Network modeling of inflammatory dynamics induced by biomass smoke leading to chronic obstructive pulmonary disease

Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disorder characterized by airflow obstruction and progressive damage of lung tissues. As currently more than 3 billion people use biomass fuel for cooking and heating worldwide, exposure to biomass smoke (BS) is recognized as a significant risk factor for COPD. Recent clinical data have shown that BS-COPD patients have a Th2-type inflammatory profile significantly different from that in COPD induced by cigarette smoke. As COPD is essentially proinflammatory, however, the mechanism underlying this Th2-type anti-inflammatory profile remains elusive. In this work, a network model is applied to study BS-induced inflammatory dynamics. The network model involves several positive feedback loops, activations of which are responsible for different mechanisms by which clinical phenotypes of COPD are produced. Our modeling study in this work has identified a subset of BS-COPD patients with a mixed M1-and Th2-type inflammatory profile. The model's prediction is in good agreement with clinical experiments and our in silico knockout simulations have demonstrated several important network components that play an important role in the disease. Our modeling study provides novel insight into BS-COPD progression, offering a rationale for targeted therapy and personalized medicine for treatment of the disease in future.