Abstract
Pseudomonas aeruginosa is the predominant cause of chronic biofilm infections that form in the lungs of people with cystic fibrosis (CF). These infections are highly resistant to antibiotics and persist for years in the respiratory tract. One of the main research challenges is that current laboratory models do not accurately replicate key aspects of a P. aeruginosa biofilm infection, highlighted by previous RNA-sequencing studies. We compared the P. aeruginosa PA14 transcriptome in an ex vivo pig lung (EVPL) model of CF and a well-studied synthetic cystic fibrosis sputum medium (SCFM). P. aeruginosa was grown in the EVPL model for 1, 2, and 7 days, and in vitro in SCFM for 1 and 2 days. The RNA was extracted and sequenced at each time point. Our findings demonstrate that expression of antimicrobial resistance genes was cued by growth in the EVPL model, highlighting the importance of growth environment in determining accurate resistance profiles. The EVPL model created two distinct growth environments: tissue-associated biofilm and the SCFM surrounding tissue, each cuing a transcriptome distinct from that seen in SCFM in vitro. The expression of quorum sensing associated genes in the EVPL tissue-associated biofilm at 48 h relative to in vitro SCFM was similar to CF sputum versus in vitro conditions. Hence, the EVPL model can replicate key aspects of in vivo biofilm infection that are missing from other current models. It provides a more accurate P. aeruginosa growth environment for determining antimicrobial resistance that quickly drives P. aeruginosa into a chronic-like infection phenotype. IMPORTANCE Pseudomonas aeruginosa lung infections that affect people with cystic fibrosis are resistant to most available antimicrobial treatments. The lack of a laboratory model that captures all key aspects of these infections hinders not only research progression but also clinical diagnostics. We used transcriptome analysis to demonstrate how a model using pig lungs can more accurately replicate key characteristics of P. aeruginosa lung infection, including mechanisms of antibiotic resistance and infection establishment. Therefore, this model may be used in the future to further understand infection dynamics to develop novel treatments and more accurate treatment plans. This could improve clinical outcomes as well as quality of life for individuals affected by these infections.