differences in time-to-positivity between ratio mixtures. Only when intermediate results were considered could BDQ heteroresistance be detected at frequencies of approximately 1% by pDST at a critical concentration of 1 {micro}g/mL using BACTEC MGIT960 coupled with EpiCenter TBeXiST software. The ability of pDST, a widely available DST technique, to reveal the presence of BDQ-resistant subpopulations at the phenotypic testing stage could improve resistance determination and potentially reduce time to effective treatment. ImportanceMultidrug resistant tuberculosis (MDR-TB) is estimated to cause up to 19% of all antimicrobial resistance-attributable deaths worldwide. Further, the success rate for the treatment of drug-resistant TB, in the presence of adherence, is poor at only 68%. The advent of bedaquiline (BDQ) has revolutionized MDR-TB care, but BDQ resistance determination is hampered by several obstacles facing both phenotypic and genotypic testing. Specifically for phenotypic susceptibility testing, BDQ-resistant Mycobacterium tuberculosis isolates with variants in MmpR5, which may display minimal inhibitory concentration values just below the critical concentration or are present at low frequencies (heteroresistance; the presence of mixed mutant and wild-type populations within a specimen), are typically designated as susceptible. This may lead to prescription of an ineffective regimen and amplification of resistance. The BACTEC MGIT960 platform coupled with EpiCenter TBeXiST software for phenotypic testing, which is currently the only routinely used method of BDQ DST, can be used to derive more information about underlying resistant populations. We demonstrate how this is possible through the consideration of intermediate results (i.e., when growth units in a drug-containing tube reach the threshold for resistance but only after a further week of incubation). These intermediate results, commonly disregarded by TB laboratories, could lead to earlier detection of BDQ resistance. This is especially crucial when the genetic mechanism of resistance is unknown, a variant has not been associated with resistance in the interim, and in cases of heteroresistance.

microbiology↗

bioRxiv2025

An improved catalogue for whole-genome sequencing prediction of bedaquiline resistance in M. tuberculosis using a reproduciblealgorithmic approach.

Bedaquiline (BDQ) has only been approved for use for a little over a decade yet is a key drug for treating multi-drug resistant tuberculosis, however rising levels of resistance threaten to reduce its effectiveness. Catalogues of mutations associated with resistance to bedaquiline are key to detecting resistance genetically for either diagnosis or surveillance. At present building catalogues requires considerable expert knowledge, often requires the use of complex grading rules, and is an irreproducible process. We developed an automated method, catomatic, that associates genetic variants with resistance (or susceptibility) using a two-tailed binomial test with a stated background rate and applied it to a dataset of 11,867 Mycobacterium tuberculosis samples with whole genome and bedaquline susceptibility testing data. Using this framework we investigated how to best classify variants and the phenotypic significance of minor alleles. The genes mmpS5 and mmpL5 are not directly associated with bedaquline resistance, and our catalogue of Rv0678, atpE, and pepQ variants attains a cross-validated sensitivity and specificity of 79.4 {+/-} 1.8 % and 98.5 {+/-} 0.3%, respectively, for 94 {+/-} 0.4% of samples. Identifying samples with subpopulations containing Rv0678 variants improves sensitivity, and detection thresholds in bioinformatic pipelines should therefore be lowered. By using a more permissive and deterministic algorithm trained on a sufficient number of resistant samples we have reproducibly constructed an AMR catalogue for BDQ resistance-associated variants that is comprehensive and accurate. Impact StatementBedaquiline has recently received global endorsement for tuberculosis treatment, yet the genetic determinants of antimicrobial resistance remain incompletely understood. Existing gold-standard methods for building mutation catalogs lack public accessibility and reproducibility. We introduce catomatic, a reproducible and publicly available method that employs simpler statistics to increase sensitivity to resistance-associated variants. This approach has enabled investigations into mechanisms of resistance, the significance of genetic subpopulations, and key data attributes that influence the ease of classifying effects and the accuracy of BDQ resistance phenotype prediction in clinical samples. We strongly emphasise the utility in using reproducible statistics and sustainably developed software in genetics-focussed microbiology.

microbiology↗

bioRxiv2024

Enhancement and validation of the antibiotic resistance prediction performance of a cloud-based genetics processing platform for Mycobacteria

Tuberculosis remains a global health problem. Making it easier and quicker to identify which antibiotics an infection is likely to be susceptible to will be a key part of the solution. Whilst whole-genome sequencing offers many advantages, the processing of the genetic reads to produce the relevant public health and clinical information is, surprisingly, often the responsibility of the end user which inhibits uptake. Here we characterise how well a freely-available tool we have developed, gnomonicus, predicts the antibiotic resistance profile of a sample (given its variant call file) using our implementation of the second edition of the WHO catalogue of resistance-associated variants (WHOv2). To facilitate this, we have constructed a Diverse Testset of 2,663 publicly-available M. tuberculosis samples which have both genetic and drug susceptibility testing (DST) data. We have chosen to apply the catalogue such that our tool will return a result of (i) Fail if there are insufficient reads at a genetic locus associated with resistance, (ii) Unknown if a genetic variant in a resistance gene not listed in the catalogue is encountered and (iii) Resistant if three or more short-reads support the presence of a resistance-associated variant. The last step increases the sensitivity for all 15 antibiotics but only reaches significance in a few in our testset. Comparing our results to those of TB-Profiler, an existing tool, highlights the different design choices and demonstrates the performance of both tools on our Diverse Testset is comparable. By only considering high confidence DST results we show that gnomonicus, in combination with our translation of WHOv2, achieves sensitivities and specificities in excess of 95% for both isoniazid and rifampicin. Impact StatementWhole genome sequencing clinical samples taken from patients with tuberculosis is a potentially fast and accurate method for determining to which antibiotics the infection will be susceptible. Two barriers need to be overcome; the first, which is knowing which mutations are associated with resistance (or not) to a range of antibiotics is well on the way to be solved thanks to the efforts of the World Health Organization (WHO) who have published extensive catalogues containing lists of such mutations. The second barrier is that the processing of the raw genetic files remains a largely manual process overseen by bioinformaticians. Here we describe gnomonicus, our open-source AMR prediction tool, and report the performance of our translation of the second edition of the WHO catalogue using a carefully designed publicly-available dataset of 2,663 M. tuberculosis samples. We hope that not only will this tool be useful but also that this dataset will be used by other researchers to facilitate comparisons between pipelines, approaches and tools. Data SummaryThe attendant GitHub repository1 allows gnomonicus to be rerun on all 2,663 samples in the Diverse Testset; it therefore includes instructions, the necessary input files (all the variant call files, the version of the WHOv2 catalogue and a link to the H37Rv GenBank file used in this study) and also the output JSON files. The ENA accession numbers for all 2,663 samples (including a bash script to download them) and their corresponding phenotypic drug susceptibility testing results are included with the intention that people can either reproduce our results, or use the same dataset for other analyses. The JSON files output by TB-Profiler have also been added for comparison. The repository contains a series of Juypter notebooks containing Python3 code that allows the user to discover and parse the output JSON files from either tool and save the results as data tables. Other notebooks allow the user to reproduce all the analysis underlying this work, including reproducing the figures and many of the tables.

microbiology↗

bioRxiv2022

Repeated evolution of bedaquiline resistance in Mycobacterium tuberculosis is driven by truncation of mmpR5

The antibiotic Bedaquiline (BDQ) is a key component of new WHO regimens for drug resistant tuberculosis (TB) but predicting BDQ resistance (BDQ-R) from genotypes remains challenging. We analysed a collection (n=505) of Mycobacterium tuberculosis from two high prevalence areas in South Africa (Cape Town and Johannesburg, 2019-2020), and found 53 independent acquisitions of 31 different mutations within the mmpR5 regulatory gene, with a particular enrichment of truncated MmpR5 in BDQ-R isolates by either frameshift or introduction of an insertion element. Truncations occurred across three M. tuberculosis lineages, impacting 66% of BDQ-R isolates. Extending our analysis to 1,961 isolates with minimum inhibitory concentrations (MICs) revealed that mmpR5-disrupted isolates had a median BDQ MIC of 0.25 mg/L, compared to the wild-type median of 0.06 mg/L. By matching mmpR5-disrupted isolates with phylogenetically close control isolates without the disruption, we were able to estimate the impact on MIC of individual mutations. In conclusion, as the MIC increase borders the ECOFF threshold for BDQ-R, we recommend the continued use of MICs and detection of MmpR5 truncations to identify modest shifts in BDQ-R.

microbiology↗

bioRxiv2019

Modeling missing cases and transmission links in networks of extensively drug-resistant tuberculosis in KwaZulu-Natal, South Africa

The transmission patterns of drug-resistant tuberculosis (TB) remain poorly understood, despite over half a million incident cases in 2017. Modeling TB transmission networks can provide insight into the nature and drivers of transmission, but incomplete and non-random sampling of TB cases can pose challenges to making inferences from epidemiologic and molecular data. We conducted a quantitative bias analysis to assess the effect of missing cases on a transmission network inferred from Mtb sequencing data on extensively drug-resistant (XDR) TB cases in South Africa. We tested scenarios in which cases were missing at random, differentially by clinical characteristics, or differentially by transmission (i.e., cases with many links were under or over-sampled). Under the assumption cases were missing at random, cases in the complete, modeled network would have had a mean of 20 or more transmission links, which is far higher than expected, in order to reproduce the observed, partial network. Instead, we found that the most likely scenario involved undersampling of high-transmitting cases, and further models provided evidence for superspreading behavior. This is, to our knowledge, the first study to define and assess the support for different mechanisms of missingness in a study of TB transmission. Our findings should caution interpretation of results of future studies of TB transmission in high-incidence settings, given the potential for biased sampling, and should motivate further research aimed at identifying the specific host, pathogen, or environmental factors contributing to superspreading.

epidemiology↗