Determinants of Ligand Specificity and Functional Plasticity in Type I Interferon Signaling

Abstract

The Type I Interferon family of cytokines all act through the same cell surface receptor and induce phosphorylation of the same subset of response regulators of the STAT family. Despite their shared receptor, different Type I Interferons have different functions during immune response to infection. In particular, they differ in the potency of their induced anti-viral and anti-proliferative responses in target cells. It remains not fully understood how these functional differences can arise in a ligand-specific manner both at the level of STAT phosphorylation and the downstream function. We use a minimal computational model of Type I Interferon signaling, focusing on Interferon-α and Interferon-β. We validate the model with quantitative experimental data to identify the key determinants of specificity and functional plasticity in Type I Interferon signaling. We investigate different mechanisms of signal discrimination, and how multiple system components such as binding affinity, receptor expression levels and their variability, receptor internalization, short-term negative feedback by SOCS1 protein, and differential receptor expression play together to ensure ligand specificity on the level of STAT phosphorylation. Based on these results, we propose phenomenological functional mappings from STAT activation to downstream anti-viral and anti-proliferative activity to investigate differential signal processing steps downstream of STAT phosphorylation. We find that the negative feedback by the protein USP18, which enhances differences in signaling between Interferons via ligand-dependent refractoriness, can give rise to functional plasticity in Interferon-α and Interferon-β signaling, and explore other factors that control functional plasticity. Beyond Type I Interferon signaling, our results have a broad applicability to questions of signaling specificity and functional plasticity in signaling systems with multiple ligands acting through a bottleneck of a small number of shared receptors.

Deciphering signal transduction networks in the liver by mechanistic mathematical modelling

“…However, experiments have shown that the interferon pathway does not only qualitatively discriminate between the ligands, but also quantitatively responds differently to specific ligand doses [ 40 ]. By combining diverse data sets from different experimental systems using a minimal computational model, it was recently shown that while these differences in ligand binding strengths are theoretically sufficient for ligand discrimination, experiments failed to show a region of absolute discrimination between IFNα and IFNβ [ 41 ]. Rather, the mathematical model based on previous experimental data [ 42 ] showed that the negative feedback loop via USP18 contributes to ligand discrimination.…”

Ligand induced receptor multimerization achieves the specificity enhancement of kinetic proofreading without associated costs

“…Ligand-receptor signaling kinetics is the first step in signaling cascades that strongly influences the communication fidelity and the reliability of the resulting cellular responses [1][2][3]. Many receptor signaling systems require high signaling specificity -the degree to which a cell can respond differently to different ligands based on the differences in their binding affinities to the receptor [3][4][5][6][7][8][9][10][11].…”

Stochastic Modelling of HIV-1 Replication in a CD4 T Cell with an IFN Response

“…Sustained IFN-I expression bears deleterious effects on host immunity [ 6 , 7 , 8 ]. The mechanisms of the virus escaping intracellular defences are not completely understood [ 9 , 10 , 11 ] because they include multiple layers of control of IFN-I signalling [ 12 ] and diverse roles of interferon-stimulated gene (ISG) products [ 13 ].…”

“…The aim of the current work was to formulate and calibrate a stochastic model of HIV-1 replication that includes antiviral responses of an infected cell and to apply it to examine the dependence of virus production and IFN-I secretion on antiviral defence reactions. This requires the application of computational modelling tools, which corresponds to the mainstream trends in immunology and virology [ 12 , 18 ]. Specifically, we aimed to: Develop an analytical tool for data assimilation and analysis of HIV-1 replication and the IFN-I response; Identify the control points underlying the viral ability to evade IFN-I-mediated defences as they represent potential targets for antiviral and immune therapies; Predict the effective reproduction number of single-cell infection resulting from the competition of multiple factors related to viral and IFN-I-dependent products.…”