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There is plenty of evidence to suggest that the lung is not uniform. The internal surface area is 30 times that of skin, and the different bronchioles/bronchi/alveoli differ greatly in blood perfusion, temperature, oxygen tension, and pH. Also, particularly in the context of respiratory disease, notable differences are present in the structure of epithelial cells, cilia, production of mucus, and inflammatory/immune responses. All of these factors are known to impact the physiology of bacteria, yet, there is very little understanding of how they impact a) the presence/absence of particular bacterial species throughout the respiratory tract, or b) the metabolic processes used by these bacteria within the human host environment. A greater understanding of the relationships between environmental (chemical) gradients in the lungs of diseased patients (particularly those with cystic fibrosis) and the microbial communities that are present may lead to novel hypotheses about manipulation of the respiratory environment for therapeutic benefit. To investigate this further, the investigators propose to use explanted lung specimens from cystic fibrosis patients to test the following hypothesis:
Hypothesis: In patients with cystic fibrosis, bacterial community composition, metabolism and environmental chemistry will vary depending on their spatial location within the airways.
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To study this in greater detail, the investigators propose to study explanted tissue of CF patients that are scheduled to undergo single or double lung transplant surgery as a late-stage disease therapeutic strategy. This population will be limited to the Adult CF clinic, as pediatric subjects are rarely candidates for lung transplantation. The Adult CF Clinic performs upwards of 20 surgeries per year, and tissue that is explanted is typically discarded. Using this tissue, the investigators propose the following objectives:
Information collected in these three objectives will then be paired with patient data (age, genotype, prior medical treatments, clinical microbiology data) to generate better working models of late-stage disease in CF patients.
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Ryan C Hunter, PhD; Jordan Dunitz, MD
Data sourced from clinicaltrials.gov
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