Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.
Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa.
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DOI：
10.1111/nyas.13358
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发表时间：
2019-01
影响因子：
5.2
通讯作者：
Levesque RC
中科院分区：
文献类型：
--
作者：
Jeukens J;Freschi L;Kukavica-Ibrulj I;Emond-Rheault JG;Tucker NP;Levesque RC
抗生素耐药性是一个全球性的健康问题，在人类和动物病原体以及环境细菌中迅速传播。抗生素的滥用会影响耐药细菌的选择，从而导致通过自发突变出现或通过水平基因转移获得的耐药基因型的出现增加。我们不仅迫切需要检测抗菌素耐药性，而且还需要通过计算机模拟预测抗生素耐药性。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。即将推出的新颖的生物信息学工具可以以前所未有的复杂程度来预测抗性组和移动组。结合细菌菌株收藏和包含菌株元数据的数据库，抗生素耐药性和毒力潜力的预测正在迅速朝着分子流行病学的新方法发展。在这里，我们提出了抗生素耐药性预测的模型系统，及其前景和局限性。由于铜绿假单胞菌通常具有多重耐药性，因此引起的感染通常难以根除。我们回顾了抗生素耐药性基因型预测的新方法。我们讨论了用于实时患者管理和抗生素耐药性预测的微生物序列数据的生成。
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.
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