skip to main content

Explore Research Products in the NSF-PAR

  • NSF Public Access
  • Search Results
  • Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection
Title: Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection
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.  more » « less
Award ID(s):
1943777
NSF-PAR ID:
10323140
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Viruses
Volume:
14
Issue:
5
ISSN:
1999-4915
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ( , PLOS Computational Biology)
    Kosakovsky Pond, Sergei L. (Ed.)
    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. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; ; et al ( , Journal of Virology)
    Viral Hemorrhagic Septicemia virus (VHSV) is a pathogenic fish rhabdovirus found in discrete locales throughout the northern hemisphere. VHSV infection of fish cells leads to upregulation of the host's virus detection response, but the virus quickly suppresses interferon (IFN) production and antiviral genes expression. By systematically screening each of the six VHSV structural and nonstructural genes, we have identified matrix protein (M) as its most potent anti-host protein. VHSV-IVb M alone suppressed mitochondrial antiviral signaling protein (MAVS) and type I IFN-induced gene expression in a dose-dependent manner. M also suppressed the constitutively active SV40 promoter and globally decreased cellular RNA levels. Chromatin immunoprecipitation (ChIP) studies illustrated that M inhibited RNA polymerase II (RNAP II) recruitment to gene promoters, and decreased RNAP II CTD Ser2 phosphorylation during VHSV infection. However, transcription directed by RNAP I-III was suppressed by M. To identify regions of functional importance, M proteins from a variety of VHSV strains were tested in cell-based transcriptional inhibition assays. M protein of a particular VHSV-Ia strain, F1, was significantly less potent than -IVb M at inhibiting SV40/luc expression, yet differed by just four amino acids. Mutation of D62 to alanine alone, or in combination with an E181 to alanine mutation (D62A/E181A), dramatically reduced the ability of -IVb M to suppress host transcription. Introducing either M D62A or D62A/E181A mutations into VHSV-IVb via reverse genetics resulted in viruses that replicated efficiently but exhibited less cytotoxicity and reduced anti-transcriptional activities, implicating M as a primary regulator of cytopathicity and host transcriptional suppression. Importance: Viruses must suppress host antiviral responses to replicate and spread between hosts. In these studies, we identified the matrix protein of the deadly fish Novirhabdovirus, VHSV, as a critical mediator of host suppression during infection. Our studies indicated that M alone could block cellular gene expression at very low expression levels. We identified several subtle mutations in M that were less potent at suppressing host transcription. When these mutations were engineered back into recombinant viruses, the resulting viruses replicated well but elicited less toxicity in infected cells and activated host innate immune responses more robustly. These data demonstrated that VHSV M plays an important role in mediating both virus-induced cell toxicity and viral replication. Our data suggest that its roles in these two processes can be separated to design effective attenuated viruses for vaccine candidates. 
    more » « less
  3. ; ; ( , Mathematics)
    SARS-CoV-2 continues to upend human life by posing novel threats related to disease spread and mutations. Current models for the disease burden of SARS-CoV-2 consider the aggregate nature of the virus without differentiating between the potency of its multiple strains. Hence, there is a need to create a fundamental modeling framework for multi-strain viruses that considers the competing viral pathogenic pathways. Alongside the consideration that other viral pathogens may coexist, there is also a need for a generalizable modeling framework to account for multiple epidemics (i.e., multi-demics) scenarios, such as influenza and COVID-19 occurring simultaneously. We present a fundamental network thermodynamics approach for assessing, determining, and predicting viral outbreak severity, which extends well-known standard epidemiological models. In particular, we use historical data from New York City’s 2011–2019 influenza seasons and SARS-CoV-2 spread to identify the model parameters. In our model-based analysis, we employ a standard susceptible–infected–recovered (SIR) model with pertinent generalizations to account for multi-strain and multi-demics scenarios. We show that the reaction affinities underpinning the formation processes of our model can be used to categorize the severity of infectious or deceased populations. The spontaneity of occurrence captured by the change in Gibbs free energy of reaction (ΔG) in the system suggests the stability of forward occurring population transfers. The magnitude of ΔG is used to examine past influenza outbreaks and infer epidemiological factors, such as mortality and case burden. This method can be extrapolated for wide-ranging utility in computational epidemiology. The risk of overlapping multi-demics seasons between influenza and SARS-CoV-2 will persist as a significant threat in forthcoming years. Further, the possibility of mutating strains requires novel ways of analyzing the network of competing infection pathways. The approach outlined in this study allows for the identification of new stable strains and the potential increase in disease burden from a complex systems perspective, thereby allowing for a potential response to the significant question: are the effects of a multi-demic greater than the sum of its individual viral epidemics? 
    more » « less
  4. ; ( , 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). 
    more » « less
  5. ; ; ; ; ; ; ; ; ; ; et al ( , 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. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email