Improvement of a mouse infection model to capture Pseudomonas aeruginosa chronic physiology in cystic fibrosis

Abstract

Laboratory models are central to microbiology research, advancing the understanding of bacterial physiology by mimicking natural environments, from soil to the human microbiome. When studying host–bacteria interactions, animal models enable investigators to examine bacterial dynamics associated with a host, and in the case of human infections, animal models are necessary to translate basic research into clinical treatments. Efforts toward improving animal infection models are typically based on reproducing host genotypes/phenotypes and disease manifestations, leaving a gap in how well the physiology of microbes reflects their behavior in a human host. Understanding bacterial physiology is vital because it dictates host response and bacterial interactions with antimicrobials. Thus, our goal was to develop an animal model that accurately recapitulates bacterial physiology in human infection. The system we chose to model was a chronic Pseudomonas aeruginosa respiratory infection in cystic fibrosis (CF). To accomplish this goal, we leveraged a framework that we recently developed to evaluate model accuracy by calculating the percentage of bacterial genes that are expressed similarly in a model to how they are expressed in their infection environment. We combined two complementary models of P. aeruginosa infection—an in vitro synthetic CF sputum model (SCFM2) and a mouse acute pneumonia model. This combined model captured the chronic physiology of P. aeruginosa in CF better than the standard mouse infection model, showing the power of a data-driven approach to refining animal models. In addition, the results of this work challenge the assumption that a chronic infection model requires long-term colonization.