Despite the increasing public health burden associated with Chronic Obstructive Pulmonary Disease (COPD), there are currently no effective drug therapies to prevent COPD or reverse the progressive decline in lung function that characterizes COPD. The recent application by our group and others of genome-wide technologies in airway and lung tissue from smokers with COPD had led to a detailed characterization of the gene- expression events associated with a number of COPD phenotypes. However, it is difficult to move beyond these gene-expression signatures to distinguish the molecular events that mediate disease pathogenesis from those that are downstream consequences of the disease process. The goal of our proposal is to identify molecular determinants that underlie the pathogenesis of COPD and identify new and existing therapies that can modulate these disease drivers. Our proposal leverages a unique set of gene-expression signatures that we have developed in airway epithelium and lung tissue of smokers with COPD. Through innovative computational approaches, we will integrate these signatures with publically available gene-expression data as well as existing whole-genome miRNA and SNP datasets from these same samples in order to identify molecular determinants of COPD. Candidate mediators that localize to epithelium and / or fibroblasts in vivo will be perturbed in primary human fibroblasts or epithelial cells in vitro to determine whether modulation of these molecular determinants is sufficient to recapitulate disease-associated gene expression signatures. Integration of these data with the Connectivity Map will enable identification of pharmacologic agents that might antagonize these disease determinants and therefore serve as anti-COPD therapeutics. This will be tested in lung fibroblasts and / or epithelial cells derived from smokers with and without COPD in order to determine whether the disease-specific activation of these determinants is necessary for expression of disease-specific cellular phenotypes. Successful completion of these aims will set the stage for moving promising therapeutics and drug targets identified in this proposal into animal models of COPD to further evaluate their impact on disease-related phenotypes in vivo.

Public Health Relevance

COPD is a disease for which there are no effective curative therapies and it is now the 3rd leading cause of death in the United States. This proposal seeks to combine multiple genome-wide datasets to identify the key molecular events that lead to COPD development and verify that targeting them in disease relevant cell models reverses the disease processes. Drugs that target these disease determinants could be effective treatments for this deadly and debilitating disease.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
1R01HL118542-01
Application #
8494246
Study Section
Special Emphasis Panel (ZRG1-GGG-A (50))
Program Officer
Gan, Weiniu
Project Start
2013-08-15
Project End
2017-05-31
Budget Start
2013-08-15
Budget End
2014-05-31
Support Year
1
Fiscal Year
2013
Total Cost
$744,400
Indirect Cost
$213,374
Name
Boston University
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
604483045
City
Boston
State
MA
Country
United States
Zip Code
02118
Hogg, James C; Paré, Peter D; Hackett, Tillie-Louise (2017) The Contribution of Small Airway Obstruction to the Pathogenesis of Chronic Obstructive Pulmonary Disease. Physiol Rev 97:529-552
Luo, Wei; Obeidat, Ma'en; Di Narzo, Antonio Fabio et al. (2016) Airway Epithelial Expression Quantitative Trait Loci Reveal Genes Underlying Asthma and Other Airway Diseases. Am J Respir Cell Mol Biol 54:177-87
Christenson, Stephanie A; Steiling, Katrina; van den Berge, Maarten et al. (2015) Asthma-COPD overlap. Clinical relevance of genomic signatures of type 2 inflammation in chronic obstructive pulmonary disease. Am J Respir Crit Care Med 191:758-66
Di Narzo, Antonio Fabio; Cheng, Haoxiang; Lu, Jianwei et al. (2014) Meta-eQTL: a tool set for flexible eQTL meta-analysis. BMC Bioinformatics 15:392