ORCID Profile
0000-0002-0738-7557
Current Organisation
University of Oxford
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Publisher: Cold Spring Harbor Laboratory
Date: 16-09-2021
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.
Publisher: eLife Sciences Publications, Ltd
Date: 21-02-2019
Publisher: Springer Science and Business Media LLC
Date: 21-12-2015
DOI: 10.1038/NCOMMS10063
Abstract: The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical s les, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial ersity can be used to identify species and resistance profiles of clinical isolates. We implement this method for Staphylococcus aureus and Mycobacterium tuberculosis in a software package (‘Mykrobe predictor’) that takes raw sequence data as input, and generates a clinician-friendly report within 3 minutes on a laptop. For S . aureus , the error rates of our method are comparable to gold-standard phenotypic methods, with sensitivity/specificity of 99.1%/99.6% across 12 antibiotics (using an independent validation set, n =470). For M . tuberculosis , our method predicts resistance with sensitivity/specificity of 82.6%/98.5% (independent validation set, n =1,609) sensitivity is lower here, probably because of limited understanding of the underlying genetic mechanisms. We give evidence that minor alleles improve detection of extremely drug-resistant strains, and demonstrate feasibility of the use of emerging single-molecule nanopore sequencing techniques for these purposes.
Publisher: Elsevier BV
Date: 04-2022
Publisher: Springer Science and Business Media LLC
Date: 05-07-2022
DOI: 10.1186/S13059-022-02714-X
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 s les from three bacterial species and an outbreak of 385 Mycobacterium tuberculosis s les. Minos also enables joint genotyping we demonstrate on a large ( N =13 k ) M. tuberculosis cohort, building a map of non-synonymous SNPs and indels in a region where all such variants are assumed to cause rif icin resistance. We quantify the correlation with phenotypic resistance and then replicate in a second cohort ( N =10 k ).
Publisher: eLife Sciences Publications, Ltd
Date: 22-02-2019
DOI: 10.7554/ELIFE.42486
Abstract: Pyomyositis is a severe bacterial infection of skeletal muscle, commonly affecting children in tropical regions, predominantly caused by Staphylococcus aureus. To understand the contribution of bacterial genomic factors to pyomyositis, we conducted a genome-wide association study of S. aureus cultured from 101 children with pyomyositis and 417 children with asymptomatic nasal carriage attending the Angkor Hospital for Children, Cambodia. We found a strong relationship between bacterial genetic variation and pyomyositis, with estimated heritability 63.8% (95% CI 49.2–78.4%). The presence of the Panton–Valentine leucocidin (PVL) locus increased the odds of pyomyositis 130-fold (p=10-17.9). The signal of association mapped both to the PVL-coding sequence and to the sequence immediately upstream. Together these regions explained over 99.9% of heritability (95% CI 93.5–100%). Our results establish staphylococcal pyomyositis, like tetanus and diphtheria, as critically dependent on a single toxin and demonstrate the potential for association studies to identify specific bacterial genes promoting severe human disease.
Publisher: Springer Science and Business Media LLC
Date: 08-07-2021
DOI: 10.1038/S41586-021-03767-X
Abstract: The genetic make-up of an in idual contributes to the susceptibility and response to viral infection. Although environmental, clinical and social factors have a role in the chance of exposure to SARS-CoV-2 and the severity of COVID-19 1,2 , host genetics may also be important. Identifying host-specific genetic factors may reveal biological mechanisms of therapeutic relevance and clarify causal relationships of modifiable environmental risk factors for SARS-CoV-2 infection and outcomes. We formed a global network of researchers to investigate the role of human genetics in SARS-CoV-2 infection and COVID-19 severity. Here we describe the results of three genome-wide association meta-analyses that consist of up to 49,562 patients with COVID-19 from 46 studies across 19 countries. We report 13 genome-wide significant loci that are associated with SARS-CoV-2 infection or severe manifestations of COVID-19. Several of these loci correspond to previously documented associations to lung or autoimmune and inflammatory diseases 3–7 . They also represent potentially actionable mechanisms in response to infection. Mendelian randomization analyses support a causal role for smoking and body-mass index for severe COVID-19 although not for type II diabetes. The identification of novel host genetic factors associated with COVID-19 was made possible by the community of human genetics researchers coming together to prioritize the sharing of data, results, resources and analytical frameworks. This working model of international collaboration underscores what is possible for future genetic discoveries in emerging pandemics, or indeed for any complex human disease.
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Sarah Earle.