UC Irvine

Increased understanding of the ecology of the cystic fibrosis (CF) lung microbiome has im- proved treatment options for CF lung infections by allowing clinicians to target microbes known to be associated with acute disease. The microbial community responsible for CF lung infections is a complex collection of bacteria, viruses, and fungi with varying nutritional sources and metabolisms. Oxygen is a key resources in this environment, however it has different effects on the various microbial species with some species requiring it for respiration and others inhibited by it. Episodes of acute disease known as cystic fibrosis pulmonary exacerbations (CFPEs) are now known to be associated with higher abundances of fermentative anaerobes which can propagate in low oxygen conditions. The goal of this dissertation is to characterize the CF lung microbiome quantitatively, specifically the inter- action between anaerobic and aerobic microbes, the role of oxygen, and the potential for oxygen-based therapies and optimized antibiotic usage. We begin by developing a model for the in vitro growth of CF pathogens and estimating basic growth parameters for individual pathogens. We use these parameterizations to model the competition between aerobic and anaerobic communities inside of a CF airway and to predict antibiotic treatments and oxy- gen conditions that maintain a low anaerobic population. Next, we extend our modeling of aerobic-anaerobic competition to include spatial effects and stochasticity to investigate how oxygen gradients affect community dynamics within aggregated mucus. We use our spatial models to predict physical properties such as the minimum mucus plug diameter necessary to support anaerobic growth and show that an antibiotic with a high permeability into mucus is the most effective for controlling the anaerobic community. The results from this dissertation will aid the clinical treatment of CF lung infections by predicting antibiotic treatments and oxygen therapies which can be tested in laboratory conditions and eventually be applied clinically.