Understanding inhibition of viral proteins on type I IFN signaling pathways with modeling and optimization

Classical Swine Fever Virus Envelope Glycoproteins Erns, E1, and E2 Activate IL-10-STAT1-MX1/OAS1 Antiviral Pathway via Replacing Classical IFNα/β

Classical swine fever (CSF) is an acute and often fatal disease caused by CSF virus (CSFV) infection. In the present study, we investigated the transcriptional profiles of peripheral blood mononuclear cells (PBMCs) in pigs infected with CSFV. The results revealed that CSFV inhibits IFNα/β production, but up-regulates the expression of signal transducer and activator of transcription 1 (STAT1); this result was verified in vitro. Interestingly, STAT1 is typically a downstream target of IFNα/β, raising the question of how CSFV can inhibit IFNα/β expression, yet up-regulate STAT1 expression. To explore this further, we observed that UV-treated CSFV induced STAT1 expression. Our results demonstrated that CSFV Erns, E1, and E2 could up-regulate STAT1 expression within the host cell cytoplasm and facilitate its translocation into the nucleus. The Erns, E1, and E2 proteins also separately induced the up-regulation of interleukin (IL)-10; IL-10 acts as the communicator connecting Erns, E1, and E2 proteins to STAT1, leading to the subsequent up-regulation, phosphorylation, and nuclear translocation of STAT1. Silencing of IL-10 down-regulated STAT1 expression. Finally, MX1 and OAS1 were identified as downstream targets of the IL-10-STAT1 pathway. In summary, a novel IL-10-STAT1 pathway independent of IFNα/β induced by CSFV Erns, E1, and E2 was identified in this study.

Mathematical modeling and dynamical analysis of anti-tumor drug dose-response

Cancer is a serious threat to human health and life. Using anti-tumor drugs is one of the important ways for treating cancer. A large number of experiments have shown that the hormesis appeared in the dose-response relationship of various anti-tumor drugs. Modeling this phenomenon will contribute to finding the appropriate dose. However, few studies have used dynamical models to quantitatively explore the hormesis phenomenon in anti-tumor drug dose-response. In this study, we present a mathematical model and dynamical analysis to quantify hormesis of anti-tumor drugs and reveal the critical threshold of antibody dose. Firstly, a dynamical model is established to describe the interactions among tumor cells, natural killer cells and M2-polarized macrophages. Model parameters are fitted through the published experimental data. Secondly, the positivity of solution and bounded invariant set are given. The stability of equilibrium points is proved. Thirdly, through bifurcation analysis and numerical simulations, the hormesis phenomenon of low dose antibody promoting tumor growth and high dose antibody inhibiting tumor growth is revealed. Furthermore, we fit out the quantitative relationship of the dose-response of antibodies. Finally, the critical threshold point of antibody dose changing from promoting tumor growth to inhibiting tumor growth is obtained. These results can provide suggestions for the selection of appropriate drug dosage in the clinical treatment of cancer.

Quantification of Type I Interferon Inhibition by Viral Proteins: Ebola Virus as a Case Study

Type I interferons (IFNs) are cytokines with both antiviral properties and protective roles in innate immune responses to viral infection. They induce an antiviral cellular state and link innate and adaptive immune responses. Yet, viruses have evolved different strategies to inhibit such host responses. One of them is the existence of viral proteins which subvert type I IFN responses to allow quick and successful viral replication, thus, sustaining the infection within a host. We propose mathematical models to characterise the intra-cellular mechanisms involved in viral protein antagonism of type I IFN responses, and compare three different molecular inhibition strategies. We study the Ebola viral protein, VP35, with this mathematical approach. Approximate Bayesian computation sequential Monte Carlo, together with experimental data and the mathematical models proposed, are used to perform model calibration, as well as model selection of the different hypotheses considered. Finally, we assess if model parameters are identifiable and discuss how such identifiability can be improved with new experimental data.

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

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.

Identification of the molecular mechanisms for cell-fate selection in budding yeast through mathematical modeling

The specification and maintenance of cell fates is essential to the development of multicellular organisms. However, the precise molecular mechanisms in cell fate selection are, to our knowledge, poorly understood due to the complexity of multiple interconnected pathways. In this study, model-based quantitative analysis is used to explore how to maintain distinguished cell fates between cell-cycle commitment and mating arrest in budding yeast. We develop a full mathematical model of an interlinked regulatory network based on the available experimental data. By theoretically defining the Start transition point, the model is able to reproduce many experimental observations of the dynamical behaviors in wild-type cells as well as in Ste5-8A and Far1-S87A mutants. Furthermore, we demonstrate that a moderate ratio between Cln1/2→Far1 inhibition and Cln1/2→Ste5 inhibition is required to ensure a successful switch between different cell fates. We also show that the different ratios of the mutual Cln1/2 and Far1 inhibition determine the different cell fates. In addition, based on a new, definition of network entropy, we find that the Start point in wild-type cells coincides with the system's point of maximum entropy. This result indicates that Start is a transition point in the network entropy. Therefore, we theoretically explain the Start point from a network dynamics standpoint. Moreover, we analyze the biological bistablity of our model through bifurcation analysis. We find that the Cln1/2 and Cln3 production rates and the nonlinearity of SBF regulation on Cln1/2 production are potential determinants for irreversible entry into a new cell fate. Finally, the quantitative computations further reveal that high specificity and fidelity of the cell-cycle and mating pathways can guarantee specific cell-fate selection. These findings show that quantitative analysis and simulations with a mathematical model are useful tools for understanding the molecular mechanisms in cell-fate decisions.

Systematic analysis of the mechanisms of virus-triggered type I IFN signaling pathways through mathematical modeling

Based on biological experimental data, we developed a mathematical model of the virus-triggered signaling pathways that lead to induction of type I IFNs and systematically analyzed the mechanisms of the cellular antiviral innate immune responses, including the negative feedback regulation of ISG56 and the positive feedback regulation of IFNs. We found that the time between 5 and 48 hours after viral infection is vital for the control and/or elimination of the virus from the host cells and demonstrated that the ISG56-induced inhibition of MITA activation is stronger than the ISG56-induced inhibition of TBK1 activation. The global parameter sensitivity analysis suggests that the positive feedback regulation of IFNs is very important in the innate antiviral system. Furthermore, the robustness of the innate immune signaling network was demonstrated using a new robustness index. These results can help us understand the mechanisms of the virus-induced innate immune response at a system level and provide instruction for further biological experiments.

Modeling and dynamical analysis of virus-triggered innate immune signaling pathways

The investigation of the dynamics and regulation of virus-triggered innate immune signaling pathways at a system level will enable comprehensive analysis of the complex interactions that maintain the delicate balance between resistance to infection and viral disease. In this study, we developed a delayed mathematical model to describe the virus-induced interferon (IFN) signaling process by considering several key players in the innate immune response. Using dynamic analysis and numerical simulation, we evaluated the following predictions regarding the antiviral responses: (1) When the replication ratio of virus is less than 1, the infectious virus will be eliminated by the immune system’s defenses regardless of how the time delays are changed. (2) The IFN positive feedback regulation enhances the stability of the innate immune response and causes the immune system to present the bistability phenomenon. (3) The appropriate duration of viral replication and IFN feedback processes stabilizes the innate immune response. The predictions from the model were confirmed by monitoring the virus titer and IFN expression in infected cells. The results suggest that the balance between viral replication and IFN-induced feedback regulation coordinates the dynamical behavior of virus-triggered signaling and antiviral responses. This work will help clarify the mechanisms of the virus-induced innate immune response at a system level and provide instruction for further biological experiments.

Apoptosis - an Ubiquitous T cell Immunomodulator

Apoptosis is a natural process where cells that are no longer required can be eliminated in a highly regulated, controlled manner. Apoptosis is important in maintaining the mammalian immune system and plays a significant role in immune response, positive and negative T cell selection, and cytotoxic death of target cells. When the apoptotic pathways are impaired or are not tightly regulated, autoimmune diseases, inflammatory diseases, viral and bacterial infections and cancers ensue. An imbalance in the anti-apoptotic and pro-apoptotic factors has been implicated in these diseases. Moreover, current therapies directed towards these diseases focus on the modulation of the apoptotic death pathways to regulate the immune response. In this review, we will focus on the process of T cell activation and apoptosis in autoimmune reactions, in response to tumor progression as well as in response to bacterial and viral infections.