Negative feedback contributes to the stochastic expression of the interferon-β gene in virus-triggered type I interferon signaling pathways

被引:7
|
作者
Zhang, Wei [1 ,2 ]
Tian, Tianhai [3 ]
Zou, Xiufen [1 ]
机构
[1] Wuhan Univ, Sch Math & Stat, Wuhan 430072, Peoples R China
[2] East China Jiaotong Univ, Sch Sci, Nanchang 330013, Peoples R China
[3] Monash Univ, Sch Math Sci, Melbourne, Vic 3800, Australia
基金
中国国家自然科学基金; 澳大利亚研究理事会;
关键词
Mathematical model; Type I IFN signaling pathways; stochasticity; Bifurcation analysis; Feedbacks; LINEAR NOISE APPROXIMATION; INNATE IMMUNITY; STEADY-STATE; POSITIVE FEEDBACK; RNA HELICASE; TRANSCRIPTION; ACTIVATION; MECHANISMS; NETWORKS; SYSTEMS;
D O I
10.1016/j.mbs.2015.04.003
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Type I interferon (IFN) signaling pathways play an essential role in the defense against early viral infections; however, the diverse and intricate molecular mechanisms of virus-triggered type I IFN responses are still poorly understood. In this study, we analyzed and compared two classes of models i.e., deterministic ordinary differential equations (ODEs) and stochastic models to elucidate the dynamics and stochasticity of type I IFN signaling pathways. Bifurcation analysis based on an ODE model reveals that the system exhibits a bistable switch and a one-way switch at high or low levels when the strengths of the negative and positive feedbacks are tuned. Furthermore, we compared the stochastic simulation results under the Master and Langevin equations. Both of the stochastic equations generate the bistable switch phenomenon, and the distance between two stable states are smaller than normal under the simulation of the Langevin equation. The quantitative computations also show that a moderate ratio between positive and negative feedback strengths is required to ensure a reliable switch between the different IFN concentrations that regulate the immune response. Moreover, we propose a multi-state stochastic model based on the above deterministic model to describe the multi-cellular system coupled with the diffusion of IFNs. The perturbation and inhibition analysis showed that the positive feedback, as well as noises, has little effect on the stochastic expression of IFNs, but the negative feedback of ISG56 on the activation of IRF7 has a great influence on IFN stochastic expression. Together, these results reveal that positive feedback stabilizes IFN gene expression, and negative feedback may be the main contribution to the stochastic expression of the IFN gene in the virus-triggered type I IFN response. These findings will provide new insight into the molecular mechanisms of virus-triggered type I IFN signaling pathways. (C) 2015 Elsevier Inc. All rights reserved.
引用
收藏
页码:12 / 27
页数:16
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