Transcriptome Profiling of Antimicrobial Resistance in Pseudomonas aeruginosa

Abstract

e Emerging resistance to 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 to the antibiotic classes fluoroquinolones, aminoglycosides, and ␤-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 to establish genotype-based molecular diagnostic tools for the identification of the current resistance profiles of bacterial pathogens and paves the way for faster diagnostics for more efficient, targeted treatment strategies to also mitigate the future potential for resistance evolution.

Transcriptional and Mutational Profiling of an Aminoglycoside-Resistant Pseudomonas aeruginosa Small-Colony Variant

“…We have previously identified three clonally related tobramycin-resistant clinical P. aeruginosa isolates (30). The resistance phenotype of those isolates could not be explained by the presence of a gene encoding an aminoglycoside-modifying enzyme, nor did they express the mexXY multidrug efflux pump genes at highly elevated levels.…”

“…For this study, RNA sequencing of the revertant strains generated was performed in the same way. Sequencing data analysis was performed as described before (30).…”

Genomics of antibiotic‐resistance prediction in Pseudomonas aeruginosa

“…Analysis of genotype versus phenotype confirmed expected resistance evolution trajectories, but also revealed new pathways. Qualitative RNA sequencing was used to identify the key genetic determinants of AMR in 135 clinical P. aeruginosa isolates from diverse geographic origins and infection sites . By applying transcriptome‐wide association studies, adaptive variations associated with AMR of fluoroquinolones, aminoglycosides, and β‐lactams were identified.…”

“…Qualitative RNA sequencing was used to identify the key genetic determinants of AMR in 135 clinical P. aeruginosa isolates from diverse geographic origins and infection sites. 16 By applying transcriptome-wide association studies, adaptive variations associated with AMR of fluoroquinolones, aminoglycosides, and ␤-lactams were identified. Besides potential novel biomarkers with a direct correlation to AMR, global patterns of phenotype-associated gene expression and sequence variations were identified by machine learning approaches.…”

Environment‐driven changes of mRNA and protein levels in Pseudomonas aeruginosa

“…Due to the technological advances in next generation sequencing, profiling of a large panel of different cells under various environmental conditions has become possible and led to the acquisition of extensive RNA-sequencing datasets (Wang et al, 2009;Yamane et al, 2012;Dötsch et al, 2015;Khaledi et al, 2016). Those have been used to shed light on environment-driven flexible changes in the transcriptional profile.…”