Immune response modeling of interferon beta-pretreated influenza virus-infected human dendritic cells

High-resolution kinetic characterization of the RIG-I-signaling pathway and the antiviral response

RIG-I recognizes viral dsRNA and activates a cell-autonomous antiviral response. Upon stimulation, it triggers a signaling cascade leading to the production of type I and III IFNs. IFNs are secreted and signal to elicit the expression of IFN-stimulated genes, establishing an antiviral state of the cell. The topology of this pathway has been studied intensively, however, its exact dynamics are less understood. Here, we employed electroporation to synchronously activate RIG-I, enabling us to characterize cell-intrinsic innate immune signaling at a high temporal resolution. Employing IFNAR1/IFNLR-deficient cells, we could differentiate primary RIG-I signaling from secondary signaling downstream of the IFN receptors. Based on these data, we developed a comprehensive mathematical model capable of simulating signaling downstream of dsRNA recognition by RIG-I and the feedback and signal amplification by IFN. We further investigated the impact of viral antagonists on signaling dynamics. Our work provides a comprehensive insight into the signaling events that occur early upon virus infection and opens new avenues to study and disentangle the complexity of the host-virus interface.

A multiscale mechanistic model of human dendritic cells for in-silico investigation of immune responses and novel therapeutics discovery

Dendritic cells (DCs) are professional antigen-presenting cells (APCs) with the unique ability to mediate inflammatory responses of the immune system. Given the critical role of DCs in shaping immunity, they present an attractive avenue as a therapeutic target to program the immune system and reverse immune disease disorders. To ensure appropriate immune response, DCs utilize intricate and complex molecular and cellular interactions that converge into a seamless phenotype. Computational models open novel frontiers in research by integrating large-scale interaction to interrogate the influence of complex biological behavior across scales. The ability to model large biological networks will likely pave the way to understanding any complex system in more approachable ways. We developed a logical and predictive model of DC function that integrates the heterogeneity of DCs population, APC function, and cell-cell interaction, spanning molecular to population levels. Our logical model consists of 281 components that connect environmental stimuli with various layers of the cell compartments, including the plasma membrane, cytoplasm, and nucleus to represent the dynamic processes within and outside the DC, such as signaling pathways and cell-cell interactions. We also provided three sample use cases to apply the model in the context of studying cell dynamics and disease environments. First, we characterized the DC response to Sars-CoV-2 and influenza co-infection by in-silico experiments and analyzed the activity level of 107 molecules that play a role in this co-infection. The second example presents simulations to predict the crosstalk between DCs and T cells in a cancer microenvironment. Finally, for the third example, we used the Kyoto Encyclopedia of Genes and Genomes enrichment analysis against the model's components to identify 45 diseases and 24 molecular pathways that the DC model can address. This study presents a resource to decode the complex dynamics underlying DC-derived APC communication and provides a platform for researchers to perform in-silico experiments on human DC for vaccine design, drug discovery, and immunotherapies.

Multicellular spatial model of RNA virus replication and interferon responses reveals factors controlling plaque growth dynamics

Respiratory viruses present major public health challenges, as evidenced by the 1918 Spanish Flu, the 1957 H2N2, 1968 H3N2, and 2009 H1N1 influenza pandemics, and the ongoing severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic. Severe RNA virus respiratory infections often correlate with high viral load and excessive inflammation. Understanding the dynamics of the innate immune response and its manifestations at the cell and tissue levels is vital to understanding the mechanisms of immunopathology and to developing strain-independent treatments. Here, we present a novel spatialized multicellular computational model of RNA virus infection and the type-I interferon-mediated antiviral response that it induces within lung epithelial cells. The model is built using the CompuCell3D multicellular simulation environment and is parameterized using data from influenza virus-infected cell cultures. Consistent with experimental observations, it exhibits either linear radial growth of viral plaques or arrested plaque growth depending on the local concentration of type I interferons. The model suggests that modifying the activity of signaling molecules in the JAK/STAT pathway or altering the ratio of the diffusion lengths of interferon and virus in the cell culture could lead to plaque growth arrest. The dependence of plaque growth arrest on diffusion lengths highlights the importance of developing validated spatial models of cytokine signaling and the need for in vitro measurement of these diffusion coefficients. Sensitivity analyses under conditions leading to continuous or arrested plaque growth found that plaque growth is more sensitive to variations of most parameters and more likely to have identifiable model parameters when conditions lead to plaque arrest. This result suggests that cytokine assay measurements may be most informative under conditions leading to arrested plaque growth. The model is easy to extend to include SARS-CoV-2-specific mechanisms or to use as a component in models linking epithelial cell signaling to systemic immune models.

Decoding the dynamics of multilayered stochastic antiviral IFN-I responses

Type I Interferon (IFN-I) responses were first recognized for their role in antiviral immunity, but it is now widely appreciated that IFN-Is have many immunomodulatory functions, influencing antitumor responses, autoimmune manifestations, and antimicrobial defenses. Given these pivotal roles, it may be surprising that multilayered stochastic events create highly heterogeneous, but tightly regulated, all-or-nothing cellular decisions. Recently, mathematical models have provided crucial insights into the stochastic nature of antiviral IFN-I responses, which we critically evaluate in this review. In this context, we emphasize the need for innovative single-cell technologies combined with mathematical models to further reveal, understand, and predict the complexity of the IFN-I system in physiological and pathological conditions that may be relevant to a plethora of diseases.

Mathematical Modeling of RNA Virus Sensing Pathways Reveals Paracrine Signaling as the Primary Factor Regulating Excessive Cytokine Production

RNA viruses, such as influenza and Severe Acute Respiratory Syndrome (SARS), invoke excessive immune responses; however, the kinetics that regulate inflammatory responses within infected cells remain unresolved. Here, we develop a mathematical model of the RNA virus sensing pathways, to determine the intracellular events that primarily regulate interferon, an important protein for the activation and management of inflammation. Within the ordinary differential equation (ODE) model, we incorporate viral replication, cell death, interferon stimulated genes’ antagonistic effects on viral replication, and virus sensor protein (TLR and RIG-I) kinetics. The model is parameterized to influenza infection data using Markov chain Monte Carlo and then validated against infection data from an NS1 knockout strain of influenza, demonstrating that RIG-I antagonism significantly alters cytokine signaling trajectory. Global sensitivity analysis suggests that paracrine signaling is responsible for the majority of cytokine production, suggesting that rapid cytokine production may be best managed by influencing extracellular cytokine levels. As most of the model kinetics are host cell specific and not virus specific, the model presented provides an important step to modeling the intracellular immune dynamics of many RNA viruses, including the viruses responsible for SARS, Middle East Respiratory Syndrome (MERS), and Coronavirus Disease (COVID-19).

Regulation of interferon stimulated gene expression levels at homeostasis

Interferon stimulated genes (ISGs), a collection of genes important in the early innate immune response, are upregulated in response to stimulation by extracellular type I interferons. The regulation of ISGs has been extensively studied in cells exposed to significant interferon stimulation, but less is known about ISG regulation in homeostatic regimes in which extracellular interferon levels are low. Using a collection of pre-existing, publicly available microarray datasets, we investigated ISG regulation at homeostasis in CD4, pulmonary epithelial, fibroblast and macrophage cells. We used a linear regression model to predict ISG expression levels from regulator expression levels. Our results suggest significant regulation of ISG expression at homeostasis, both through the ISGF3 molecule and through IRF7 and IRF8 associated pathways. We find that roughly 50% of ISGs have expression levels significantly correlated with ISGF3 expression levels at homeostasis, supporting previous results suggesting that homeostatic IFN levels have broad functional consequences. We find that ISG expression levels varied in their correlation with ISGF3, with epithelial and macrophage cells showing more correlation than CD4 and fibroblast cells. Our analysis provides a novel approach for decomposing and quantifying ISG regulation.

Kinetic Modeling of Virus Growth in Cells

When a virus infects a host cell, it hijacks the biosynthetic capacity of the cell to produce virus progeny, a process that may take less than an hour or more than a week. The overall time required for a virus to reproduce depends collectively on the rates of multiple steps in the infection process, including initial binding of the virus particle to the surface of the cell, virus internalization and release of the viral genome within the cell, decoding of the genome to make viral proteins, replication of the genome, assembly of progeny virus particles, and release of these particles into the extracellular environment. For a large number of virus types, much has been learned about the molecular mechanisms and rates of the various steps. However, in only relatively few cases during the last 50 years has an attempt been made-using mathematical modeling-to account for how the different steps contribute to the overall timing and productivity of the infection cycle in a cell. Here we review the initial case studies, which include studies of the one-step growth behavior of viruses that infect bacteria (Qβ, T7, and M13), human immunodeficiency virus, influenza A virus, poliovirus, vesicular stomatitis virus, baculovirus, hepatitis B and C viruses, and herpes simplex virus. Further, we consider how such models enable one to explore how cellular resources are utilized and how antiviral strategies might be designed to resist escape. Finally, we highlight challenges and opportunities at the frontiers of cell-level modeling of virus infections.

Multi-epitope Models Explain How Pre-existing Antibodies Affect the Generation of Broadly Protective Responses to Influenza

The development of next-generation influenza vaccines that elicit strain-transcendent immunity against both seasonal and pandemic viruses is a key public health goal. Targeting the evolutionarily conserved epitopes on the stem of influenza’s major surface molecule, hemagglutinin, is an appealing prospect, and novel vaccine formulations show promising results in animal model systems. However, studies in humans indicate that natural infection and vaccination result in limited boosting of antibodies to the stem of HA, and the level of stem-specific antibody elicited is insufficient to provide broad strain-transcendent immunity. Here, we use mathematical models of the humoral immune response to explore how pre-existing immunity affects the ability of vaccines to boost antibodies to the head and stem of HA in humans, and, in particular, how it leads to the apparent lack of boosting of broadly cross-reactive antibodies to the stem epitopes. We consider hypotheses where binding of antibody to an epitope: (i) results in more rapid clearance of the antigen; (ii) leads to the formation of antigen-antibody complexes which inhibit B cell activation through Fcγ receptor-mediated mechanism; and (iii) masks the epitope and prevents the stimulation and proliferation of specific B cells. We find that only epitope masking but not the former two mechanisms to be key in recapitulating patterns in data. We discuss the ramifications of our findings for the development of vaccines against both seasonal and pandemic influenza.

The current influenza vaccine requires frequent updating in order to protect against small changes in the virus from one year to the next as well as larger changes associated with the emergence of new influenza strains from zoonotic reservoirs that cause pandemics. There is a considerable interest in developing “universal” vaccines that will boost immune responses to the conserved regions of the virus, in particular, to the stem region of the major virus surface molecule hemagglutinin (HA). However, recent data reveals that vaccination results in very limited boosting of antibodies to the stem of HA. We use mathematical models to explore different hypotheses that may explain why vaccination does not boost antibodies to the conserved parts of the virus. By confronting our models with the data from the human vaccination trials we found that the key mechanism preventing effective boosting of the responses to the stem of HA is masking of the stem by pre-existing antibodies developed during previous infections and vaccinations. We discuss how this masking effect could be overcome in a “universal” influenza vaccine.

Model of influenza A virus infection: dynamics of viral antagonism and innate immune response

Viral antagonism of host responses is an essential component of virus pathogenicity. The study of the interplay between immune response and viral antagonism is challenging due to the involvement of many processes acting at multiple time scales. Here we develop an ordinary differential equation model to investigate the early, experimentally measured, responses of human monocyte-derived dendritic cells to infection by two H1N1 influenza A viruses of different clinical outcomes: pandemic A/California/4/2009 and seasonal A/New Caledonia/20/1999. Our results reveal how the strength of virus antagonism, and the time scale over which it acts to thwart the innate immune response, differs significantly between the two viruses, as is made clear by their impact on the temporal behavior of a number of measured genes. The model thus sheds light on the mechanisms that underlie the variability of innate immune responses to different H1N1 viruses.

Transient oscillatory dynamics of interferon beta signaling in macrophages

Background

Interferon-beta (IFN-beta) activates the immune response through the type I IFN signaling pathway. IFN-beta is important in the response to pathogen infections and is used as a therapy for Multiple Sclerosis. The mechanisms of self-regulation and control of this pathway allow precise and environment-dependent response of the cells in different conditions. Here we analyzed type I IFN signaling in response to IFN-beta in the macrophage cell line RAW 264.7 by RT-PCR, ELISA and xMAP assays. The experimental results were interpreted by means of a theoretical model of the pathway.

Results

Phosphorylation of the STAT1 protein (pSTAT1) and mRNA levels of the pSTAT1 inhibitor SOCS1 displayed an attenuated oscillatory behavior after IFN-beta activation. In turn, mRNA levels of the interferon regulatory factor IRF1 grew rapidly in the first 50–90 minutes after stimulation until a maximum value, and started to decrease slowly around 200–250 min. The analysis of our kinetic model identified a significant role of the negative feedback from SOCS1 in driving the observed damped oscillatory dynamics, and of the positive feedback from IRF1 in increasing STAT1 basal levels. Our study shows that the system works as a biological damped relaxation oscillator based on a phosphorylation-dephosphorylation network centered on STAT1. Moreover, a bifurcation analysis identified translocation of pSTAT1 dimers to the nucleus as a critical step for regulating the dynamics of type I IFN pathway in the first steps, which may be important in defining the response to IFN-beta therapy.

Conclusions

The immunomodulatory effect of IFN-beta signaling in macrophages takes the form of transient oscillatory dynamics of the JAK-STAT pathway, whose specific relaxation properties determine the lifetime of the cellular response to the cytokine.

Model-based simulation and prediction of an antiviral strategy against influenza A infection

There is a strong need to develop novel strategies in using antiviral agents to efficiently treat influenza infections. Thus, we constructed a rule-based mathematical model that reflects the complicated interactions of the host immunity and viral life cycle and analyzed the key controlling steps of influenza infections. The main characteristics of the pandemic and seasonal influenza strains were estimated using parameter values derived from cells infected with Influenza A/California/04/2009 and Influenza A/NewCaledonia/20/99, respectively. The quantitative dynamics of the infected host cells revealed a more aggressive progression of the pandemic strain than the seasonal strain. The perturbation of each parameter in the model was then tested for its effects on viral production. In both the seasonal and pandemic strains, the inhibition of the viral release (k_C), the reinforcement of viral attachment (k_V), and an increased transition rate of infected cells into activated cells (k_I) exhibited significant suppression effects on the viral production; however, these inhibitory effects were only observed when the numerical perturbations were performed at the early stages of the infection. In contrast, combinatorial perturbations of both the inhibition of viral release and either the reinforcement of the activation of infected cells or the viral attachment exhibited a significant reduction in the viral production even at a later stage of infection. These results suggest that, in addition to blocking the viral release, a combination therapy that also enhances either the viral attachment or the transition of the infected cells might provide an alternative for effectively controlling progressed influenza infection.

Computational approaches to understanding dendritic cell responses to influenza virus infection

The evolution of immunology research from measurements of single entities to large-scale data-intensive assays necessitates the integration of experimental work with bioinformatics and computational approaches. The introduction of physics into immunology has led to the study of new phenomena, such as cellular noise, which is likely to prove increasingly important to understand immune system responses. The fusion of "hard science" and biology is also leading to a re-examination of data acquisition, analysis, and statistical validation and is resulting in the development of easy-to-access tools for immunology research. Here, we review some of our models, computational tools, and results related to studies of the innate immune response of human dendritic cells to viral infection. Our project functions on an open model across institutions with electronic record keeping and public sharing of data. Our tools, models, and data can be accessed at http://tsb.mssm.edu/primeportal/ .

Biphasic RLR-IFN-β response controls the balance between antiviral immunity and cell damage

In RNA virus-infected cells, retinoic acid-inducible gene-I-like receptors (RLRs) sense foreign RNAs and activate signaling cascades to produce IFN-α/β. However, not every infected cell produces IFN-α/β that exhibits cellular heterogeneity in antiviral immune responses. Using the IFN-β-GFP reporter system, we observed bimodal IFN-β production in the uniformly stimulated cell population with intracellular dsRNA. Mathematical simulation proposed the strength of autocrine loop via RLR as one of the contributing factor for biphasic IFN-β expression. Bimodal IFN-β production with intracellular dsRNA was disturbed by blockage of IFN-α/β secretion or by silencing of the IFN-α/β receptor. Amplification of RLRs was critical in the generation of bimodality of IFN-β production, because IFN-β(high) population expressed more RLRs than IFN-β(low) population. In addition, bimodality in IFN-β production results in biphasic cellular response against infection, because IFN-β(high) population was more prone to apoptosis than IFN-β(low) population. These results suggest that RLR-mediated biphasic cellular response may act to restrict the number of cells expressing IFN-β and undergoing apoptosis in the infected population.

Cytokine response is determined by duration of receptor and signal transducers and activators of transcription 3 (STAT3) activation

Paradoxically, the pro-inflammatory cytokine IL-6 and the anti-inflammatory cytokine IL-10 both activate STAT3, yet generate nearly opposing cellular responses. Here, we show that the temporal pattern of STAT3 activation codes for the specific cytokine response. A computational model of IL-6 and IL-10 signaling predicted that IL-6 stimulation results in transient activation of STAT3, with a rapid decline in phosphorylation and nuclear localization. In contrast, simulated IL-10 signaling resulted in sustained STAT3 activation. The predicted STAT3 patterns produced by each cytokine were confirmed experimentally in human dendritic cells. Time course microarray studies further showed that the dynamic genome-wide transcriptional responses were nearly identical at early time points following stimulation (when STAT3 is active in response to both IL-6 and IL-10) but divergent at later times (when STAT3 is active only in response to IL-10). Truncating STAT3 activation after IL-10 stimulation caused IL-10 to elicit an IL-6-like transcriptional and secretory response. That the duration of IL-10 receptor and STAT3 activation can direct distinct responses reveals a complex cellular information-coding mechanism that may be relevant to improving the prediction of the effects of drug candidates using this mechanism.

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.

Modelling the interactions between TLR4 and IFNβ pathways

Bacterial lipopolysaccharide (LPS) association with their connate receptor TLR4 triggers Type I interferon signaling cascade through its MyD88 independent downstream. Compared to plethora of reported empirical data on both TLR4 and Type I interferon pathways, there is no known model to decipher crosstalk mechanisms between these two crucial innate immune pathogen activated pathways regulating vital transcriptional factors such as nuclear factor-κB (NFκB), IFNβ, the interferon-stimulated gene factor-3 (ISGF3) and an important cancer drug target protein kinase-R (PKR). Innate immune system is based on a sensitive balance of intricate interactions. In elucidating these interactions, in silico integration of pathways has great potential. Attempts confined to single pathway may not be effective in truly addressing source of real systems behavior. This is the first report combining toll-like receptor-4 (TLR4) and interferon beta (IFNβ) pathways in a single in silico model, analyzing their interactions, pinpointing the source of delay in PKR late phase activity and limiting the transcription of IFN and PKR by using a method including an statistical physics technique in reaction equations. The model quite successfully recapitulates published interferon regulatory factor-3 (IRF3) and IFNβ data from mouse macrophages and PKR data from mouse embryonic fibroblast cell lines. The simulations end up with an estimate of IRF3, IFNβ, ISGF3 dose dependent profiles mimicking nonlinear dose response characteristic of the system. Involvement of concomitant PKR downstream can unravel elusive mechanisms in specific profiles like NFκB regulation.

Trypsin promotes efficient influenza vaccine production in MDCK cells by interfering with the antiviral host response

Trypsin is commonly used in Madin-Darby canine kidney (MDCK) cell culture-based influenza vaccine production to facilitate virus infection by proteolytic activation of viral haemagglutinin, which enables multi-cycle replication. In this study, we were able to demonstrate that trypsin also interferes with pathogen defence mechanisms of host cells. In particular, a trypsin concentration of 5 BAEE U/mL (4.5 μg/mL porcine trypsin) used in vaccine manufacturing strongly inhibited interferon (IFN) signalling by proteolytic degradation of secreted IFN. Consequently, absence of trypsin during infection resulted in a considerably stronger induction of IFN signalling and apoptosis, which significantly reduced virus yields. Under this condition, multi-cycle virus replication in MDCK cells was not prevented but clearly delayed. Therefore, incomplete infection can be ruled out as the reason for the lower virus titres. However, suppression of IFN signalling by overexpression of viral IFN antagonists (influenza virus PR8-NS1, rabies virus phosphoprotein) partially rescued virus titres in the absence of trypsin. In addition, virus yields could be almost restored by using the influenza strain A/WSN/33 in combination with fetal calf serum (FCS). For this strain, FCS enabled trypsin-independent fast propagation of virus infection, probably outrunning cellular defence mechanisms and apoptosis induction in the absence of trypsin. Overall, addition of trypsin provided optimal conditions for high yield vaccine production in MDCK cells by two means. On the one hand, proteolytic degradation of IFN keeps cellular defence at a low level. On the other hand, enhanced virus spreading enables viruses to replicate before the cellular response becomes fully activated.

Role of cell-to-cell variability in activating a positive feedback antiviral response in human dendritic cells

In the first few hours following Newcastle disease viral infection of human monocyte-derived dendritic cells, the induction of IFNB1 is extremely low and the secreted type I interferon response is below the limits of ELISA assay. However, many interferon-induced genes are activated at this time, for example DDX58 (RIGI), which in response to viral RNA induces IFNB1. We investigated whether the early induction of IFNBI in only a small percentage of infected cells leads to low level IFN secretion that then induces IFN-responsive genes in all cells. We developed an agent-based mathematical model to explore the IFNBI and DDX58 temporal dynamics. Simulations showed that a small number of early responder cells provide a mechanism for efficient and controlled activation of the DDX58-IFNBI positive feedback loop. The model predicted distributions of single cell responses that were confirmed by single cell mRNA measurements. The results suggest that large cell-to-cell variation plays an important role in the early innate immune response, and that the variability is essential for the efficient activation of the IFNB1 based feedback loop.

Novel Nipah virus immune-antagonism strategy revealed by experimental and computational study

Nipah virus is an emerging pathogen that causes severe disease in humans. It expresses several antagonist proteins that subvert the immune response and that may contribute to its pathogenicity. Studies of its biology are difficult due to its high pathogenicity and requirement for biosafety level 4 containment. We integrated experimental and computational methods to elucidate the effects of Nipah virus immune antagonists. Individual Nipah virus immune antagonists (phosphoprotein and V and W proteins) were expressed from recombinant Newcastle disease viruses, and the responses of infected human monocyte-derived dendritic cells were determined. We developed an ordinary differential equation model of the infectious process that that produced results with a high degree of correlation with these experimental results. In order to simulate the effects of wild-type virus, the model was extended to incorporate published experimental data on the time trajectories of immune-antagonist production. These data showed that the RNA-editing mechanism utilized by the wild-type Nipah virus to produce immune antagonists leads to a delay in the production of the most effective immune antagonists, V and W. Model simulations indicated that this delay caused a disconnection between attenuation of the antiviral response and suppression of inflammation. While the antiviral cytokines were efficiently suppressed at early time points, some early inflammatory cytokine production occurred, which would be expected to increase vascular permeability and promote virus spread and pathogenesis. These results suggest that Nipah virus has evolved a unique immune-antagonist strategy that benefits from controlled expression of multiple antagonist proteins with various potencies.