Metabolic adaptations of Pseudomonas aeruginosa during cystic fibrosis chronic lung infections - V. Behrends; B. Ryall; J. E. A. Zlosnik; D. P. Speert; J. G. Bundy; H. D. Williams (Environmental Microbiology, 2013)
Summary Pseudomonas aeruginosa forms chronic infections in the lungs of cystic fibrosis (CF) patients, and is the leading cause of morbidity and mortality in patients with CF. Understanding how this opportunistic pathogen adapts to the CF lung during chronic infections is important to increase the efficacy of treatment and is likely to increase insight into other long‐term infections. Previous studies of P. aeruginosa adaptation and divergence in CF infections have focused on the genetic level, both identifying characteristic mutations and patterns of gene expression. However, these approaches are not sufficient to fully understand the metabolic changes that occur during long‐term infection, as metabolic regulation is complex and takes place on different biological levels. We used untargeted metabolic profiling (metabolomics) of cell supernatants (exometabolome analysis, or metabolic footprinting) to compare 179 strains, collected over time periods ranging from 4 to 24 years for the individual patients, representing a series of mostly clonal lineages from 18 individual patients. There was clear evidence of metabolic adaptation to the CF lung environment: acetate production was highly significantly negatively associated with length of infection. For amino acids, which are available to the bacterium in the lung environment, the tendency of isolates to evolve more efficient uptake was related to the biosynthetic cost of producing each metabolite; conversely, for the non‐mammalian metabolite trehalose, isolates had significantly reduced tendency to utilize this compound with length of infection. However, as well as adaptation across patients, there was also a striking degree of metabolic variation between the different clonal lineages: in fact, the patient the strains were isolated from was a greater source of variance than length of infection for all metabolites observed. Our data highlight the potential for metabolomic investigation of complex phenotypic adaptations during infection.