Modeling the acute and chronic phases of Theiler murine encephalomyelitis virus infection

Protective and stochastic correlation between infectious diseases and autoimmune disorders

A priori, early exposure to a wide range of bacteria, viruses, and parasites appears to fortify and regulate the immune system, potentially reducing the risk of autoimmune diseases. However, improving hygiene conditions in numerous societies has led to a reduction in these microbial exposures, which, according to certain theories, could contribute to an increase in autoimmune diseases. Indeed, molecular mimicry is a key factor triggering immune system reactions; while it seeks pathogens, it can bind to self-molecules, leading to autoimmune diseases associated with microbial infections. On the other hand, a hygiene-based approach aimed at reducing the load of infectious agents through better personal hygiene can be beneficial for such pathologies. This review sheds light on how the evolution of the innate immune system, following the evolution of molecular patterns associated with microbes, contributes to our protection but may also trigger autoimmune diseases linked to microbes. Furthermore, it addresses how hygiene conditions shield us against autoimmune diseases related to microbes but may lead to autoimmune pathologies not associated with microbes.

Modeling suggests that virion production cycles within individual cells is key to understanding acute hepatitis B virus infection kinetics

Hepatitis B virus (HBV) infection kinetics in immunodeficient mice reconstituted with humanized livers from inoculation to steady state is highly dynamic despite the absence of an adaptive immune response. To recapitulate the multiphasic viral kinetic patterns, we developed an agent-based model that includes intracellular virion production cycles reflecting the cyclic nature of each individual virus lifecycle. The model fits the data well predicting an increase in production cycles initially starting with a long production cycle of 1 virion per 20 hours that gradually reaches 1 virion per hour after approximately 3-4 days before virion production increases dramatically to reach to a steady state rate of 4 virions per hour per cell. Together, modeling suggests that it is the cyclic nature of the virus lifecycle combined with an initial slow but increasing rate of HBV production from each cell that plays a role in generating the observed multiphasic HBV kinetic patterns in humanized mice.

CD28-signaling can be partially compensated in CD28-knockout mice but is essential for virus elimination in a murine model of multiple sclerosis

The intracerebral infection of mice with Theiler’s murine encephalomyelitis virus (TMEV) represents a well-established animal model for multiple sclerosis (MS). Because CD28 is the main co-stimulatory molecule for the activation of T cells, we wanted to investigate its impact on the course of the virus infection as well as on a potential development of autoimmunity as seen in susceptible mouse strains for TMEV. In the present study, 5 weeks old mice on a C57BL/6 background with conventional or tamoxifen-induced, conditional CD28-knockout were infected intracerebrally with TMEV-BeAn. In the acute phase at 14 days post TMEV-infection (dpi), both CD28-knockout strains showed virus spread within the central nervous system (CNS) as an uncommon finding in C57BL/6 mice, accompanied by histopathological changes such as reduced microglial activation. In addition, the conditional, tamoxifen-induced CD28-knockout was associated with acute clinical deterioration and weight loss, which limited the observation period for this mouse strain to 14 dpi. In the chronic phase (42 and 147 dpi) of TMEV-infection, surprisingly only 33% of conventional CD28-knockout mice showed chronic TMEV-infection with loss of motor function concomitant with increased spinal cord inflammation, characterized by T- and B cell infiltration, microglial activation and astrogliosis at 33-42 dpi. Therefore, the clinical outcome largely depends on the time point of the CD28-knockout during development of the immune system. Whereas a fatal clinical outcome can already be observed in the early phase during TMEV-infection for conditional, tamoxifen-induced CD28-knockout mice, only one third of conventional CD28-knockout mice develop clinical symptoms later, accompanied by ongoing inflammation and an inability to clear the virus. However, the development of autoimmunity could not be observed in this C57BL/6 TMEV model irrespective of the time point of CD28 deletion.

Epstein-Barr Virus (EBV) Epithelial Associated Malignancies: Exploring Pathologies and Current Treatments

Epstein-Barr virus (EBV) is one of eight known herpesviruses with the potential to infect humans. Globally, it is estimated that between 90-95% of the population has been infected with EBV. EBV is an oncogenic virus that has been strongly linked to various epithelial malignancies such as nasopharyngeal and gastric cancer. Recent evidence suggests a link between EBV and breast cancer. Additionally, there are other, rarer cancers with weaker evidence linking them to EBV. In this review, we discuss the currently known epithelial malignancies associated with EBV. Additionally, we discuss and establish which treatments and therapies are most recommended for each cancer associated with EBV.

Serum Cytokines Predict Neurological Damage in Genetically Diverse Mouse Models

Viral infections contribute to neurological and immunological dysfunction driven by complex genetic networks. Theiler's murine encephalomyelitis virus (TMEV) causes neurological dysfunction in mice and can model human outcomes to viral infections. Here, we used genetically distinct mice from five Collaborative Cross mouse strains and C57BL/6J to demonstrate how TMEV-induced immune responses in serum may predict neurological outcomes in acute infection. To test the hypothesis that serum cytokine levels can provide biomarkers for phenotypic outcomes of acute disease, we compared cytokine levels at pre-injection, 4 days post-injection (d.p.i.), and 14 d.p.i. Each strain produced unique baseline cytokine levels and had distinct immune responses to the injection procedure itself. Thus, we eliminated the baseline responses to the injection procedure itself and identified cytokines and chemokines induced specifically by TMEV infection. Then, we identified strain-specific longitudinal cytokine profiles in serum during acute disease. Using stepwise regression analysis, we identified serum immune markers predictive for TMEV-induced neurological phenotypes of the acute phase, e.g., IL-9 for limb paralysis; and TNF-α, IL-1β, and MIP-1β for limb weakness. These findings indicate how temporal differences in immune responses are influenced by host genetic background and demonstrate the potential of serum biomarkers to track the neurological effects of viral infection.

Advances in Parameter Estimation and Learning from Data for Mathematical Models of Hepatitis C Viral Kinetics

Mathematical models, some of which incorporate both intracellular and extracellular hepatitis C viral kinetics, have been advanced in recent years for studying HCV–host dynamics, antivirals mode of action, and their efficacy. The standard ordinary differential equation (ODE) hepatitis C virus (HCV) kinetic model keeps track of uninfected cells, infected cells, and free virus. In multiscale models, a fourth partial differential equation (PDE) accounts for the intracellular viral RNA (vRNA) kinetics in an infected cell. The PDE multiscale model is substantially more difficult to solve compared to the standard ODE model, with governing differential equations that are stiff. In previous contributions, we developed and implemented stable and efficient numerical methods for the multiscale model for both the solution of the model equations and parameter estimation. In this contribution, we perform sensitivity analysis on model parameters to gain insight into important properties and to ensure our numerical methods can be safely used for HCV viral dynamic simulations. Furthermore, we generate in-silico patients using the multiscale models to perform machine learning from the data, which enables us to remove HCV measurements on certain days and still be able to estimate meaningful observations with a sufficiently small error.

A Parameter Estimation Method for Multiscale Models of Hepatitis C Virus Dynamics

Mathematical models that are based on differential equations require detailed knowledge about the parameters that are included in the equations. Some of the parameters can be measured experimentally while others need to be estimated. When the models become more sophisticated, such as in the case of multiscale models of hepatitis C virus dynamics that deal with partial differential equations (PDEs), several strategies can be tried. It is possible to use parameter estimation on an analytical approximation of the solution to the multiscale model equations, namely the long-term approximation, but this limits the scope of the parameter estimation method used and a long-term approximation needs to be derived for each model. It is possible to transform the PDE multiscale model to a system of ODEs, but this has an effect on the model parameters themselves and the transformation can become problematic for some models. Finally, it is possible to use numerical solutions for the multiscale model and then use canned methods for the parameter estimation, but the latter is making the user dependent on a black box without having full control over the method. The strategy developed here is to start by working directly on the multiscale model equations for preparing them toward the parameter estimation method that is fully coded and controlled by the user. It can also be adapted to multiscale models of other viruses. The new method is described, and illustrations are provided using a user-friendly simulator that incorporates the method.

Numerical schemes for solving and optimizing multiscale models with age of hepatitis C virus dynamics

Age-structured PDE models have been developed to study viral infection and treatment. However, they are notoriously difficult to solve. Here, we investigate the numerical solutions of an age-based multiscale model of hepatitis C virus (HCV) dynamics during antiviral therapy and compare them with an analytical approximation, namely its long-term approximation. First, starting from a simple yet flexible numerical solution that also considers an integral approximated over previous iterations, we show that the long-term approximation is an underestimate of the PDE model solution as expected since some infection events are being ignored. We then argue for the importance of having a numerical solution that takes into account previous iterations for the associated integral, making problematic the use of canned solvers. Second, we demonstrate that the governing differential equations are stiff and the stability of the numerical scheme should be considered. Third, we show that considerable gain in efficiency can be achieved by using adaptive stepsize methods over fixed stepsize methods for simulating realistic scenarios when solving multiscale models numerically. Finally, we compare between several numerical schemes for the solution of the equations and demonstrate the use of a numerical optimization scheme for the parameter estimation performed directly from the equations.

Neuronal CCL2 expression drives inflammatory monocyte infiltration into the brain during acute virus infection

Background

Viral encephalitis is a dangerous compromise between the need to robustly clear pathogen from the brain and the need to protect neurons from bystander injury. Theiler’s murine encephalomyelitis virus (TMEV) infection of C57Bl/6 mice is a model of viral encephalitis in which the compromise results in hippocampal damage and permanent neurological sequelae. We previously identified brain-infiltrating inflammatory monocytes as the primary driver of this hippocampal pathology, but the mechanisms involved in recruiting these cells to the brain were unclear.

Methods

Chemokine expression levels in the hippocampus were assessed by microarray, ELISA, RT-PCR, and immunofluorescence. Monocyte infiltration during acute TMEV infection was measured by flow cytometry. CCL2 levels were manipulated by immunodepletion and by specific removal from neurons in mice generated by crossing a line expressing the Cre recombinase behind the synapsin promoter to animals with floxed CCL2.

Results

Inoculation of the brain with TMEV induced hippocampal production of the proinflammatory chemokine CCL2 that peaked at 6 h postinfection, whereas inoculation with UV-inactivated TMEV did not elicit this response. Immunofluorescence revealed that hippocampal neurons expressed high levels of CCL2 at this timepoint. Genetic deletion of CCR2 and systemic immunodepletion of CCL2 abrogated or blunted the infiltration of inflammatory monocytes into the brain during acute infection. Specific genetic deletion of CCL2 from neurons reduced serum and hippocampal CCL2 levels and inhibited inflammatory monocyte infiltration into the brain.

Conclusions

We conclude that intracranial inoculation with infectious TMEV rapidly induces the expression of CCL2 in neurons, and this cellular source is necessary for CCR2-dependent infiltration of inflammatory monocytes into the brain during the most acute stage of encephalitis. These findings highlight a unique role for neuronal production of chemokines in the initiation of leukocytic infiltration into the infected central nervous system.

A Robust and Efficient Numerical Method for RNA-Mediated Viral Dynamics

The multiscale model of hepatitis C virus (HCV) dynamics, which includes intracellular viral RNA (vRNA) replication, has been formulated in recent years in order to provide a new conceptual framework for understanding the mechanism of action of a variety of agents for the treatment of HCV. We present a robust and efficient numerical method that belongs to the family of adaptive stepsize methods and is implicit, a Rosenbrock type method that is highly suited to solve this problem. We provide a Graphical User Interface that applies this method and is useful for simulating viral dynamics during treatment with anti-HCV agents that act against HCV on the molecular level.

Models of Viral Population Dynamics

Models of viral population dynamics have contributed enormously to our understanding of the pathogenesis and transmission of several infectious diseases, the coevolutionary dynamics of viruses and their hosts, the mechanisms of action of drugs, and the effectiveness of interventions. In this chapter, we review major advances in the modeling of the population dynamics of the human immunodeficiency virus (HIV) and briefly discuss adaptations to other viruses.

Viral kinetic modeling: state of the art

Viral kinetic (VK) modeling has led to increased understanding of the within host dynamics of viral infections and the effects of therapy. Here we review recent developments in the modeling of viral infection kinetics with emphasis on two infectious diseases: hepatitis C and influenza. We review how VK modeling has evolved from simple models of viral infections treated with a drug or drug cocktail with an assumed constant effectiveness to models that incorporate drug pharmacokinetics and pharmacodynamics, as well as phenomenological models that simply assume drugs have time varying-effectiveness. We also discuss multiscale models that include intracellular events in viral replication, models of drug-resistance, models that include innate and adaptive immune responses and models that incorporate cell-to-cell spread of infection. Overall, VK modeling has provided new insights into the understanding of the disease progression and the modes of action of several drugs. We expect that VK modeling will be increasingly used in the coming years to optimize drug regimens in order to improve therapeutic outcomes and treatment tolerability for infectious diseases.