Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics

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.
Author summary Respiratory lung infections form lesions in the lungs, whose number and size correlate with the severity of illness. In some severe cases, respiratory disease triggers a severe inflammatory condition known as a cytokine storm. In order to link molecular signaling at the site of infection to its impact on the overall interferon response and the occurrence of severe inflammation, we created a computational model of the early stages of infection that simulates lung cells infected with RNA viruses, such as those responsible for COVID-19 and flu, to help explore how the disease forms viral plaques, an in vitro analog to lesion growth in the lung. Consistent with experimental observations, our model suggests that the treatment of cells with type-I interferons, which are currently being evaluated for the treatment of COVID-19, may have a protective effect. We found that enhancing certain aspects of the inflammatory response, such as the JAK/STAT pathway, may be able to arrest viral plaque growth, suggesting molecules involved in this pathway as possible drug candidates. Analysis of the model also shows that to better estimate the parameter values needed to model interferon signaling and viral replication, experiments should be performed under conditions that inhibit viral growth, e.g., by pretreating cells with interferon. We present a computational framework built using the CompuCell3D multicellular simulation environment that lays the groundwork for constructing larger models of respiratory-infection-induced immune responses and that can inform experiments to improve our fundamental understanding of the mechanisms regulating the immune response.