Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection

Abstract

Secretion of type I interferons (IFN) by infected cells mediates protection against many viruses, but prolonged or excessive type I IFN secretion can lead to immune pathology. A proper type I IFN response must therefore maintain a balance between protection and excessive IFN secretion. It has been widely noted that the type I IFN response is driven by positive feedback and is heterogeneous, with only a fraction of infected cells upregulating IFN expression even in clonal cell lines, but the functional roles of feedback and heterogeneity in balancing protection and excessive IFN secretion are not clear. To investigate the functional roles for feedback and heterogeneity, we constructed a mathematical model coupling IFN and viral dynamics that extends existing mathematical models by accounting for feedback and heterogeneity. We fit our model to five existing datasets, reflecting different experimental systems. Fitting across datasets allowed us to compare the IFN response across the systems and suggested different signatures of feedback and heterogeneity in the different systems. Further, through numerical experiments, we generated hypotheses of functional roles for IFN feedback and heterogeneity consistent with our mathematical model. We hypothesize an inherent tradeoff in the IFN response: a positive feedback loop prevents excessive IFN secretion, but also makes the IFN response vulnerable to viral antagonism. We hypothesize that cellular heterogeneity of the IFN response functions to protect the feedback loop from viral antagonism. Verification of our hypotheses will require further experimental studies. Our work provides a basis for analyzing the type I IFN response across systems.

Host‐pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

“…where F is the efficiency of IFN in reducing virus production. 14 85 However, we must reconcile data from some IFN perturbation experiments that suggest viral loads are altered only in the later stages of infection when IFN is absent. 63 These data may indicate that IFN has more potent effects (eg, on inflammation) other than limiting virus infection of target cells.…”

“…While it remains difficult to directly connect these dynamics, the data are provocative. New models investigating IFN heterogeneity and viral antagonism may help interpret the data . However, we must reconcile data from some IFN perturbation experiments that suggest viral loads are altered only in the later stages of infection when IFN is absent .…”

Heterogeneity in viral infections increases the rate of deleterious mutation accumulation

“…Viral output from cells can be affected by host cell characteristics such as size, cell type, and cell cycle stage (Brooke et al, 2013; Schulte and Andino, 2014; Heldt et al, 2015; Golumbeanu et al, 2018; Leviyang and Griva, 2018; Russell et al, 2018; Xin et al, 2018; Phipps et al, 2019; Vera et al, 2019). To consider the effect of heterogeneity in virus output on deleterious mutation accumulation, we extend our base model described above by adapting an approach used by Lloyd-Smith et al (2005) to describe population-level viral transmission heterogeneity (superspreading dynamics).…”

Validated models of immune response to virus infection

“…However, the generation and efficacy of various cellular (e.g., macrophages, T cells, or B cells) and soluble (e.g., type I interferons (IFNs) or antibodies (Abs)) host immune factors responsible for controlling viral spread have also been assessed mathematically (Figure 1). In addition, some models have examined how spatial heterogeneity and cell-to-cell viral spread influences viral kinetics [26,28–32]. The use of different functional forms, such as saturating or time-dependent functions [20,33–35], can achieve more complex dynamics than those in Figure 1 while simultaneously retaining model simplicity.…”

“…Kinetic models yield substantial insight about the rates of virus production and clearance, the complex host-pathogen interactions that govern the viral infection, and the formation of protective immunity for a variety of viruses (reviewed in [9,54–59], among others, and herein). In addition, an understanding of how direct cell-to-cell viral spread and spatial heterogeneity influences the disease course is beginning to be understood for viruses like HSV, HIV, HCV, HBV, and HPV [26,28–32]. How population kinetics correlate to tissue-level measurements is also increasing for IAV [9].…”