(648e) Multicellular Spatial Model of RNA Virus Replication and Interferon Responses Reveals Factors Controlling Plaque Growth Dynamics | AIChE

(648e) Multicellular Spatial Model of RNA Virus Replication and Interferon Responses Reveals Factors Controlling Plaque Growth Dynamics


Shoemaker, J. E., University of Pittsburgh
Glazier, J., Indiana University Bloomington
Aponte-Serrano, J. O., Indiana University Bloomington
Sego, T. J., Indiana University Bloomington
Respiratory virus infections contribute significantly to death rates worldwide. Seasonal influenza virus infection is responsible for 290,000 – 650,000 average annual deaths globally (1), and occasional, highly pathogenic pandemics strains can emerge, such as the 1918 Spanish Flu (2), resulting in significantly higher mortality rates. As of February 16th, 2021, the SARS-CoV-2 virus has caused over 108.3 million recorded infections and 2.3 million deaths worldwide (3). Both influenza and SARS-CoV-2 are RNA viruses, and studies of severe SARS-CoV-2 and influenza infections find that impaired innate immune responses correlate with more severe outcomes (4–6). In highly pathogenic infections, aberrant innate immune responses – specifically elevated inflammation and high production of type-I interferons leading to hypercytokinemia (cytokine storm) (7) – are believed to be significant drivers of mortality (8,9). Excessive inflammation exacerbates tissue damage and hinders clinical recovery (10,11). Influenza studies show that immunomodulation can improve infection outcomes. Prestimulation of toll-like receptors is protective against highly pathogenic influenza strains in mice (12), while cell culture prestimulation with type-I interferons prevents viral plaque growth by SARS-CoV (13), SARS-CoV-2 (14), and influenza (15). Nebulized interferon α2b and interferon β are being investigated as an early treatment for COVID-19 (16,17). Collectively, these studies demonstrate the necessity of immune response regulation as a means to balance tissue damage from inflammatory responses with viral clearance. Computational modeling may reveal how complex responses emerge during infection, rapidly identify and optimize treatments, and reveal dynamic insights into immunoregulation during respiratory infection that can drive the discovery and design of immune targeted treatments.

Recent computational models have considered many aspects of virus infection induced immune responses (18–21), however, few models describe virus replication and interferon regulation in a cell culture. Ordinary Differential Equation (ODE) based models assume either homogeneity or a compartment based structure, and typically ignore the diffusion of virions and cytokine signaling, heterogeneity of cell response to stimuli, and stochasticity of individual cells’ response (19,22). Many recent models (20,23,24) of interferon response use a simple virally resistant cell state which does not capture interferon stimulated genes’ (ISGs) effect on viral growth (23,24). A spatial model of viral spread and plaque growth (22) mentions the impact of diffusion constants on viral plaque formation, but does not incorporate the cells’ innate immune response to infection and paracrine induced activation, limiting the ability of the model to explain plaque growth arrest due to ISGs.

We developed a multiscale, multicellular, spatiotemporal model of the innate immune response to RNA viral respiratory infections in vitro in CompuCell3D (25) (CC3D), with the ability to simulate plaque growth, cytokine response, and plaque arrest. The model of IFN production and virus replication determines the conditions leading to arrest or promotion of plaque growth during the infection of lung epithelial cells with an RNA virus. Plaque growth assays seed the virus at low multiplicity of infection (MOI) and allow it to replicate across a sheet of host cells in a cell culture to form visible plaques. Our aim in replicating plaque growth experiments in a spatial computer simulation was twofold. First, realistic simulations of physical experiments allow in silico experimentation for cheaper, faster, and higher throughput hypothesis testing of more simultaneous outputs than would be experimentally viable. Second, our model replicates familiar biological experimental measurements and imaging, making our results accessible to wet lab biologists. The model includes two competing mechanisms, viral replication and the host cells’ innate immune response. Viral reproduction consists of the virus production within and export from infected cells, and viral particle spread via diffusion in the extracellular matrix. The host immune response includes interferon production, export, and diffusion, and the initiation of virally resistant cell states via ISGs. The extracellular environment allows diffusion of virions to spread the virus and form viral plaques and type-I interferons responsible for spatiotemporal paracrine signaling. Viral plaque growth is shown to be arrested under elevated STATP activity, when the epithelial cells are pretreated with type-I interferons, and when interferon diffusion is sufficiently elevated over the diffusion of viral particles, with the relationship between the two diffusion constants being nonlinear. Targeted local sensitivity analyses under both arrested and continuous plaque growth conditions reveals that which factors control plaque growth vary significantly based on these same conditions. Since epithelial regulation of IFN is an important early regulator of the immune system more broadly, the model could be expanded in future work to account for additional regulation via immune cells.

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