Center for Communicable Disease Dynamics, Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA.
Department of Biomedical Informatics and Laboratory of Systems Pharmacology, Harvard Medical School, Boston, MA, USA.
Nat Microbiol. 2020 Mar;5(3):455-464. doi: 10.1038/s41564-019-0656-6. Epub 2020 Feb 10.
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.
耐药菌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通常不会在影响患者治疗和结果的时间尺度上发生。在这里，我们提出了一种称为“基因组邻居分型”的方法，通过在具有元数据的基因组数据库中识别其最接近的亲缘关系来推断细菌样本的表型。我们表明，该技术可推断肺炎链球菌和淋病奈瑟菌的抗生素敏感性和耐药性。我们通过快速 k-mer 匹配来实现这一点，当在牛津纳米孔 MinION 数据上使用时，它可以实时运行。这导致在开始测序后 10 分钟内（肺炎链球菌的敏感性为 91%，特异性为 100%，代表性数据库中的分离物的特异性为 100%；淋病奈瑟菌的敏感性为 81%，特异性为 100%）确定耐药性，以及在样本采集后 4 小时内（肺炎链球菌的敏感性为 75%，特异性为 100%）确定临床宏基因组痰样本中的耐药性。这种灵活的方法具有广泛的病原体监测应用，可用于大大加快经验性抗生素治疗的速度。
