A data-driven framework improves a mouse infection model to capture Pseudomonas aeruginosa chronic physiology in cystic fibrosis

Animal infection models advance basic and translational research on human infections, but evaluating and improving these models is hindered efforts to reproduce host phenotypes without understanding how well models mimic the physiology of infecting bacteria. Using a data driven approach, we improved a common mouse acute pneumonia model of the opportunistic human pathogen Pseudomonas aeruginosa, resulting in a mouse model that more accurately represents chronic respiratory infection in cystic fibrosis. Thus, we provide the first account that animal models can be systematically and quantitatively refined. Furthermore, improving a short-term animal model demonstrated that studying chronic infection does not necessarily require a long-term model, showing that approaching animal model development from a quantitative perspective has the potential to reshape evaluation of model relevancy across human diseases.