Clinical Effects of Evolved Variation in Infecting Bacterial Populations

University Of Washington, Seattle WA

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Abstract

Chronic infections cause continuous organ dysfunction, and disease progresses at highly variable rates. The chronic Pseudomonas aeruginosa (Pa) airway infections that afflict people cystic fibrosis (CF) are a prime example. Pronounced variability in the rate of lung function decline is seen in different patients even when host CF genotype and airway microbiology are similar. Rates of lung function decline also vary at different times in individual patients, even though most patients are infected with a single, dominant Pa. Understanding the mechanisms responsible for stable or accelerated clinical decline could suggest novel approaches to slow disease. Several findings have led us to investigate the contribution of genetic variation that evolves in Pa strains during CF infections. CF Pa strains genetically diversify in vivo producing clonally-related variants that can differ markedly in virulence. In addition, our data shows that the composition of genetic variants changes over time and can be associated with changes in disease. Finally, many groups are investigating host genetic and environmental factors that contribute to disease, but the contributions of bacterial evolution is relatively unexplored. Here we exploit unique clinical and technical resources, and our knowledge of Pa pathogenesis to test the hypothesis that genetic variants that evolve in Pa populations infecting CF airways can affect lung disease severity. We will test this hypothesis in three steps. First, we will measure the prevalence of Pa gene variants in CF sputum collected at time points that capture disease variability. Second, we will perform genetic association- type statistical analysis to link specific Pa gene variants, genes, or gene pathways to lung disease. Third, we will investigate functional effects of gene variants on Pa virulence, host injury and inflammation. This step will establish biological plausibility and mechanistic understanding of potential cause and effect relationships between Pa gene variants and disease. This work will provide a proof of principle test of a new idea to explain disease variability that could have implications for many chronic infections.