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1. Mathematical Modeling of RNA Virus Sensing Pathways Reveals Paracrine Signaling as the Primary Factor Regulating Excessive Cytokine Production

   https://doi.org/10.3390/pr8060719  

   Weaver, Jordan J. ; Shoemaker, Jason E. ( June 2020 , Processes)

   null (Ed.)

   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). 

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2. Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

   https://doi.org/10.3390/v14050906  

   Ackerman, Emily E. ; Weaver, Jordan J. ; Shoemaker, Jason E. ( May 2022 , Viruses)

   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. 

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3. Spatially distributed infection increases viral load in a computational model of SARS-CoV-2 lung infection

   https://doi.org/10.1371/journal.pcbi.1009735  

   Moses, Melanie E. ; Hofmeyr, Steven ; Cannon, Judy L. ; Andrews, Akil ; Gridley, Rebekah ; Hinga, Monica ; Leyba, Kirtus ; Pribisova, Abigail ; Surjadidjaja, Vanessa ; Tasnim, Humayra ; et al ( December 2021 , PLOS Computational Biology)

   Smith, Amber M (Ed.)

   A key question in SARS-CoV-2 infection is why viral loads and patient outcomes vary dramatically across individuals. Because spatial-temporal dynamics of viral spread and immune response are challenging to study in vivo, we developed Spatial Immune Model of Coronavirus (SIMCoV), a scalable computational model that simulates hundreds of millions of lung cells, including respiratory epithelial cells and T cells. SIMCoV replicates viral growth dynamics observed in patients and shows how spatially dispersed infections can lead to increased viral loads. The model also shows how the timing and strength of the T cell response can affect viral persistence, oscillations, and control. By incorporating spatial interactions, SIMCoV provides a parsimonious explanation for the dramatically different viral load trajectories among patients by varying only the number of initial sites of infection and the magnitude and timing of the T cell immune response. When the branching airway structure of the lung is explicitly represented, we find that virus spreads faster than in a 2D layer of epithelial cells, but much more slowly than in an undifferentiated 3D grid or in a well-mixed differential equation model. These results illustrate how realistic, spatially explicit computational models can improve understanding of within-host dynamics of SARS-CoV-2 infection. 

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4. Interferon‐induced transmembrane protein 3 (IFITM3) limits lethality of SARS‐CoV‐2 in mice

   https://doi.org/10.15252/embr.202256660  

   Kenney, Adam D. ; Zani, Ashley ; Kawahara, Jeffrey ; Eddy, Adrian C. ; Wang, Xiao‐Liang ; Mahesh, KC ; Lu, Mijia ; Thomas, Jeronay ; Kohlmeier, Jacob E. ; Suthar, Mehul S. ; et al ( March 2023 , EMBO reports)

   Interferon‐induced transmembrane protein 3 (IFITM3) is an antiviral protein that alters cell membranes to block fusion of viruses. Conflicting reports identified opposing effects of IFITM3 on SARS‐CoV‐2 infection of cells, and its impact on viral pathogenesisin vivoremains unclear. Here, we show that IFITM3 knockout (KO) mice infected with SARS‐CoV‐2 experience extreme weight loss and lethality compared to mild infection in wild‐type (WT) mice. KO mice have higher lung viral titers and increases in inflammatory cytokine levels, immune cell infiltration, and histopathology. Mechanistically, we observe disseminated viral antigen staining throughout the lung and pulmonary vasculature in KO mice, as well as increased heart infection, indicating that IFITM3 constrains dissemination of SARS‐CoV‐2. Global transcriptomic analysis of infected lungs shows upregulation of gene signatures associated with interferons, inflammation, and angiogenesis in KO versus WT animals, highlighting changes in lung gene expression programs that precede severe lung pathology and fatality. Our results establish IFITM3 KO mice as a new animal model for studying severe SARS‐CoV‐2 infection and overall demonstrate that IFITM3 is protective in SARS‐CoV‐2 infectionsin vivo.

    

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5. Learning the language of viral evolution and escape

   https://doi.org/10.1126/science.abd7331  

   Hie, Brian ; Zhong, Ellen D. ; Berger, Bonnie ; Bryson, Bryan ( January 2021 , Science)

   The ability for viruses to mutate and evade the human immune system and cause infection, called viral escape, remains an obstacle to antiviral and vaccine development. Understanding the complex rules that govern escape could inform therapeutic design. We modeled viral escape with machine learning algorithms originally developed for human natural language. We identified escape mutations as those that preserve viral infectivity but cause a virus to look different to the immune system, akin to word changes that preserve a sentence’s grammaticality but change its meaning. With this approach, language models of influenza hemagglutinin, HIV-1 envelope glycoprotein (HIV Env), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Spike viral proteins can accurately predict structural escape patterns using sequence data alone. Our study represents a promising conceptual bridge between natural language and viral evolution.

    

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