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非结核分枝杆菌的全基因组测序及抗菌药敏预测

Whole genome sequencing and prediction of antimicrobial susceptibilities in non-tuberculous mycobacteria.

作者信息

Solanki Priya, Lipman Marc, McHugh Timothy D, Satta Giovanni

机构信息

UCL-TB and UCL Centre for Clinical Microbiology, University College London, London, United Kingdom.

UCL-TB and UCL Respiratory, University College London, London, United Kingdom.

出版信息

Front Microbiol. 2022 Nov 29;13:1044515. doi: 10.3389/fmicb.2022.1044515. eCollection 2022.

Abstract

Non-tuberculous mycobacteria (NTM) are opportunistic pathogens commonly causing chronic, pulmonary disease which is notoriously hard to treat. Current treatment for NTM infections involves at least three active drugs (including one macrolide: clarithromycin or azithromycin) over 12 months or longer. At present there are limited phenotypic drug susceptibility testing options for NTM which are standardised globally. As seen with tuberculosis, whole genome sequencing has the potential to transform drug susceptibility testing in NTM, by utilising a genotypic approach. The Comprehensive Resistance Prediction for Tuberculosis is a database used to predict resistance: at present there are no similar databases available to accurately predict NTM resistance. Recent studies have shown concordance between phenotypic and genotypic NTM resistance results. To benefit from the advantages of whole genome sequencing, further advances in resistance prediction need to take place, as well as there being better information on novel drug mutations and an understanding of the impact of whole genome sequencing on NTM treatment outcomes.

摘要

非结核分枝杆菌(NTM)是常见的机会致病菌,通常会引发难以治疗的慢性肺部疾病。目前针对NTM感染的治疗需要在12个月或更长时间内使用至少三种活性药物(包括一种大环内酯类药物:克拉霉素或阿奇霉素)。目前,全球范围内标准化的NTM表型药敏试验选项有限。与结核病一样,全基因组测序有潜力通过采用基因型方法来改变NTM的药敏试验。结核病综合耐药预测是一个用于预测耐药性的数据库:目前尚无类似的数据库可准确预测NTM耐药性。最近的研究表明NTM表型和基因型耐药结果之间具有一致性。为了受益于全基因组测序的优势,耐药性预测需要进一步发展,同时还需要更好地了解新的药物突变以及全基因组测序对NTM治疗结果的影响。

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7
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8
Potency of Omadacycline against Mycobacteroides abscessus Clinical Isolates and in a Mouse Model of Pulmonary Infection.
Antimicrob Agents Chemother. 2022 Jan 18;66(1):e0170421. doi: 10.1128/AAC.01704-21. Epub 2021 Oct 18.
9
Epidemiology of in England: an observational study.
Lancet Microbe. 2021 Oct;2(10):e498-e507. doi: 10.1016/S2666-5247(21)00128-2.
10
Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning.
Bioinformatics. 2022 Jan 3;38(2):325-334. doi: 10.1093/bioinformatics/btab681.

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