Stochastic dynamics of Type-I interferon responses

Abstract

Interferon (IFN) activates the transcription of several hundred of IFN stimulated genes (ISGs) that constitute a highly effective antiviral defense program. Cell-to-cell variability in the induction of ISGs is well documented, but its source and effects are not completely understood. The molecular mechanisms behind this heterogeneity have been related to randomness in molecular events taking place during the JAK-STAT signaling pathway. Here, we study the sources of variability in the induction of the IFN-alpha response by using MxA and IFIT1 activation as read-out. To this end, we integrate time-resolved flow cytometry data and stochastic modeling of the JAK-STAT signaling pathway. The complexity of the IFN response was matched by fitting probability distributions to time-course flow cytometry snapshots. Both, experimental data and simulations confirmed that the MxA and IFIT1 induction circuits generate graded responses rather than all-or-none responses. Subsequently, we quantify the size of the intrinsic variability at different steps in the pathway. We found that stochastic effects are transiently strong during the ligand-receptor activation steps and the formation of the ISGF3 complex, but negligible for the final induction of the studied ISGs. We conclude that the JAK-STAT signaling pathway is a robust biological circuit that efficiently transmits information under stochastic environments.

The population context is a driver of the heterogeneous response of epithelial cells to interferons

“…Stochastic events arise from ‘noise’, a term describing some randomness of molecular interactions in the cellular environment (Andrews et al, 2009 ). This model of stochastic origin of cell-to-cell variability was supported with experimental data (stimulation of Huh7.5 with IFNα) and mathematical modeling simulations conducted by Maier et al (Maier et al, 2022 ). Altogether, stochastic events are pivotal factors contributing to cell-to-cell variability during IFN-signaling.…”

“…Importantly, when Rand et al (Rand et al, 2012 ) analyzed the induction of an antiviral state, the non-responder population was permissive to virus infection, while the responder population was protected. Similar follow-up studies supported a heterogonous response to IFNs, including to IFNβ (type I), IFNα (type I) and IFNλ3 (type III), in a variety of cell types (human airway epithelial cell line A549, hepatocyte-derived epithelial-like cell line Huh7.5, primary human hepatocytes, and murine intestinal epithelial cells) (Schmid et al, 2015b ; Bhushal et al, 2017a ; Maier et al, 2022 ; Bauhofer et al, 2012 ). Rand et al (Rand et al, 2012 ) developed a mathematical model and suggested that cell intrinsic stochasticity is responsible for a heterogeneous response to IFNβ in murine fibroblasts.…”

“…However, our understanding of the molecular basis for this cell-to-cell heterogeneity is only at its infancy. It has been reported that individual cells within a population induce ISG expression at different times post type I IFN treatment (Schmid et al, 2015a ; Maier et al, 2022 ). Moreover, low concentrations of type I IFNs are known to induce a heterogeneous pattern of ISG expression levels with highly responsive, less responsive, and non-responder cell subpopulations (Rand et al, 2012 ; Bhushal et al, 2017b ; Maier et al, 2022 ; Schmid et al, 2015a ).…”

Mathematical modeling of regulatory networks of intracellular processes – Aims and selected methods

“…Each year, new models appear, describing systems that previously have not been dealt with. However, most efforts concentrate on pathways involved in determination of cell fate [64] , cell cycle [3] , response to stress [63] , [89] , carcinogenesis [125] , immune system responses [29] , [76] , or, most recently, micro-RNA-mediated regulation of intracellular processes [61] , [92] , [104] .…”

Antagonism between viral infection and innate immunity at the single-cell level

“…Understanding the complex interplay between viruses and the innate immune response at the system level requires experimentation at the single-cell resolution combined with data-driven single-cell level computational modeling (Talemi & Höfer, 2018; Van Eyndhoven et al , 2021). Thus far, integration of quantitative data on Ifnb1 (IFNβ gene) and ISGs expression in single cells over time and stochastic modeling allowed to demonstrate that paracrine signaling has a major impact on heterogeneous cell responses to infection (Patil et al , 2015; Rand et al, 2012; Maier et al , 2022). Recently, benefits of interferon expression heterogeneity have been studied within a spatial agent-based model of an infection with a “generic” virus (Gregg et al , 2021).…”