Friday, January 3, 2025
1/3/2025
Cystic fibrosis (CF) is among the most common fatal genetic diseases in the U.S. and involves progressive lung function loss and structural remodeling, leading to lung transplant or death. Though life expectancy in CF patients has increased due to improved treatments, pathological changes still occur within the first year of life. It has been difficult to detect these early changes, because conventional measures of lung function such as spirometry (e.g., forced expiratory volume in 1 second, FEV1) are lagging indicators and insensitive to early disease. In contrast, ultra-short echo-time (UTE) and hyperpolarized (HP) 129Xe MRI can detect pathology years before FEV1. Addi- tionally, proteomic biomarkers from high-precisions mass spectrometry (MS), when coupled with modeling based on Functional Data (FD) analysis, accurately forecast CF lung disease progression. However, these biomarkers have only been validated in patients with established disease. The long-term goal of this research is to validate proteomic markers that detect and predict lung function decline and structural remodeling in early lung disease. The objective of this application is to use state-of-the-art HP 129Xe and UTE MRI to validate proteomic markers in early CF. This will be accomplished using blood serum and clinically obtained bronchoalveolar lavage (BAL) fluid from CF patients with known lung pathology. Our central hypothesis is that image-guided proteomics can forecast pathophysiology before spirometric changes are observed. Our rationale is that, while 129Xe and UTE MRI are currently limited to specialized centers, MS proteomics can be performed on readily obtained clinical specimens, and translated with FD analysis into an easily disseminated tool to predict impending lung disease progression, and thus enable interventions before permanent lung damage occurs. Guided by combined MRI and proteomic data and the utility of FD analysis to predict lung function decline, our central hypothesis will be tested by completing the following Specific Aims: 1) Validate our predictive biomarkers in CF patients with normal spirometry but abnormal ventilation; 2) determine the sensitivity and specificity of systemic biomarkers in pre- dicting early structural re-modeling in CF lung disease; and 3) perform clinical bronchoscopy to identify molecular signatures of irreversible lung remodeling. We have developed the MRI sequences and reconstruction pipeline needed to complete the work. For Aims 1 & 2, we have used MRI and MS proteomics to identify key biomarkers to predict structural and functional abnormalities in CF. For Aim 3, we have used BAL proteomics to identify molecular changes at the pathway level in CF patients. The proposed research is innovative, because it will use cutting-edge imaging to validate molecular tools to assess early lung disease. These results will be significant, because they will produce an easily disseminated tool to predict permanent structural remodelling and irreversi- ble functional losses. This work will have an immediate positive impact by developing and translating non-inva- sive tests to identify CF patients at high risk of lung damage and intervene before irreversible changes occur. It will also provide a unique platform to assess pathological progression in a wide range of lung diseases.
HHS’ Tracking Accountability in Government Grants System (TAGGS) website provides access to detailed descriptions of grants, loans, aggregated direct payments and other types of financial assistance awarded by the HHS Operating Divisions (OPDIVs) and the Office of the Secretary Staff Divisions (STAFFDIVs).