Transcriptome profiling of antimicrobial resistance in Pseudomonas aeruginosa
Antimicrob Agents and Chemother, 66 (8):
4722-4733 (2016)Abstract
Emerging resistance towards antimicrobials and the lack of new antibiotic drug candidates underscore the need for optimization of current diagnostics and therapies to diminish the evolution and spread of multidrug-resistance. As the antibiotic resistance status of a bacterial pathogen is defined by its genome, resistance profiling by applying next-generation sequencing (NGS) technologies may in the future accomplish pathogen identification, prompt initiation of targeted individualized treatment and the implementation of optimized infection control measures. In this study, qualitative RNA-sequencing was used to identify key genetic determinants of antibiotic resistance in 135 clinical Pseudomonas aeruginosa isolates from diverse geographic and infection site origins. By applying transcriptome-wide association studies adaptive variations associated with resistance towards the antibiotic classes of fluoroquinolones, aminoglycosides and β-lactams were identified. Besides potential novel biomarkers with a direct correlation to resistance, global patterns of phenotype-associated gene expression and sequence variations were identified by predictive machine learning approaches. Our research serves the establishment of genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way to faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential of resistance evolution.