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 ofEscherichia colipathogenicity genes during urinary tract infections (UTI) in humans and to assign the UTI-associatedE. coliisolates to different phylogenetic groups. Whereas thein vivogene expression profiles of the majority of genes were conserved among 21E. colistrains 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 transcribedin vivorelative 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.IMPORTANCEUrinary tract infections (UTI) are very common; at least half of all women experience UTI, most of which are caused by pathogenicEscherichia colistrains. In this study, we applied massive parallel cDNA sequencing (RNA-seq) to provide unbiased, deep, and accurate insight into the nature and the dimension of the uropathogenicE. coligene expression profile during an acute UTI within the human host. This work was undertaken to identify key players in physiological adaptation processes and, hence, potential targets for new infection prevention and therapy interventions specifically aimed at sabotaging bacterial adaptation to the human host.
Discovery of glpC, an Organic Solvent Tolerance-Related Gene in Escherichia coli, Using Gene Expression Profiles from DNA Microarrays
