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.

The opportunistic bacterial pathogen Pseudomonas aeruginosa causes acute and chronic infections that are notoriously difficult to treat. In people with cystic fibrosis, P. aeruginosa can cause lifelong lung infections, and isolation of mucoid P. aeruginosa , resulting from the overproduction of alginate, is associated with chronic infection. The histone-like protein AlgP has previously been implicated in the control of alginate gene expression in mucoid strains, but this regulation is unclear. To explore AlgP in further detail, we deleted algP in mucoid strains and demonstrated that the deletion of algP did not result in a nonmucoid phenotype or a decrease in alginate production. We showed that the algP promoter is expressed by both the nonmucoid strain PAO1 and the isogenic mucoid strain PDO300, suggesting that there may be genes that are differentially regulated between these strains. In support of this, using RNA sequencing, we identified a small AlgP regulon that has no significant overlap between PAO1 and PDO300 and established that alginate genes were not differentially regulated by the deletion of algP. Of note, we found that deleting algP in PAO1 increased expression of the nitric oxide operon norCBD and the nitrous oxide reductase genes nosRZ and subsequently promoted growth of PAO1 under anaerobic conditions. Altogether, we have defined a narrow regulon of genes controlled by AlgP and provided evidence that alginate production is not greatly affected by AlgP, countering the long-standing premise in the field.