Genomics of antibiotic-resistance prediction in Pseudomonas aeruginosa

Julie Jeukens, Luca Freschi, Irena Kukavica-Ibrulj, Jean-Guillaume Emond-Rheault, Nicholas P Tucker, Roger C Levesque

Research output: Contribution to journalArticlepeer-review

47 Citations (Scopus)
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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.

Original languageEnglish
Number of pages13
JournalAnnals of the New York Academy of Sciences
Early online date2 Jun 2017
Publication statusE-pub ahead of print - 2 Jun 2017


  • genomics
  • antibiotic resistance
  • in silico prediction
  • emerging technologies
  • bioinformatics
  • Pseudomonas aeruginosa


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