摘要

      对耐药菌的监测对于医疗保健提供者提供有效的经验性抗生素治疗至关重要。然而，传统的分子流行病学并不典型地发生在可能影响患者治疗和结果的时间尺度上。在这里，我们提出了一种称为“基因组邻域分型”的方法，通过在带有元数据的基因组数据库中识别细菌样本的最近亲属来推断其表型。结果表明，该技术可同时推断肺炎链球菌和淋病奈瑟菌的药敏和耐药性。我们通过快速k-mer匹配实现了这一点，当它用于牛津纳米孔模型数据时，可以实时运行。这导致在开始测序的10 min（对肺炎链球菌的敏感性为91%，特异性为100%，对具有代表性数据库的分离株的淋病链球菌的敏感性为81%，特异性为100%）内，以及在采集样本的4 h（对肺炎链球菌的敏感性为75%，特异性为100%）内，对临床亚基因组痰标本。这种灵活的方法在病原菌监测方面有着广泛的应用，可用于大大加快适当的经验性抗生素治疗。

      Surveillance of drug-resistant bacteria is essential for healthcare providers to deliver effective empirical antibiotic therapy. However, traditional molecular epidemiology does not typically occur on a timescale that could affect patient treatment and outcomes. Here, we present a method called ‘genomic neighbour typing’ for inferring the phenotype of a bacterial sample by identifying its closest relatives in a database of genomes with metadata. We show that this technique can infer antibiotic susceptibility and resistance for both Streptococcus pneumoniae and Neisseria gonorrhoeae. We implemented this with rapid k-mer matching, which, when used on Oxford Nanopore MinION data, can run in real time. This resulted in the determination of resistance within 10 min (91% sensitivity and 100% specificity for S. pneumoniae and 81% sensitivity and 100% specificity for N. gonorrhoeae from isolates with a representative database) of starting sequencing, and within 4 h of sample collection (75% sensitivity and 100% specificity for S. pneumoniae) for clinical metagenomic sputum samples. This flexible approach has wide application for pathogen surveillance and may be used to greatly accelerate appropriate empirical antibiotic treatment.

      https://www.nature.com/articles/s41564-019-0656-6