Probing Cellular and Molecular Mechanisms of Cigarette Smoke-Induced Immune Response in the Progression of Chronic Obstructive Pulmonary Disease Using Multiscale Network Modeling

Abstract

Chronic obstructive pulmonary disease (COPD) is a chronic inflammatory disorder characterized by progressive destruction of lung tissues and airway obstruction. COPD is currently the third leading cause of death worldwide and there is no curative treatment available so far. Cigarette smoke (CS) is the major risk factor for COPD. Yet, only a relatively small percentage of smokers develop the disease, showing that disease susceptibility varies significantly among smokers. As smoking cessation can prevent the disease in some smokers, quitting smoking cannot halt the progression of COPD in others. Despite extensive research efforts, cellular and molecular mechanisms of COPD remain elusive. In particular, the disease susceptibility and smoking cessation effects are poorly understood. To address these issues in this work, we develop a multiscale network model that consists of nodes, which represent molecular mediators, immune cells and lung tissues, and edges describing the interactions between the nodes. Our model study identifies several positive feedback loops and network elements playing a determinant role in the CS-induced immune response and COPD progression. The results are in agreement with clinic and laboratory measurements, offering novel insight into the cellular and molecular mechanisms of COPD. The study in this work also provides a rationale for targeted therapy and personalized medicine for the disease in future.

Cigarette Smoke-Induced Chronic Inflammation Leading to COPD and Lung Cancer: A Multiscale Modeling Study

“…But when M2 (Treg) cells are dominant over M1 (Th17 and CD8+ T) cells, the acute inflammation turns into the low-level chronic inflammation, and COPD does not happen. Our simulations offer rationales for the above-mentioned issues regarding CS-induced COPD as seen in Figure 2a and 2b [17]. Different smokers have distinct parameters, to which the TD outcome is sensitive, in the ODEs.…”

“…This network bears a feature with multiple temporal and spatial scales. For example, cytokine regulation of signal transduction for cell function usually happens on a sub-second timescale, while cell secretion of cytokines takes minutes to hours [17].…”

“…In this network model, the nodes represent important cytokines, immune cells, and lung tissues, whereas the edges represent the interactions between these network components [17]. The network dynamics for the cytokines, immune cells and tissue damage (TD) can be evaluated by a set of ordinary differential equations (ODEs) [17]. In our modelling study, several positive feedback loops and network components are identified, playing a determinant role in the CS-induced inflammatory response in COPD progression.…”

“…There are two types of inputs initiated from a node: a positive or an upregulation input (denoted by →) indicates that an increasing of the concentration of the tail node will lead to an increasing of that of the head node or an up-regulation of the process when the input arrow ends at an edge between two nodes, and vice versa for a negative or a down-regulation (inhibition) input (denoted by ┫). Interactions between various nodes that represent cytokines, immune cells, and TD are described [17]. (a) k13<2.6 × 10 -2 ml/(cell day) corresponds to resistant smokers (TD<30%), while k13 ≥ 2.6 × 10 -2 ml/(cell day) is associated with susceptible smokers.…”

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

“…In our previous study [12,13], a network model was proposed to describe the dynamics of CS-induced immune responses in COPD progression. This network model is then extended to study the immune response dynamics in the progression of ulcerative colitis [14].…”