Phenotypic variability among bacteria depends on gene expression in response to different environments, and it also reflects differences in genomic structure. In this study, we analyzed transcriptome sequencing (RNA-seq) profiles of 151 Pseudomonas aeruginosa clinical isolates under standard laboratory conditions and of one P. aeruginosa type strain under 14 different environmental conditions. Our approach allowed dissection of the impact of the genetic background versus environmental cues on P. aeruginosa gene expression profiles and revealed that phenotypic variation was larger in response to changing environments than between genomically different isolates. We demonstrate that mutations within the global regulator LasR affect more than one trait (pleiotropy) and that the interaction between mutations (epistasis) shapes the P. aeruginosa phenotypic plasticity landscape. Because of pleiotropic and epistatic effects, average genotype and phenotype measures appeared to be uncorrelated in P. aeruginosa.

ABSTRACTmRNA profiling of pathogens during the course of human infections gives detailed information on the expression levels of relevant genes that drive pathogenicity and adaptation and at the same time allows for the delineation of phylogenetic relatedness of pathogens that cause specific diseases. In this study, we used mRNA sequencing to acquire information on the expression of Escherichia coli pathogenicity genes during urinary tract infections (UTI) in humans and to assign the UTI-associated E. coli isolates to different phylogenetic groups. Whereas the in vivo gene expression profiles of the majority of genes were conserved among 21 E. coli strains in the urine of elderly patients suffering from an acute UTI, the specific gene expression profiles of the flexible genomes was diverse and reflected phylogenetic relationships. Furthermore, genes transcribed in vivo relative to laboratory media included well-described virulence factors, small regulatory RNAs, as well as genes not previously linked to bacterial virulence. Knowledge on relevant transcriptional responses that drive pathogenicity and adaptation of isolates to the human host might lead to the introduction of a virulence typing strategy into clinical microbiology, potentially facilitating management and prevention of the disease.

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.