The Edison Family Center for Genome Sciences and Systems Biology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Department of Pathology & Immunology, Washington University in St. Louis School of Medicine, St. Louis, MO, USA.
Nat Rev Genet. 2019 Jun;20(6):356-370. doi: 10.1038/s41576-019-0108-4.
Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.
抗微生物药物耐药每年使细菌对抗生素产生耐药性,从而导致高发病率、死亡率和经济成本。鉴定和了解抗微生物药物耐药性对于临床实践治疗耐药感染以及公共卫生努力限制耐药性传播至关重要。下一代测序等技术正在扩展我们检测和研究抗微生物药物耐药性的能力。本综述详细概述了抗微生物药物耐药性的鉴定和特征描述方法,从传统的抗微生物药物敏感性测试到最近的深度学习方法。我们重点介绍基于测序的耐药性发现,并讨论了在抗微生物药物耐药性研究中使用的工具和数据库。