A Simulation of Semi-Infectious Particles and Genome Complementation Reproduces Interferon Response by Respiratory Epithelial Cells in vitro during Influenza A Virus Infection

Abstract

In the respiratory epithelium, interferon (IFN)-induced antiviral resistance acts as a defense against infection. Influenza A viruses (IAVs) have evolved multiple strategies to counteract these defenses, including expression of the viral protein NS1, which inhibits both IFN production and the IFN-mediated transcription of Interferon Stimulated Genes (ISG) in infected cells. However, experiments show that this inhibition is imperfect, especially at a low multiplicity of infection (MOI). One hypothesis to describe this phenomenon relies on the presence of Semi-infectious Particles (SIPs) that fail to express NS1. In this scenario, the IFN response is incompletely suppressed at low MOI, while it is successfully inhibited at high MOI because most cells are infected by multiple virions, allowing complementation to rescue NS1 expression. To test this hypothesis, we developed a computer simulation that models viral gene defects and complementation. We compared the model outputs with in vitro experiments at different MOIs. To assess inter-host reproducibility and calibrate the model parameters, we measured IFN levels and viral load over time in bronchial epithelial cell cultures from five human donors. We observed no statistically significant heterogeneity in IFN response or virus production between donors, and the calibrated simulation fits the experimental time series for IFN and viral load. Consistent with literature (1,2), the model predicted higher IFN levels at low MOI than at high MOI. Finally, simulations of IFN treatment applied before and during infection showed reduced viral load, in agreement with our experiments. Increasing the viral genome defect rate above the experimentally estimated rate increased IFN levels and reduced viral load. High MOI simulations showed lower cumulative IFN levels, while NS1 knockout recovered high IFN levels. These results demonstrate the ability of mechanistic models of viral dynamics to predict the innate immune response of epithelial cells during viral infection. Author SummaryRespiratory viruses such as influenza A are highly infectious and pose significant challenges for the human immune system. Through laboratory experiments and computer simulations, we investigated how cells in the respiratory epithelium defend themselves and their neighbors against infection. Using cells collected from different donors, we generated 3-dimensional cell cultures that mimic human airways and measured how they respond to IAV. When a tissue was initially exposed to a small amount of virus, cells could successfully slow or stop the spread of the infection. This phenomenon is hypothesized to be due in part to the high error rate in IAV replication, resulting in many viral particles that are not fully functional. We recapitulated this experimental result with our computational model, validating the model design and parameter estimates. We then simulated a scenario in which cells were pre-treated with interferon, a protective cytokine important to early immune response, and showed that this pre-treatment could successfully limit infection. Laboratory experiments subsequently confirmed this predicted behavior. The computational model reproduced key observations across infection conditions and identified nonfunctional viral particles as important drivers of the early immune response.