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The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains. 

Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models. 

Abstract Motivation The cGAS pathway is a component of the innate immune system responsible for the detection of pathogenic DNA and upregulation of interferon beta (IFNβ). Experimental evidence shows that IFNβ signaling occurs in highly heterogeneous cells and is stochastic in nature; however, the benefits of these attributes remain unclear. To investigate how stochasticity and heterogeneity affect IFNβ production, an agent-based model is developed to simulate both DNA transfection and viral infection. Results We show that heterogeneity can enhance IFNβ responses during infection. Furthermore, by varying the degree of IFNβ stochasticity, we find that only a percentage of cells (20–30%) need to respond during infection. Going beyond this range provides no additional protection against cell death or reduction of viral load. Overall, these simulations suggest that heterogeneity and stochasticity are important for moderating immune potency while minimizing cell death during infection. Availability and implementation Model repository is available at: https://github.com/ImmuSystems-Lab/AgentBasedModel-cGASPathway. Contact jason.shoemaker@pitt.edu Supplementary information Supplementary data are available at Bioinformatics online. 

RNA viruses, such as influenza and Severe Acute Respiratory Syndrome (SARS), invoke excessive immune responses; however, the kinetics that regulate inflammatory responses within infected cells remain unresolved. Here, we develop a mathematical model of the RNA virus sensing pathways, to determine the intracellular events that primarily regulate interferon, an important protein for the activation and management of inflammation. Within the ordinary differential equation (ODE) model, we incorporate viral replication, cell death, interferon stimulated genes’ antagonistic effects on viral replication, and virus sensor protein (TLR and RIG-I) kinetics. The model is parameterized to influenza infection data using Markov chain Monte Carlo and then validated against infection data from an NS1 knockout strain of influenza, demonstrating that RIG-I antagonism significantly alters cytokine signaling trajectory. Global sensitivity analysis suggests that paracrine signaling is responsible for the majority of cytokine production, suggesting that rapid cytokine production may be best managed by influencing extracellular cytokine levels. As most of the model kinetics are host cell specific and not virus specific, the model presented provides an important step to modeling the intracellular immune dynamics of many RNA viruses, including the viruses responsible for SARS, Middle East Respiratory Syndrome (MERS), and Coronavirus Disease (COVID-19).