Chronic obstructive pulmonary disease (COPD) is a fatal lung disease that is the fourth leading cause of death in the U.S. Despite an estimated $50 billion in yearly healthcare costs, it has no current cure and only palliat- ive treatments. COPD is clearly characterized by chronic lung inflammation that likely arises from dysregulat- ion of complex networks of immune factors and cells across multiple tissue compartments. Although multiple individual genes and proteins have been associated with COPD risk and progression, global mechanistic understanding of its pathophysiology is lacking, particularly regarding the marked heterogeneity in COPD phenotypes. The overall objective of our study is to gain systems-level insight into complex inflammatory and immune mechanisms underlying COPD, by applying data-driven (also called ?machine learning?) modeling approaches to clinical samples collected from human pulmonary microenvironments and matched immune cell networks from peripheral blood. Our central hypothesis is that immune networks will be more predictive of COPD phenotype, progression, and exacerbation than individual factors. We will test this hypothesis in three Specific Aims, using matched brochoalveolar lavage (BAL) and blood samples collected in SPIROMICS I and II clinical trials.
Aim 1 will identify changes in immune cell-cell communication networks, by high-throughput cytokine measurements from stimulated systems of peripheral blood immune cells from smokers with and without COPD and never-smoking controls (collected from the upcoming SPIROMICS II visit; n=150).
Aim 2 will determine systems-level changes that occur in the inflamed lung microenvironment, using high-throughput cytokine measurements in BAL samples (both archival from SPIROMICS I, n=200, and collected during upcoming SPIROMICS II bronchoscopies) from smokers with and without COPD and never-smoking controls. We will identify networks associated with longitudinal clinical progression and exacerbation frequency.
Aim 3 will integrate measurements across lung and blood tissue compartments to define key combinatorial relationships associated with progression and exacerbation events. Overall, this project will provide systems- level insight into COPD pathogenesis and progression, and create a new paradigm for the study of other pulmonary conditions involving chronic inflammation, including idiopathic pulmonary fibrosis, asthma, and lung transplant. Results will aid in the future development of new non-invasive diagnostic assays and will guide systems-level mechanistic studies that could result in new combinatorial therapies.

Public Health Relevance

The goal of this proposal is to gain new systems-level insight into chronic obstructive pulmonary disease (COPD), which is the third leading cause of death worldwide and an enormous cost burden in the U.S. yearly (~50 billion). Despite progress in identifying individual immune factors associated with COPD, there are only palliative treatment options and no cure. New insight generated by this project into how immune cell communication networks and inflammatory pulmonary microenvironments are altered in COPD could be used to identify diagnostic biomarkers and to guide the design of combinatorial therapies.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Punturieri, Antonello
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University of Michigan Ann Arbor
Biomedical Engineering
Biomed Engr/Col Engr/Engr Sta
Ann Arbor
United States
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