Pseudomonas aeruginosa populations in the cystic fibrosis lung lose susceptibility to newly applied β-lactams within 3 days.

<bold>Background: </bold>Chronic pulmonary infections by Pseudomonas aeruginosa require frequent intravenous antibiotic treatment in cystic fibrosis (CF) patients. Emergence of antimicrobial resistance is common in these patients, which to date has been investigated at long-term intervals only.<bold>Objectives: </bold>To investigate under close to real-time conditions the dynamics of the response by P. aeruginosa to a single course of antibiotic therapy and the potentially associated rapid spread of antimicrobial resistance, as well as the impact on the airway microbiome.<bold>Methods: </bold>We investigated a cohort of adult CF patients that were treated with a single course of antimicrobial combination therapy. Using daily sampling during treatment, we quantified the expression of resistance by P. aeruginosa (median of six isolates per daily sample, 347 isolates in total), measured bacterial load by P. aeruginosa-specific quantitative PCR and characterized the airway microbiome with a 16S rRNA-based approach. WGS was performed to reconstruct intrapatient strain phylogenies.<bold>Results: </bold>In two patients, we found rapid and large increases in resistance to meropenem and ceftazidime. Phylogenetic reconstruction of strain relationships revealed that resistance shifts are probably due to de novo evolution and/or the selection of resistant subpopulations. We observed high interindividual variation in the reduction of bacterial load, microbiome composition and antibiotic resistance.<bold>Conclusions: </bold>We show that CF-associated P. aeruginosa populations can quickly respond to antibiotic therapy and that responses are patient specific. Thus, resistance evolution can be a direct consequence of treatment, and drug efficacy can be lost much faster than usually assumed. The consideration of these patient-specific rapid resistance shifts can help to improve treatment of CF-associated infections, for example by deeper sampling of bacteria for diagnostics, repeated monitoring of pathogen susceptibility and switching between drugs. [ABSTRACT FROM AUTHOR]