摘要

抗生素耐药性是一种在人类和动物病原体以及环境细菌中迅速传播的全球健康问题。滥用抗生素对抗性细菌的选择有影响，因此导致通过自发突变出现的抗性基因型的出现增加或通过水平基因转移获得。不仅需要检测抗微生物药物耐药性，还需要预测硅片上的抗生素耐药性。我们现在有能力每周对数百个细菌基因组进行测序，包括组装和注释。新颖且即将推出的生物信息学工具可以预测以前不可能的复杂程度的resistome和mobilome。加上含有菌株元数据的细菌菌种收集和数据库，预测抗生素耐药性和毒力潜力正迅速转向分子流行病学的新方法。在这里，我们提出了抗生素耐药性预测的模型系统，以及其承诺和限制。由于它通常具有多重耐药性，铜绿假单胞菌引起通常难以根除的感染。我们回顾了抗生素耐药基因型预测的新方法。我们讨论了用于实时病人管理和预测抗微生物药物耐药性的微生物序列数据的产生。

Antibiotic resistance is a worldwide health issue spreading quickly among human and animal pathogens, as well as environmental bacteria. Misuse of antibiotics has an impact on the selection of resistant bacteria, thus contributing to an increase in the occurrence of resistant genotypes that emerge via spontaneous mutation or are acquired by horizontal gene transfer. There is a specific and urgent need not only to detect antimicrobial resistance but also to predict antibiotic resistance in silico. We now have the capability to sequence hundreds of bacterial genomes per week, including assembly and annotation. Novel and forthcoming bioinformatics tools can predict the resistome and the mobilome with a level of sophistication not previously possible. Coupled with bacterial strain collections and databases containing strain metadata, prediction of antibiotic resistance and the potential for virulence are moving rapidly toward a novel approach in molecular epidemiology. Here, we present a model system in antibiotic‐resistance prediction, along with its promises and limitations. As it is commonly multidrug resistant, Pseudomonas aeruginosa causes infections that are often difficult to eradicate. We review novel approaches for genotype prediction of antibiotic resistance. We discuss the generation of microbial sequence data for real‐time patient management and the prediction of antimicrobial resistance.

https://nyaspubs.onlinelibrary.wiley.com/doi/full/10.1111/nyas.13358