Genomics of antibiotic‐resistance prediction in Pseudomonas aeruginosa. Issue 1 (2nd June 2017)

• Record Type: Journal Article
• Authors: Jeukens, Julie; Freschi, Luca; Kukavica‐Ibrulj, Irena; Emond‐Rheault, Jean‐Guillaume; Tucker, Nicholas P.; Levesque, Roger C.
• Other Names: Wright Gerard D. guestEditor.
• Is Part Of: Annals of the New York Academy of Sciences. Volume 1435:Issue 1(2019)
• Page Start: 5
• Page End: 17
• Publication Date: 2017-06-02
• Subjects: antibiotic resistance -- in silico prediction -- emerging technologies -- genomics; Medical sciences -- Periodicals; Medicine -- Periodicals; Science -- Periodicals; 610
• DOI: 10.1111/nyas.13358
• Languages: English
• ISSNs: 0077-8923

Abstract

Abstract: 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.