(560d) Modeling Viral Replication and Cytokine Response to Reveal Mechanisms of Inflammation

Influenza remains a global health concern, causing an annual 3 - 5 million infections and 290,000 – 650,000 deaths¹. The disease presents a significant pediatric burden, causing 374,000 annual hospitalizations of children under 1 years old, globally². Seasonal monovalent vaccines have achieved only 69% efficacy³, leaving room for improvement through fundamental understanding of the immune response to viral detection. The adaptive immune response, vaccines’ primary route of protection, is predicated by the actions of the innate immune response to create an immunocompetent environment during vaccination or infection⁴. Improper innate immune activation has been implicated in a wide range of pathologies from inflammatory bowel⁵ and liver⁶ diseases to Alzheimer’s Disease⁷ and beyond.

Immune responses can help or hinder an organism’s ability to overcome an infection; excessively inflammatory responses⁸ can cause greater tissue damage, higher mortality, and slow recovery⁹, while an adequate and well-regulated response to a threat is a prerequisite for survival and the management of the adaptive immune system. Thus, tight regulation of the immune response is critical. The first step to occur during an immune response is the detection of the pathogens, leading to the early, localized, innate immune response¹⁰. This response to viral infection leads to the production of Type I Interferons (IFNs). Interferons serve to establish an antiviral state by activating and inducing Mx proteins, RNA-activated protein kinase, and the 2-5A system¹¹; they also regulate other immune responses by acting on NK cells, T cells, B cells, DCs, and phagocytic cells¹². Without the initial sensing protein activity, no immune responses would be mounted. Understanding the dynamics of these sensors is vital to quantifying the innate immune response. The presence of influenza virus is primarily sensed by cytoplasmic retinoic acid-inducible gene 1 (RIG-I) and endosomal Toll-like receptors (TLR)^13,14. RIG-I senses viral RNA in the cytoplasm¹⁵, but is antagonized by influenza A nonstructural protein I (NS1) to varying, strain-specific magnitudes^16,17; TLR7 is free of this antagonism^18,19, but is only activated after the influenza envelop has been degraded by endosomal proteases²⁰. The activation of either sensor leads to the phosphorylation of IRF7 and the production of IFN to act as a signaling cytokine. IFN induces secondary messenger molecules, ultimately leading to the induction of immune activity, inflammation, and antiviral genes²¹

Ordinary Differential Equations (ODEs) have become a common approach in systems biology after their demonstrable success in analyzing the robustness of biological signaling^22–24, the highly dynamic behaviors of NF-kB²⁵, and Xenopus oocytes’ ultrasensitive cell fate binary response²⁶. ODE’s allow for interpolation of the dynamics between a finite number of time points at which RNA profiles have been measured, based on hypothesis of the mechanisms regulating the system’s components.

Here, a novel ODE model was constructed to quantify the innate immune response of human bronchial epithelial cells (HBECs) to influenza A infection. The model incorporates both the classical viral dynamic model^27,28 and the proportionality of sensor protein activity to vRNA levels in the cytoplasm, the first such integration of intracellular immune dynamics and viral replication. The feedback of interferon production on viral replication through the induction of the interferon stimulated gene (ISG) family²⁸ is included. Sensitivity analysis of the model revealed IRF7 phosphorylation and induction as the primary drivers of IFN production in the model. An NS1 knockout strain was simulated as a validation study, revealing lower cell lethality, lower peak viral titers, and a stronger cytokine response, with only the removal of RIG-I antagonism. This work establishes the first cell-level model of interferon signaling induced by influenza infection that can be used to compare host responses between infections with different infecting agents and antagonism motifs through modification of the sensor proteins’ kinetics

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