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

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).

Expression pattern of the interferon regulatory factor family members in influenza virus induced local and systemic inflammatory responses

Type I interferon is considered to be a key cytokine in influenza virus-induced acute lung injury (ALI), in which IRF3 and IRF7 play particularly important roles. However, whether all nine members of IRF family are involved in influenza virus-induced immune response is currently unknown. In this study, we found that all members of IRF family responded to influenza virus. The IRF family expression profile seems to be related to the pathogenicity of the particular influenza virus strain. The influenza virus mainly relies on endosomal TLR signals and the positive feedback loop of IFN-I to cause either direct or indirect different expression of all IRF family members locally or systemically. Interestingly, IRF6 was somewhat different from other IRF family members during influenza virus infection. Overall, the expression profile of the IRF family may be a valuable reference for the prevention and treatment of influenza complications, which encourage further, more in-depth research.

Expression Pattern Analysis of Antiviral Genes and Inflammatory Cytokines in PEDV-Infected Porcine Intestinal Epithelial Cells

Porcine diarrhea disease in newborn and suckling piglets due to infection with porcine epidemic diarrhea virus (PEDV) is a leading cause of economic loss in the pig industry globally. In this study, we investigated the molecular mechanism of the host innate immune response to PEDV infection. The expression dynamics of antiviral genes (e.g., RIG-1, PKR, OAS1, Mx1, and Mx2) and inflammatory cytokines (e.g., IFN-α, IFN-β, TNF-α, IL-6, IL-8, and IL-12) in porcine small intestinal epithelial (IPEC-J2) cells were analyzed following PEDV stimulation. The results showed that the expression of antiviral genes (e.g., PKR, OAS1, and Mx2) and inflammatory cytokines (e.g., IFN-α and TNF-α) were significantly reduced within 0–4 h post-infection (P < 0.05). However, all antiviral genes and inflammatory cytokines were up-regulated from 12 to 24 h (P < 0.05), and cytopathic changes were observed during this time. The expression of RIG-1, PKR, OAS1, Mx1, and Mx2 were significantly and positively correlated to each other during the entire infection (P < 0.01). The results suggested that the RIG-1, PKR, OAS1, Mx1, and Mx2 genes may play an important role in PEDV infection in piglets. Initially, PEDV displayed cellular invasion by inhibiting IFN-α transcription and interfering with the antiviral function of PKR, OAS1, and Mx2, ultimately induced an intense inflammatory response. The relationship between antiviral genes and inflammatory cytokines with PEDV infection at the cellular level provides a reference for studying the mechanism of resistance to PEDV infection in piglets.

Spatio-temporal dynamics of Host-Virus competition: A model study of influenza A

We present results of a study of the early-time response of the innate immune system to influenza virus infection in an agent-based model (ABM) of epithelial cell layers. We find that the competition between the anti-viral immune response and viral antagonism can lead to viral titers non-monotonic in the initial infection fraction as found in experiments. Our model includes a coarse-grained version of intra-cellular processes and inter-cellular communication via cytokine and virion diffusion. We use ABM to follow the propagation of viral infection in the layer and the increase of the viral load as a function of time for different values of the multiplicity of infection (MOI), the initial number of viruses added per cell. We find that for moderately strong host immune response, the number of infected cells and viral load for a smaller MOI exceeds that for larger MOI, as seen in experiments. We elucidate the mechanism underlying this result as the synergistic action of cytokines secreted by infected cells in controlling viral amplification for larger MOI. We investigate the length and time scales that determine this non-monotonic behavior within the ABM. We study the diffusive spread of virions and cytokines from a single infected cell in an absorbing medium analytically and numerically and deduce the length scale that yields a good estimate of the MOI at which we find non-monotonicity. Detailed computations of the temporal behavior of averaged quantities and spatial measures provide further insights into host-viral interactions and connections to experimental observations.

Optimal control of acute myeloid leukaemia

Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corresponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin's Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.

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.

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.

Intranasal co-administration of 1,8-cineole with influenza vaccine provide cross-protection against influenza virus infection

BACKGROUND

Vaccination is the most efficient means for protection against influenza. However, the various vaccines have low efficacy to protect against pandemic strains because of antigenic drift and recombination of influenza virus. Adjuvant therapy is one of the attempts to improve influenza vaccine effective cross-protection against influenza virus infection. Our previous study confirmed that 1,8-cineole inhibits the NF-κB, reduces pro-inflammatory cytokines, and relieves the pathological changes of viral pneumonia in mice infected with influenza virus.

HYPOTHESIS/PURPOSE

1,8-cineole, administered via intranasal (i.n.) route, may also have the capacity to be an adjuvant of the influenza vaccine. This study was designed to investigate the potential use of i.n. co-administration of 1,8-cineole, a major component of the Eucalyptus essential oils, with influenza vaccine and whether could provide cross-protection against influenza virus infection in a mouse model.

STUDY DESIGN

I.n. co-administration of 1,8-cineole in two doses (6.25 and 12.5 mg/kg) with influenza vaccine was investigated in a mouse model in order to see whether it could provide cross-protection against influenza virus infection.

METHODS

The mice were intranasally immunized three times at the 0, 7 and 14 day with vaccine containing 0.2 µg hemagglutinin (HA) and/or without 1,8-cineole. Seven days after the 3rd immunization dose, the mice were infected with 50 µl of 15 LD₅₀ (50% mouse lethal dose) influenza virus A/FM/1/47 (H1N1). On day 6 post-infection, 10 mice per group were sacrificed to collect samples, to take the body weight and lung, and detect the viral load, pathological changes in the lungs and antibody, etc. The collected samples included blood serum and nasal lavage fluids. In addition, the survival experiments were carried out  to investigate the survival of mice.

RESULTS

Mice i.n. inoculated with influenza vaccine and 12.5 mg/kg 1,8-cineole increased the production of influenza-specific serum immunoglobulin (Ig) G2a antibodies, stimulated mucosal secretive IgA (s-IgA) responses at the nasal cavity, improved the expression of respiratory tract intraepithelial lymphocytes (IELs) in the upper respiratory tract, and promoted dendritic cell (DC) maturation and the expression of co-stimulatory molecules cluster of differentiation (CD)40, CD80 and CD86 in peripheral blood. Importantly, mice that had received 1,8-cineole-supplemented influenza vaccine showed longer survival time, milder inflammation, less weight loss and mortality rate and lower lung index and viral titers compared to that of mice immunized a non-1,8-cineole-adjuvanted split vaccine. Thus, i.n. immunization with 1,8-cineole-adjuvanted vaccine induces a superior cross-protective immunity against infection with influenza than an inactivated vaccine only.

CONCLUSION

These results suggest that 1,8-cineole (12.5 mg/kg) has a cross-protection against influenza virus, co-administered with inactivated influenza viral antigen in a mouse model.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

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.

Mosaic gene network modelling identified new regulatory mechanisms in HCV infection

Modelling of gene networks is widely used in systems biology to study the functioning of complex biological systems. Most of the existing mathematical modelling techniques are useful for analysis of well-studied biological processes, for which information on rates of reactions is available. However, complex biological processes such as those determining the phenotypic traits of organisms or pathological disease processes, including pathogen-host interactions, involve complicated cross-talk between interacting networks. Furthermore, the intrinsic details of the interactions between these networks are often missing. In this study, we developed an approach, which we call mosaic network modelling, that allows the combination of independent mathematical models of gene regulatory networks and, thereby, description of complex biological systems. The advantage of this approach is that it allows us to generate the integrated model despite the fact that information on molecular interactions between parts of the model (so-called mosaic fragments) might be missing. To generate a mosaic mathematical model, we used control theory and mathematical models, written in the form of a system of ordinary differential equations (ODEs). In the present study, we investigated the efficiency of this method in modelling the dynamics of more than 10,000 simulated mosaic regulatory networks consisting of two pieces. Analysis revealed that this approach was highly efficient, as the mean deviation of the dynamics of mosaic network elements from the behaviour of the initial parts of the model was less than 10%. It turned out that for construction of the control functional, data on perturbation of one or two vertices of the mosaic piece are sufficient. Further, we used the developed method to construct a mosaic gene regulatory network including hepatitis C virus (HCV) as the first piece and the tumour necrosis factor (TNF)-induced apoptosis and NF-κB induction pathways as the second piece. Thus, the mosaic model integrates the model of HCV subgenomic replicon replication with the model of TNF-induced apoptosis and NF-κB induction. Analysis of the mosaic model revealed that the regulation of TNF-induced signaling by the HCV network is crucially dependent on the RIP1, TRADD, TRAF2, FADD, IKK, IκBα, c-FLIP, and BAR genes. Overall, the developed mosaic gene network modelling approach demonstrated good predictive power and allowed the prediction of new regulatory nodes in HCV action on apoptosis and the NF-κB pathway. Those theoretical predictions could be a basis for further experimental verification.

Fluorescence-Activated Cell Sorting-Based Analysis Reveals an Asymmetric Induction of Interferon-Stimulated Genes in Response to Seasonal Influenza A Virus

Influenza A virus (IAV) infection provokes an antiviral response involving the expression of type I and III interferons (IFN) and IFN-stimulated genes (ISGs) in infected cell cultures. However, the spatiotemporal dynamics of the IFN reaction are incompletely understood, as previous studies investigated mainly the population responses of virus-infected cultures, although substantial cell-to-cell variability has been documented. We devised a fluorescence-activated cell sorting-based assay to simultaneously quantify expression of viral antigens and ISGs, such as ISG15, MxA, and IFIT1, in IAV-infected cell cultures at the single-cell level. This approach revealed that seasonal IAV triggers an unexpected asymmetric response, as the major cell populations expressed either viral antigen or ISG, but rarely both. Further investigations identified a role of the viral NS1 protein in blocking ISG expression in infected cells, which surprisingly did not reduce paracrine IFN signaling to noninfected cells. Interestingly, viral ISG control was impaired in cultures infected with avian-origin IAV, including the H7N9 virus from eastern China. This phenotype was traced back to polymorphic NS1 amino acids known to be important for stable binding of the polyadenylation factor CPSF30 and concomitant suppression of host cell gene expression. Most significantly, mutation of two amino acids within the CPSF30 attachment site of NS1 from seasonal IAV diminished the strict control of ISG expression in infected cells and substantially attenuated virus replication. In conclusion, our approach revealed an asymmetric, NS1-dependent ISG induction in cultures infected with seasonal IAV, which appears to be essential for efficient virus propagation.

IMPORTANCE

Interferons are expressed by infected cells in response to IAV infection and play important roles in the antiviral immune response by inducing hundreds of interferon-stimulated genes (ISGs). Unlike many previous studies, we investigated the ISG response at the single-cell level, enabling novel insights into this virus-host interaction. Hence, cell cultures infected with seasonal IAV displayed an asymmetric ISG induction that was confined almost exclusively to noninfected cells. In comparison, ISG expression was observed in larger cell populations infected with avian-origin IAV, suggesting a more resolute antiviral response to these strains. Strict control of ISG expression by seasonal IAV was explained by the binding of the viral NS1 protein to the polyadenylation factor CPSF30, which reduces host cell gene expression. Mutational disruption of CPSF30 binding within NS1 concomitantly attenuated ISG control and replication of seasonal IAV, illustrating the importance of maintaining an asymmetric ISG response for efficient virus propagation.