摘要

       抗生素抗性基因 (ARG) 的垂直和/或水平转移促进了抗生素或抗菌素耐药性 (AR)，这是一项严重的全球健康挑战。虽然传统上与临床环境中的病原体有关，但越来越清楚的是，非临床环境也可能是 ARG 的储存库。最近在快速且经济实惠的下一代测序技术以及复杂的生物信息学平台方面的改进有可能彻底改变诊断微生物学和微生物监测。通过对细菌基因组和复杂宏基因组中 ARGs 的研究和表征，我们现在能够揭示 AR 在单个细菌和复杂群落中的遗传范围，并在物种、种群和群落水平上获得对 AR 动态的重要见解，提供新的流行病学研究和生态观点。目前有一套生物信息学管道和 ARG 数据库可用于基因组和宏基因组数据分析。但是，不同的平台可能会有很大差异，因此，选择最适合正在进行的特定分析的工具至关重要。本综述详细介绍了可用的生物信息学平台，用于鉴定和表征单个细菌分离株和复杂环境样品中的 ARG 和相关遗传元件。它主要关注当前可用的 ARG 数据库，采用全面的基准测试流程来识别四种细菌基因组（杀鲑气单胞菌、蜡状芽孢杆菌、伯克霍尔德菌属和大肠杆菌）和三种鸟枪法宏基因组（人类肠道、家禽垫料和土壤）中的 ARG，从而提供洞察力哪些数据库应该用于不同的分析场景。

       Antibiotic or antimicrobial resistance (AR) facilitated by the vertical and/or horizontal transfer of antibiotic resistance genes (ARGs), is a serious global health challenge. While traditionally associated with pathogens in clinical environments, it is becoming increasingly clear that non-clinical environments may also be reservoirs of ARGs. The recent improvements in rapid and affordable next generation sequencing technologies along with sophisticated bioinformatics platforms has the potential to revolutionize diagnostic microbiology and microbial surveillance. Through the study and characterization of ARGs in bacterial genomes and complex metagenomes, we are now able to reveal the genetic scope of AR in single bacteria and complex communities, and obtain important insights into AR dynamics at species, population and community levels, providing novel epidemiological and ecological perspectives. A suite of bioinformatics pipelines and ARG databases are currently available for genomic and metagenomic data analyses. However, different platforms may significantly vary and therefore, it is crucial to choose the tools that are most suitable for the specific analysis being conducted. This review provides a detailed account of available bioinformatics platforms for identification and characterization of ARGs and associated genetic elements within single bacterial isolates and complex environmental samples. It focuses primarily on currently available ARG databases, employing a comprehensive benchmarking pipeline to identify ARGs in four bacterial genomes (Aeromonas salmonicida, Bacillus cereus, Burkholderia sp. and Escherichia coli) and three shotgun metagenomes (human gut, poultry litter and soil) providing insight into which databases should be used for different analytical scenarios.

https://www.sciencedirect.com/science/article/pii/S0160412019342424