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

• 成果类型: Article
• 署名作者: Duncan, Rebecca P.; Moustafa, Dina A.; Lewin, Gina R.; Diggle, Frances L.; Bomberger, Jennifer M.; Whiteley, Marvin; Goldberg, Joanna B.
• 署名单位: Emory University; University System of Georgia; Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology; Pennsylvania Commonwealth System of Higher Education (PCSHE); University of Pittsburgh; Centers for Disease Control & Prevention - USA; University System of Ohio; Case Western Reserve University; Dartmouth College
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-13023
• DOI: 10.1073/pnas.2406234121
• 发表日期: 2024-08-13

摘要

Laboratory models are central to microbiology research, advancing the understanding of bacterial physiology by mimicking natural environments, from soil to the human micro- biome. 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.