Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.
Rapid antibiotic-resistance predictions from genome sequence data for Staphylococcus aureus and Mycobacterium tuberculosis.
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DOI：
10.1038/ncomms10063
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发表时间：
2015-12-21
影响因子：
16.6
通讯作者：
Iqbal Z
中科院分区：
文献类型：
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作者：
Bradley P;Gordon NC;Walker TM;Dunn L;Heys S;Huang B;Earle S;Pankhurst LJ;Anson L;de Cesare M;Piazza P;Votintseva AA;Golubchik T;Wilson DJ;Wyllie DH;Diel R;Niemann S;Feuerriegel S;Kohl TA;Ismail N;Omar SV;Smith EG;Buck D;McVean G;Walker AS;Peto TE;Crook DW;Iqbal Z
抗药性细菌的增加导致迫切需要快速检测临床样本中的抗药性，并改善全球监测。在这里，我们展示了如何用de Bruijn图表示细菌多样性来识别临床分离株的物种和耐药性。我们在一个软件包（Mykrobe predictor）中实现了这种针对金黄色葡萄球菌和结核分枝杆菌的方法，该软件包将原始序列数据作为输入，并在3分钟内在笔记本电脑上生成临床医生友好的报告。对于鼠伤寒沙门氏金黄色葡萄球菌，我们的方法的错误率与金标准表型方法相当，12种抗生素的灵敏度/特异性为99.1%/99.6%（使用独立验证集，n=470）。支原体对于结核病，我们的方法预测耐药性的灵敏度/特异性为82.6%/98.5%（独立验证集，n= 1，609）;这里的灵敏度较低，可能是因为对潜在遗传机制的了解有限。我们提供的证据表明，次要等位基因提高了对极端耐药菌株的检测，并证明了使用新兴的单分子纳米孔测序技术用于这些目的的可行性。 新测序技术的临床应用有望加速病原体鉴定。在此，布拉德利等人提出了一个临床医生友好的软件包，该软件包使用测序数据快速准确地预测链球菌的抗生素耐药性。aureus和M.结核
The rise of antibiotic-resistant bacteria has led to an urgent need for rapid detection of drug resistance in clinical samples, and improvements in global surveillance. Here we show how de Bruijn graph representation of bacterial diversity 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. The clinical application of new sequencing techniques is expected to accelerate pathogen identification. Here, Bradley et al. present a clinician-friendly software package that uses sequencing data for quick and accurate prediction of antibiotic resistance profiles for S. aureus and M. tuberculosis.