Publications
We maintain an update list of our publications below.
Members of the group have their name in bold.
Preprints under review
- Parker MJ, Hopkins KMV, Chau KK, Cregan J, Oakley S, Barrett L, Jeffery K, Butcher L, Paulus S, Young BC, Eyre DW, Fowler PW, Stoesser N, Sanderson ND, Bejon P (2026).
Genomic epidemiology of multi-modal ESBL gene transmission among Enterobacterales in a neonatal unit.
medRxiv preprint doi:10.64898/2026.07.03.26357218
Abstract
Antimicrobial resistance genes (ARGs) can spread via horizontal transfer or clonal expansion. We investigated the genomic epidemiology of extended-spectrum beta-lactamase (ESBL)–producing Klebsiella pneumoniae and Escherichia coli in a neonatal unit. Between January and November 2023, 53 ESBL isolates were obtained from 23 neonates via routine screening and clinical sampling. Long-read nanopore sequencing identified bla CTX-M-15 as the dominant ESBL gene, alongside bla CTX-M-65 , and bla CTX-M-27 . Among 49 bla CTX-M-15 isolates, 33 carried the gene on plasmids and in the remainder it was located on the chromosome. ESBL isolates belonged to one of seven MLST sequence types; E. coli isolates were dominated by ST131/ST131-like/ST5640 lineages, while K. pneumoniae were predominantly ST13. Plasmids (ESBL and non-ESBL-associated) clustered into 18 communities, five of which contained plasmid bearing bla CTX-M genes. The largest cluster comprised IncFIB(K) plasmids from K. pneumoniae ST13, although, these were predicted as “non-mobilizable” by MOB-suite, and belonged to isolates from a clonally disseminated strain. No evidence of bla CTX-M dissemination via shared plasmids was identified. Meanwhile, chromosomal phylogenetic analysis identified four distinct clonal clusters with ≤7 SNP differences (n=10, 5, 5, and 2 patients). In this setting, genomic analysis supported clonal dissemination of several bla CTX-M -associated strains as the main outbreak mechanism, affecting 20/23 neonates, rather than plasmid-mediated transmission.
- Colpus M, Baker CS, Roghi E, Hong HN, Trieu PP, Thub DDA, Hall A, Fowler PW, Walker TM, Spies R, Webster H, Westhead J, Thai H, Turner RD, Peto TEA, Quang NL, Thuong NTT, Omar SV, Crook DW (2026).
Long-read sequencing of Mycobacterial tuberculosis is comparable to short-read sequencing for antimicrobial resistance prediction and epidemiological studies.
bioRxiv preprint doi:10.64898/2026.04.08.717216
Abstract
Background Short-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. Methods A 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. Findings Of 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. Interpretation The 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. Funding The 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 context Evidence before this study We 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 (R9·4·1) and base-caller (guppy version 5·0·16). 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 6·4·6. 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 study We 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 evidence This 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.
- Constantinides B, Webster H, Gentry J, Bastable J, Dunn L, Oakley S, Swann J, Sanderson N, Fowler PW, Peto TEA, Stoesser N, Street T, Crook DW (2021).
Rapid turnaround multiplex sequencing of SARS-CoV-2: comparing tiling amplicon protocol performance.
medRxiv preprint doi:10.1101/2021.12.28.21268461
Abstract
Genome sequencing is pivotal to virus surveillance, revealing the emergence and global dissemination of acquired genetic mutations. Amplicon sequencing has proven effective for sequencing SARS-CoV-2, but prevalent mutations disrupting primer binding sites have necessitated frequent revision of sequencing protocols in order to maintain performance for emerging virus lineages. We compared the performance of Oxford Nanopore Technologies (ONT) Midnight and ARTIC tiling amplicon protocols using 96-plexed nanopore sequencing of 193 Delta-lineage SARS-CoV-2 clinical specimens with complete paired results and 71 S-gene target failure-selected specimens collected during emergence of Omicron BA.1. For Delta samples, ARTIC v4 recovered ≥90% of the genome in 82.4% (159/193) of specimens compared with 71.5% (138/193) for Midnight. ARTIC v4 was more robust in low-titre, high-Ct specimens, whereas Midnight achieved slightly greater completeness at lower Ct. In the S-gene target failure-selected cohort, revised Midnight v2 and ARTIC v4.1 primer schemes substantially improved genome completeness: ≥99% genome recovery was achieved in 74.6% (53/71) with Midnight v2 versus 18.3% (13/71) with Midnight, and 57.7% (41/71) with ARTIC v4.1 versus 0% (0/71) with ARTIC v4. Estimated consumable costs were lower for Midnight than ARTIC (£12 versus £16 per sample at Q1 2022 prices), and the interval from reverse transcription to flow cell loading was shorter (~7 versus ~9.5 hours). Midnight proved a practical alternative for rapid nanopore sequencing, with lower consumable costs and shorter preparation time, although ARTIC v4 was more robust in high-Ct Delta specimens. Impact statement Tiling amplicon whole genome sequencing enables fast and cost-effective virus sequencing, yet is adversely affected by mutations at primer binding sites. We evaluated ARTIC and Midnight nanopore sequencing protocols using SARS-CoV-2 clinical samples in terms of genome recovery, turnaround time and consumable costs. Our evaluation contributed to deployment of a nanopore sequencing service embedded in an NHS hospital microbiology laboratory supporting genomic surveillance and subsequently the RECOVERY platform trial.
2026
- Adlard D, Malone KM, Westhead J, Hunt M, Thai H, Colpus M, Turner RD, Omar SV, Eyre DW, Ismail N, Walker TM, Peto TEA, Crook DW, Iqbal Z, Fowler PW (2026).
Rapidly and reproducibly building a comprehensive catalogue of resistance-associated variants for M. tuberculosis.
accepted for publication in Lancet Microbe
bioRxiv preprint doi:10.1101/2025.10.02.679941
Abstract
Background Catalogues 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. Methods We 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. Findings By 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. Interpretation Performant 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. Funding The National Institute for Health and Care Research (NIHR), Engineering and Physics Sciences Research Council (EPSRC) and ORACLE Corporation. Research in context Evidence before this study We 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 study We 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 evidence Catalogues 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.
- Holt KE, Berends MS, Chindelevitch L, Couto N, Duncan DB, Dyson ZA, Foster-Nyarko E, Hawkey J, Lees JA, Maguire F, Prasad AB, Baines SL, ESGEM-AMR Working Group (2026).
A call for sharing phenotypic antimicrobial susceptibility data alongside bacterial genome sequence data in public archives.
npj Antimicrob Resist doi:10.1038/s44259-026-00278-5
Zenodo preprint doi:10.1038/s44259-026-00278-5
Abstract
Genomics is widely used in the surveillance and investigation of antimicrobial resistance (AMR), with standard practice to deposit bacterial whole-genome sequence data in public archives to support research and public health. Here, we call for open sharing of matched phenotypic antimicrobial susceptibility data alongside sequence data to support equitable data-driven development and validation of new technologies to address AMR, and highlight key principles and resources to facilitate such sharing.
- Baker CS, Colpus M, Gentry J, Hall A, Roghi E, Webster H, Drummond B, Cooper R, Thai H, Westhead J, Turner R, Peto TEA, Fowler PW, Morgan M, Crook DW (2026).
Validation of an optimized Oxford Nanopore sequencing workflow versus Illumina for mycobacteria from primary MGIT culture.
Microbiology Spectrum doi:10.1128/spectrum.01402-26
bioRxiv preprint doi:10.64898/2026.02.04.703726
Abstract
Illumina short-read sequencing of primary Mycobacterial Growth Indicator Tube (MGIT) cultures is an established approach for mycobacterial genomic characterization but relies on labor-intensive workflows and centralized sequencing facilities. Oxford Nanopore Technologies (ONT) offers long-read sequencing and is increasingly being explored for in-house implementation in diagnostic laboratories; however, low DNA yields from primary MGIT cultures frequently limit standard PCR-free ONT workflows, restricting routine implementation. We developed and evaluated a semi-automated DNA extraction and Rapid PCR Barcoding workflow for ONT sequencing and compared its performance with Illumina for species identification and Mycobacterium tuberculosis complex (MTBC) single-nucleotide polymorphism (SNP) detection. A platform-agnostic bioinformatics pipeline was used for human read removal, taxonomic assignment, and MTBC genomic characterization. The time required to achieve reliable species identification was assessed by subsampling ONT data at 1, 6, and 72 h. Concordance between ONT and Illumina for species classification was 98.3% (95% CI: 95.8%–99.5%), with all discordant classifications attributable to potential mixed infections. MTBC SNP concordance was high, with a mean of 0.3 and a median of 0 SNP differences between platforms after masking. Reliable species identification was achieved for 233/237 (98.3%) samples within 6 h of sequencing, with no incorrect primary species assignments. These findings demonstrate that an optimized PCR-amplified ONT workflow provides a practical approach for routine in-house genomic characterization of primary MGIT cultures, achieving high concordance with Illumina while addressing a major barrier to ONT implementation in diagnostic laboratories. IMPORTANCE Rapid genomic characterization of mycobacterial infections is important for timely patient management, infection control, and public health surveillance. However, many diagnostic laboratories rely on referral of positive cultures to regional reference centers for sequencing, introducing delays associated with transport, batching, and processing. Although Oxford Nanopore Technologies (ONT) can support local sequencing, implementation has been limited by the low DNA yields typically obtained from primary Mycobacterial Growth Indicator Tube (MGIT) cultures and the absence of practical workflows suitable for routine diagnostic laboratories. In this study, we developed and validated a semi-automated DNA extraction and PCR-based ONT workflow designed to support implementation in routine diagnostic laboratories. The workflow generated genomic results highly concordant with Illumina sequencing while overcoming a major barrier to ONT implementation by enabling reliable sequencing from low-yield MGIT-derived DNA. This approach supports routine in-house genomic characterization of mycobacterial cultures and has the potential to reduce turnaround times and dependence on external sequencing services.
- Baranowski C, Cortade DL, Adler B, Bayliss S, Fowler PW, Greenberg D, Hawkey J, Holt K, Huseby D, Muwonge K, Knight-Connoni V, Mistretta M, Reider Apel A, Tobin M, Wonderly Trainor B, Zgurskaya H (2026).
Predicting Quantitative MIC from Whole Genome Sequencing.
Zenodo preprint doi:10.5281/zenodo.21456141
Abstract
Machine-learning models that predict antimicrobial minimum inhibitory concentrations (MICs) from whole-genome sequencing (WGS) are currently built mostly for single species and trained on geographically narrow collections, so it remains unclear whether they generalise across regions, sub-lineages, or taxa. This pilot proposes a globally representative benchmark dataset of paired WGS and quantitative MICs, including approximately 5,000 E. coli and 5,000 K. pneumoniae isolates from ten countries spanning all six inhabited continents, phenotyped on a standardised broth-microdilution panel and short-read sequenced with a long-read subset and designed to extend existing resources such as CABBAGE. It would then train and evaluate both species-specific and cross-species MIC prediction models under phylogenetically informed cross-validation, systematically testing how taxonomic, geographic, and antibiotic diversity affect predictive performance and generalisability. This proposal is a product from “Roadmapping the Future of AI-Driven Antimicrobial Resistance Research”, a community alignment initiative led by The Align Foundation and supported by Google DeepMind, with the goal of prioritizing key challenges and opportunities for the use of AI/ML in helping combat AMR.
- Dissanayake D, Brunner VM, Adlard D, Morrone J, Fowler PW (2026).
Predicting pyrazinamide resistance in M. tuberculosis using a graph convolutional network.
BMC Microbiology doi:10.1186/s12866-026-04876-1
bioRxiv preprint doi:10.1101/2025.10.28.685176
Abstract
BACKGROUND: Pyrazinamide is an important first-line antibiotic for treating tuberculosis, with resistance primarily driven by mutations in the pncA gene. Traditional machine learning models are able to predict pyrazinamide resistance with some success but are limited in their ability to incorporate 3-dimensional protein structural information. Graph neural networks offer the potential to integrate protein structure and residue-level features to better predict the impact of mutations on drug resistance. RESULTS: We trained a graph convolutional network on PncA variants containing missense mutations and evaluated its ability to classify resistance to pyrazinamide. Each PncA variant was represented as an amino acid-level graph, with edges calculated from 3-dimensional spatial proximity, and node features derived from chemical properties and mutation meta-predictors. We used AlphaFold2 to generate predicted structures of the PncA variants, which we used to create the protein graphs. The predicted structures of resistant PncA variants showed greater deviation from the wild-type structure compared to susceptible variants. Our model achieved an F1 score of 81.6%, sensitivity of 81.6% and specificity of 80.4% on the test set and either matched or exceeded the performance of a published set of traditional machine learning models. We show that both structural graph connectivity and node features contribute significantly to model performance. Furthermore, we employ additional train/test dataset splits to demonstrate the GCN’s ability to generalise and predict resistance in samples with mutations in unseen positions and structural regions. CONCLUSIONS: Our study demonstrates that graph-based deep learning can leverage protein structure and biochemical features to accurately predict antimicrobial resistance, despite being trained on a small dataset with little variation. We present this as a proof-of-concept for these methods to be applied to resistance phenotype prediction in more genetically diverse pathogens to predict the more complex observed patterns of antimicrobial resistance.
- Hunt M, Hinrichs A S, Anderson D, Karim L, Dearlove B L, Knaggs J, Constantinides B, Fowler P W, Rodger G, Street T, Lumley S, Webster H, Sanderson T, Ruis C, Kotzen B, de Maio N, Amenga-Etego L N, Amuzu D S Y, Avaro M, Awandare G A, Ayivor-Djanie R, Barkham T, Bashton M, Batty E M, Bediako Y, De Belder D, Benedetti E, Bergthaler A, Boers S A, Campos J, Carr R A A, Chen Y Y C, Cuba F, Dattero M E, Dejnirattisai W, Dilthey A, Duedu K O, Endler L, Engelmann I, Francisco N M, Fuchs J, Gnimpieba E Z, Groc S, Gyamfi J, Heemskerk D, Houwaart T, Hsiao N y, Huska M, Hölzer M, Iranzadeh A, Jarva H, Jeewandara C, Jolly B, Joseph R, Kant R, Ki K K K, Kurkela S, Lappalainen M, Lataretu M, Lemieux J, Liu C, Malavige G N, Mashe T, Mongkolsapaya J, Montes B, Mora J A M, Morang’a C M, Mvula B, Nagarajan N, Nelson A, Ngoi J M, da Paixão J P, Panning M, Poklepovich T, Quashie P K, Ranasinghe D, Russo M, San J E, Sanderson N D, Scaria V, Screaton G, Sessions O M, Sironen T, Sisay A, Smith D, Smura T, Supasa P, Suphavilai C, Swann J, Tegally H, Tegomoh B, Vapalahti O, Walker A, Wilkinson R J, Williamson C, Zair X, IMSSC Laboratory Network Consortium, Biere B, Dürrwald R, Mache C, Oh D Y, Schulze J, Wedde M, Wolff T, Fuchs S, Semmler T, Paraskevopoulou S, Kerber R, Kröger S, Haas W, Bode K, Corman V, Erren M, Finzer P, Grosser R, Haffner M, Hermann B, Kiel C, Krumbholz A, Lorentz T, Meinck K, Nitsche A, Petzold M, Schwanz T, Szabados F, Tewald F, Tiemann C, de Oliveira T, Peto T E, Crook D, Corbett-Detig R, Iqbal Z (2026).
Addressing pandemic-wide systematic errors in the SARS-CoV-2 phylogeny.
Nature Methods doi:10.1038/s41592-025-02947-1
bioRxiv preprint doi:10.1101/2024.04.29.591666
Abstract
The SARS-CoV-2 genome occupies a unique place in infection biology - it is the most highly sequenced genome on earth (making up over 20% of public sequencing datasets) with fine scale information on sampling date and geography, and has been subject to unprecedented intense analysis. As a result, these phylogenetic data are an incredibly valuable resource for science and public health. However, the vast majority of the data was sequenced by tiling amplicons across the full genome, with amplicon schemes that changed over the pandemic as mutations in the viral genome interacted with primer binding sites. In combination with the disparate set of genome assembly workflows and lack of consistent quality control (QC) processes, the current genomes have many systematic errors that have evolved with the virus and amplicon schemes. These errors have significant impacts on the phylogeny, and therefore over the last few years, many thousands of hours of researchers time has been spent in "eyeballing" trees, looking for artefacts, and then patching the tree. Given the huge value of this dataset, we therefore set out to reprocess the complete set of public raw sequence data in a rigorous amplicon-aware manner, and build a cleaner phylogeny. Here we provide a global tree of 4,471,579 samples, built from a consistently assembled set of high quality consensus sequences from all available public data as of June 2024, viewable at https://viridian.taxonium.org. Each genome was constructed using a novel assembly tool called Viridian (https://github.com/iqbal-lab-org/viridian), developed specifically to process amplicon sequence data, eliminating artefactual errors and mask the genome at low quality positions. We provide simulation and empirical validation of the methodology, and quantify the improvement in the phylogeny. We hope the tree, consensus sequences and Viridian will be a valuable resource for researchers.
2025
- Westhead J, Baker CS, Brouard M, Colpus M, Constantinides B, Hall A, Knaggs J, Lopes Alves M, Spies R, Thai H, Surrall S, Govender K, Peto TEA, Crook DW, Omar SV, Turner R, Fowler PW (2025).
Characterising the performance of an antibiotic resistance prediction tool, gnomonicus, using a diverse testset of 2,663 Mycobacterium tuberculosis samples.
Microbial Genomics 11:001592 doi:10.1099/mgen.0.001592
bioRxiv preprint doi:10.1101/2024.11.08.622466
Abstract
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 characterize 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 World Health Organization (WHO) catalogue of resistance-associated variants (WHOv2). To facilitate this, we have constructed a diverse test set of 2,663 publicly available Mycobacterium 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 test set. Comparing our results with those of TB-Profiler, an existing tool, highlights the different design choices and demonstrates that the performance of both tools on our diverse test set 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.
- Spies R, Crook DW, Peto TEA, Fowler PW, Turner R, Thai H, Watson J, Walker TM (2025).
Evaluating 12 automated, whole-genome sequencing analysis pipelines for Mycobacterium tuberculosis complex: a comparative study.
Lancet Microbe doi:10.1016/j.lanmic.2025.101210
Lancet SSRN preprint doi:10.2139/ssrn.5064085
Abstract
BACKGROUND: Reliance on complex, custom-built bioinformatics pipelines is a barrier to the implementation of whole-genome sequencing (WGS) of Mycobacterium tuberculosis in high-burden settings in some low-income and middle-income countries (LMICs). Automated analysis pipelines could address this inequity in access to WGS-based diagnostics and surveillance. This study aimed to systematically evaluate the performance and usability of publicly available WGS pipelines for M tuberculosis. METHODS: We identified automated M tuberculosis WGS analysis pipelines through searches of PubMed and GitHub from database inception up to Aug 31, 2024. Accuracy, cost, accessibility, and scalability were assessed for each pipeline. We evaluated the accuracy of genotypic drug susceptibility testing (gDST) using publicly available sequences with phenotypic susceptibility data for 12 antituberculosis drugs. We estimated pooled sensitivity and specificity for each pipeline, across all drugs, by conducting a bivariate meta-analysis, with random effects representing between-drug variability. Lineage classifications were compared, and a previously epidemiologically well-characterised dataset was used to compare measures of genomic relatedness. FINDINGS: Among 28 candidate pipelines, 16 were excluded as they were unmaintained and inexecutable. 12 pipelines (11 compatible with Illumina and four compatible with Nanopore), all free to use, were included for evaluation. Six pipelines processed and stored data remotely, but for five of these six, scalability was limited by the need to upload sequences through web portals. For local processing pipelines, scalability was dependent on substantial local computational resources, data storage capacity, and command-line interfaces that limited user-friendliness. Only one of six remote-processing pipelines removed human DNA sequences before server upload. gDST was similarly accurate across ten of 11 Illumina-compatible pipelines and three of four Nanopore-compatible pipelines. All pipelines classified the main lineages consistently, although there were differences at sublineage resolution. Outputs from three of four pipelines reporting genomic relatedness were compatible with commonly cited single nucleotide polymorphism difference thresholds. INTERPRETATION: Numerous automated analysis pipelines capable of enhancing equity in M tuberculosis WGS are available. Given the overall similarities between the pipelines evaluated in this study in terms of gDST performance, lineage classification, and genomic relatedness inference, non-functional attributes such as availability, accessibility, scalability, and privacy could represent the point of difference for prospective users in LMICs with a high burden of tuberculosis. FUNDING: The Rhodes Trust, Wellcome, Ellison Institute of Technology, and the UK National Institute for Health and Care Research Oxford Biomedical Research Centre.
- Brunner VM, Fowler PW (2025).
Subpopulations in clinical samples of M. tuberculosis can give rise to rifampicin resistance and shed light on how resistance is acquired.
JAC-Antimicrobial Resistance 7:dlaf175 doi:10.1093/jacamr/dlaf175
bioRxiv preprint doi:10.1101/2025.04.09.647945
Abstract
Objectives: infections, but discrepancies between genotypic and phenotypic drug susceptibility testing can hinder effective treatment and surveillance. This study investigated the impact of resistant subpopulations and compensatory mutations in WGS-based rifampicin resistance prediction. Methods: samples, the sensitivity and specificity of resistance classification were evaluated with and without considering subpopulations and compensatory mutations. Results: By lowering the fraction of reads required to identify a resistance-associated variant in a sample from 0.90 to 0.05, the sensitivity increased significantly from 94.3% to 96.4% without a significant impact on specificity. Allowing compensatory mutations to predict resistance further lowered the false negative rate. Finally, we found that samples with resistant subpopulations were less likely to be compensated than homogeneous resistant samples. Further analysis of these samples revealed distinct clusters with differing amounts of within-sample diversity, pointing towards different mechanisms of resistance acquisition, such as within-host evolution and secondary infections. Conclusions: Our results indicate that a substantial fraction of false negative calls in WGS-based rifampicin resistance prediction can be explained by masked resistant subpopulations. The genetic diversity within the heterogeneous samples is consistent with at least 28% of the rifampicin resistance arising from secondary infections.
- Zhang X, Wan S, Bhati AP, Fowler PW, Coveney PV (2025).
Rapid, accurate and reproducible de novo prediction of resistance to antituberculars.
mSphere e00571 doi:10.1128/msphere.00571-25
Lancet SSRN preprint doi:10.2139/ssrn.5166252
Abstract
As one of the deadliest infectious diseases in the world, tuberculosis is responsible for millions of new cases and deaths reported annually. The rise of drug-resistant tuberculosis, particularly resistance to first-line treatments like rifampicin, presents a critical challenge for global health, which complicates the treatment strategies and calls for effective diagnostic and predictive tools. In this study, we apply an ensemble-based molecular dynamics computer simulation method, TIES_PM, to estimate the binding affinity through free energy calculations and predict rifampicin resistance in RNA polymerase. By analyzing 61 mutations, including those in the rifampicin resistance-determining region, TIES_PM produces reliable results in good agreement with clinical reference and identifies abnormal data points indicating alternative mechanisms of resistance. In the future, TIES_PM is capable of identifying and selecting leads with a lower risk of resistance evolution and, for smaller proteins, it may systematically predict antibiotic resistance by analyzing all possible codon permutations. Moreover, its flexibility allows for extending predictions to other first-line drugs and drug-resistant diseases. TIES_PM provides a rapid, accurate, low-cost, and scalable supplement to current diagnostic pipelines, particularly for drug resistance screening in both research and clinical domains.IMPORTANCEAntimicrobial resistance (AMR), a global threat, challenges early diagnosis and treatment of tuberculosis (TB). This study employs TIES_PM, a free-energy calculation method, to efficiently predict AMR by quantifying how mutations in bacterial RNA polymerase (RNAP) affect rifampicin (RIF) binding. On simulating 61 clinically observed mutations, the results align with WHO classifications and reveal ambiguous cases, suggesting alternative resistance mechanisms. Each mutation requires ~5 h, offering rapid, cost-effective predictions. An ensemble approach ensures statistical robustness. TIES_PM can be extended to smaller proteins for systematic codon permutation analysis, enabling comprehensive antibiotic resistance prediction, or adapted to identify low-resistance-risk drug leads. It also applies to other TB drugs and resistant pathogens, supporting personalized therapy and global AMR surveillance. This work provides novel tools to refine resistance mutation databases and phenotypic classification standards, enhancing early diagnosis while advancing translational research and infectious disease control.
- Vo HAT, Nguyen S, Tran AQT, Nguyen H, Bich HH, Fowler PW, Walker TM, Nguyen TT (2025).
Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing.
Computational and Structural Biotechnology Journal 27:2208 doi:10.1016/j.csbj.2025.05.030
bioRxiv preprint doi:10.1101/2025.02.14.638231
Abstract
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 summary Tuberculosis (TB) is one of the world’s 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.
- Adlard D, Joseph L, Webster H, O’Reilly A, Knaggs J, Peto TEA, Crook DW, Omar SV, Fowler PW (2025).
An improved catalogue for whole-genome sequencing prediction of bedaquiline resistance in M. tuberculosis using a reproducible algorithmic approach.
Microbial Genomics 11:001429 doi:10.1099/mgen.0.001429
bioRxiv preprint doi:10.1101/2025.01.30.635633
Abstract
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 Statement Bedaquiline 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.
- Lynch CI, Adlard D, Fowler PW (2025).
Predicting rifampicin resistance in M. tuberculosis using machine learning informed by protein structural and chemical features.
ERJ Open Research 00952 doi:10.1183/23120541.00952-2024
bioRxiv preprint doi:10.1101/2024.08.15.608097
Abstract
Background: , will be encountered in clinical samples, complicating the task of using genetics to predict whether a sample is resistant or not to rifampicin. We have trained a series of machine learning models with the aim of complementing genetics-based drug susceptibility testing. Methods: We built a Test+Train dataset comprising 219 susceptible mutations and 46 RAVs. Features derived from the structure of the RNA polymerase or the change in chemistry introduced by the mutation were considered; however, only a few, notably the distance from the rifampicin binding site, were found to be predictive on their own. Owing to the paucity of RAVs we used Monte Carlo cross-validation with 50 repeats to train four different machine learning models. Results: All four models behaved similarly with sensitivities and specificities in the range 0.84-0.88 and 0.94-0.97, although we preferred the ensemble of decision tree models as they are easy to inspect and understand. We showed that measuring distances from molecular dynamics simulations did not improve performance. Conclusions: confers resistance to rifampicin using a machine learning model trained on a combination of structural, chemical and evolutionary features; however, performance is moderate and training is complicated by the lack of data.
2024
- Farrar A, Feehily C, Turner P, Zagajewski A, Chatzimichail S, Crook D, Andersson M, Oakley S, Barrett L, El Sayyed H, Fowler PW, Nellåker C, Kapanidis AN, Stoesser N (2024).
Infection Inspection: Using the power of citizen science to help with image-based prediction of antibiotic resistance in Escherichia coli treated with ciprofloxacin.
Sci Rep 14:19543 doi:10.1038/s41598-024-69341-3
medRxiv preprint doi:10.1101/2023.12.11.23299807
Abstract
Antibiotic resistance is an urgent global health challenge, necessitating rapid diagnostic tools to combat its threat. This study uses citizen science and image feature analysis to profile the cellular features associated with antibiotic resistance in Escherichia coli. Between February and April 2023, we conducted the Infection Inspection project, in which 5273 volunteers made 1,045,199 classifications of single-cell images from five E. coli strains, labelling them as antibiotic-sensitive or antibiotic-resistant based on their response to the antibiotic ciprofloxacin. User accuracy in image classification reached 66.8 ± 0.1%, lower than our deep learning model's performance at 75.3 ± 0.4%, but both users and the model were more accurate when classifying cells treated at a concentration greater than the strain's own minimum inhibitory concentration. We used the users' classifications to elucidate which visual features influence classification decisions, most importantly the degree of DNA compaction and heterogeneity. We paired our classification data with an image feature analysis which showed that most of the incorrect classifications happened when cellular features varied from the expected response. This understanding informs ongoing efforts to enhance the robustness of our diagnostic methodology. Infection Inspection is another demonstration of the potential for public participation in research, specifically increasing public awareness of antibiotic resistance.
- The CRyPTIC Consortium (2024).
Quantitative drug susceptibility testing for Mycobacterium tuberculosis using unassembled sequencing data and machine learning.
PLoS Comp Biol doi:10.1371/journal.pcbi.1012260
bioRxiv preprint doi:10.1101/2021.09.14.458035
Abstract
There remains a clinical need for better approaches to rapid drug susceptibility testing in view of the increasing burden of multidrug resistant tuberculosis. Binary susceptibility phenotypes only capture changes in minimum inhibitory concentration when these cross the critical concentration, even though other changes may be clinically relevant. We developed a machine learning system to predict minimum inhibitory concentration from unassembled whole-genome sequencing data for 13 anti-tuberculosis drugs. We trained, validated and tested the system on 10,859 isolates from the CRyPTIC dataset. Essential agreement rates (predicted MIC within one doubling dilution of observed MIC) were above 92% for first-line drugs, 91% for fluoroquinolones and aminoglycosides, and 90% for new and repurposed drugs, albeit with a significant drop in performance for the very few phenotypically resistant isolates in the latter group. To further validate the model in the absence of external MIC datasets, we predicted MIC and converted values to binary for an external set of 15,239 isolates with binary phenotypes, and compare their performance against a previously validated mutation catalogue, the expected performance of existing molecular assays, and World Health Organization Target Product Profiles. The sensitivity of the model on the external dataset was greater than 90% for all drugs except ethionamide, clofazimine and linezolid. Specificity was greater than 95% for all drugs except ethambutol, ethionamide, bedaquiline, delamanid and clofazimine. The proposed system can provide quantitative susceptibility phenotyping to help guide antimicrobial therapy, although further data collection and validation are required before machine learning can be used clinically for all drugs.
- Carter JJ, Walker TM, Waker AS, Whitfield M, Morlock GP, Lynch CI, Adlard D, Peto TEA, Posey JE, Crook DW, Fowler PW (2024).
Prediction of pyrazinamide resistance in Mycobacterium tuberculosis using structure-based machine learning approaches.
JAC-Antimicrobial Resistance 6:dlae037 doi:10.1093/jacamr/dlae037
bioRxiv preprint doi:10.1101/518142
Abstract
Background Pyrazinamide 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, encoding an enzyme that converts pyrazinamide into its active form. Methods We 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. Results The 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 4027 samples harbouring 367 unique missense mutations in pncA derived from 24 231 clinical isolates. Conclusions This 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.
- Brunner V, Fowler PW (2024).
Compensatory mutations are associated with increased in vitro growth in resistant clinical samples of Mycobacterium tuberculosis.
mGen 10:001187 doi:10.1099/mgen.0.001187
bioRxiv preprint doi:10.1101/2023.06.21.545231
Abstract
Mutations in Mycobacterium tuberculosis associated with resistance to antibiotics often come with a fitness cost for the bacteria. Resistance to the first-line drug rifampicin leads to lower competitive fitness of M. tuberculosis populations when compared to susceptible populations. This fitness cost, introduced by resistance mutations in the RNA polymerase, can be alleviated by compensatory mutations (CMs) in other regions of the affected protein. CMs are of particular interest clinically since they could lock in resistance mutations, encouraging the spread of resistant strains worldwide. Here, we report the statistical inference of a comprehensive set of CMs in the RNA polymerase of M. tuberculosis , using over 70,000 M. tuberculosis genomes that were collated as part of the CRyPTIC project. The unprecedented size of this data set gave the statistical tests to investigate the association of putative CMs with resistance-conferring mutations much more power. Overall, we propose 51 high-confidence CMs by means of statistical association testing and suggest hypotheses for how they exert their compensatory mechanism by mapping them onto the protein structure. In addition, we were able to show an association of CMs with higher in vitro growth densities, and hence presumably with higher fitness, in resistant samples in the more virulent M. tuberculosis Lineages 2 and 3. In Lineage 2, our results even suggest the association of CM presence with significantly higher in vitro growth than for wild-type samples, although this association is confounded with lineage and sub-lineage affiliation. Our findings emphasise the integral role of CMs and lineage affiliation in resistance spread and increases the urgency for antibiotic stewardship, which implies accurate, cheap and widely accessible diagnostics for M. tuberculosis infections to not only improve patient outcomes but also to prevent the spread of resistant strains.
- The CRyPTIC Consortium (2024).
Quantitative measurement of antibiotic resistance in M. tuberculosis reveals genetic determinants of resistance and susceptibility in a target gene approach.
Nature Comms 15:488 doi:10.1038/s41467-023-44325-5
bioRxiv preprint doi:10.1101/2021.09.14.460353
Abstract
The World Health Organization has a goal of universal drug susceptibility testing for patients with tuberculosis. However, molecular diagnostics to date have focused largely on first-line drugs and predicting susceptibilities in a binary manner (classifying strains as either susceptible or resistant). Here, we used a multivariable linear mixed model alongside whole genome sequencing and a quantitative microtiter plate assay to relate genomic mutations to minimum inhibitory concentration (MIC) in 15,211 Mycobacterium tuberculosis clinical isolates from 23 countries across five continents. We identified 492 unique MIC-elevating variants across 13 drugs, as well as 91 mutations likely linked to hypersensitivity. Our results advance genetics-based diagnostics for tuberculosis and serve as a curated training/testing dataset for development of drug resistance prediction algorithms.
2023
- Amoako D, Anh NT, Bastable J, Brouard M, Campano Romero C, Castillo Ramirez A, Constantinides B, Crook DW, Cuong PM, Diagne MM, Diallo A, Dung NT, Dunn L, Duyet LV, Everatt J, Fletcher K, Fowler PW, Gail M, Gentry J, Gharbia S, Hospital for Tropical Diseases SARS-CoV-2 testing team, Hong NTT, Hunt M, Iqbal Z, Jeffery K, Kekana D, Kesteman T, Knaggs J, Lopes Alves M, Man DNH, Mathers AJ, Ngoc MN, Oakley S, Parikh H, Peto TEA, Quan P, Rojas Herrera M, Sanderson N, Sintchenko V, Swann J, Takata J, Tam NT, Tan LV, Thach PN, Top NM, Trang NT, Trang VD, Turner R, van Doorn HR, von Gottberg A, Westhead J, Wolter N, Young BC (2023).
A validated cloud-based genomic platform for co-ordinated, expedient global analysis of SARS-CoV-2 genomic epidemiology.
medRxiv preprint doi:10.1101/2023.11.27.23298986
Abstract
Background Viral sequencing has made critical contributions to our understanding of and response to the COVID-19 pandemic, but sequencing capacity and bioinformatic expertise remain limited in many settings. This proof-of-principle study aimed to demonstrate the utility of a cloud-based sequencing analysis pipeline, the Tiled Amplicon Pipeline (TAP), for rapid and collaborative SARS-CoV-2 sequencing across seven globally distributed sites. Methods In this cross-sectional study from July to August 2022, seven international sites submitted all SARS-CoV-2 sequence data generated over a two-week period to our cloud platform. No patient identifying information was uploaded, and human reads were removed prior to upload to the cloud. Users could opt in to share sample information with collaborators via a tagging system. The pipeline performed sequence assembly, lineage identification and relatedness analysis. Results Seven sites contributed 5,432 sequences, of which 5,342 (98.3%) were from clinical samples and 90 (1.7%) were controls. 4,470/5,342 (83.7%) clinical samples had sufficient coverage for lineage assignment. Omicron lineages dominated, with BA.5, BA.4 and BA.2 comprising the vast majority, consistent with contemporary epidemiological observations at the time. Phylogenetic analysis demonstrated low diversity within lineages, and genotypically identical or highly similar sequences were recovered from globally disparate sites. Conclusions A cloud-based analysis platform like TAP addresses bioinformatic bottlenecks and facilitates international capacity building and collaboration in pathogen surveillance, enhancing global epidemic and pandemic preparedness.
- Davies TJ, Swann J, Sheppard AE, Pickford H, Lipworth S, AbuOun M, Ellington M, Fowler PW, Hopkins S, Hopkins KL, Crook DW, Peto TEA, Anjum MF, Walker AS, Stoesser N (2023).
Discordance between different bioinformatic methods for identifying resistance genes from short-read genomic data, with a focus on Escherichia coli.
mGen 9:001151 doi:10.1099/mgen.0.001151
bioRxiv preprint doi:10.1101/2021.11.03.467004
Abstract
Several bioinformatics genotyping algorithms are now commonly used to characterise antimicrobial resistance (AMR) gene profiles in whole genome sequencing (WGS) data, with a view to understanding AMR epidemiology and developing resistance prediction workflows using WGS in clinical settings. Accurately evaluating AMR in Enterobacterales, particularly Escherichia coli , is of major importance, because this is a common pathogen. However, robust comparisons of different genotyping approaches on relevant simulated and large real-life WGS datasets are lacking. Here, we used both simulated datasets and a large set of real E. coli WGS data (n=1818 isolates) to systematically investigate genotyping methods in greater detail. Simulated constructs and real sequences were processed using four different bioinformatic programs (ABRicate, ARIBA, KmerResistance, and SRST2, run with the ResFinder database) and their outputs compared. For simulations tests where 3,079 AMR gene variants were inserted into random sequence constructs, KmerResistance was correct for 3,076 (99.9%) simulations, ABRicate for 3,054 (99.2%), ARIBA for 2,783 (90.4%) and SRST2 for 2,108 (68.5%). For simulations tests where two closely related gene variants were inserted into random sequence constructs, KmerResistance the correct alleles in 35,338/46,318 (76.3%) ABRicate identified in 11,842/46,318 (25.6%) of simulations, ARIBA in 1679/46,318 (3.6%), and SRST2 in 2000/46,318 (4.3%). In real data, across all methods, 1392/1818 (76%) isolates had discrepant allele calls for at least one gene. Our evaluations revealed poor performance in scenarios that would be expected to be challenging (e.g. identification of AMR genes at <10x coverage, discriminating between closely related AMR gene sequences), but also identified systematic sequence classification (i.e. naming) errors even in straightforward circumstances, which contributed to 1081/3092 (35%) errors in our most simple simulations and at least 2530/4321 (59%) discrepancies in real data. Further, many of the remaining discrepancies were likely “artefactual” with reporting cut-off differences accounting for at least 1430/4321 (33%) discrepants. Comparing outputs generated by running multiple algorithms on the same dataset can help identify and resolve these artefacts, but ideally new and more robust genotyping algorithms are needed. 3. Impact statement Whole-genome sequencing is widely used for studying the epidemiology of antimicrobial resistance (AMR) genes in bacteria; however, there is some concern that outputs are highly dependent on the bioinformatics methods used. This work evaluates these concerns in detail by comparing four different, commonly used AMR gene typing methods using large simulated and real datasets. The results highlight performance issues for most methods in at least one of several simulated and real-life scenarios. However most discrepancies between methods were due to differential labelling of the same sequences related to the assumptions made regarding the underlying structure of the reference resistance gene database used (i.e. that resistance genes can be easily classified in well-defined groups). This study represents a major advance in quantifying and evaluating the nature of discrepancies between outputs of different AMR typing algorithms, with relevance for historic and future work using these algorithms. Some of the discrepancies can be resolved by choosing methods with fewer assumptions about the reference AMR gene database and manually resolving outputs generated using multiple programs. However, ideally new and better methods are needed.
- The CRyPTIC Consortium (2023).
Reply: “Epidemiological cut-off values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis.”.
Eur Resp J 61:2300426 doi:10.1183/13993003.00426-2023
Abstract
The recent publication by the (crypticproject?) proposing ECOFF/ECVs for research attracted two letters to the editor. Here is the response from the CRyPTIC Consortium to both.https://bit.ly/40vRLye
- Brankin AE, Fowler PW (2023).
Inclusion of minor alleles improves catalogue-based prediction of fluoroquinolone resistance in M. tuberculosis.
JAC-Antimicrobial Resistance 5:dlad039 doi:10.1093/jacamr/dlad039
bioRxiv preprint doi:10.1101/2022.11.09.515757
Abstract
Objectives Fluoroquinolone resistance poses a threat to the successful treatment of tuberculosis. WGS, and the subsequent detection of catalogued resistance-associated mutations, offers an attractive solution to fluoroquinolone susceptibility testing but sensitivities are often less than 90%. We hypothesize that this is partly because the bioinformatic pipelines used usually mask the recognition of minor alleles that have been implicated in fluoroquinolone resistance. Methods We analysed the Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) dataset of globally diverse WGS Mycobacterium tuberculosis isolates, with matched MICs for two fluoroquinolone drugs and allowed putative minor alleles to contribute to resistance prediction. Results Detecting minor alleles increased the sensitivity of WGS for moxifloxacin resistance prediction from 85.4% to 94.0%, without significantly reducing specificity. We also found no correlation between the proportion of an M. tuberculosis population containing a resistance-conferring allele and the magnitude of resistance. Conclusions Together our results highlight the importance of detecting minor resistance-conferring alleles when using WGS, or indeed any sequencing-based approach, to diagnose fluoroquinolone resistance.
- Dreyer V, Mandal A, Dev P, Merker M, Barilar I, Utpatel C, Nilgiriwala K, Rodrigues K, Crook DW, The CRyPTIC Consortium, Rasigade JP, Wirth T, Mistry N, Niemann S (2023).
High fluoroquinolone resistance proportions among multidrug-resistant tuberculosis driven by dominant L2 Mycobacterium tuberculosis clones in the Mumbai Metropolitan Region.
Genome Med 22:14 doi:10.1186/s13073-022-01076-0
Abstract
BACKGROUND: Multidrug-resistant (MDR) Mycobacterium tuberculosis complex (MTBC) strains are a serious health problem in India, also contributing to one-fourth of the global MDR tuberculosis (TB) burden. About 36% of the MDR MTBC strains are reported fluoroquinolone (FQ) resistant leading to high pre-extensively drug-resistant (pre-XDR) and XDR-TB (further resistance against bedaquiline and/or linezolid) rates. Still, factors driving the MDR/pre-XDR epidemic in India are not well defined. METHODS: In a retrospective study, we analyzed 1852 consecutive MTBC strains obtained from patients from a tertiary care hospital laboratory in Mumbai by whole genome sequencing (WGS). Univariate and multivariate statistics was used to investigate factors associated with pre-XDR. Core genome multi locus sequence typing, time scaled haplotypic density (THD) method and homoplasy analysis were used to analyze epidemiological success, and positive selection in different strain groups, respectively. RESULTS: In total, 1016 MTBC strains were MDR, out of which 703 (69.2%) were pre-XDR and 45 (4.4%) were XDR. Cluster rates were high among MDR (57.8%) and pre-XDR/XDR (79%) strains with three dominant L2 (Beijing) strain clusters (Cl 1-3) representing half of the pre-XDR and 40% of the XDR-TB cases. L2 strains were associated with pre-XDR/XDR-TB (P < 0.001) and, particularly Cl 1-3 strains, had high first-line and FQ resistance rates (81.6-90.6%). Epidemic success analysis using THD showed that L2 strains outperformed L1, L3, and L4 strains in short- and long-term time scales. More importantly, L2 MDR and MDR + strains had higher THD success indices than their not-MDR counterparts. Overall, compensatory mutation rates were highest in L2 strains and positive selection was detected in genes of L2 strains associated with drug tolerance (prpB and ppsA) and virulence (Rv2828c). Compensatory mutations in L2 strains were associated with a threefold increase of THD indices, suggesting improved transmissibility. CONCLUSIONS: Our data indicate a drastic increase of FQ resistance, as well as emerging bedaquiline resistance which endangers the success of newly endorsed MDR-TB treatment regimens. Rapid changes in treatment and control strategies are required to contain transmission of highly successful pre-XDR L2 strains in the Mumbai Metropolitan region but presumably also India-wide.
- Sonnenkalb L, Carter JJ, Spitaleri A, Iqbal Z, Hunt M, Malone K, Utpatel C, Cirrillo DM, Rodrigues C, Nilgiriwala KS, the CRyPTIC Consortium, Fowler PW, Merker M, Niemann S (2023).
Deciphering Bedaquiline and Clofazimine Resistance in Tuberculosis: An Evolutionary Medicine Approach.
Lancet Microbe 54:e358 00002 doi:10.1016/S2666-5247(23)00002-2
bioRxiv preprint doi:10.1101/2021.03.19.436148
Abstract
BACKGROUND: Bedaquiline is a core drug for the treatment of multidrug-resistant tuberculosis; however, the understanding of resistance mechanisms is poor, which is hampering rapid molecular diagnostics. Some bedaquiline-resistant mutants are also cross-resistant to clofazimine. To decipher bedaquiline and clofazimine resistance determinants, we combined experimental evolution, protein modelling, genome sequencing, and phenotypic data. METHODS: For this in-vitro and in-silico data analysis, we used a novel in-vitro evolutionary model using subinhibitory drug concentrations to select bedaquiline-resistant and clofazimine-resistant mutants. We determined bedaquiline and clofazimine minimum inhibitory concentrations and did Illumina and PacBio sequencing to characterise selected mutants and establish a mutation catalogue. This catalogue also includes phenotypic and genotypic data of a global collection of more than 14 000 clinical Mycobacterium tuberculosis complex isolates, and publicly available data. We investigated variants implicated in bedaquiline resistance by protein modelling and dynamic simulations. FINDINGS: We discerned 265 genomic variants implicated in bedaquiline resistance, with 250 (94%) variants affecting the transcriptional repressor (Rv0678) of the MmpS5-MmpL5 efflux system. We identified 40 new variants in vitro, and a new bedaquiline resistance mechanism caused by a large-scale genomic rearrangement. Additionally, we identified in vitro 15 (7%) of 208 mutations found in clinical bedaquiline-resistant isolates. From our in-vitro work, we detected 14 (16%) of 88 mutations so far identified as being associated with clofazimine resistance and also seen in clinically resistant strains, and catalogued 35 new mutations. Structural modelling of Rv0678 showed four major mechanisms of bedaquiline resistance: impaired DNA binding, reduction in protein stability, disruption of protein dimerisation, and alteration in affinity for its fatty acid ligand. INTERPRETATION: Our findings advance the understanding of drug resistance mechanisms in M tuberculosis complex strains. We have established an extended mutation catalogue, comprising variants implicated in resistance and susceptibility to bedaquiline and clofazimine. Our data emphasise that genotypic testing can delineate clinical isolates with borderline phenotypes, which is essential for the design of effective treatments. FUNDING: Leibniz ScienceCampus Evolutionary Medicine of the Lung, Deutsche Forschungsgemeinschaft, Research Training Group 2501 TransEvo, Rhodes Trust, Stanford University Medical Scientist Training Program, National Institute for Health and Care Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Bill & Melinda Gates Foundation, Wellcome Trust, and Marie Skłodowska-Curie Actions.
2022
- Hunt M, Letcher B, Malone KM, Nguyen G, Hall MB, Colquhoun RM, Lima L, Schatz M, Ramakrishnan S, The CRyPTIC Consortium, Iqbal Z (2022).
Minos: variant adjudication and joint genotyping of cohorts of bacterial genomes.
Genome Biology 23:147 doi:10.1186/s13059-022-02714-x
bioRxiv preprint: doi:10.1101/2021.09.15.460475
Abstract
There are many short-read variant-calling tools, with different strengths and weaknesses. We present a tool, Minos, which combines outputs from arbitrary variant callers, increasing recall without loss of precision. We benchmark on 62 samples from three bacterial species and an outbreak of 385 Mycobacterium tuberculosis samples. Minos also enables joint genotyping; we demonstrate on a large (N=13k) M. tuberculosis cohort, building a map of non-synonymous SNPs and indels in a region where all such variants are assumed to cause rifampicin resistance. We quantify the correlation with phenotypic resistance and then replicate in a second cohort (N=10k).
- The CRyPTIC Consortium (2022).
A data compendium associating the genomes of 12,289 Mycobacterium tuberculosis isolates with quantitative resistance phenotypes to 13 antibiotics.
PLoS Biology 20:e3001721 doi:10.1371/journal.pbio.3001721
bioRxiv preprint doi:10.1101/2021.09.14.460274
Abstract
The Comprehensive Resistance Prediction for Tuberculosis: an International Consortium (CRyPTIC) presents here a data compendium of 12,289 Mycobacterium tuberculosis global clinical isolates, all of which have undergone whole-genome sequencing and have had their minimum inhibitory concentrations to 13 antitubercular drugs measured in a single assay. It is the largest matched phenotypic and genotypic dataset for M. tuberculosis to date. Here, we provide a summary detailing the breadth of data collected, along with a description of how the isolates were selected, collected, and uniformly processed in CRyPTIC partner laboratories across 23 countries. The compendium contains 6,814 isolates resistant to at least 1 drug, including 2,129 samples that fully satisfy the clinical definitions of rifampicin resistant (RR), multidrug resistant (MDR), pre-extensively drug resistant (pre-XDR), or extensively drug resistant (XDR). The data are enriched for rare resistance-associated variants, and the current limits of genotypic prediction of resistance status (sensitive/resistant) are presented by using a genetic mutation catalogue, along with the presence of suspected resistance-conferring mutations for isolates resistant to the newly introduced drugs bedaquiline, clofazimine, delamanid, and linezolid. Finally, a case study of rifampicin monoresistance demonstrates how this compendium could be used to advance our genetic understanding of rare resistance phenotypes. The data compendium is fully open source and it is hoped that it will facilitate and inspire future research for years to come.
- The CRyPTIC Consortium (2022).
Genome-wide association studies of global Mycobacterium tuberculosis resistance to thirteen antimicrobials in 10,228 genomes.
PLoS Biology 20:e3001755 doi:10.1371/journal.pbio.3001755
bioRxiv preprint doi:10.1101/2021.09.14.460272
Abstract
The emergence of drug-resistant tuberculosis is a major global public health concern that threatens the ability to control the disease. Whole-genome sequencing as a tool to rapidly diagnose resistant infections can transform patient treatment and clinical practice. While resistance mechanisms are well understood for some drugs, there are likely many mechanisms yet to be uncovered, particularly for new and repurposed drugs. We sequenced 10,228 Mycobacterium tuberculosis (MTB) isolates worldwide and determined the minimum inhibitory concentration (MIC) on a grid of 2-fold concentration dilutions for 13 antimicrobials using quantitative microtiter plate assays. We performed oligopeptide- and oligonucleotide-based genome-wide association studies using linear mixed models to discover resistance-conferring mechanisms not currently catalogued. Use of MIC over binary resistance phenotypes increased sample heritability for the new and repurposed drugs by 26% to 37%, increasing our ability to detect novel associations. For all drugs, we discovered uncatalogued variants associated with MIC, including in the Rv1218c promoter binding site of the transcriptional repressor Rv1219c (isoniazid), upstream of the vapBC20 operon that cleaves 23S rRNA (linezolid) and in the region encoding an α-helix lining the active site of Cyp142 (clofazimine, all p < 10-7.7). We observed that artefactual signals of cross-resistance could be unravelled based on the relative effect size on MIC. Our study demonstrates the ability of very large-scale studies to substantially improve our knowledge of genetic variants associated with antimicrobial resistance in M. tuberculosis.
- Brankin AE, Fowler PW (2022).
Predicting antibiotic resistance in complex protein targets using alchemical free energy methods.
J Comp Chem 43:1771 doi:10.1002/jcc.26979
chemRxiv preprint doi:10.26434/chemrxiv-2021-shfgp
Abstract
Drug resistant Mycobacterium tuberculosis , which mostly results from single nucleotide polymorphisms in antibiotic target genes, poses a major threat to tuberculosis treatment outcomes. Relative binding free energy (RBFE) calculations can rapidly predict the effects of mutations, but this approach has not been tested on large, complex proteins. We use RBFE calculations to predict the effects of M. tuberculosis RNA polymerase and DNA gyrase mutations on rifampicin and moxifloxacin susceptibility respectively. These mutations encompass a range of amino acid substitutions with known effects and include large steric perturbations and charged moieties. We find that moderate numbers ( n = 3–15) of short RBFE calculations can predict resistance in cases where the mutation results in a large change in the binding free energy. We show that the method lacks discrimination in cases with either a small change in energy or that involve charged amino acids, and we investigate how these calculation errors may be decreased.
- Fowler PW, Wright C, Spiers H, Zhu T, Baeten EML, Hoosdally SW, Gibertoni Cruz AL, Roohi A, Kouchaki S, Walker TM, Peto TEA, Miller G, Lintott C, Clifton D, Crook DW, Walker AS, The CRyPTIC Consortium (2022).
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.
eLife 11:e75046 doi:10.7554/eLife.75046
bioRxiv preprint doi:10.1101/2021.07.20.453060
Abstract
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, each being present at a pre-determined concentration, thereby determining if the sample isresistant or susceptible to each sample. The minimum inhibitory concentration (MIC) of a drug is the lowestconcentration that inhibits growth and is a more useful quantity but requires each sample to be tested at a range ofconcentrations for 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 still 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.
- Hunt M, Swann J, Constantinides B, Fowler PW, Iqbal Z (2022).
ReadItAndKeep: rapid decontamination of SARS-CoV-2 sequencing reads.
Bioinformatics 38:3291 doi:10.1093/bioinformatics/btac311
bioRxiv preprint doi:10.1101/2022.01.21.477194
Abstract
SUMMARY: Viral sequence data from clinical samples frequently contain contaminating human reads, which must be removed prior to sharing for legal and ethical reasons. To enable host read removal for SARS-CoV-2 sequencing data on low-specification laptops, we developed ReadItAndKeep, a fast lightweight tool for Illumina and nanopore data that only keeps reads matching the SARS-CoV-2 genome. Peak RAM usage is typically below 10 MB, and runtime less than 1 min. We show that by excluding the polyA tail from the viral reference, ReadItAndKeep prevents bleed-through of human reads, whereas mapping to the human genome lets some reads escape. We believe our test approach (including all possible reads from the human genome, human samples from each of the 26 populations in the 1000 genomes data and a diverse set of SARS-CoV-2 genomes) will also be useful for others. AVAILABILITY AND IMPLEMENTATION: ReadItAndKeep is implemented in C++, released under the MIT license, and available from https://github.com/GenomePathogenAnalysisService/read-it-and-keep. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
- World Health Organization (2022).
Optimized broth microdilution plate methodology for drug susceptibility testing of Mycobacterium tuberculosis complex.
- The CRyPTIC Consortium (2022).
Epidemiological cutoff values for a 96-well broth microdilution plate for high-throughput research antibiotic susceptibility testing of M. tuberculosis.
Eur Resp J 60:2200239 doi:10.1183/13993003.00239-2022
medRxiv preprint doi:10.1101/2021.02.24.21252386
Abstract
Drug susceptibility testing of M. tuberculosis is rooted in a binary susceptible/resistant paradigm. Whilst there are considerable advantages in measuring the minimum inhibitory concentrations (MICs) of a panel of drugs for an isolate it is n ecessary to measure the epidemiological cutoff values (ECOFF/ECVs) to permit comparison with qualitative data. Here we present ECOFF/ECVs for 13 anti-TB compounds, including bedaquiline and delamanid, derived from 20,637 clinical isolates collected by 14 laboratories based in 11 countries on five continents. Each isolate was incubated for 14 days on a dry 96-well broth microdilution plate and then read. Resistance to most of the drugs due to prior exposure is expected and the MIC distributions for many of the compounds are complex and therefore a phenotypically wild-type population could not be defined. Since a majority of samples also underwent genetic sequencing, we defined a genotypically wild-type population and measured the MIC of the 99 th percentile by direct measurement and via fitting a Gaussian using interval regression. The proposed ECOFF/ECV values were then validated by comparing to the MIC distributions of high-confidence genetic variants that confer resistance and to qualitative drug susceptibility tests obtained via Mycobacterial Growth Indicator Tube and the Microscopic-Observation Drug-Susceptibility assay. These ECOFF/ECV values will inform and encourage the more widespread adoption of broth microdilution – this is a cheap culture-based method that tests the susceptibility of 12-14 antibiotics on a single 96-well plate and so could help personalise the treatment of tuberculosis.
- Walker TM, Miotto P, Köser CU, Fowler PW, Knaggs J, Iqbal Z, Hunt M, Chindelevitch L, Farhat M, Cirillo DM, Comas I, Posey JE, Omar SV, Peto TEA, Suresh A, Uplekar S, Laurent S, Colman R, Nathanson CM, Zignol M, Walker AS, the CRyPTIC Consortium, the Seq&Treat Consortium, Crook DW, Ismail N, Rodwell TC (2022).
The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: A new global standard for molecular diagnostics.
Lancet Microbe 3:E265 doi:10.1016/S2666-5247(21)00301-3
Lancet SSRN preprint doi:10.2139/ssrn.3923444
Abstract
Background Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC).We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. MethodsIn this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs.We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature.We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3).For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values.Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%.A series of expert rules were applied for final confidence grading of each mutation. FindingsWe analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries.38 215 MTBC isolates passed quality control steps and were included in the final analysis.15 667 associations were computed for 13 211 unique mutations linked to one or more drugs.1149 (7•3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0•7%) were deemed consistent with susceptibility.For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%.Specificity was over 95% for all drugs except ethionamide (91•4%), moxifloxacin (91•6%) and ethambutol (93•3%).Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs.Interpretation We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation.The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes.
- Yang Y, Walker TM, Kouchaki S, Wang C, Peto TEA, Crook DW, CRYPTIC Consortium, Clifton DA (2022).
An end-to-end heterogeneous graph attention network for Mycobacterium tuberculosis drug-resistance prediction.
Briefings in Bioinformatics 22:bbab299 doi:10.1093/bib/bbab299
Abstract
Antimicrobial resistance (AMR) poses a threat to global public health. To mitigate the impacts of AMR, it is important to identify the molecular mechanisms of AMR and thereby determine optimal therapy as early as possible. Conventional machine learning-based drug-resistance analyses assume genetic variations to be homogeneous, thus not distinguishing between coding and intergenic sequences. In this study, we represent genetic data from Mycobacterium tuberculosis as a graph, and then adopt a deep graph learning method-heterogeneous graph attention network ('HGAT-AMR')-to predict anti-tuberculosis (TB) drug resistance. The HGAT-AMR model is able to accommodate incomplete phenotypic profiles, as well as provide 'attention scores' of genes and single nucleotide polymorphisms (SNPs) both at a population level and for individual samples. These scores encode the inputs, which the model is 'paying attention to' in making its drug resistance predictions. The results show that the proposed model generated the best area under the receiver operating characteristic (AUROC) for isoniazid and rifampicin (98.53 and 99.10%), the best sensitivity for three first-line drugs (94.91% for isoniazid, 96.60% for ethambutol and 90.63% for pyrazinamide), and maintained performance when the data were associated with incomplete phenotypes (i.e. for those isolates for which phenotypic data for some drugs were missing). We also demonstrate that the model successfully identifies genes and SNPs associated with drug resistance, mitigating the impact of resistance profile while considering particular drug resistance, which is consistent with domain knowledge.
2021
- Adams O, Deme JC, Parker JL, the CRyPTIC Consortium, Fowler PW, Lea SM, Newstead S (2021).
Cryo-EM Structure and Resistance Landscape of M. tuberculosis MmpL3: An Emergent Therapeutic Target.
Structure 29:1182 doi:10.1016/j.str.2021.06.013
Abstract
Tuberculosis (TB) is the leading cause of death from a single infectious agent and in 2019 an estimated 10 million people worldwide contracted the disease. Although treatments for TB exist, continual emergence of drug-resistant variants necessitates urgent development of novel antituberculars. An important new target is the lipid transporter MmpL3, which is required for construction of the unique cell envelope that shields Mycobacterium tuberculosis (Mtb) from the immune system. However, a structural understanding of the mutations in Mtb MmpL3 that confer resistance to the many preclinical leads is lacking, hampering efforts to circumvent resistance mechanisms. Here, we present the cryoelectron microscopy structure of Mtb MmpL3 and use it to comprehensively analyze the mutational landscape of drug resistance. Our data provide a rational explanation for resistance variants local to the central drug binding site, and also highlight a potential alternative route to resistance operating within the periplasmic domain.
- Ismail NA, Nathanson CM, Korobitsyn A, Zignol M, Kasaeva T, Rodwell T, Miotto P, Walker TM, 2021 66 Fowler PW, Knaggs J, Iqbal Z, Hunt M, Chindelevitch L, Farhat M, Cirillo D, Crook DW, Comas I, Posey J, Vally Omar S, Peto TEA, Walker AS, Suresh A, Uplekar S, Laurent S, Colman R (2021).
Catalogue of mutations in M. tuberculosis complex and their association with drug resistance.
- Lumley SF, Rodger G, Constantinides B, Sanderson N, Chau KK, Street TL, O’Donnell D, Howarth A, Hatch SB, Marsden BD, Cox S, James T, Warren F, Peck LJ, Ritter TG, de Toledo Z, Warren L, Axten D, Cornall RJ, Jones EY, Stuart DI, Screaton G, Ebner D, Hoosdally S, Chand M, Oxford University Hospitals Staff Testing Group, Crook DW, O’Donnell A, Conlon CP, Pouwels KB, Walker AS, Peto TEA, Hopkins S, Walker TM, Stoesser NE, Matthews PC, Jeffery K, Eyre DW (2021).
An observational cohort study on the incidence of SARS-CoV-2 infection and B.1.1.7 variant infection in healthcare workers by antibody and vaccination status.
Clin Infec Dis 74:1208 doi:10.1093/cid/ciab608
medRxiv preprint doi:10.1101/2021.03.09.21253218
Abstract
BACKGROUND: Natural and vaccine-induced immunity will play a key role in controlling the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. SARS-CoV-2 variants have the potential to evade natural and vaccine-induced immunity. METHODS: In a longitudinal cohort study of healthcare workers (HCWs) in Oxfordshire, United Kingdom, we investigated the protection from symptomatic and asymptomatic polymerase chain reaction (PCR)-confirmed SARS-CoV-2 infection conferred by vaccination (Pfizer-BioNTech BNT162b2, Oxford-AstraZeneca ChAdOx1 nCOV-19) and prior infection (determined using anti-spike antibody status), using Poisson regression adjusted for age, sex, temporal changes in incidence and role. We estimated protection conferred after 1 versus 2 vaccinations and from infections with the B.1.1.7 variant identified using whole genome sequencing. RESULTS: In total, 13 109 HCWs participated; 8285 received the Pfizer-BioNTech vaccine (1407 two doses), and 2738 the Oxford-AstraZeneca vaccine (49 two doses). Compared to unvaccinated seronegative HCWs, natural immunity and 2 vaccination doses provided similar protection against symptomatic infection: no HCW vaccinated twice had symptomatic infection, and incidence was 98% lower in seropositive HCWs (adjusted incidence rate ratio 0.02 [95% confidence interval {CI} < .01-.18]). Two vaccine doses or seropositivity reduced the incidence of any PCR-positive result with or without symptoms by 90% (0.10 [95% CI .02-.38]) and 85% (0.15 [95% CI .08-.26]), respectively. Single-dose vaccination reduced the incidence of symptomatic infection by 67% (0.33 [95% CI .21-.52]) and any PCR-positive result by 64% (0.36 [95% CI .26-.50]). There was no evidence of differences in immunity induced by natural infection and vaccination for infections with S-gene target failure and B.1.1.7. CONCLUSIONS: Natural infection resulting in detectable anti-spike antibodies and 2 vaccine doses both provide robust protection against SARS-CoV-2 infection, including against the B.1.1.7 variant.
- Lumley S F, O’Donnell D, Stoesser N E, Matthews P C, Howarth A, Hatch S B, Marsden B D, Cox S, James T, Warren F, Peck L J, Ritter T G, de Toledo Z, Warren L, Axten D, Cornall R J, Jones E Y, Stuart D I, Screaton G, Ebner D, Hoosdally S, Chand M, Crook D W, O’Donnell A M, Conlon C P, Pouwels K B, Walker A S, Peto T E, Hopkins S, Walker T M, Jeffery K, Eyre D W (2021).
Antibody Status and Incidence of SARS-CoV-2 Infection in Health Care Workers.
N Eng J Med 384:533 doi:10.1056/NEJMoa2034545
Abstract
BACKGROUND: The relationship between the presence of antibodies to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the risk of subsequent reinfection remains unclear. METHODS: We investigated the incidence of SARS-CoV-2 infection confirmed by polymerase chain reaction (PCR) in seropositive and seronegative health care workers attending testing of asymptomatic and symptomatic staff at Oxford University Hospitals in the United Kingdom. Baseline antibody status was determined by anti-spike (primary analysis) and anti-nucleocapsid IgG assays, and staff members were followed for up to 31 weeks. We estimated the relative incidence of PCR-positive test results and new symptomatic infection according to antibody status, adjusting for age, participant-reported gender, and changes in incidence over time. RESULTS: A total of 12,541 health care workers participated and had anti-spike IgG measured; 11,364 were followed up after negative antibody results and 1265 after positive results, including 88 in whom seroconversion occurred during follow-up. A total of 223 anti-spike-seronegative health care workers had a positive PCR test (1.09 per 10,000 days at risk), 100 during screening while they were asymptomatic and 123 while symptomatic, whereas 2 anti-spike-seropositive health care workers had a positive PCR test (0.13 per 10,000 days at risk), and both workers were asymptomatic when tested (adjusted incidence rate ratio, 0.11; 95% confidence interval, 0.03 to 0.44; P = 0.002). There were no symptomatic infections in workers with anti-spike antibodies. Rate ratios were similar when the anti-nucleocapsid IgG assay was used alone or in combination with the anti-spike IgG assay to determine baseline status. CONCLUSIONS: The presence of anti-spike or anti-nucleocapsid IgG antibodies was associated with a substantially reduced risk of SARS-CoV-2 reinfection in the ensuing 6 months. (Funded by the U.K. Government Department of Health and Social Care and others.).
- Lumley S F, Wei J, O’Donnell D, Stoesser N E, Matthews P C, Howarth A, Hatch S B, Marsden B D, Cox S, James T, Peck L J, Ritter T G, de Toledo Z, Cornall R J, Jones E Y, Stuart D I, Screaton G, Ebner D, Hoosdally S, Crook D W, Conlon C P, Pouwels K B, Walker A S, Peto T E A, Walker T M, Jeffery K, Eyre D W, Oxford University Hospitals Staff Testing Group, Abbott M, Abhari R, Abuelgasim H, Ahmed-Firani T, Alhussni A, Argles I, Ashworth R, Back F, Black S, Brennan I, Cansdale L, Chakravorty T, Choudhury Z, Clay H, Conway-Jones R, Curtis A, Cutteridge J, Danbury H, Davies H, Denly L, Dequaire J, Dimitrov S, Dunlop E, Dunn P, East C, Eastwood B, Edwardes C, Evans M, Fields C, Flaherty S, Foord T, Freer R, Fuchs H, Grassam-Rowe A, Greenan-Barrett L, Gunnell J, Harries C, Harris E, Hatton E, Hennessy C, Holland C, Holloway B, Howgego G, Barnham I J, Johnson S, Johnson T, Johnstone D, Jurdon R, Kelly G, Kerneis S, Khulusi B, Killen A, Kourdov S, Kumarendran M, Laurenson-Schafer H, Lawrence R, Lee C, Livingstone A, Lynch R, Madsen T, Manji A, Marsh J, Mason C, Mccance A, Mcgivern E, Mckinlay A, Mighiu A, Millar L, Mohamed Y, Motohashi K, Myat S, Nezhentsev A, O’Byrne K, Okuwoga T, Oliver M, Pattenden S, Perera V, Petit Y, Pickles F, Pikoula M, Randhawa G, Ravi K, Reed L, Rigler C, Robinson C, Rothwell E, Santos-Paulo S, Savva C, Scharmeli A, Scott S, Shabir Z, Sharma I, Sheehan R, Shibu A, Somanathan A, Sussmes S, Sweeney O, Swift E, Tadikamalla G, Tamblyn M, Taylor A, Naudé R T W, Thomas S, Thompson C, Till L, Toward R, Virgo C, Vorley I, Waite S, Ward H, Ward J, Watson A, Watson L, Wedlich M, Wilkins L, Wilson J, Wilson J, Wong W, Young R, Doherty G, Kavanagh J, Chau K K, Fowler P W, Swann J, Volk D, Yang-Turner F, Skelly D, Karpe F, Neville M J, Ferreira L M, Christott T, Rodger G, Pickford H, Cameron S, Tamblin-Hopper P, Amini A, Andersson M, Anson K, Aston L, Axten D, Barot S, Bellinger T, Bialek A, Bodo N, Brent A, Campbell M, Cox V, Cronin T, Davies T, Deales A, Downs L, Drennan P, Dudareva M, Gates L, Gates M, Georgiou D, Gergely B, Holland L, Jones N, Jones S, Kerr S, Lawson E, Looms S, Luciw M, Martins D, Mcknight M, Mentzer A, Millard R, Mitchell T, Morgan L, Morrow J, Mortimore E, Mullins H, Nevard H, O’Donnell A M, Patel K, Peto L, Philips J, Ponting J, Ray R, Robles M, Rughani S, Sande N, Shaw R, Simons E J, Sims E, Smit K, Smith L, Szczurkowska J, Thompson Z, Turford R, Vilca K, Vogt A, Warren F, Warren L, Wharton V, Whitty A, Woodley H, Wozniak B, Young B, Justice A, Jesuthasan G, Wareing S, Fadzillah N H M, Cann K, Kirton R, Sutton C, Salvagno C, D’Amato G, Pill G, Butcher L, Rylance-Knight L, Tabirao M, Moroney R, Wright S (2021).
The Duration, Dynamics, and Determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Antibody Responses in Individual Healthcare Workers.
Clinical Infectious Diseases 73:e699 doi:10.1093/cid/ciab004
medRxiv preprint doi:10.1101/2020.11.02.20224824
Abstract
BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibody measurements can be used to estimate the proportion of a population exposed or infected and may be informative about the risk of future infection. Previous estimates of the duration of antibody responses vary. METHODS: We present 6 months of data from a longitudinal seroprevalence study of 3276 UK healthcare workers (HCWs). Serial measurements of SARS-CoV-2 anti-nucleocapsid and anti-spike IgG were obtained. Interval censored survival analysis was used to investigate the duration of detectable responses. Additionally, Bayesian mixed linear models were used to investigate anti-nucleocapsid waning. RESULTS: Anti-spike IgG levels remained stably detected after a positive result, for example, in 94% (95% credibility interval [CrI] 91-96%) of HCWs at 180 days. Anti-nucleocapsid IgG levels rose to a peak at 24 (95% CrI 19-31) days post first polymerase chain reaction (PCR)-positive test, before beginning to fall. Considering 452 anti-nucleocapsid seropositive HCWs over a median of 121 days from their maximum positive IgG titer, the mean estimated antibody half-life was 85 (95% CrI 81-90) days. Higher maximum observed anti-nucleocapsid titers were associated with longer estimated antibody half-lives. Increasing age, Asian ethnicity, and prior self-reported symptoms were independently associated with higher maximum anti-nucleocapsid levels and increasing age and a positive PCR test undertaken for symptoms with longer anti-nucleocapsid half-lives. CONCLUSIONS: SARS-CoV-2 anti-nucleocapsid antibodies wane within months and fall faster in younger adults and those without symptoms. However, anti-spike IgG remains stably detected. Ongoing longitudinal studies are required to track the long-term duration of antibody levels and their association with immunity to SARS-CoV-2 reinfection.
2020
- Fowler PW (2020).
How quickly can we predict trimethoprim resistance using alchemical free energy methods?.
Interface Focus 10:20190141 doi:10.1098/rsfs.2019.0141
bioRxiv preprint doi:10.1098/rsfs.2019.0141
Abstract
The emergence of antimicrobial resistance threatens modern medicine and necessitates more personalized treatment of bacterial infections. Sequencing the whole genome of the pathogen(s) in a clinical sample offers one way to improve clinical microbiology diagnostic services, and has already been adopted for tuberculosis in some countries. A key weakness of a genetics clinical microbiology is it cannot return a result for rare or novel genetic variants and therefore predictive methods are required. Non-synonymous mutations in the S. aureusdfrB gene can be successfully classified as either conferring resistance (or not) by calculating their effect on the binding free energy of the antibiotic, trimethoprim. The underlying approach, alchemical free energy methods, requires large numbers of molecular dynamics simulations to be run. We show that a large number (N = 15) of binding free energies calculated from a series of very short (50 ps) molecular dynamics simulations are able to satisfactorily classify all seven mutations in our clinically derived testset. A result for a single mutation could therefore be returned in less than an hour, thereby demonstrating that this or similar methods are now sufficiently fast and reproducible for clinical use.
- Eyre D W, Lumley S F, O’Donnell D, Campbell M, Sims E, Lawson E, Warren F, James T, Cox S, Howarth A, Doherty G, Hatch S B, Kavanagh J, Chau K K, Fowler P W, Swann J, Volk D, Yang-Turner F, Stoesser N, Matthews P C, Dudareva M, Davies T, Shaw R H, Peto L, Downs L O, Vogt A, Amini A, Young B C, Drennan P G, Mentzer A J, Skelly D T, Karpe F, Neville M J, Andersson M, Brent A J, Jones N, Martins Ferreira L, Christott T, Marsden B D, Hoosdally S, Cornall R, Crook D W, Stuart D I, Screaton G, Oxford University Hospitals Staff Testing Group, Watson A J, Taylor A, Chetwynd A, Grassam-Rowe A, Mighiu A S, Livingstone A, Killen A, Rigler C, Harries C, East C, Lee C, Mason C J, Holland C, Thompson C, Hennesey C, Savva C, Kim D S, Harris E W, McGivern E J, Qian E, Rothwell E, Back F, Kelly G, Watson G, Howgego G, Chase H, Danbury H, Laurenson-Schafer H, Ward H L, Hendron H, Vorley I C, Tol I, Gunnell J, Ward J L, Drake J, Wilson J D, Morton J, Dequaire J, O’Byrne K, Motohashi K, Harper K, Ravi K, Millar L J, Peck L J, Oliver M, English M R, Kumarendran M, Wedlich M, Ambler O, Deal O T, Sweeney O, Cowie P, Naudé R t W, Young R, Freer R, Scott S, Sussmes S, Peters S, Pattenden S, Waite S, Johnson S A, Kourdov S, Santos-Paulo S, Dimitrov S, Kerneis S, Ahmed-Firani T, King T B, Ritter T G, Foord T H, De Toledo Z, Christie T, Gergely B, Axten D, Simons E J, Nevard H, Philips J, Szczurkowska J, Patel K, Smit K, Warren L, Morgan L, Smith L, Robles M, McKnight M, Luciw M, Gates M, Sande N, Turford R, Ray R, Rughani S, Mitchell T, Bellinger T, Wharton V, Justice A, Jesuthasan G, Wareing S, Huda Mohamad Fadzillah N, Cann K, Kirton R, Sutton C, Salvagno C, DAmato G, Pill G, Butcher L, Rylance-Knight L, Tabirao M, Moroney R, Wright S, Peto T E, Holthof B, O’Donnell A M, Ebner D, Conlon C P, Jeffery K, Walker T M (2020).
Differential occupational risks to healthcare workers from SARS-CoV-2 observed during a prospective observational study.
eLife 9:e60675 doi:10.7554/eLife.60675
medRxiv preprint doi:10.1101/2020.06.24.20135038
Abstract
We conducted voluntary Covid-19 testing programmes for symptomatic and asymptomatic staff at a UK teaching hospital using naso-/oro-pharyngeal PCR testing and immunoassays for IgG antibodies. 1128/10,034 (11.2%) staff had evidence of Covid-19 at some time. Using questionnaire data provided on potential risk-factors, staff with a confirmed household contact were at greatest risk (adjusted odds ratio [aOR] 4.82 [95%CI 3.45-6.72]). Higher rates of Covid-19 were seen in staff working in Covid-19-facing areas (22.6% vs. 8.6% elsewhere) (aOR 2.47 [1.99-3.08]). Controlling for Covid-19-facing status, risks were heterogenous across the hospital, with higher rates in acute medicine (1.52 [1.07-2.16]) and sporadic outbreaks in areas with few or no Covid-19 patients. Covid-19 intensive care unit staff were relatively protected (0.44 [0.28-0.69]), likely by a bundle of PPE-related measures. Positive results were more likely in Black (1.66 [1.25-2.21]) and Asian (1.51 [1.28-1.77]) staff, independent of role or working location, and in porters and cleaners (2.06 [1.34-3.15]).
- Davies TJ, Stoesser N, Sheppard AE, Abuoun M, Fowler PW, Swann J, Quan P, Griffiths D, Vaughan A, Morgan M, Phan HTT, Jeffery KJM, Andersson M, Eillington M, Ekelund O, Mathers A, Bonomo R, Woodford N, Crook DW, Peto TEA, Anjum M, Walker AS (2020).
Reconciling the potentially irreconcilable? Genotypic and phenotypic amoxicillin-clavulanate resistance in E. coli.
Antimicrobial Agent Chemo 64:e02026 doi:10.1128/aac.02026-19
bioRxiv preprint doi:10.1101/511402
Abstract
Resistance to amoxicillin-clavulanate, a widely used beta-lactam/beta-lactamase inhibitor combination antibiotic, is rising globally, yet susceptibility testing remains challenging. To test whether whole-genome sequencing (WGS) could provide a more reliable assessment of susceptibility than traditional methods, we predicted resistance from WGS for 976 E. coli bloodstream infection isolates from Oxfordshire, UK, comparing against phenotypes from the BD Phoenix (calibrated against EUCAST guidelines). 339/976 (35%) isolates were amoxicillin-clavulanate resistant. Predictions based solely on beta-lactamase presence/absence performed poorly (sensitivity 23% (78/339)) but improved when genetic features associated with penicillinase hyper-production (e.g. promoter mutations, copy number estimates) were considered (sensitivity 82% (277/339); p<0.0001). Most discrepancies occurred in isolates with peri-breakpoint MICs. We investigated two potential causes; the phenotypic reference and the binary resistant/susceptible classification. We performed reference standard, replicated phenotyping in a random stratified subsample of 261/976 (27%) isolates using agar dilution, following both EUCAST and CLSI guidelines, which use different clavulanate concentrations. As well as disagreeing with each other, neither agar dilution phenotype aligned perfectly with genetic features. A random-effects model investigating associations between genetic features and MICs showed that some genetic features had small, variable and additive effects, resulting in variable resistance classification. Using model fixed-effects to predict MICs for the non-agar dilution isolates, predicted MICs were in essential agreement (±1 doubling dilution) with observed (BD Phoenix) MICs for 691/715 (97%) isolates. This suggests amoxicillin-clavulanate resistance in E. coli is quantitative, rather than qualitative, explaining the poorly reproducible binary (resistant/susceptible) phenotypes and suboptimal concordance between different phenotypic methods and with WGS-based predictions.
- Kouchaki S, Yang Y, Lachapelle A, Walker TM, Walker AS, CRyPTIC Consortium, Peto TEA, Crook DW, Clifton DA (2020).
Multi-label random forest model for tuberculosis drug resistance classification and mutation ranking.
Frontiers in Microbiology 11:667 doi:10.3389/fmicb.2020.00667
Abstract
Resistance prediction and mutation ranking are important tasks in the analysis of Tuberculosis sequence data. Due to standard regimens for the use of first-line antibiotics, resistance co-occurrence, in which samples are resistant to multiple drugs, is common. Analysing all drugs simultaneously should therefore enable patterns reflecting resistance co-occurrence to be exploited for resistance prediction. Here, multi-label random forest (MLRF) models are compared with single-label random forest (SLRF) for both predicting phenotypic resistance from whole genome sequences and identifying important mutations for better prediction of four first-line drugs in a dataset of 13402 Mycobacterium tuberculosis isolates. Results confirmed that MLRFs can improve performance compared to conventional clinical methods (by 18.10%) and SLRFs (by 0.91%). In addition, we identified a list of candidate mutations that are important for resistance prediction or that are related to resistance co-occurrence. Moreover, we found that retraining our analysis to a subset of top-ranked mutations was sufficient to achieve satisfactory performance. The source code can be found at http://www.robots.ox.ac.uk/~davidc/code.php.
- Wilson DJ, CRyPTIC Consortium (2020).
GenomegaMap: within-species genome-wide dN/dS estimation from over 10,000 genomes.
Mol Biol Evol 37:2450 doi:10.1093/molbev/msaa069
biorXiv preprint doi:10.1101/523316
Abstract
The dN/dS ratio provides evidence of adaptation or functional constraint in protein-coding genes by quantifying the relative excess or deficit of amino acid-replacing versus silent nucleotide variation. Inexpensive sequencing promises a better understanding of parameters, such as dN/dS, but analyzing very large data sets poses a major statistical challenge. Here, I introduce genomegaMap for estimating within-species genome-wide variation in dN/dS, and I apply it to 3,979 genes across 10,209 tuberculosis genomes to characterize the selection pressures shaping this global pathogen. GenomegaMap is a phylogeny-free method that addresses two major problems with existing approaches: 1) It is fast no matter how large the sample size and 2) it is robust to recombination, which causes phylogenetic methods to report artefactual signals of adaptation. GenomegaMap uses population genetics theory to approximate the distribution of allele frequencies under general, parent-dependent mutation models. Coalescent simulations show that substitution parameters are well estimated even when genomegaMap's simplifying assumption of independence among sites is violated. I demonstrate the ability of genomegaMap to detect genuine signatures of selection at antimicrobial resistance-conferring substitutions in Mycobacterium tuberculosis and describe a novel signature of selection in the cold-shock DEAD-box protein A gene deaD/csdA. The genomegaMap approach helps accelerate the exploitation of big data for gaining new insights into evolution within species.
- Merker M, Kohl TA, Barilar I, Andres S, Fowler PW, Chryssanthou E, Ängeby K, Jureen P, Moradigaravand D, Parkhill J, Peacock SJ, Schön T, Maurer F, Walker TM, Köser C, Niemann S (2020).
Phylogenetically informative mutations in genes implicated in antibiotic resistance in Mycobacterium tuberculosis complex.
Genome Medicine 12:27 doi:10.1186/s13073-020-00726-5
Abstract
BACKGROUND: A comprehensive understanding of the pre-existing genetic variation in genes associated with antibiotic resistance in the Mycobacterium tuberculosis complex (MTBC) is needed to accurately interpret whole-genome sequencing data for genotypic drug susceptibility testing (DST). METHODS: We investigated mutations in 92 genes implicated in resistance to 21 anti-tuberculosis drugs using the genomes of 405 phylogenetically diverse MTBC strains. The role of phylogenetically informative mutations was assessed by routine phenotypic DST data for the first-line drugs isoniazid, rifampicin, ethambutol, and pyrazinamide from a separate collection of over 7000 clinical strains. Selected mutations/strains were further investigated by minimum inhibitory concentration (MIC) testing. RESULTS: Out of 547 phylogenetically informative mutations identified, 138 were classified as not correlating with resistance to first-line drugs. MIC testing did not reveal a discernible impact of a Rv1979c deletion shared by M. africanum lineage 5 strains on resistance to clofazimine. Finally, we found molecular evidence that some MTBC subgroups may be hyper-susceptible to bedaquiline and clofazimine by different loss-of-function mutations affecting a drug efflux pump subunit (MmpL5). CONCLUSIONS: Our findings underline that the genetic diversity in MTBC has to be studied more systematically to inform the design of clinical trials and to define sound epidemiologic cut-off values (ECOFFs) for new and repurposed anti-tuberculosis drugs. In that regard, our comprehensive variant catalogue provides a solid basis for the interpretation of mutations in genotypic as well as in phenotypic DST assays.
2019
- Hunt M, Bradley P, Lapierre SG, Heys S, Thomsit M, Hall MB, Malone KM, Wintringer P, Walker TM, Cirillo DM, Comas I, Farhat MR, Fowler PW, Gardy J, Ismail N, Kohl TA, Mathys V, Merker M, Niemann S, Omar SV, Sintchenko V, Smith G, van Soolingen D, Supply P, Tahseen S, Wilcox M, Arandjelovic I, Peto TEA, Crook DW, Iqbal Z (2019).
Antibiotic resistance prediction for Mycobacterium tuberculosis from genome sequence data with Mykrobe.
Wellcome Open Research 4:191 doi:10.12688/wellcomeopenres.15603.1
Abstract
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.
- Brankin AE, Fowler PW (2019).
Predicting Resistance is (Not) Futile.
ACS Cent Sci 5:1312 doi:10.1021/acscentsci.9b00791
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVFirst ReactionsNEXTPredicting Resistance Is (Not) FutileTackling drug resistance needs predictive methods rooted in chemistry that can contribute to both diagnostics and drug development.Alice E. BrankinAlice E. BrankinNuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, United KingdomMore by Alice E. Brankinhttp://orcid.org/0000-0002-5070-6525 and Philip W. FowlerPhilip W. FowlerNuffield Department of Medicine, John Radcliffe Hospital, University of Oxford, Headley Way, Oxford, OX3 9DU, United KingdomE-mail: [email protected]More by Philip W. Fowlerhttp://orcid.org/0000-0003-0912-4483Cite this: ACS Cent. Sci. 2019, 5, 8, 1312–1314Publication Date (Web):August 15, 2019Publication History Published online15 August 2019Published inissue 28 August 2019https://doi.org/10.1021/acscentsci.9b00791Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceArticle Views1417Altmetric-Citations3LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit PDF (426 KB) Get e-Alerts Get e-Alerts
- Collins JJ, Brown E, Baym M, Wright GD, Dantas G, Burrows L, Liu GY, Fowler PW, Whitchurch CB, Skelly AN, Honda K, Strathdee SA, Patterson TL (2019).
Overcoming Antibiotic Resistance.
Cell Host Microbe 26:8 doi:10.1016/j.chom.2019.06.007
Abstract
We invited eleven experts in their fields to share their insights on the growing threats of antibiotic resistance, and how we, as a community, can address them.
- Yang-Turner F, Volk D, Fowler PW, Hoosdally S, Peto TEA, Crook DW (2019).
Scalable Pathogen Pipeline Platform (SP3) Enabling Unified Genomic Data Analysis with Elastic Cloud Computing.
IEEE Cloud 2019 478 doi:10.1109/CLOUD.2019.00083
Abstract
Pathogen genomic data analysis can be extremely bespoke and diverse. This paper presents our plan and progress towards creating a Scalable Pathogen Pipeline Platform (SP3) providing an efficient and unified process of collecting, analysing and comparing genomic data analysis with the benefit of elastic cloud computing. SP3enables container-centric bioinformatic workflows run on personal computers, High-performance computing (HPC) clusters and cloud platforms. We have deployed and tested SP3on local HPC, Google Cloud Platform (GCP), Microsoft Azure and OpenStack Platforms. SP3allows users to fetch genomic sequencing data from European Nucleotide Archive (ENA) and conduct analysis with open-source bioinformatic pipelines. We believe SP3will promote common standards around pathogen genomic data quality, data processing and data analysis, helping answer the challenges of tools divergence and leveraging a pool of public genomic data repository and cloud resources.
- Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, Shamout F, CRyPTIC Consortium, Zhu T, Clifton DA (2019).
DeepAMR for predicting co-occurrent resistance of Mycobacterium tuberculosis.
Bioinformatics 35:3240 doi:10.1093/bioinformatics/btz067
Abstract
MOTIVATION: Resistance co-occurrence within first-line anti-tuberculosis (TB) drugs is a common phenomenon. Existing methods based on genetic data analysis of Mycobacterium tuberculosis (MTB) have been able to predict resistance of MTB to individual drugs, but have not considered the resistance co-occurrence and cannot capture latent structure of genomic data that corresponds to lineages. RESULTS: We used a large cohort of TB patients from 16 countries across six continents where whole-genome sequences for each isolate and associated phenotype to anti-TB drugs were obtained using drug susceptibility testing recommended by the World Health Organization. We then proposed an end-to-end multi-task model with deep denoising auto-encoder (DeepAMR) for multiple drug classification and developed DeepAMR_cluster, a clustering variant based on DeepAMR, for learning clusters in latent space of the data. The results showed that DeepAMR outperformed baseline model and four machine learning models with mean AUROC from 94.4% to 98.7% for predicting resistance to four first-line drugs [i.e. isoniazid (INH), ethambutol (EMB), rifampicin (RIF), pyrazinamide (PZA)], multi-drug resistant TB (MDR-TB) and pan-susceptible TB (PANS-TB: MTB that is susceptible to all four first-line anti-TB drugs). In the case of INH, EMB, PZA and MDR-TB, DeepAMR achieved its best mean sensitivity of 94.3%, 91.5%, 87.3% and 96.3%, respectively. While in the case of RIF and PANS-TB, it generated 94.2% and 92.2% sensitivity, which were lower than baseline model by 0.7% and 1.9%, respectively. t-SNE visualization shows that DeepAMR_cluster captures lineage-related clusters in the latent space. AVAILABILITY AND IMPLEMENTATION: The details of source code are provided at http://www.robots.ox.ac.uk/∼davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
- Kouchaki S, Yang Y, Walker TM, Walker AS, Wilson DJ, Peto TEA, Crook DW, CRyPTIC Consortium, Clifton DA (2019).
Application of machine learning techniques to tuberculosis drug resistance analysis.
Bioinformatics 35:2276 doi:10.1093/bioinformatics/bty949
Abstract
MOTIVATION: Timely identification of Mycobacterium tuberculosis (MTB) resistance to existing drugs is vital to decrease mortality and prevent the amplification of existing antibiotic resistance. Machine learning methods have been widely applied for timely predicting resistance of MTB given a specific drug and identifying resistance markers. However, they have been not validated on a large cohort of MTB samples from multi-centers across the world in terms of resistance prediction and resistance marker identification. Several machine learning classifiers and linear dimension reduction techniques were developed and compared for a cohort of 13 402 isolates collected from 16 countries across 6 continents and tested 11 drugs. RESULTS: Compared to conventional molecular diagnostic test, area under curve of the best machine learning classifier increased for all drugs especially by 23.11%, 15.22% and 10.14% for pyrazinamide, ciprofloxacin and ofloxacin, respectively (P < 0.01). Logistic regression and gradient tree boosting found to perform better than other techniques. Moreover, logistic regression/gradient tree boosting with a sparse principal component analysis/non-negative matrix factorization step compared with the classifier alone enhanced the best performance in terms of F1-score by 12.54%, 4.61%, 7.45% and 9.58% for amikacin, moxifloxacin, ofloxacin and capreomycin, respectively, as well increasing area under curve for amikacin and capreomycin. Results provided a comprehensive comparison of various techniques and confirmed the application of machine learning for better prediction of the large diverse tuberculosis data. Furthermore, mutation ranking showed the possibility of finding new resistance/susceptible markers. AVAILABILITY AND IMPLEMENTATION: The source code can be found at http://www.robots.ox.ac.uk/ davidc/code.php. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
2018
- Fowler PW, Gibertoni-Cruz AL, Hoosdally S, Jarrett L, Borroni E, Chiacchiaretta M, Rathod P, Lehmann S, Molodtsov N, Grazian C, Walker TM, Robinson E, Hoffmann H, Peto TEA, Cirillo DM, Smith EG, Crook DW (2018).
Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of Mycobacterium tuberculosis.
Microbiology 164:1522 doi:10.1099/mic.0.000733
biorXiv preprint doi:10.1101/229427
Abstract
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 MICs to be elucidated. The three participating laboratories each inoculated 38 96-well plates with 15 known M. tuberculosis strains (including the standard H37Rv reference strain) and, after 2 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. The 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 (>30000) of samples of M. tuberculosis from patients over the next few years.
- The CRyPTIC Consortium, the 100, 000 Genomes Project (2018).
Prediction of Susceptibility to First-Line Tuberculosis Drugs by DNA Sequencing.
N Eng J Med 379:1403 doi:10.1056/NEJMoa1800474
Abstract
BACKGROUND: The World Health Organization recommends drug-susceptibility testing of Mycobacterium tuberculosis complex for all patients with tuberculosis to guide treatment decisions and improve outcomes. Whether DNA sequencing can be used to accurately predict profiles of susceptibility to first-line antituberculosis drugs has not been clear. METHODS: We obtained whole-genome sequences and associated phenotypes of resistance or susceptibility to the first-line antituberculosis drugs isoniazid, rifampin, ethambutol, and pyrazinamide for isolates from 16 countries across six continents. For each isolate, mutations associated with drug resistance and drug susceptibility were identified across nine genes, and individual phenotypes were predicted unless mutations of unknown association were also present. To identify how whole-genome sequencing might direct first-line drug therapy, complete susceptibility profiles were predicted. These profiles were predicted to be susceptible to all four drugs (i.e., pansusceptible) if they were predicted to be susceptible to isoniazid and to the other drugs or if they contained mutations of unknown association in genes that affect susceptibility to the other drugs. We simulated the way in which the negative predictive value changed with the prevalence of drug resistance. RESULTS: A total of 10,209 isolates were analyzed. The largest proportion of phenotypes was predicted for rifampin (9660 [95.4%] of 10,130) and the smallest was predicted for ethambutol (8794 [89.8%] of 9794). Resistance to isoniazid, rifampin, ethambutol, and pyrazinamide was correctly predicted with 97.1%, 97.5%, 94.6%, and 91.3% sensitivity, respectively, and susceptibility to these drugs was correctly predicted with 99.0%, 98.8%, 93.6%, and 96.8% specificity. Of the 7516 isolates with complete phenotypic drug-susceptibility profiles, 5865 (78.0%) had complete genotypic predictions, among which 5250 profiles (89.5%) were correctly predicted. Among the 4037 phenotypic profiles that were predicted to be pansusceptible, 3952 (97.9%) were correctly predicted. CONCLUSIONS: Genotypic predictions of the susceptibility of M. tuberculosis to first-line drugs were found to be correlated with phenotypic susceptibility to these drugs. (Funded by the Bill and Melinda Gates Foundation and others.).
- Rancoita PMV, Cugnata F, Gibertoni-Cruz AL, Borroni E, Hoosdally SJ, Walker TM, Grazian C, Davies TJ, Peto TEA, Crook DW, Fowler PW, Cirillo DM, the CRyPTIC consortium (2018).
Validating a 14-Drug Microtiter Plate Containing Bedaquiline and Delamanid for Large-Scale Research Susceptibility Testing of Mycobacterium tuberculosis.
Antimicrob Agent Chemo 62:e00344 doi:10.1128/AAC.00344-18
biorXiv preprint doi:10.1101/244731
Abstract
isolates. Unlike the MYCOTB plate, on which the UKMYC5 plate is based, the UKMYC5 plate includes two new (bedaquiline and delamanid) and two repurposed (clofazimine and linezolid) compounds. UKMYC5 plates were tested by seven laboratories on four continents by use of 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 of incubation. In addition, all EQA strains were subjected to whole-genome sequencing and phenotypically characterized by the 7H10/7H11 agar proportion method (APM) and by use of MGIT960 mycobacterial growth indicator tubes. We concluded that the UKMYC5 plate is optimally read using the Vizion system after 14 days of incubation, achieving an interreader agreement of 97.9% and intra- and interlaboratory reproducibility rates of 95.6% and 93.1%, respectively. The mirrored box had a similar reproducibility. Strains classified as resistant by APM, MGIT960, or the presence of mutations known to confer resistance consistently showed elevated MICs compared to those for strains classified as susceptible. Finally, the UKMYC5 plate records intermediate MICs for one strain for which the APM measured MICs close to the applied critical concentration, providing early evidence that the UKMYC5 plate can quantitatively measure the magnitude of resistance to anti-TB compounds that is due to specific genetic variation.
- Fowler PW, Coles K, Gordon NC, Kearns AM, Llewelyn MJ, Peto TEA, Crook DW, Walker AS (2018).
Robust prediction of resistance to trimethoprim in S. aureus.
Cell Chem Biol 25:339 doi:10.1016/j.chembiol.2017.12.009
Abstract
The rise of antibiotic resistance threatens modern medicine; to combat it new diagnostic methods are required. Sequencing the whole genome of a pathogen offers the potential to accurately determine which antibiotics will be effective to treat a patient. A key limitation of this approach is that it cannot classify rare or previously unseen mutations. Here we demonstrate that alchemical free energy methods, a well-established class of methods from computational chemistry, can successfully predict whether mutations in Staphylococcus aureus dihydrofolate reductase confer resistance to trimethoprim. We also show that the method is quantitatively accurate by calculating how much the most common resistance-conferring mutation, F99Y, reduces the binding free energy of trimethoprim and comparing predicted and experimentally measured minimum inhibitory concentrations for seven different mutations. Finally, by considering up to 32 free energy calculations for each mutation, we estimate its specificity and sensitivity.
2017
- Duncan A, Reddy T, Koldsø H, Helie J, Fowler PW, Chavent M, Sansom MSP (2017).
Protein crowding and lipid complexity influence the nanoscale dynamic organization of ion channels in cell membranes.
Sci. Rep. 7:16647 doi:10.1038/s41598-017-16865-6
Abstract
Cell membranes are crowded and complex environments. To investigate the effect of protein-lipid interactions on dynamic organization in mammalian cell membranes, we have performed coarse-grained molecular dynamics simulations containing >100 copies of an inwardly rectifying potassium (Kir) channel which forms specific interactions with the regulatory lipid phosphatidylinositol 4,5-bisphosphate (PIP2). The tendency of protein molecules to cluster has the effect of organizing the membrane into dynamic compartments. At the same time, the diversity of lipids present has a marked effect on the clustering behavior of ion channels. Sub-diffusion of proteins and lipids is observed. Protein crowding alters the sub-diffusive behavior of proteins and lipids such as PIP2which interact tightly with Kir channels. Protein crowding also affects bilayer properties, such as membrane undulations and bending rigidity, in a PIP2-dependent manner. This interplay between the diffusion and the dynamic organization of Kir channels may have important implications for channel function.
- Fowler PW, Sansom MSP, Reithmeier RAF (2017).
Effect of the Southeast Asian Ovalocytosis Deletion on the Conformational Dynamics of the Signal-Anchor Transmembrane Segment 1 of the Red Cell Anion Exchanger 1 (AE1, Band 3).
Biochemistry 56:712 doi:10.1021/acs.biochem.6b00966
Abstract
The first transmembrane (TM1) helix in the red cell anion exchanger (AE1, Band 3, or SLC4A1) acts as an internal signal anchor that binds the signal recognition particle and directs the nascent polypeptide chain to the endoplasmic reticulum (ER) membrane where it moves from the translocon laterally into the lipid bilayer. The sequence N-terminal to TM1 forms an amphipathic helix that lies at the membrane interface and is connected to TM1 by a bend at Pro403. Southeast Asian ovalocytosis (SAO) is a red cell abnormality caused by a nine-amino acid deletion (Ala400-Ala408) at the N-terminus of TM1. Here we demonstrate, by extensive (∼4.5 μs) molecular dynamics simulations of TM1 in a model 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine membrane, that the isolated TM1 peptide is highly dynamic and samples the structure of TM1 seen in the crystal structure of the membrane domain of AE1. The SAO deletion not only removes the proline-induced bend but also causes a "pulling in" of the part of the amphipathic helix into the hydrophobic phase of the bilayer, as well as the C-terminal of the peptide. The dynamics of the SAO peptide very infrequently resembles the structure of TM1 in AE1, demonstrating the disruptive effect the SAO deletion has on AE1 folding. These results provide a precise molecular view of the disposition and dynamics of wild-type and SAO TM1 in a lipid bilayer, an important early biosynthetic intermediate in the insertion of AE1 into the ER membrane, and extend earlier results of cell-free translation experiments.
2016
- Fowler PW, Helie J, Duncan AL, Chavent M, Koldsø H, Sansom MSP (2016).
Membrane stiffness is modified by integral membrane proteins.
Soft Matter 12:7792 doi:10.1039/c6sm01186a
Abstract
). We demonstrate by computer simulation that adding integral membrane proteins at physiological densities alters the stiffness of the membrane. First we establish that the coarse-grained MARTINI forcefield is able to accurately reproduce the bending rigidity of a small patch of 1500 phosphatidyl choline lipids by comparing the calculated value to both experiment and an atomistic simulation of the same system. This enables us to simulate the dynamics of large (ca. 50 000 lipids) patches of membrane using the MARTINI coarse-grained description. We find that altering the lipid composition changes the bending rigidity. Adding integral membrane proteins to lipid bilayers also changes the bending rigidity, whilst adding a simple peripheral membrane protein has no effect. Our results suggest that integral membrane proteins can have different effects, and in the case of the bacterial outer membrane protein, BtuB, the greater the density of protein, the larger the reduction in stiffness.
- Fowler PW, Williamson JJ, Sansom MSP, Olmsted PD (2016).
Roles of interleaflet coupling and hydrophobic mismatch in lipid membrane phase-separation kinetics.
J Am Chem Soc 138:11633 doi:10.1021/jacs.6b04880
Abstract
Characterizing the nanoscale dynamic organization within lipid bilayer membranes is central to our understanding of cell membranes at a molecular level. We investigate phase separation and communication across leaflets in ternary lipid bilayers, including saturated lipids with between 12 and 20 carbons per tail. Coarse-grained molecular dynamics simulations reveal a novel two-step kinetics due to hydrophobic mismatch, in which the initial response of the apposed leaflets upon quenching is to increase local asymmetry (antiregistration), followed by dominance of symmetry (registration) as the bilayer equilibrates. Antiregistration can become thermodynamically preferred if domain size is restricted below ∼20 nm, with implications for the symmetry of rafts and nanoclusters in cell membranes, which have similar reported sizes. We relate our findings to theory derived from a semimicroscopic model in which the leaflets experience a "direct" area-dependent coupling, and an "indirect" coupling that arises from hydrophobic mismatch and is most important at domain boundaries. Registered phases differ in composition from antiregistered phases, consistent with a direct coupling between the leaflets. Increased hydrophobic mismatch purifies the phases, suggesting that it contributes to the molecule-level lipid immiscibility. Our results demonstrate an interplay of competing interleaflet couplings that affect phase compositions and kinetics, and lead to a length scale that can influence lateral and transverse bilayer organization within cells.
- Koldsø H, Reddy T, Fowler PW, Duncan AL, Sansom MSP (2016).
Membrane compartmentalization reduces the mobility of lipids and proteins within a model plasma membrane.
J Phys Chem B 120:8873 doi:10.1021/acs.jpcb.6b05846
Abstract
The cytoskeleton underlying cell membranes may influence the dynamic organization of proteins and lipids within the bilayer by immobilizing certain transmembrane (TM) proteins and forming corrals within the membrane. Here, we present coarse-grained resolution simulations of a biologically realistic membrane model of asymmetrically organized lipids and TM proteins. We determine the effects of a model of cytoskeletal immobilization of selected membrane proteins using long time scale coarse-grained molecular dynamics simulations. By introducing compartments with varying degrees of restraints within the membrane models, we are able to reveal how compartmentalization caused by cytoskeletal immobilization leads to reduced and anomalous diffusional mobility of both proteins and lipids. This in turn results in a reduced rate of protein dimerization within the membrane and of hopping of membrane proteins between compartments. These simulations provide a molecular realization of hierarchical models often invoked to explain single-molecule imaging studies of membrane proteins.
- Samsudin F, Parker JL, Sansom MSP, Newstead, Fowler PW (2016).
Accurate prediction of ligand affinities for a peptide transporter.
Cell Chem Biol 23:299 doi:10.1016/j.chembiol.2015.11.015
Abstract
Membrane transporters are critical modulators of drug pharmacokinetics, efficacy, and safety. One example is the proton-dependent oligopeptide transporter PepT1, also known as SLC15A1, which is responsible for the uptake of the ?-lactam antibiotics and various peptide-based prodrugs. In this study, we modeled the binding of various peptides to a bacterial homolog, PepT(St), and evaluated a range of computational methods for predicting the free energy of binding. Our results show that a hybrid approach (endpoint methods to classify peptides into good and poor binders and a theoretically exact method for refinement) is able to accurately predict affinities, which we validated using proteoliposome transport assays. Applying the method to a homology model of PepT1 suggests that the approach requires a high-quality structure to be accurate. Our study provides a blueprint for extending these computational methodologies to other pharmaceutically important transporter families.
2015
- Beale JH, Parker JL, Samsudin F, Barret AL, Senan A, Bird LE, Scott D, Owens RJ, Sansom MSP, Tucker SJ, Meredith D, Fowler PW, Newstead S (2015).
Crystal structures of the extracellular domain from PepT1 and PepT2 provide novel insights into mammalian peptide transport.
Structure 23:1889 doi:10.1016/j.str.2015.07.016
Abstract
Mammals obtain nitrogen via the uptake of di- and tri-peptides in the gastrointestinal tract through the action of PepT1 and PepT2, which are members of the POT family of proton-coupled oligopeptide transporters. PepT1 and PepT2 also play an important role in drug transport in the human body. Recent crystal structures of bacterial homologs revealed a conserved peptide-binding site and mechanism of transport. However, a key structural difference exists between bacterial and mammalian homologs with only the latter containing a large extracellular domain, the function of which is currently unknown. Here, we present the crystal structure of the extracellular domain from both PepT1 and PepT2 that reveal two immunoglobulin-like folds connected in tandem, providing structural insight into mammalian peptide transport. Functional and biophysical studies demonstrate that these domains interact with the intestinal protease trypsin, suggesting a role in clustering proteolytic activity to the site of peptide transport in eukaryotic cells.
- Jefferys E, Sands Z, Shi J, Sansom MSP, Fowler PW (2015).
Alchembed: A computational method for incorporating proteins into complex lipid geometries.
J Chem Theo Comp 11:2743 doi:10.1021/ct501111d
Abstract
A necessary step prior to starting any membrane protein computer simulation is the creation of a well-packed configuration of protein(s) and lipids. Here, we demonstrate a method, alchembed, that can simultaneously and rapidly embed multiple proteins into arrangements of lipids described using either atomistic or coarse-grained force fields. During a short simulation, the interactions between the protein(s) and lipids are gradually switched on using a soft-core van der Waals potential. We validate the method on a range of membrane proteins and determine the optimal soft-core parameters required to insert membrane proteins. Since all of the major biomolecular codes include soft-core van der Waals potentials, no additional code is required to apply this method. A tutorial is included in the Supporting Information.
- Reddy T, Shorthouse D, Parton D, Jefferys E, Fowler PW, Chavent M, Baaden M, Sansom MSP (2015).
Nothing to sneeze at: a dynamic and integrative computational model of an influenza A virion.
Structure 23:584 doi:10.1016/j.str.2014.12.019
Abstract
The influenza virus is surrounded by an envelope composed of a lipid bilayer and integral membrane proteins. Understanding the structural dynamics of the membrane envelope provides biophysical insights into aspects of viral function, such as the wide-ranging survival times of the virion in different environments. We have combined experimental data from X-ray crystallography, nuclear magnetic resonance spectroscopy, cryo-electron microscopy, and lipidomics to build a model of the intact influenza A virion. This is the basis of microsecond-scale coarse-grained molecular dynamics simulations of the virion, providing simulations at different temperatures and with varying lipid compositions. The presence of the Forssman glycolipid alters a number of biophysical properties of the virion, resulting in reduced mobility of bilayer lipid and protein species. Reduced mobility in the virion membrane may confer physical robustness to changes in environmental conditions. Our simulations indicate that viral spike proteins do not aggregate and thus are competent for multivalent immunoglobulin G interactions.
- Fowler PW, Orwick-Rydmark M, Radestock S, Solcan N, Dijkman P, Lyons JA, Kwok J, Caffery M, Watts A, Forrest LR, Newstead S (2015).
Gating topology of the proton coupled oligopeptide symporters.
Structure 23:290 doi:10.1016/j.str.2014.12.012
Abstract
Proton-coupled oligopeptide transporters belong to the major facilitator superfamily (MFS) of membrane transporters. Recent crystal structures suggest the MFS fold facilitates transport through rearrangement of their two six-helix bundles around a central ligand binding site; how this is achieved, however, is poorly understood. Using modeling, molecular dynamics, crystallography, functional assays, and site-directed spin labeling combined with double electron-electron resonance (DEER) spectroscopy, we present a detailed study of the transport dynamics of two bacterial oligopeptide transporters, PepTSo and PepTSt. Our results identify several salt bridges that stabilize outward-facing conformations and we show that, for all the current structures of MFS transporters, the first two helices of each of the four inverted-topology repeat units form half of either the periplasmic or cytoplasmic gate and that these function cooperatively in a scissor-like motion to control access to the peptide binding site during transport.
2014
- Fowler PW, Bollepalli MK, Rapedius M, Shang L, Nematian E, Sansom MSP, Tucker SJ, Baukrowitz T (2014).
Insights into the structural nature of the transition state in the Kir channel gating pathway.
Channels 8:551 doi:10.4161/19336950.2014.962371
Abstract
In a previous study we identified an extensive gating network within the inwardly rectifying Kir1.1 (ROMK) channel by combining systematic scanning mutagenesis and functional analysis with structural models of the channel in the closed, pre-open and open states. This extensive network appeared to stabilize the open and pre-open states, but the network fragmented upon channel closure. In this study we have analyzed the gating kinetics of different mutations within key parts of this gating network. These results suggest that the structure of the transition state (TS), which connects the pre-open and closed states of the channel, more closely resembles the structure of the pre-open state. Furthermore, the G-loop, which occurs at the center of this extensive gating network, appears to become unstructured in the TS because mutations within this region have a 'catalytic' effect upon the channel gating kinetics.
- Bollepalli MK, Fowler PW, Rapedius M, Shang L, Sansom MSP, Tucker SJ, Baukrowitz T (2014).
State dependent network connectivity determines gating in a K+ channel.
Structure 22:1037 doi:10.1016/j.str.2014.04.018
Abstract
X-ray crystallography has provided tremendous insight into the different structural states of membrane proteins and, in particular, of ion channels. However, the molecular forces that determine the thermodynamic stability of a particular state are poorly understood. Here we analyze the different X-ray structures of an inwardly rectifying potassium channel (Kir1.1) in relation to functional data we obtained for over 190 mutants in Kir1.1. This mutagenic perturbation analysis uncovered an extensive, state-dependent network of physically interacting residues that stabilizes the pre-open and open states of the channel, but fragments upon channel closure. We demonstrate that this gating network is an important structural determinant of the thermodynamic stability of these different gating states and determines the impact of individual mutations on channel function. These results have important implications for our understanding of not only K+ channel gating but also the more general nature of conformational transitions that occur in other allosteric proteins.
- Jefferys E, Sansom MSP, Fowler PW (2014).
NRas slows the rate at which a model lipid bilayer phase separates.
RSC Faraday Discussion 169:209 doi:10.1039/c3fd00131h
Abstract
The Ras family of small membrane-associated GTP-ases are important components in many different cell signalling cascades. They are thought to cluster on the cell membrane through association with cholesterol-rich nanodomains. This process remains poorly understood. Here we test the effect of adding multiple copies of NRas, one of the canonical Ras proteins, to a three-component lipid bilayer that rapidly undergoes spinodal decomposition (i.e. unmixing), thereby creating ordered and disordered phases. Coarse-grained molecular dynamics simulations of a large bilayer containing 6000 lipids, with and without protein, are compared. NRas preferentially localises to the interface between the domains and slows the rate at which the domains grow. We infer that this doubly-lipidated cell signalling protein is reducing the line tension between the ordered and disordered regions. This analysis is facilitated by our use of techniques borrowed from image-processing. The conclusions above are contingent upon several assumptions, including the use of a model lipid with doubly unsaturated tails and the limited structural data available for the C-terminus of NRas, which is where the lipid anchors are found.
- Stelzl L, Fowler PW, Sansom MSP, Beckstein O (2014).
Flexible gates generate occluded intermediates in the transport cycle of LacY.
J Mol Biol 426:735 doi:10.1016/j.jmb.2013.10.024
Abstract
The major facilitator superfamily (MFS) transporter lactose permease (LacY) alternates between cytoplasmic and periplasmic open conformations to co-transport a sugar molecule together with a proton across the plasma membrane. Indirect experimental evidence suggested the existence of an occluded transition intermediate of LacY, which would prevent leaking of the proton gradient. As no experimental structure is known, the conformational transition is not fully understood in atomic detail. We simulated transition events from a cytoplasmic open conformation to a periplasmic open conformation with the dynamic importance sampling molecular dynamics method and observed occluded intermediates. Analysis of water permeation pathways and the electrostatic free-energy landscape of a solvated proton indicated that the occluded state contains a solvated central cavity inaccessible from either side of the membrane. We propose a pair of geometric order parameters that capture the state of the pathway through the MFS transporters as shown by a survey of available crystal structures and models. We present a model for the occluded state of apo-LacY, which is similar to the occluded crystal structures of the MFS transporters EmrD, PepTSo, NarU, PiPT and XylE. Our simulations are consistent with experimental double electron spin–spin distance measurements that have been interpreted to show occluded conformations. During the simulations, a salt bridge that has been postulated to be involved in driving the conformational transition formed. Our results argue against a simple rigid-body domain motion as implied by a strict “rocker-switch mechanism” and instead hint at an intricate coupling between two flexible gates.
2013
- Fowler PW, Beckstein O, Abad E, Sansom MSP (2013).
Detailed examination of a single conduction event in a potassium channel.
J Phys Chem Lett 4:3104 doi:10.1021/jz4014079
Abstract
Although extensively studied, it has proved difficult to describe in detail how potassium ion channels conduct cations and water. We present a computational study that, by using stratified umbrella sampling, examines nearly an entire conduction event of the Kv1.2/2.1 paddle chimera and thereby identifies the expected stable configurations of ions and waters in the selectivity filter of the channel. We describe in detail the motions of the ions and waters during a conduction event, focusing on how waters and ions enter the filter, the rotation of water molecules inside the filter, and how potassium ions are coordinated as they move from a water to a protein environment. Finally, we analyze the small conformational changes undergone by the protein, showing that the stable configurations are most similar to the experimental crystal structure.
- Fowler PW, Abad E, Beckstein O, Sansom MSP (2013).
Energetics of multi-ion conduction pathways in potassium ion channels.
J Chem Theo Comp 9:5176 doi:10.1021/ct4005933
Abstract
Potassium ion channels form pores in cell membranes, allowing potassium ions through while preventing the passage of sodium ions. Despite numerous high-resolution structures, it is not yet possible to relate their structure to their single molecule function other than at a qualitative level. Over the past decade, there has been a concerted effort using molecular dynamics to capture the thermodynamics and kinetics of conduction by calculating potentials of mean force (PMF). These can be used, in conjunction with the electro-diffusion theory, to predict the conductance of a specific ion channel. Here, we calculate seven independent PMFs, thereby studying the differences between two potassium ion channels, the effect of the CHARMM CMAP forcefield correction, and the sensitivity and reproducibility of the method. Thermodynamically stable ion-water configurations of the selectivity filter can be identified from all the free energy landscapes, but the heights of the kinetic barriers for potassium ions to move through the selectivity filter are, in nearly all cases, too high to predict conductances in line with experiment. This implies it is not currently feasible to predict the conductance of potassium ion channels, but other simpler channels may be more tractable.
- Fowler PW, Sansom MSP (2013).
The pore of voltage-gated potassium ion channels is strained when closed.
Nature Communications 4:1872 doi:10.1038/ncomms2858
Abstract
Voltage-gated potassium channels form potassium-selective pores in cell membranes. They open or close in response to changes in the transmembrane potential and are essential for generating action potentials, and thus for the functioning of heart and brain. While a mechanism for how these channels close has been proposed, it is not clear what drives their opening. Here we use free energy molecular dynamics simulations to show that work must be done on the pore to reduce the kink in the pore-lining (S6) α-helices, thereby forming the helix bundle crossing and closing the channel. Strain is built up as the pore closes, which subsequently drives opening. We also determine the effect of mutating the PVPV motif that causes the kink in the S6 helix. Finally, an approximate upper limit on how far the S4 helix is displaced as the pore closes is estimated. Voltage-gated potassium channels open and close in response to changes in transmembrane potential, but their opening mechanism is poorly understood. Here, free energy molecular dynamics simulations show that strain accumulates as the pore closes, which subsequently drives opening.
2012
- Solcan N, Kwok J, Fowler PW, Cameron AD, Drew D, Iwata S, Newstead S (2012).
Alternating access mechanism in the POT family of oligopeptide transporters.
EMBO J 31:3411 doi:10.1038/emboj.2012.157
Abstract
Short chain peptides are actively transported across membranes as an efficient route for dietary protein absorption and for maintaining cellular homeostasis. In mammals, peptide transport occurs via PepT1 and PepT2, which belong to the proton-dependent oligopeptide transporter, or POT family. The recent crystal structure of a bacterial POT transporter confirmed that they belong to the major facilitator superfamily of secondary active transporters. Despite the functional characterization of POT family members in bacteria, fungi and mammals, a detailed model for peptide recognition and transport remains unavailable. In this study, we report the 3.3-Å resolution crystal structure and functional characterization of a POT family transporter from the bacterium Streptococcus thermophilus. Crystallized in an inward open conformation the structure identifies a hinge-like movement within the C-terminal half of the transporter that facilitates opening of an intracellular gate controlling access to a central peptide-binding site. Our associated functional data support a model for peptide transport that highlights the importance of salt bridge interactions in orchestrating alternating access within the POT family.
2011
- Newstead S, Drew D, Cameron AD, Postis VLG, Xia X, Fowler PW, Carpenter EP, Sansom MSP, McPheron MJ, Baldwin SA, Iwata S (2011).
Crystal structure of a prokaryotic homologue of the mammalian oligopeptide-proton symporters, PepT1 and PepT2.
EMBO J 30:417 doi:10.1038/emboj.2010.309
Abstract
PepT1 and PepT2 are major facilitator superfamily (MFS) transporters that utilize a proton gradient to drive the uptake of di- and tri-peptides in the small intestine and kidney, respectively. They are the major routes by which we absorb dietary nitrogen and many orally administered drugs. Here, we present the crystal structure of PepT\(_{So}\), a functionally similar prokaryotic homologue of the mammalian peptide transporters from . This structure, refined using data up to 3.6 Å resolution, reveals a ligand-bound occluded state for the MFS and provides new insights into a general transport mechanism. We have located the peptide-binding site in a central hydrophilic cavity, which occludes a bound ligand from both sides of the membrane. Residues thought to be involved in proton coupling have also been identified near the extracellular gate of the cavity. Based on these findings and associated kinetic data, we propose that PepT\(_{So}\) represents a sound model system for understanding mammalian peptide transport as catalysed by PepT1 and PepT2.
2010
- Paynter JJ, Andres-Enguix I, Fowler PW, Tottey S, Cheng W, Enkvetchakul D, Bavro VN, Kusakabe Y, Sansom MSP, Robinson NJ, Nichols CG, Tucker SJ (2010).
Functional complementation and genetic deletion studies of KirBac channels.
J Biol Chem 285:40754 doi:10.1074/jbc.M110.175687
Abstract
The superfamily of prokaryotic inwardly rectifying (KirBac) potassium channels is homologous to mammalian Kir channels. However, relatively little is known about their regulation or about their physiological role in vivo. In this study, we have used random mutagenesis and genetic complementation in K(+)-auxotrophic Escherichia coli and Saccharomyces cerevisiae to identify activatory mutations in a range of different KirBac channels. We also show that the KirBac6.1 gene (slr5078) is necessary for normal growth of the cyanobacterium Synechocystis PCC6803. Functional analysis and molecular dynamics simulations of selected activatory mutations identified regions within the slide helix, transmembrane helices, and C terminus that function as important regulators of KirBac channel activity, as well as a region close to the selectivity filter of KirBac3.1 that may have an effect on gating. In particular, the mutations identified in TM2 favor a model of KirBac channel gating in which opening of the pore at the helix-bundle crossing plays a far more important role than has recently been proposed.
2009
- Newstead S, Fowler PW, Bilton P, Carpenter E, Sadler P, Campopiano D, Sansom M, Iwata S (2009).
Insights into how nucleotide-binding domains power ABC transport.
Structure 17:1213 doi:10.1016/j.str.2009.07.009
Abstract
The mechanism by which nucleotide-binding domains (NBDs) of ABC transporters power the transport of substrates across cell membranes is currently unclear. Here we report the crystal structure of an NBD, FbpC, from the Neisseria gonorrhoeae ferric iron uptake transporter with an unusual and substantial domain swap in the C-terminal regulatory domain. This entanglement suggests that FbpC is unable to open to the same extent as the homologous protein MalK. Using molecular dynamics we demonstrate that this is not the case: both NBDs open rapidly once ATP is removed. We conclude from this result that the closed structures of FbpC and MalK have higher free energies than their respective open states. This result has important implications for our understanding of the mechanism of power generation in ABC transporters, because the unwinding of this free energy ensures that the opening of these two NBDs is also powered.
- Abad E, Reingruber J, Fowler PW, Sansom MSP (2009).
A novel rate theory approach to transport in ion channels.
TACC 1102:236 doi:10.1063/1.3108380
Abstract
We present a novel rate theory based on the notions of splitting probability and mean first passage time to describe conduction of single ions in narrow, effectively 1D membrane channels. In contrast to traditional approaches such as transition state theory or Kramers theory, transitions between different conduction states in our model are governed by rates which depend on the full geometry of the potential of mean force (PMF) resulting from the superposition of an equilibrium free energy profile and a transmembrane potential induced by a nonequilibrium constraint. If a detailed theoretical PMF is available (e.g. from atomistic molecular dynamics simulations), it can be used to compute characteristic conductance curves in the framework of our model, thereby bridging the gap between the atomistic and the mesoscopic level of description. Explicit analytic solutions for the rates, the ion flux and the associated electric current can be obtained by approximating the actual PMF by a piecewise linear potential.
2008
- Tai K, Fowler PW, Mokrab Y, Stansfield P, Sansom MSP (2008).
Molecular modeling and simulation studies of ion channel structures, dynamics & mechanisms.
Methods Nano Cell Biol 90:233 doi:10.1016/S0091-679X(08)00812-1
Abstract
Ion channels are integral membrane proteins that enable selected ions to flow passively across membranes. Channel proteins have been the focus of computational approaches to relate their three-dimensional (3D) structure to their physiological function. We describe a number of computational tools to model ion channels. Homology modeling may be used to construct structural models of channels based on available X-ray structures. Electrostatics calculations enable an approximate evaluation of the energy profile of an ion passing through a channel. Molecular dynamics simulations and free-energy calculations provide information on the thermodynamics and kinetics of channel function.
- Psachoulia E, Fowler PW, Bond PJ, Sansom MSP (2008).
Helix-helix interactions in membrane proteins: Coarse grained simulations of Glycophorin helix dimerization.
Biochemistry 47:10503 doi:10.1021/bi800678t
Abstract
Oligomerization of transmembrane (TM) helices is a key stage in the folding of membrane proteins. Glycophorin A (GpA) is a well-documented test system for this process. Coarse-grained molecular dynamics (CG-MD) allows us to simulate the self-assembly of TM helices into dimers, for both wild-type (WT) and mutant GpA sequences. For the WT sequences, dimers formed rapidly and remained stable in all simulations. The resultant dimers exhibited right-handed crossing and the same interhelix contacts as in NMR structures. Simulations of disruptive mutants revealed the dimers were less stable, with values of DeltaDelta G dimerization consistent with experimental data. The dimers of disruptive mutants were distorted relative to the WT and showed left-handed crossing of their helices. CG-MD can therefore be used to explore the interactions of TM helices, an important stage in the folding of membrane proteins. In particular, CG-MD has been shown to be sensitive enough to detect disruptions introduced by mutation. Future refinement of such models via atomistic simulations will enable a multiscale approach to predict the folding of membrane proteins.
- Fowler PW, Tai K, Sansom MSP (2008).
The selectivity of K+ ion channels: testing the hypotheses.
Biophys J 95:5062 doi:10.1529/biophysj.108.132035
Abstract
How K+ channels are able to conduct certain cations yet not others remains an important but unresolved question. The recent elucidation of the structure of NaK, an ion channel that conducts both Na+ and K+ ions, offers an opportunity to test the various hypotheses that have been put forward to explain the selectivity of K+ ion channels. We test the snug-fit, field-strength, and over-coordination hypotheses by comparing their predictions to the results of classical molecular dynamics simulations of the K+ selective channel KcsA and the less selective channel NaK embedded in lipid bilayers. Our results are incompatible with the so-called strong variant of the snug-fit hypothesis but are consistent with the over-coordination hypothesis and neither confirm nor refute the field-strength hypothesis. We also find that the ions and waters in the NaK selectivity filter unexpectedly move to a new conformation in seven K+ simulations: the two K+ ions rapidly move from site S4 to S2 and from the cavity to S4. At the same time, the selectivity filter narrows around sites S1 and S2 and the carbonyl oxygen atoms rotate 20°−40° inwards toward the ion. These motions diminish the large structural differences between the crystallographic structures of the selectivity filters of NaK and KcsA and appear to allow the binding of ions to S2 of NaK at physiological temperature.
2007
- Rapedius M, Paynter J, Fowler PW, Shang L, Sansom MSP, Tucker SJ, Baukrowitz T (2007).
Control of pH and PIP2 gating in heteromeric Kir4.1/Kir5.1 channels by H-bonding at the helix bundle crossing.
Channels 1:327 doi:10.4161/chan.5176
Abstract
Inhibition by intracellular H(+) (pH gating) and activation by phosphoinositides such as PIP(2) (PIP(2)-gating) are key regulatory mechanisms in the physiology of inwardly-rectifying potassium (Kir) channels. Our recent findings suggest that PIP(2) gating and pH gating are controlled by an intra-subunit H-bond at the helix-bundle crossing between a lysine in TM1 and a backbone carbonyl group in TM2. This interaction only occurs in the closed state and channel opening requires this H-bond to be broken, thereby influencing the kinetics of PIP(2) and pH gating in Kir channels. In this addendum, we explore the role of H-bonding in heteromeric Kir4.1/Kir5.1 channels. Kir5.1 subunits do not possess a TM1 lysine. However, homology modelling and molecular dynamics simulations demonstrate that the TM1 lysine in Kir4.1 is capable of H-bonding at the helix-bundle crossing. Consistent with this, the rates of pH and PIP2 gating in Kir4.1/Kir5.1 channels (two H-bonds) were intermediate between those of wild-type homomeric Kir4.1 (four H-bonds) and Kir4.1(K67M) channels (no H-bonds) suggesting that the number of H-bonds in the tetrameric channel complex determines the gating kinetics. Furthermore, in heteromeric Kir4.1(K67M)/Kir5.1 channels, where the two remaining H-bonds are disrupted, we found that the gating kinetics were similar to Kir4.1(K67M) homomeric channels despite the fact that these two channels differ considerably in their PIP(2) affinities. This indicates that Kir channel PIP(2) affinity has little impact on either the PIP(2) or pH gating kinetics.
- Rapedius M, Fowler PW, Shang L, Sansom MSP, Tucker SJ, Baukrowitz T (2007).
H-Bonding at the helix-bundle crossing controls gating in Kir potassium channels.
Neuron 55:602 doi:10.1016/j.neuron.2007.07.026
Abstract
Specific stimuli such as intracellular H+ and phosphoinositides (e.g., PIP2) gate inwardly rectifying potassium (Kir) channels by controlling the reversible transition between the closed and open states. This gating mechanism underlies many aspects of Kir channel physiology and pathophysiology; however, its structural basis is not well understood. Here, we demonstrate that H+ and PIP2 use a conserved gating mechanism defined by similar structural changes in the transmembrane (TM) helices and the selectivity filter. Our data support a model in which the gating motion of the TM helices is controlled by an intrasubunit hydrogen bond between TM1 and TM2 at the helix-bundle crossing, and we show that this defines a common gating motif in the Kir channel superfamily. Furthermore, we show that this proposed H-bonding interaction determines Kir channel pH sensitivity, pH and PIP2 gating kinetics, as well as a K+-dependent inactivation process at the selectivity filter and therefore many of the key regulatory mechanisms of Kir channel physiology.
- Fowler PW, Geroult S, Jha S, Waksman G, Coveney PV (2007).
Rapid, accurate and precise calculation of relative binding affinities for the SH2 domain using a computational grid.
J Comp Theo Chem 3:1193 doi:10.1021/ct6003017
Abstract
We describe and apply a method that reduces the time taken to calculate binding free energies using thermodynamic integration. This method uses a stack of grid software, which we call STIMD, that allows the scientist to easily distribute the necessary simulations around a computational grid thereby accelerating the process. We use this method to study how a series of phosphopeptides binds to the Src SH2 domain. The binding of phosphopeptides to the Src SH2 domain is described by the "two-pronged plug two-holed socket" model, and we investigate this model by reducing the length of the aliphatic side chain that engages the second of the two sockets through two successive alchemical mutations. Seven different values of ΔΔG have been calculated, and we report good agreement with experiment. We then propose an extension to this model using the insights gained from a free energy component analysis.
- Fowler PW, Balali-Mood K, Deol SS, Coveney PV, Sansom MSP (2007).
Monotopic proteins and lipid bilayers: a comparative study.
Biochemistry 46:3108 doi:10.1021/bi602455n
Abstract
Monotopic proteins make up a class of membrane proteins that bind tightly to, but do not span, cell membranes. We examine and compare how two monotopic proteins, monoamine oxidase B (MAO-B) and cyclooxygenase-2 (COX-2), interact with a phospholipid bilayer using molecular dynamics simulations. Both enzymes form between three and seven hydrogen bonds with the bilayer in our simulations with basic side chains accounting for the majority of these interactions. By analyzing lipid order parameters, we show that, to a first approximation, COX-2 disrupts only the upper leaflet of the bilayer. In contrast, the top and bottom halves of the lipid tails surrounding MAO-B are more and less ordered, respectively, than in the absence of the protein. Finally, we identify which residues are important in binding individual phospholipids by counting the number and type of lipid atoms that come close to each amino acid residue. The existing models that explain how these proteins bind to bilayers were proposed following inspection of the X-ray crystallographic structures. Our results support these models and suggest that basic residues contribute significantly to the binding of these monotopic proteins to bilayers through the formation of hydrogen bonds with phospholipids.
2006
- Fowler PW, Coveney PV (2006).
A computational protocol for the integration of the monotopic protein PGHS into a bilayer. Biophys J 91:401.
doi:10.1529/biophysj.105.077784
Abstract
Prostaglandin H2 synthase (PGHS) synthesizes PGH\(_2\), a prostaglandin precursor, from arachidonic acid and was the first monotopic enzyme to have its structure experimentally determined. Both isozymes of PGHS are inhibited by nonsteroidal antiinflammatory drugs, an important class of drugs that are the primary means of relieving pain and inflammation. Selectively inhibiting the second isozyme, PGHS-2, minimizes the gastrointestinal side-effects. This had been achieved by the new PGHS-2 selective NSAIDs (i.e., COX-2 inhibitors) but it has been recently suggested that they suffer from additional side-effects. The design of these drugs only made use of static structures from x-ray crystallographic experiments. Investigating the dynamics of both PGHS-1 and PGHS-2 using classical molecular dynamics is expected to generate new insight into the differences in behavior between the isozymes, and therefore may allow improved PGHS-2 selective inhibitors to be designed. We describe a molecular dynamics protocol that integrates PGHS monomers into phospholipid bilayers, thereby producing in silico atomistic models of the PGHS system. Our protocol exploits the vacuum created beneath the protein when several lipids are removed from the top leaflet of the bilayer. The protein integrates into the bilayer during the first 5ns in a repeatable process. The integrated PGHS monomer is stable and forms multiple hydrogen bonds between the phosphate groups of the lipids and conserved basic residues (Arg, Lys) on the protein. These interactions stabilize the system and are similar to interactions observed for transmembrane proteins.
2005
- Coveney PV, Fowler PW (2005).
Modelling biological complexity: a physical scientist’s perspective.
J R Soc Interface 2:267 doi:10.1098/rsif.2005.0045
Abstract
We discuss the modern approaches of complexity and self-organization to understanding dynamical systems and how these concepts can inform current interest in systems biology. From the perspective of a physical scientist, it is especially interesting to examine how the differing weights given to philosophies of science in the physical and biological sciences impact the application of the study of complexity. We briefly describe how the dynamics of the heart and circadian rhythms, canonical examples of systems biology, are modelled by sets of nonlinear coupled differential equations, which have to be solved numerically. A major difficulty with this approach is that all the parameters within these equations are not usually known. Coupled models that include biomolecular detail could help solve this problem. Coupling models across large ranges of length- and time-scales is central to describing complex systems and therefore to biology. Such coupling may be performed in at least two different ways, which we refer to as hierarchical and hybrid multiscale modelling. While limited progress has been made in the former case, the latter is only beginning to be addressed systematically. These modelling methods are expected to bring numerous benefits to biology, for example, the properties of a system could be studied over a wider range of length- and time-scales, a key aim of systems biology. Multiscale models couple behaviour at the molecular biological level to that at the cellular level, thereby providing a route for calculating many unknown parameters as well as investigating the effects at, for example, the cellular level, of small changes at the biomolecular level, such as a genetic mutation or the presence of a drug. The modelling and simulation of biomolecular systems is itself very computationally intensive; we describe a recently developed hybrid continuum-molecular model, HybridMD, and its associated molecular insertion algorithm, which point the way towards the integration of molecular and more coarse-grained representations of matter. The scope of such integrative approaches to complex systems research is circumscribed by the computational resources available. Computational grids should provide a step jump in the scale of these resources; we describe the tools that RealityGrid, a major UK e-Science project, has developed together with our experience of deploying complex models on nascent grids. We also discuss the prospects for mathematical approaches to reducing the dimensionality of complex networks in the search for universal systems-level properties, illustrating our approach with a description of the origin of life according to the RNA world view.
- Fowler PW, Jha S, Coveney PV (2005).
Grid-based steered thermodynamic integration accelerates the calculation of binding affinities. Phil Trans R Soc Lond A 363(1833):1999.
doi:10.1098/rsta.2005.1625
Abstract
The calculation of binding free energies is important in many condensed matter problems. Although formally exact computational methods have the potential to complement, add to, and even compete with experimental approaches, they are difficult to use and extremely time consuming. We describe a Grid-based approach for the calculation of relative binding free energies, which we call Steered Thermodynamic Integration calculations using Molecular Dynamics (STIMD), and its application to Src homology 2 (SH2) protein cell signalling domains. We show that the time taken to compute free energy differences using thermodynamic integration can be significantly reduced: potentially from weeks or months to days of wall-clock time. To be able to perform such accelerated calculations requires the ability to both run concurrently and control in realtime several parallel simulations on a computational Grid. We describe how the RealityGrid computational steering system, in conjunction with a scalable classical MD code, can be used to dramatically reduce the time to achieve a result. This is necessary to improve the adoption of this technique and further allows more detailed investigations into the accuracy and precision of thermodynamic integration. Initial results for the Src SH2 system are presented and compared to a reported experimental value. Finally, we discuss the significance of our approach.
- Giordanetto F, Fowler PW, Saqi M, and Coveney PV (2005).
Large scale molecular dynamics simulation of native and mutant DHPS complexes suggests the molecular basis for dihydropteroate synthase drug resistance.
Phil Trans R Soc Lond A 363:(1833):2055 doi:10.1098/rsta.2005.1629
Abstract
Antibiotic resistance is hampering the efficacy of drugs in the treatment of several pathological infections. Dihydropteroate synthase (DHPS) has been targeted by sulphonamide inhibitors for the past 60 years and has developed different amino acid mutations to survive sulpha drug action. We couple homology modelling techniques and massively parallel molecular dynamics simulations to study both the drug-bound and apo forms of native and mutant DHPS. Simulations of the complex between sulphanilamide and Streptomyces pneumoniae, DHPS shows how sulphanilamide is able to position itself close to 6-hydroxymethyl-7, 8-dihydropteridine-phosphate in a suitable position for the enzymatic transformation whereas in the mutant complex the sulpha drug is expelled from the catalytic site. Our simulations, therefore, provide insight into the molecular basis for drug resistance with S. pneumoniae DHPS.
2004
- Fowler PW, Coveney PV, Jha S, Wan S (2004).
Exact calculation of peptide-protein binding energies by steered thermodynamic integration using high performance computing grids.
Proceedings of the UK e-Science All Hands Meeting
1998
- Al-Mushadani O, Boghosian BM, Coveney PV, Fowler PW, Maillet JB, Wilson JL (1998).
Lattice-Gas Simulations of Ternary Amphiphilic Fluid Flow in Porous Media.
Int J Mod Phys C 9:1479 doi:10.1142/S0129183198001345
Abstract
We develop our existing two-dimensional lattice-gas model to simulate the flow of single phase, binary immiscible and ternary amphiphilic fluids. This involves the inclusion of fixed obstacles on the lattice, together with the inclusion of "no-slip" boundary conditions. Here we report on preliminary applications of this model to the flow of such fluids within model porous media. We also construct fluid invasion boundary conditions, and the effects of invading aqueous solutions of surfactant on oil-saturated rock during imbibition and drainage are described.
