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

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus

In silico knockout simulations.(a) TD dynamics, (b) Iα dynamics, (c) I6 dynamics, and (d) I17 dynamics. In silico knockouts of M1 (red dashed line), DC (red circles), Th1 (black stars), Th17 (black plus), CD8+T (black squares), TNF-α (blue dash-and-dot line), INF-γ (black asterisk), IL-6 (blue triangles), and IL-17 (black cross) [wild type is denoted by WT (black solid line)].
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pone.0163192.g010: In silico knockout simulations.(a) TD dynamics, (b) Iα dynamics, (c) I6 dynamics, and (d) I17 dynamics. In silico knockouts of M1 (red dashed line), DC (red circles), Th1 (black stars), Th17 (black plus), CD8+T (black squares), TNF-α (blue dash-and-dot line), INF-γ (black asterisk), IL-6 (blue triangles), and IL-17 (black cross) [wild type is denoted by WT (black solid line)].

Mentions: To identify important network elements for CS-induced COPD, in silico knockout simulations for the proinflammatory elements, e.g., M1, DC, Th1, Th17, CD8+T cells and TNF-α, IL-6, IFN-γ, and IL-17 were conducted in the following discussion. Deletion of a network element in the model was performed by setting all parameters of the element and the rate to zero [69]. Here, the parameters used in the simulations are listed in Table A in S1 File [dTD = 2.9×10−3 (1/day)] and the CS dose (S = 1.67) is the same as that shown in Figs 2–4. The results for the time courses of TD, and Iα, I6 and I17 are presented in Fig 10.


Probing Cellular and Molecular Mechanisms of Cigarette Smoke-Induced Immune Response in the Progression of Chronic Obstructive Pulmonary Disease Using Multiscale Network Modeling
In silico knockout simulations.(a) TD dynamics, (b) Iα dynamics, (c) I6 dynamics, and (d) I17 dynamics. In silico knockouts of M1 (red dashed line), DC (red circles), Th1 (black stars), Th17 (black plus), CD8+T (black squares), TNF-α (blue dash-and-dot line), INF-γ (black asterisk), IL-6 (blue triangles), and IL-17 (black cross) [wild type is denoted by WT (black solid line)].
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5036797&req=5

pone.0163192.g010: In silico knockout simulations.(a) TD dynamics, (b) Iα dynamics, (c) I6 dynamics, and (d) I17 dynamics. In silico knockouts of M1 (red dashed line), DC (red circles), Th1 (black stars), Th17 (black plus), CD8+T (black squares), TNF-α (blue dash-and-dot line), INF-γ (black asterisk), IL-6 (blue triangles), and IL-17 (black cross) [wild type is denoted by WT (black solid line)].
Mentions: To identify important network elements for CS-induced COPD, in silico knockout simulations for the proinflammatory elements, e.g., M1, DC, Th1, Th17, CD8+T cells and TNF-α, IL-6, IFN-γ, and IL-17 were conducted in the following discussion. Deletion of a network element in the model was performed by setting all parameters of the element and the rate to zero [69]. Here, the parameters used in the simulations are listed in Table A in S1 File [dTD = 2.9×10−3 (1/day)] and the CS dose (S = 1.67) is the same as that shown in Figs 2–4. The results for the time courses of TD, and Iα, I6 and I17 are presented in Fig 10.

View Article: PubMed Central - PubMed

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.

No MeSH data available.


Related in: MedlinePlus