Ecology of Cystic Fibrosis

点击翻译按钮获取中文摘要

DESCRIPTION (provided by applicant): Cystic Fibrosis (CF) is a genetic disease that dramatically decreases life expectancy and quality. The disease is characterized by polymicrobial infections, which lead to lung remodeling and airway mucus plugging. CF patients experience intermittent exacerbations that cause airway scarring and eventually result in seriously impaired respiration. Despite aggressive therapy, most CF patients will die from respiratory failure caused by the chronic lung infections. Standard clinical microbiological studies and antibiotic susceptibility testing have severe limitations in predicting medical outcomes from specific therapies. This is due in part because the dynamics of microbes and their viral predators (phage) are essentially unknown in the CF lung. The goal of this study is to use metagenomics and microscopy to characterize these two communities and determine how they change in response to perturbations such as disease severity, antibiotic treatments, and chemical treatments designed to break up the mucus plugs. The metagenomic data will be used to determine how the types and metabolic functions of both the viral and microbial communities change. Microbial growth rates and phage-mediated death will also be measured. To better understand the spatial dynamics of the microbial and viral communities, we will also be dissecting lungs of CF patients. Viral and microbial metagenomes from individual lobes will be sequenced. To determine where specific species of microbes reside in the mucus plugs, fluorescent in situ hybridization with 16S rDNA probes will used on tissue samples. Together these data sets will be used to parameterize three complementary ecological models of the CF lung. Then these models will be used as in silico tests of alternative treatment regimes (e.g., adjust timing of antibiotic administration). When completed, we will have generated an unprecedented view of the viral and microbial communities in CF and how they respond to treatments. With the models, we will be able to better understand the dynamics of these communities and test alternative treatment regimes to prolong and improve the lives of CF patients.