Polymicrobial disease and inflammation in cystic fibrosis

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DESCRIPTION (provided by applicant): This project seeks to understand the complex interactions between competing species of bacteria and the human immune system in the airways of patients with cystic fibrosis (CF). Our overall hypothesis is that complex interactions between bacterial species in the airways of patients with CF determine host defense responses and control the progression of airway disease. Our overall aim is to understand the interactions between Staphylococcus aureus and Pseudomonas aeruginosa, the most common bacteria infecting the lung in CF, and their interactions with the airway. These interactions appear to change the risk of death and vary the progression of the severe inflammation that causes airway damage and worsening lung function. Specifically, we will use epidemiologic and mathematical models to make precise testable predictions about the interactions between bacteria and the response of the airway. To provide data to modify and shape our models, we will use real-time quantitative PCR methods that we have newly developed to measure the numbers of each type of bacteria in the airway. We will perform measurements in a number of different clinical situations in order to understand how the burdens of these bacteria alter the course of disease. Using well established methods, we will measure inflammation as others have done before in CF, but, in contrast to past efforts, our focus will be to study how the intensity of inflammation varies as the burden of bacteria and the clinical circumstances vary. Our project will improve our understanding of how the two main species of bacteria in the CF airway alter disease course. Our new real-time quantitative PCR methods for rapid, precise and accurate measurement of bacteria will provide new clinical tools for assessing the extent of infection. These new tools will improve our ability to care for CF patients and other patients with infections by S. aureus and P. aeruginosa, for example, by improving our ability to apply antibiotics at the most favorable time to control flares of disease at the least effort and cost. Mathematically relating intensity of inflammation to the extent of infection will provide new opportunities for understanding the consequences of infection and the impact of treatments and may suggest new potential therapies and strategies for treating CF.