Introduction: Chronic obstructive pulmonary disease (COPD) is the fourth leading cause of death in the United States, with high associated costs. Most of the cost burden results from acute exacerbations of COPD (AE-COPD), events associated with heightened symptoms and mortality. Cellular mechanisms underlying AE-COPD are poorly understood, likely because they arise from dysregulation of complex immune networks across multiple tissue compartments.
Methods: To gain systems-level insight into cellular environments relevant to exacerbation, we applied data-driven modeling approaches to measurements of immune factors (cytokines and flow cytometry) measured previously in two different human tissue environments (sputum and peripheral blood) during the stable and exacerbated state.
Results: Using partial least squares discriminant analysis, we identified a unique signature of cytokines in serum that differentiated stable and AE-COPD better than individual measurements. Furthermore, we found that models integrating data across tissue compartments (serum and sputum) trended towards being more accurate. The resulting paracrine signature defining AE-COPD events combined elevations of proteins associated with cell adhesion (sVCAM-1, sICAM-1) and increased levels of neutrophils and dendritic cells in blood with elevated chemoattractants (IP-10 and MCP-2) in sputum.
Conclusions: Our results supported a new hypothesis that AE-COPD is driven by immune cell trafficking into the lung, which requires expression of cell adhesion molecules and raised levels of innate immune cells in blood, with parallel upregulated expression of specific chemokines in pulmonary tissue. Overall, this work serves as a proof-of-concept for using data-driven modeling approaches to generate new insights into cellular processes involved in complex pulmonary diseases.
Keywords: Data-driven models; Immune system; Inflammation; Pulmonary disease; Systems biology.
© Biomedical Engineering Society 2019.