Unraveling Molecular Mechanisms Underlying Chronic Obstructive Pulmonary Disease Heterogeneity Using Combinatorial Barcoding and Single-Nuclear RNA Sequencing
Abstract
Topic Area: Respiratory Health Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death due to chronic illness in the United States. U.S. Veterans are three times more likely to develop COPD than non-Veterans, and approximately 25% of Veterans have COPD. The leading risk factor for COPD is chronic exposure to cigarette smoke and 30% of deployed military service personnel have or are currently smoking. Additionally, many military personnel have been exposed to other causes of COPD including (a) smoke from open burn pits, oil fires, and other mechanical fuels; (b) sand, dust, and particulate matter; and( c) general air pollution. There are no cures for COPD, no therapies that halt disease progression, and current therapies are only modestly effective at limiting symptoms. Therefore, identifying therapies for COPD will improve the health and well-being of military Service Members, Veterans, and beneficiaries. The biggest challenge for identifying new therapies is our limited understanding of COPD heterogeneity. COPD heterogeneity refers to the many clinical and biologic manifestations of COPD that vary widely among those affected with this disease. For example, patients can suffer from a variety of clinical manifestations including chronic cough, wheezing, difficulty breathing, and susceptibility to infection. In each person with disease, a specific manifestation can be mild, severe, or not occur at all. The same is true for underlying pathologic manifestations, such as airway inflammation or lung tissue destruction. These too can occur to varying degrees and in an overlapping manner. COPD researchers have been unable to untangle all these diverse manifestations of COPD to identify targeted therapies. While other medical fields have leveraged a deeper understanding of biologic and clinical heterogeneity to develop targeted therapies, this has yet to occur for COPD. Here, we propose a completely different approach to studying COPD. Rather than first classifying patients based clinical features and then trying to find targeted therapies, we will identify specific cells that contribute to disease, and then classify patients based on the presence of these pathologic or bad cells. We will then find therapies to target these pathologic cells. Completion of this proposal will be a major leap forward in establish a vision of COPD clinical care where patients are tested for peripheral biomarkers that reflect the cellular profile of their lungs, and clinicians can use those biomarkers to choose the right therapies treat patients’ disease. To do this, we will apply single-cell RNA sequencing and advanced computational approaches. Single-cell RNA sequencing is a high-throughput approach for examining the genomic information of individual cells. This technique has allowed us to identify cellular subtypes in diseased tissue with unprecedented resolution. First, we will use single-cell RNA sequencing to obtain molecular profiles of all the cells in lung tissue from healthy donors and patients with varied manifestations of COPD. Second, we will identify cell subtypes only present in diseased lung. For example, we may find cellular subtypes that are inflamed or have been injured from smoke exposure. Third, rather than classify patients based on their clinical features, we will classify patients based on the frequency of these abnormal cell subtypes. Finally, we will interrogate databases of cells treated with established compounds, and use those to predict new and targeted therapies for COPD. Our goals for this proposal are: Aim 1: Identify pathologic cellular subtypes in COPD and their associations with disease severity. Aim 2: Identify novel COPD classifications and associated biomarkers. Aim 3: Identify novel therapies for COPD. Completion of this proposal also directly addresses FY21 PRMRP Topic area of Respiratory Health and the area of encouragement for research on the causes, treatment
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Dec 28, 2022
- Source ID
- W81XWH2210629
Entities
People
- Maor Sauler
Organizations
- United States Army
- Yale University