A Pseudomonas aeruginosa small RNA regulates chronic and acute infection.

The ability to switch between different lifestyles allows bacterial pathogens to thrive in diverse ecological niches1,2. However, a molecular understanding of their lifestyle changes within the human host is lacking. Here, by directly examining bacterial gene expression in human-derived samples, we discover a gene that orchestrates the transition between chronic and acute infection in the opportunistic pathogen Pseudomonas aeruginosa. The expression level of this gene, here named sicX, is the highest of the P. aeruginosa genes expressed in human chronic wound and cystic fibrosis infections, but it is expressed at extremely low levels during standard laboratory growth. We show that sicX encodes a small RNA that is strongly induced by low-oxygen conditions and post-transcriptionally regulates anaerobic ubiquinone biosynthesis. Deletion of sicX causes P. aeruginosa to switch from a chronic to an acute lifestyle in multiple mammalian models of infection. Notably, sicX is also a biomarker for this chronic-to-acute transition, as it is the most downregulated gene when a chronic infection is dispersed to cause acute septicaemia. This work solves a decades-old question regarding the molecular basis underlying the chronic-to-acute switch in P. aeruginosa and suggests oxygen as a primary environmental driver of acute lethality.

Application of a quantitative framework to improve the accuracy of a bacterial infection model.

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.

The histone-like protein AlgP regulon is distinct in mucoid and nonmucoid Pseudomonas aeruginosa and does not include alginate biosynthesis genes.

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.

Quantitative Framework for Model Evaluation in Microbiology Research Using Pseudomonas aeruginosa and Cystic Fibrosis Infection as a Test Case.

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.