Search

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., Competing Interests: Competing interests statement:The authors declare no competing interest.

Laboratory models are critical to basic and translational microbiology research. Models serve multiple purposes, from providing tractable systems to study cell biology to allowing the investigation of inaccessible clinical and environmental ecosystems. Although there is a recognized need for improved model systems, there is a gap in rational approaches to accomplish this goal. We recently developed a framework for assessing the accuracy of microbial models by quantifying how closely each gene is expressed in the natural environment and in various models. The accuracy of the model is defined as the percentage of genes that are similarly expressed in the natural environment and the model. Here, we leverage this framework to develop and validate two generalizable approaches for improving model accuracy, and as proof of concept, we apply these approaches to improve models of Pseudomonas aeruginosa infecting the cystic fibrosis (CF) lung. First, we identify two models, an in vitro synthetic CF sputum medium model (SCFM2) and an epithelial cell model, that accurately recapitulate different gene sets. By combining these models, we developed the epithelial cell-SCFM2 model which improves the accuracy of over 500 genes. Second, to improve the accuracy of specific genes, we mined publicly available transcriptome data, which identified zinc limitation as a cue present in the CF lung and absent in SCFM2. Induction of zinc limitation in SCFM2 resulted in accurate expression of 90% of P. aeruginosa genes. These approaches provide generalizable, quantitative frameworks for microbiological model improvement that can be applied to any system of interest.

Laboratory models are a cornerstone of modern microbiology, but the accuracy of these models has not been systematically evaluated. As a result, researchers often choose models based on intuition or incomplete data. We propose a general quantitative framework to assess model accuracy from RNA sequencing data and use this framework to evaluate models of Pseudomonas aeruginosa cystic fibrosis (CF) lung infection. We found that an in vitro synthetic CF sputum medium model and a CF airway epithelial cell model had the highest genome-wide accuracy but underperformed on distinct functional categories, including porins and polyamine biosynthesis for the synthetic sputum medium and protein synthesis for the epithelial cell model. We identified 211 "elusive" genes that were not mimicked in a reference strain grown in any laboratory model but found that many were captured by using a clinical isolate. These methods provide researchers with an evidence-based foundation to select and improve laboratory models. IMPORTANCE Laboratory models have become a cornerstone of modern microbiology. However, the accuracy of even the most commonly used models has never been evaluated. Here, we propose a quantitative framework based on gene expression data to evaluate model performance and apply it to models of Pseudomonas aeruginosa cystic fibrosis lung infection. We discovered that these models captured different aspects of P. aeruginosa infection physiology, and we identify which functional categories are and are not captured by each model. These methods will provide researchers with a solid basis to choose among laboratory models depending on the scientific question of interest and will help improve existing experimental models., (Copyright © 2020 Cornforth et al.)