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Title: Principles of robust innate immune response to viral infections: a multiplex network analysis

Abstract

The human innate immune response, particularly the type-I interferon (IFN) response, is highly robust and effective first line of defense against virus invasion. IFN molecules are produced and secreted frominfected cells upon virus infection and recognition. They then act as signaling/communicationmolecules to activate an antiviral response in neighboring cells so that those cells become refractory to infection. Previous experimental studies have identified the detailed molecular mechanisms for the IFN signaling and response. However, the principles underlying how host cells use IFN to communicate with each other to collectively and robustly halt an infection is not understood. Here we take a multiplex network modeling approach to provide a theoretical framework to identify key factors that determine the effectiveness of the IFN response against virus infection of a host. In this approach, we consider the virus spread among host cells and the interferon signaling to protect host cells as a competition process on a two-layer multiplex network. We focused on two types of network topology, i.e., the Erdős-Rényi (ER) network and the Geometric Random (GR) network, which represent the scenarios when infection of cells is mostly well mixed (e.g., in the blood) and when infection is spatially segregated (e.g., in tissues), respectively.Wemore » show that in general, the IFN response works effectively to stop viral infection when virus infection spreads spatially (a most likely scenario for initial virus infection of a host at the peripheral tissue). Importantly, we show that the effectiveness of the IFN response is robust against large variations in the distance of IFN diffusion as long as IFNs diffuse faster than viruses and they can effectively induce antiviral responses in susceptible host cells. This suggests that the effectiveness of the IFN response is insensitive to the specific arrangement of host cells in peripheral tissues. Thus, our work provides a quantitative explanation of why the IFN response can serve an effective and robust response in different tissue types to a wide range of viral infections of a host.« less

Authors:
 [1];  [1]; ORCiD logo [2]
  1. North Carolina State Univ., Raleigh, NC (United States)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); North Carolina State Univ., Raleigh, NC (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1558216
Report Number(s):
LA-UR-18-31437
Journal ID: ISSN 1664-3224
Grant/Contract Number:  
89233218CNA000001
Resource Type:
Accepted Manuscript
Journal Name:
Frontiers in Immunology
Additional Journal Information:
Journal Volume: 10; Journal ID: ISSN 1664-3224
Publisher:
Frontiers Research Foundation
Country of Publication:
United States
Language:
English
Subject:
59 BASIC BIOLOGICAL SCIENCES; Immune response; interferon; viral infection; mathematical modeling; multiplex network

Citation Formats

Huang, Yufan, Dai, Huaiju, and Ke, Ruian. Principles of robust innate immune response to viral infections: a multiplex network analysis. United States: N. p., 2019. Web. doi:10.3389/fimmu.2019.01736.
Huang, Yufan, Dai, Huaiju, & Ke, Ruian. Principles of robust innate immune response to viral infections: a multiplex network analysis. United States. doi:10.3389/fimmu.2019.01736.
Huang, Yufan, Dai, Huaiju, and Ke, Ruian. Wed . "Principles of robust innate immune response to viral infections: a multiplex network analysis". United States. doi:10.3389/fimmu.2019.01736. https://www.osti.gov/servlets/purl/1558216.
@article{osti_1558216,
title = {Principles of robust innate immune response to viral infections: a multiplex network analysis},
author = {Huang, Yufan and Dai, Huaiju and Ke, Ruian},
abstractNote = {The human innate immune response, particularly the type-I interferon (IFN) response, is highly robust and effective first line of defense against virus invasion. IFN molecules are produced and secreted frominfected cells upon virus infection and recognition. They then act as signaling/communicationmolecules to activate an antiviral response in neighboring cells so that those cells become refractory to infection. Previous experimental studies have identified the detailed molecular mechanisms for the IFN signaling and response. However, the principles underlying how host cells use IFN to communicate with each other to collectively and robustly halt an infection is not understood. Here we take a multiplex network modeling approach to provide a theoretical framework to identify key factors that determine the effectiveness of the IFN response against virus infection of a host. In this approach, we consider the virus spread among host cells and the interferon signaling to protect host cells as a competition process on a two-layer multiplex network. We focused on two types of network topology, i.e., the Erdős-Rényi (ER) network and the Geometric Random (GR) network, which represent the scenarios when infection of cells is mostly well mixed (e.g., in the blood) and when infection is spatially segregated (e.g., in tissues), respectively.We show that in general, the IFN response works effectively to stop viral infection when virus infection spreads spatially (a most likely scenario for initial virus infection of a host at the peripheral tissue). Importantly, we show that the effectiveness of the IFN response is robust against large variations in the distance of IFN diffusion as long as IFNs diffuse faster than viruses and they can effectively induce antiviral responses in susceptible host cells. This suggests that the effectiveness of the IFN response is insensitive to the specific arrangement of host cells in peripheral tissues. Thus, our work provides a quantitative explanation of why the IFN response can serve an effective and robust response in different tissue types to a wide range of viral infections of a host.},
doi = {10.3389/fimmu.2019.01736},
journal = {Frontiers in Immunology},
number = ,
volume = 10,
place = {United States},
year = {2019},
month = {7}
}

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