Infection dynamics and virus-induced apoptosis in cell culture-based influenza vaccine production—Flow cytometry and mathematical modeling

A mathematical model simulating the adaptive immune response in various vaccines and vaccination strategies

Vaccination has been widely recognized as an effective measure for preventing infectious diseases. To facilitate quantitative research into the activation of adaptive immune responses in the human body by vaccines, it is important to develop an appropriate mathematical model, which can provide valuable guidance for vaccine development. In this study, we constructed a novel mathematical model to simulate the dynamics of antibody levels following vaccination, based on principles from immunology. Our model offers a concise and accurate representation of the kinetics of antibody response. We conducted a comparative analysis of antibody dynamics within the body after administering several common vaccines, including traditional inactivated vaccines, mRNA vaccines, and future attenuated vaccines based on defective interfering viral particles (DVG). Our findings suggest that booster shots play a crucial role in enhancing Immunoglobulin G (IgG) antibody levels, and we provide a detailed discussion on the advantages and disadvantages of different vaccine types. From a mathematical standpoint, our model proposes four essential approaches to guide vaccine design: enhancing antigenic T-cell immunogenicity, directing the production of high-affinity antibodies, reducing the rate of IgG decay, and lowering the peak level of vaccine antigen-antibody complexes. Our study contributes to the understanding of vaccine design and its application by explaining various phenomena and providing guidance in comprehending the interactions between antibodies and antigens during the immune process.

Semi-infectious particles contribute substantially to influenza virus within-host dynamics when infection is dominated by spatial structure

Influenza is an ribonucleic acid virus with a genome that comprises eight segments. Experiments show that the vast majority of virions fail to express one or more gene segments and thus cannot cause a productive infection on their own. These particles, called semi-infectious particles (SIPs), can induce virion production through complementation when multiple SIPs are present in an infected cell. Previous within-host influenza models did not explicitly consider SIPs and largely ignore the potential effects of coinfection during virus infection. Here, we constructed and analyzed two distinct models explicitly keeping track of SIPs and coinfection: one without spatial structure and the other implicitly considering spatial structure. While the model without spatial structure fails to reproduce key aspects of within-host influenza virus dynamics, we found that the model implicitly considering the spatial structure of the infection process makes predictions that are consistent with biological observations, highlighting the crucial role that spatial structure plays during an influenza infection. This model predicts two phases of viral growth prior to the viral peak: a first phase driven by fully infectious particles at the initiation of infection followed by a second phase largely driven by coinfections of fully infectious particles and SIPs. Fitting this model to two sets of data, we show that SIPs can contribute substantially to viral load during infection. Overall, the model provides a new interpretation of the in vivo exponential viral growth observed in experiments and a mechanistic explanation for why the production of large numbers of SIPs does not strongly impede viral growth. Being simple and predictive, our model framework serves as a useful tool to understand coinfection dynamics in spatially structured acute viral infections.

Mechanistic modeling of viral particle production

Viral systems such as wild-type viruses, viral vectors, and virus-like particles are essential components of modern biotechnology and medicine. Despite their importance, the commercial-scale production of viral systems remains highly inefficient for multiple reasons. Computational strategies are a promising avenue for improving process development, optimization, and control, but require a mathematical description of the system. This article reviews mechanistic modeling strategies for the production of viral particles, both at the cellular and bioreactor scales. In many cases, techniques and models from adjacent fields such as epidemiology and wild-type viral infection kinetics can be adapted to construct a suitable process model. These process models can then be employed for various purposes such as in-silico testing of novel process operating strategies and/or advanced process control.

Heterogeneity of Rift Valley fever virus transmission potential across livestock hosts, quantified through a model-based analysis of host viral load and vector infection

Quantifying the variation of pathogens’ life history traits in multiple host systems is crucial to understand their transmission dynamics. It is particularly important for arthropod-borne viruses (arboviruses), which are prone to infecting several species of vertebrate hosts. Here, we focus on how host-pathogen interactions determine the ability of host species to transmit a virus to susceptible vectors upon a potentially infectious contact. Rift Valley fever (RVF) is a viral, vector-borne, zoonotic disease, chosen as a case study. The relative contributions of livestock species to RVFV transmission has not been previously quantified. To estimate their potential to transmit the virus over the course of their infection, we 1) fitted a within-host model to viral RNA and infectious virus measures, obtained daily from infected lambs, calves, and young goats, 2) estimated the relationship between vertebrate host infectious titers and probability to infect mosquitoes, and 3) estimated the net infectiousness of each host species over the duration of their infectious periods, taking into account different survival outcomes for lambs. Our results indicate that the efficiency of viral replication, along with the lifespan of infectious particles, could be sources of heterogeneity between hosts. Given available data on RVFV competent vectors, we found that, for similar infectious titers, infection rates in the Aedes genus were on average higher than in the Culex genus. Consequently, for Aedes-mediated infections, we estimated the net infectiousness of lambs to be 2.93 (median) and 3.65 times higher than that of calves and goats, respectively. In lambs, we estimated the overall infectiousness to be 1.93 times higher in individuals which eventually died from the infection than in those recovering. Beyond infectiousness, the relative contributions of host species to transmission depend on local ecological factors, including relative abundances and vector host-feeding preferences. Quantifying these contributions will ultimately help design efficient, targeted, surveillance and vaccination strategies.

Viruses spread by mosquitoes present a major threat to animal and public health worldwide. When these pathogenic viruses can infect multiple species, controlling their spread becomes difficult. Rift Valley fever virus (RVFV) is such a virus. It spreads predominantly among ruminant livestock but can also spill over and cause severe disease in humans. Understanding which of these ruminant species are most important for the transmission of RVFV can help for effective control. One piece of this puzzle is to assess how effective infected animals are at transmitting RVFV to mosquitoes. To answer this question, we combine mathematical models with observations from experimental infections in cattle, sheep, and goats, and model changes in viremia over time within individuals. We then quantify the relationship between hosts’ viremia and the probability to infect mosquitoes. In combining these two analyses, we estimate the overall transmission potential of sheep, when in contact with mosquitoes, to be 3 to 5 times higher than that of goats and cattle. Further, sheep that experience a lethal infection have an even larger overall transmission potential. Once applied at the level of populations, with setting-specific herd composition and exposure to mosquitoes, these results will help unravel species’ role in RVF outbreaks.

Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model

Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.

Iterative community-driven development of a SARS-CoV-2 tissue simulator

The 2019 novel coronavirus, SARS-CoV-2, is a pathogen of critical significance to international public health. Knowledge of the interplay between molecular-scale virus-receptor interactions, single-cell viral replication, intracellular-scale viral transport, and emergent tissue-scale viral propagation is limited. Moreover, little is known about immune system-virus-tissue interactions and how these can result in low-level (asymptomatic) infections in some cases and acute respiratory distress syndrome (ARDS) in others, particularly with respect to presentation in different age groups or pre-existing inflammatory risk factors. Given the nonlinear interactions within and among each of these processes, multiscale simulation models can shed light on the emergent dynamics that lead to divergent outcomes, identify actionable "choke points" for pharmacologic interventions, screen potential therapies, and identify potential biomarkers that differentiate patient outcomes. Given the complexity of the problem and the acute need for an actionable model to guide therapy discovery and optimization, we introduce and iteratively refine a prototype of a multiscale model of SARS-CoV-2 dynamics in lung tissue. The first prototype model was built and shared internationally as open source code and an online interactive model in under 12 hours, and community domain expertise is driving regular refinements. In a sustained community effort, this consortium is integrating data and expertise across virology, immunology, mathematical biology, quantitative systems physiology, cloud and high performance computing, and other domains to accelerate our response to this critical threat to international health. More broadly, this effort is creating a reusable, modular framework for studying viral replication and immune response in tissues, which can also potentially be adapted to related problems in immunology and immunotherapy.

Soft Computing Paradigms to Find the Numerical Solutions of a Nonlinear Influenza Disease Model

The aim of this work is to present the numerical results of the influenza disease nonlinear system using the feed forward artificial neural networks (ANNs) along with the optimization of the combination of global and local search schemes. The genetic algorithm (GA) and active-set method (ASM), i.e., GA-ASM, are implemented as global and local search schemes. The mathematical nonlinear influenza disease system is dependent of four classes, susceptible S(u), infected I(u), recovered R(u) and cross-immune individuals C(u). For the solutions of these classes based on influenza disease system, the design of an objective function is presented using these differential system equations and its corresponding initial conditions. The optimization of this objective function is using the hybrid computing combination of GA-ASM for solving all classes of the influenza disease nonlinear system. The obtained numerical results will be compared by the Adams numerical results to check the authenticity of the designed ANN-GA-ASM. In addition, the designed approach through statistical based operators shows the consistency and stability for solving the influenza disease nonlinear system.

Quantification of Ebola virus replication kinetics in vitro

Mathematical modelling has successfully been used to provide quantitative descriptions of many viral infections, but for the Ebola virus, which requires biosafety level 4 facilities for experimentation, modelling can play a crucial role. Ebola virus modelling efforts have primarily focused on in vivo virus kinetics, e.g., in animal models, to aid the development of antivirals and vaccines. But, thus far, these studies have not yielded a detailed specification of the infection cycle, which could provide a foundational description of the virus kinetics and thus a deeper understanding of their clinical manifestation. Here, we obtain a diverse experimental data set of the Ebola virus infection in vitro, and then make use of Bayesian inference methods to fully identify parameters in a mathematical model of the infection. Our results provide insights into the distribution of time an infected cell spends in the eclipse phase (the period between infection and the start of virus production), as well as the rate at which infectious virions lose infectivity. We suggest how these results can be used in future models to describe co-infection with defective interfering particles, which are an emerging alternative therapeutic.

Production of Defective Interfering Particles of Influenza A Virus in Parallel Continuous Cultures at Two Residence Times-Insights From qPCR Measurements and Viral Dynamics Modeling

Defective interfering particles (DIPs) are a natural byproduct of influenza A virus (IAV) replication. DIPs interfere with the propagation and spread of infectious standard virus (STV), reduce virus yields by competing for viral and cellular resources, and induce antiviral responses. These properties open exciting possibilities for the development of DIP-based antivirals. Exploring options for cell culture-based DIP production, we have established a fully continuous cultivation process, where one bioreactor is used to grow cells that are fed to two bioreactors operated in parallel for virus production. This system allows head-to-head comparisons of STV and DIP replication dynamics over extended time periods. Cultivations were performed at two residence times (RT, 22 and 36 h) using MDCK suspension cells grown in a fully defined medium. For infection, we used a virus seed generated by reverse genetics containing STVs and a known DIP carrying a deletion in segment 1 (delS1(1)). Four days post infection, DIPs achieved maximum concentrations of 7.0·10⁹ virions/mL and 8.4·10⁹ virions/mL for RTs of 22 and 36 h, respectively. Furthermore, oscillations in virus titers with two to three maxima were found for DIP accumulation at 36 and 22 h RT, respectively. To complement the study, a basic mathematical model using simple kinetics and a reasonable number of parameters to describe DIP-propagation in continuous cultures was established. Upon fitting the model individually to each of the two data sets, oscillations in the viral dynamics and the cell population dynamics were described well. Modeling suggests that both STV inactivation and virus degradation have to be taken into account to achieve good agreement of simulations and experimental data for longer RTs. Together, the high DIP titers obtained, and the successful simulation of the experimental data showed that the combination of continuous bioreactors and mathematical models can enable studies regarding DIP dynamics over extended time periods and allow large scale manufacturing of DIP-based antivirals.

Kinetic model for adherent Vero cell growth and poliovirus production in batch bioreactors

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Mathematical model for Vero cell growth in batch bioreactors.

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Mathematical model for poliovirus proliferation on Vero cells.

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Oxygen uptake rate as process analytical technology for simple process monitoring.

The production of poliovirus vaccines in adherent Vero cells in batch bioreactors usually consists of a two-step upstream process: (1) Vero cell cultivation on microcarriers and (2) poliovirus proliferation. In this study we developed a mathematical model to describe this two-step process. We introduced the calculation of the oxygen uptake rate (OUR) and a correction of measurement for the sampling effect in order to ensure the high quality data sets. Besides the data of the OUR, we selected glucose concentration, Vero cell concentration and the virus titer for daily in process control to evaluate the progress of the process. With the selected data sets, the described model can accurately describe poliovirus production by Vero cells. Several other regular in process control samples (e.g. lactate concentration, ammonia concentration, and amino acids concentration) were excluded from the model, simplifying the process control analysis and minimizing labor.

Variable selection and parameter estimation of viral amplification in vero cell cultures dedicated to the production of a dengue vaccine

In this study, a dynamic model of a Vero cell culture-based dengue vaccine production process is developed. The approach consists in describing the process dynamics as functions of the whole living (uninfected and infected) biomass whereas previous works are based on population balance approaches. Based on the assumption that infected biomass evolves faster than other variable, the model can be simplified using a slow-fast approximation. The structural identifiability of the model is analysed using differential algebra as implemented in the software DAISY. The model parameters are inferred from experimental datasets collected from an actual vaccine production process and the model predictive capability is confirmed both in direct and cross-validation. The model prediction shows the impact of the metabolism on virus yield and confirms observations reported in previous studies. Multi-modality and sensitivity analysis complement the parameter estimation, and allow to obtain confidence intervals on both parameters and state estimates. Finally, the model is used to compute the maximum infectious virus yield that can be obtained for different combinations of multiplicity of infection (MOI) and time of infection (TOI). © 2018 American Institute of Chemical Engineers Biotechnol. Prog., 35: e2687, 2019.

A comparison of RSV and influenza in vitro kinetic parameters reveals differences in infecting time

Influenza and respiratory syncytial virus (RSV) cause acute infections of the respiratory tract. Since the viruses both cause illnesses with similar symptoms, researchers often try to apply knowledge gleaned from study of one virus to the other virus. This can be an effective and efficient strategy for understanding viral dynamics or developing treatment strategies, but only if we have a full understanding of the similarities and differences between the two viruses. This study used mathematical modeling to quantitatively compare the viral kinetics of in vitro RSV and influenza virus infections. Specifically, we determined the viral kinetics parameters for RSV A2 and three strains of influenza virus, A/WSN/33 (H1N1), A/Puerto Rico/8/1934 (H1N1), and pandemic H1N1 influenza virus. We found that RSV viral titer increases at a slower rate and reaches its peak value later than influenza virus. Our analysis indicated that the slower increase of RSV viral titer is caused by slower spreading of the virus from one cell to another. These results provide estimates of dynamical differences between influenza virus and RSV and help provide insight into the virus-host interactions that cause observed differences in the time courses of the two illnesses in patients.

Influenza virus intracellular replication dynamics, release kinetics, and particle morphology during propagation in MDCK cells

Influenza viruses are respiratory pathogens and can cause severe disease. The best protection against influenza is provided by annual vaccination. These vaccines are produced in embryonated chicken eggs or using continuous animal cell lines. The latter processes are more flexible and scalable to meet the growing global demand. However, virus production in cell cultures is more expensive. Hence, further research is needed to make these processes more cost-effective and robust. We studied influenza virus replication dynamics to identify factors that limit the virus yield in adherent Madin-Darby canine kidney (MDCK) cells. The cell cycle stage of MDCK cells had no impact during early infection. Yet, our results showed that the influenza virus RNA synthesis levels out already 4 h post infection at a time when viral genome segments are exported from the nucleus. Nevertheless, virus release occurred at a constant rate in the following 16 h. Thereafter, the production of infectious viruses dramatically decreased, but cells continued to produce particles contributing to the hemagglutination (HA) titer. The majority of these particles from the late phase of infection were deformed or broken virus particles as well as large membranous structures decorated with viral surface proteins. These changes in particle characteristics and morphology need to be considered for the optimization of influenza virus production and vaccine purification steps. Moreover, our data suggest that in order to achieve higher cell-specific yields, a prolonged phase of viral RNA synthesis and/or a more efficient release of influenza virus particles is required.

Modeling Influenza Virus Infection: A Roadmap for Influenza Research

Influenza A virus (IAV) infection represents a global threat causing seasonal outbreaks and pandemics. Additionally, secondary bacterial infections, caused mainly by Streptococcus pneumoniae, are one of the main complications and responsible for the enhanced morbidity and mortality associated with IAV infections. In spite of the significant advances in our knowledge of IAV infections, holistic comprehension of the interplay between IAV and the host immune response (IR) remains largely fragmented. During the last decade, mathematical modeling has been instrumental to explain and quantify IAV dynamics. In this paper, we review not only the state of the art of mathematical models of IAV infection but also the methodologies exploited for parameter estimation. We focus on the adaptive IR control of IAV infection and the possible mechanisms that could promote a secondary bacterial coinfection. To exemplify IAV dynamics and identifiability issues, a mathematical model to explain the interactions between adaptive IR and IAV infection is considered. Furthermore, in this paper we propose a roadmap for future influenza research. The development of a mathematical modeling framework with a secondary bacterial coinfection, immunosenescence, host genetic factors and responsiveness to vaccination will be pivotal to advance IAV infection understanding and treatment optimization.

Quantifying relative within-host replication fitness in influenza virus competition experiments

Through accumulation of genetic mutations in the neuraminidase gene, the influenza virus can become resistant to antiviral drugs such as oseltamivir. Quantifying the fitness of emergent drug-resistant influenza viruses, relative to contemporary circulating viruses, provides valuable information to complement existing efforts in the surveillance of drug-resistance. We have previously developed a co-infection based method for the assessment of the relative in vivo fitness of two competing viruses. We have also introduced a model of within-host co-infection dynamics that enables relative within-host fitness to be quantified in these competitive-mixtures experiments. The model assumed that fitness differences between co-infecting strains were mediated by strain-dependent viral production rates from infected epithelial cells. Here we extend the model to enable a more complete exploration of biological processes that may differ between virus pairs and hence generate fitness differences. We use the extended model to re-analyse data from competitive-mixtures experiments that investigated the fitness of oseltamivir-resistant (OR) H1N1 pandemic 2009 ("H1N1pdm09") viruses that emerged during a community outbreak in Australia in 2011. Results are consistent with those of our previous analysis, suggesting that the within-host replication fitness of these OR viruses is not compromised relative to that of related oseltamivir-susceptible (OS) strains, and that potentially permissive mutations in the neuraminidase gene (V241I and N369K) significantly enhance the fitness of H1N1pdm09 OR viruses. These results are consistent regardless of the hypothesised biological cause of fitness difference.

Glycosylation at hemagglutinin Asn-167 protects the H6N1 avian influenza virus from tryptic cleavage at Arg-201 and maintains the viral infectivity

Cleavage of the hemagglutinin (HA) precursor (HA0) by trypsin, which produces the active HA1 and HA2 complex, is a critical step for activating the avian influenza virus (AIV). However, other tryptic cleavage sites on HA might also cause HA degradation and affect the virulence. Otherwise, HA is modified by glycosylation in the host cell. The conjugated glycans on HA may hinder the antigenic epitopes, and thus prevent the virus from being recognized and attacked by the antibodies. In this study, we observed that glycosylation at the Asn-167 (N167) site on the HA1 of the H6N1 AIV strain A/chicken/Taiwan/2838V/00 (2838V) protected Arg-201 (R201) from tryptic cleavage. The 2838V HA protein became sensitive to tryptic cleavage, whereas the glycans at N167 were removed by N-glycosidase F (PNGase-F). Furthermore, the infectivity of 2838V decreased when pretreated with PNGase-F and trypsin. Our observations suggest that the inaccessibility of the R201 residue of HA1 for tryptic cleavage, which is sterically hindered by glycosylation at N167, is a crucial factor for determining the infectivity of the AIV.

Mathematical model of adherent Vero cell growth and poliovirus production in animal component free medium

Sabin-IPV (or sIPV, inactivated polio vaccine based on attenuated Sabin strains) is anticipated to replace the oral polio vaccine for the endgame in polio eradication. Optimization of sIPV production will lead to a better economically feasible vaccine. To assist process optimization, we studied Sabin type 1 poliovirus (PV) infection kinetics on Vero cells in controlled bioreactor vessels. The aim of our study was to develop a descriptive mathematical model able to capture the dynamics of adherent Vero cell growth and PV infection kinetics in animal component free medium. The model predicts the cell density, metabolites profiles, and viral yields in time. We found that the multiplicity of infection (MOI) and the time of infection (TOI) within the investigated range did not affect maximal PV yields, but they did affect the process time. The latter may be reduced by selecting a low TOI and a high MOI. Additionally, we present a correlation between viral titers and D-antigen, a measure for immunogenicity, of Sabin type 1 PV. The developed model is adequate for further studies of the cell metabolism and infection kinetics and may be used to identify control strategies to increase viral productivity. Increased viral yields reduce costs of polio vaccines with large implications on public health.

Multiscale modeling of influenza A virus infection supports the development of direct-acting antivirals

Influenza A viruses are respiratory pathogens that cause seasonal epidemics with up to 500,000 deaths each year. Yet there are currently only two classes of antivirals licensed for treatment and drug-resistant strains are on the rise. A major challenge for the discovery of new anti-influenza agents is the identification of drug targets that efficiently interfere with viral replication. To support this step, we developed a multiscale model of influenza A virus infection which comprises both the intracellular level where the virus synthesizes its proteins, replicates its genome, and assembles new virions and the extracellular level where it spreads to new host cells. This integrated modeling approach recapitulates a wide range of experimental data across both scales including the time course of all three viral RNA species inside an infected cell and the infection dynamics in a cell population. It also allowed us to systematically study how interfering with specific steps of the viral life cycle affects virus production. We find that inhibitors of viral transcription, replication, protein synthesis, nuclear export, and assembly/release are most effective in decreasing virus titers whereas targeting virus entry primarily delays infection. In addition, our results suggest that for some antivirals therapy success strongly depends on the lifespan of infected cells and, thus, on the dynamics of virus-induced apoptosis or the host's immune response. Hence, the proposed model provides a systems-level understanding of influenza A virus infection and therapy as well as an ideal platform to include further levels of complexity toward a comprehensive description of infectious diseases.

Influenza A viruses are contagious pathogens that cause an infection of the respiratory tract in humans, commonly referred to as flu. Each year seasonal epidemics occur with three to five million cases of severe illness and occasionally new strains can create pandemics like the 1918 Spanish Flu with a high mortality among infected individuals. Currently, there are only two classes of antivirals licensed for influenza treatment. Moreover, these compounds start to lose their effectiveness as drug-resistant strains emerge frequently. Here, we use a computational model of infection to reveal the steps of virus replication that are most susceptible to interference by drugs. Our analysis suggests that the enzyme which replicates the viral genetic code, and the processes involved in virus assembly and release are promising targets for new antivirals. We also highlight that some drugs can change the dynamics of virus replication toward a later but more sustained production. Thus, we demonstrate that modeling studies can be a tremendous asset to the development of antiviral drugs and treatment strategies.

Quantification of viral infection dynamics in animal experiments

Analyzing the time-course of several viral infections using mathematical models based on experimental data can provide important quantitative insights regarding infection dynamics. Over the past decade, the importance and significance of mathematical modeling has been gaining recognition among virologists. In the near future, many animal models of human-specific infections and experimental data from high-throughput techniques will become available. This will provide us with the opportunity to develop new quantitative approaches, combining experimental and mathematical analyses. In this paper, we review the various quantitative analyses of viral infections and discuss their possible applications.

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.

Reducing uncertainty in within-host parameter estimates of influenza infection by measuring both infectious and total viral load

For in vivo studies of influenza dynamics where within-host measurements are fit with a mathematical model, infectivity assays (e.g. 50% tissue culture infectious dose; TCID₅₀) are often used to estimate the infectious virion concentration over time. Less frequently, measurements of the total (infectious and non-infectious) viral particle concentration (obtained using real-time reverse transcription-polymerase chain reaction; rRT-PCR) have been used as an alternative to infectivity assays. We investigated the degree to which measuring both infectious (via TCID₅₀) and total (via rRT-PCR) viral load allows within-host model parameters to be estimated with greater consistency and reduced uncertainty, compared with fitting to TCID₅₀ data alone. We applied our models to viral load data from an experimental ferret infection study. Best-fit parameter estimates for the “dual-measurement” model are similar to those from the TCID₅₀-only model, with greater consistency in best-fit estimates across different experiments, as well as reduced uncertainty in some parameter estimates. Our results also highlight how variation in TCID₅₀ assay sensitivity and calibration may hinder model interpretation, as some parameter estimates systematically vary with known uncontrolled variations in the assay. Our techniques may aid in drawing stronger quantitative inferences from in vivo studies of influenza virus dynamics.

Real-time monitoring of influenza virus production kinetics in HEK293 cell cultures

There is an increased interest from the vaccine industry to use mammalian cell cultures for influenza vaccine manufacturing. Therefore, it became important to study the influenza infection mechanism, the viral-host interaction, and the replication kinetics from a bioprocessing stand point to maximize the influenza viral production yield in cell culture. In the present work, influenza replication kinetics was studied in HEK293 cells. Two infection conditions were evaluated, a low (0.01) and a high multiplicity of infection (1.0). Critical time points of the viral production cycle (infection, protein synthesis, viral assembly and budding, viral release, and host-cell death) were identified in small-scale cell cultures. Additionally, cell growth, viability, and viral titers were monitored in the viral production process. The infection state of the cultivated cell population was assessed by influenza immunolabeling throughout the culture period. Influenza virus production kinetics were also on-line monitored by dielectric spectroscopy and successfully correlated to real-time capacitance measures. Overall, this work provided insights into the mechanisms associated with the infection of human HEK293 cell line by the influenza virus and demonstrated, once again, the usefulness of multifrequency scanning permittivity for in-line monitoring and supervision of cell-based viral production processes.

Quantification of the dynamics of enterovirus 71 infection by experimental-mathematical investigation

Enterovirus 71 (EV71) is the causative agent of hand-foot-and-mouth disease and can trigger neurological disorders. EV71 outbreaks are a major public health concern in Asia-Pacific countries. By performing experimental-mathematical investigation, we demonstrate here that viral productivity and transmissibility but not viral cytotoxicity are drastically different among EV71 strains and can be associated with their epidemiological backgrounds. This is the first report demonstrating the dynamics of nonenveloped virus replication in cell culture using mathematical modeling.

Production of influenza H1N1 vaccine from MDCK cells using a novel disposable packed-bed bioreactor

A process for human influenza H1N1 virus vaccine production from Madin-Darby canine kidney (MDCK) cells using a novel packed-bed bioreactor is described in this report. The mini-bioreactor was used to study the relationship between cell density and glucose consumption rate and to optimize the infection parameters of the influenza H1N1 virus (A/New Caledonia/20/99). The MDCK cell culture and virus infection were then monitored in a disposable perfusion bioreactor (AmProtein Current Perfusion Bioreactor) with proportional-integral-derivative control of pH, dissolved O(2) (DO), agitation, and temperature. During 6 days of culture, the total cell number increased from 2.0 × 10(9) to 3.2 × 10(10) cells. The maximum virus titers of 768 hemagglutinin units/100 μL and 7.8 × 10(7) 50 % tissue culture infectious doses/mL were obtained 3 days after infection. These results demonstrate that using a disposable perfusion bioreactor for large-scale cultivation of MDCK cells, which allows for the control of DO, pH, and other conditions, is a convenient and stable platform for industrial-scale production of influenza vaccines.

Identifying viral parameters from in vitro cell cultures

Current in vitro cell culture studies of viral replication deliver detailed time courses of several virological variables, like the amount of virions and the number of target cells, measured over several days of the experiment. Each of these time points solely provides a snap-shot of the virus infection kinetics and is brought about by the complex interplay of target cell infection, and viral production and cell death. It remains a challenge to interpret these data quantitatively and to reveal the kinetics of these underlying processes to understand how the viral infection depends on these kinetic properties. In order to decompose the kinetics of virus infection, we introduce a method to “quantitatively” describe the virus infection in in vitro cell cultures, and discuss the potential of the mathematical based analyses for experimental virology.

Productivity, apoptosis, and infection dynamics of influenza A/PR/8 strains and A/PR/8-based reassortants

In cell culture-based influenza vaccine production significant efforts are directed towards virus seed optimization for maximum yields. Typically, high growth reassortants (HGR) containing backbones of six gene segments of e.g. influenza A/PR/8, are generated from wild type strains. Often, however, HA and TCID₅₀ titres obtained do not meet expectations and further optimization measures are required. Flow cytometry is an invaluable tool to improve our understanding of mechanism related to progress of infection, virus-induced apoptosis, and cell-specific productivity. In this study, we performed infections with two influenza A/PR/8 variants (from NIBSC and RKI) and two A/PR/8-based HGRs (Wisconsin-like and Uruguay-like) to investigate virus replication, apoptosis and virus titres at different multiplicities of infection (MOI 0.0001, 0.1, 3). Flow cytometric analyses showed similar dynamics in the time course of infected and apoptotic cell populations for all four tested strains at MOI 0.0001. Interestingly, higher MOI resulted in an earlier increase of the populations of infected and apoptotic cells and showed strain-specific differences. Infections with A/PR/8 NIBSC resulted in an earlier increase in both cell populations compared to A/PR/8 RKI. The Uruguay-like reassortant showed the earliest increase in the concentration of infected cells and a late induction of apoptosis at all tested MOIs. In contrast, the Wisconsin-like reassortant showed strong apoptosis induction at high MOIs resulting in reduced titres compared to lower MOI. Maximum HA titres were unaffected by changes in the MOI for the two A/PR/8 and the Uruguay-like reassortant. Maximum TCID₅₀ titres, however, decreased with increasing MOI for all strains. Overall, infections at very low MOI (0.0001) resulted not only in similar dynamics concerning progress of infection and induction of apoptosis but also in maximum virus yields. Highest HA titres were obtained for virus seed strains combining a fast progress in infection with a late onset of apoptosis. Therefore, both factors should be considered for the establishment of robust influenza vaccine production processes.

Quantification system for the viral dynamics of a highly pathogenic simian/human immunodeficiency virus based on an in vitro experiment and a mathematical model

Background

Developing a quantitative understanding of viral kinetics is useful for determining the pathogenesis and transmissibility of the virus, predicting the course of disease, and evaluating the effects of antiviral therapy. The availability of data in clinical, animal, and cell culture studies, however, has been quite limited. Many studies of virus infection kinetics have been based solely on measures of total or infectious virus count. Here, we introduce a new mathematical model which tracks both infectious and total viral load, as well as the fraction of infected and uninfected cells within a cell culture, and apply it to analyze time-course data of an SHIV infection in vitro.

Results

We infected HSC-F cells with SHIV-KS661 and measured the concentration of Nef-negative (target) and Nef-positive (infected) HSC-F cells, the total viral load, and the infectious viral load daily for nine days. The experiments were repeated at four different MOIs, and the model was fitted to the full dataset simultaneously. Our analysis allowed us to extract an infected cell half-life of 14.1 h, a half-life of SHIV-KS661 infectiousness of 17.9 h, a virus burst size of 22.1 thousand RNA copies or 0.19 TCID₅₀, and a basic reproductive number of 62.8. Furthermore, we calculated that SHIV-KS661 virus-infected cells produce at least 1 infectious virion for every 350 virions produced.

Conclusions

Our method, combining in vitro experiments and a mathematical model, provides detailed quantitative insights into the kinetics of the SHIV infection which could be used to significantly improve the understanding of SHIV and HIV-1 pathogenesis. The method could also be applied to other viral infections and used to improve the in vitro determination of the effect and efficacy of antiviral compounds.

Real-time monitoring of adherent Vero cell density and apoptosis in bioreactor processes

This study proposes an easy to use in situ device, based on multi-frequency permittivity measurements, to monitor the growth and death of attached Vero cells cultivated on microporous microcarriers, without any cell sampling. Vero cell densities were on-line quantified up to 10(6) cell mL(-1). Some parameters which could potentially impact Vero cell morphological and physiological states were assessed through different culture operating conditions, such as media formulation or medium feed-harvest during cell growth phase. A new method of in situ cell death detection with dielectric spectroscopy was also successfully implemented. Thus, through permittivity frequency scanning, major rises of the apoptotic cell population in bioreactor cultures were detected by monitoring the characteristic frequency of the cell population, f(c), which is one of the culture dielectric parameters. Both cell density quantification and cell apoptosis detection are strategic information in cell-based production processes as they are involved in major events of the process, such as scale-up or choice of the viral infection conditions. This new application of dielectric spectroscopy to adherent cell culture processes makes it a very promising tool for risk-mitigation strategy in industrial processes. Therefore, our results contribute to the development of Process Analytical Technology in cell-based industrial processes.

Impact of host cell line adaptation on quasispecies composition and glycosylation of influenza A virus hemagglutinin

The genome of influenza A viruses is constantly changing (genetic drift) resulting in small, gradual changes in viral proteins. Alterations within antibody recognition sites of the viral membrane glycoproteins hemagglutinin (HA) and neuraminidase (NA) result in an antigenetic drift, which requires the seasonal update of human influenza virus vaccines. Generally, virus adaptation is necessary to obtain sufficiently high virus yields in cell culture-derived vaccine manufacturing. In this study detailed HA N-glycosylation pattern analysis was combined with in-depth pyrosequencing analysis of the virus genomic RNA. Forward and backward adaptation from Madin-Darby Canine Kidney (MDCK) cells to African green monkey kidney (Vero) cells was investigated for two closely related influenza A virus PR/8/34 (H1N1) strains: from the National Institute for Biological Standards and Control (NIBSC) or the Robert Koch Institute (RKI). Furthermore, stability of HA N-glycosylation patterns over ten consecutive passages and different harvest time points is demonstrated. Adaptation to Vero cells finally allowed efficient influenza A virus replication in Vero cells. In contrast, during back-adaptation the virus replicated well from the very beginning. HA N-glycosylation patterns were cell line dependent and stabilized fast within one (NIBSC-derived virus) or two (RKI-derived virus) successive passages during adaptation processes. However, during adaptation new virus variants were detected. These variants carried "rescue" mutations on the genomic level within the HA stem region, which result in amino acid substitutions. These substitutions finally allowed sufficient virus replication in the new host system. According to adaptation pressure the composition of the virus populations varied. In Vero cells a selection for "rescue" variants was characteristic. After back-adaptation to MDCK cells some variants persisted at indifferent frequencies, others slowly diminished and even dropped below the detection limit.

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.

Metabolic and kinetic analyses of influenza production in perfusion HEK293 cell culture

BACKGROUND

Cell culture-based production of influenza vaccine remains an attractive alternative to egg-based production. Short response time and high production yields are the key success factors for the broader adoption of cell culture technology for industrial manufacturing of pandemic and seasonal influenza vaccines. Recently, HEK293SF cells have been successfully used to produce influenza viruses, achieving hemagglutinin (HA) and infectious viral particle (IVP) titers in the highest ranges reported to date. In the same study, it was suggested that beyond 4 × 10(6) cells/mL, viral production was limited by a lack of nutrients or an accumulation of toxic products.

RESULTS

To further improve viral titers at high cell densities, perfusion culture mode was evaluated. Productivities of both perfusion and batch culture modes were compared at an infection cell density of 6 × 10(6) cells/mL. The metabolism, including glycolysis, glutaminolysis and amino acids utilization as well as physiological indicators such as viability and apoptosis were extensively documented for the two modes of culture before and after viral infection to identify potential metabolic limitations. A 3 L bioreactor with a perfusion rate of 0.5 vol/day allowed us to reach maximal titers of 3.3 × 10(11) IVP/mL and 4.0 logHA units/mL, corresponding to a total production of 1.0 × 10(15) IVP and 7.8 logHA units after 3 days post-infection. Overall, perfusion mode titers were higher by almost one order of magnitude over the batch culture mode of production. This improvement was associated with an activation of the cell metabolism as seen by a 1.5-fold and 4-fold higher consumption rates of glucose and glutamine respectively. A shift in the viral production kinetics was also observed leading to an accumulation of more viable cells with a higher specific production and causing an increase in the total volumetric production of infectious influenza particles.

CONCLUSIONS

These results confirm that the HEK293SF cell is an excellent substrate for high yield production of influenza virus. Furthermore, there is great potential in further improving the production yields through better control of the cell culture environment and viral production kinetics. Once accomplished, this cell line can be promoted as an industrial platform for cost-effective manufacturing of the influenza seasonal vaccine as well as for periods of peak demand during pandemics.

Emerging vaccine informatics

Vaccine informatics is an emerging research area that focuses on development and applications of bioinformatics methods that can be used to facilitate every aspect of the preclinical, clinical, and postlicensure vaccine enterprises. Many immunoinformatics algorithms and resources have been developed to predict T- and B-cell immune epitopes for epitope vaccine development and protective immunity analysis. Vaccine protein candidates are predictable in silico from genome sequences using reverse vaccinology. Systematic transcriptomics and proteomics gene expression analyses facilitate rational vaccine design and identification of gene responses that are correlates of protection in vivo. Mathematical simulations have been used to model host-pathogen interactions and improve vaccine production and vaccination protocols. Computational methods have also been used for development of immunization registries or immunization information systems, assessment of vaccine safety and efficacy, and immunization modeling. Computational literature mining and databases effectively process, mine, and store large amounts of vaccine literature and data. Vaccine Ontology (VO) has been initiated to integrate various vaccine data and support automated reasoning.

A review of mathematical models of influenza A infections within a host or cell culture: lessons learned and challenges ahead

Most mathematical models used to study the dynamics of influenza A have thus far focused on the between-host population level, with the aim to inform public health decisions regarding issues such as drug and social distancing intervention strategies, antiviral stockpiling or vaccine distribution. Here, we investigate mathematical modeling of influenza infection spread at a different scale; namely that occurring within an individual host or a cell culture. We review the models that have been developed in the last decades and discuss their contributions to our understanding of the dynamics of influenza infections. We review kinetic parameters (e.g., viral clearance rate, lifespan of infected cells) and values obtained through fitting mathematical models, and contrast them with values obtained directly from experiments. We explore the symbiotic role of mathematical models and experimental assays in improving our quantitative understanding of influenza infection dynamics. We also discuss the challenges in developing better, more comprehensive models for the course of influenza infections within a host or cell culture. Finally, we explain the contributions of such modeling efforts to important public health issues, and suggest future modeling studies that can help to address additional questions relevant to public health.

Effect of 1918 PB1-F2 expression on influenza A virus infection kinetics

Relatively little is known about the viral factors contributing to the lethality of the 1918 pandemic, although its unparalleled virulence was likely due in part to the newly discovered PB1-F2 protein. This protein, while unnecessary for replication, increases apoptosis in monocytes, alters viral polymerase activity in vitro, enhances inflammation and increases secondary pneumonia in vivo. However, the effects the PB1-F2 protein have in vivo remain unclear. To address the mechanisms involved, we intranasally infected groups of mice with either influenza A virus PR8 or a genetically engineered virus that expresses the 1918 PB1-F2 protein on a PR8 background, PR8-PB1-F2(1918). Mice inoculated with PR8 had viral concentrations peaking at 72 hours, while those infected with PR8-PB1-F2(1918) reached peak concentrations earlier, 48 hours. Mice given PR8-PB1-F2(1918) also showed a faster decline in viral loads. We fit a mathematical model to these data to estimate parameter values. The model supports a higher viral production rate per cell and a higher infected cell death rate with the PR8-PB1-F2(1918) virus. We discuss the implications these mechanisms have during an infection with a virus expressing a virulent PB1-F2 on the possibility of a pandemic and on the importance of antiviral treatments.

Influenza A virus is a respiratory pathogen that causes significant morbidity and mortality in infected individuals, particularly during pandemics like the 1918–1919 Spanish Flu pandemic. Recent data suggests that the influenza virus PB1-F2 protein contributes to disease severity. Here, we use data from infected mice together with quantitative analyses to understand how the PB1-F2 protein from the 1918–1919 pandemic strain influences viral kinetics. We find that the rates of virus growth and decay are increased when the 1918 PB1-F2 is present. Our analyses suggest that infection with an influenza virus possessing the 1918 PB1-F2 protein results in a higher rate of viral production from infected cells and a higher rate of infected cell death. These results provide new insights into the mechanisms of PB1-F2 and the virulence and pathogenesis of pandemic strains of influenza.

Influenza A virus infection kinetics: quantitative data and models

Influenza A virus is an important respiratory pathogen that poses a considerable threat to public health each year during seasonal epidemics and even more so when a pandemic strain emerges. Understanding the mechanisms involved in controlling an influenza infection within a host is important and could result in new and effective treatment strategies. Kinetic models of influenza viral growth and decay can summarize data and evaluate the biological parameters governing interactions between the virus and the host. Here we discuss recent viral kinetic models for influenza. We show how these models have been used to provide insight into influenza pathogenesis and treatment, and we highlight the challenges of viral kinetic analysis, including accurate model formulation, estimation of important parameters, and the collection of detailed data sets that measure multiple variables simultaneously. WIREs Syst Biol Med 2011 3 429-445 DOI: 10.1002/wsbm.129

Development and strategies of cell-culture technology for influenza vaccine

Influenza is a pandemic contagious disease and causes human deaths and huge economic destruction of poultry in the world. In order to control and prevent influenza, mainly type A, influenza vaccine for human and poultry were available since the 1940s and 1920s, respectively. In the development of vaccine production, influenza viruses were cultured originally from chicken embryos to anchorage-dependent cell lines, such as MDCK and Vero. The anchorage-independent lines have also been used to produce influenza virus, such as PER.C6 and engineering modified MDCK and Vero. During the process of influenza vaccine production, the common problem faced by all producers is how to improve the titer of influenza virus. This paper focuses on the developments of cell culture for influenza virus vaccine production, limitations of cell culture, and relative strategies for improvement virus yields in cell-culture systems.

The 2009 pandemic H1N1 and triple-reassortant swine H1N1 influenza viruses replicate efficiently but elicit an attenuated inflammatory response in polarized human bronchial epithelial cells

The pandemic H1N1 virus of 2009 (2009 H1N1) produced a spectrum of disease ranging from mild illness to severe illness and death. Respiratory symptoms were frequently associated with virus infection, with relatively high rate of gastrointestinal symptoms reported. To better understand 2009 H1N1 virus pathogenesis in humans, we studied virus and host responses following infection of two cell types: polarized bronchial and pharyngeal epithelial cells, which exhibit many features of the human airway epithelium, and colon epithelial cells to serve as a human intestinal cell model. Selected 2009 H1N1 viruses were compared to both seasonal H1N1 and triple-reassortant swine H1N1 influenza viruses that have circulated among North American pigs since before the 2009 pandemic. All H1N1 viruses replicated productively in airway cells; however, in contrast to seasonal H1N1 virus infection, infection with the 2009 H1N1 and triple-reassortant swine H1N1 viruses resulted in an attenuated inflammatory response, a weaker interferon response, and reduced cell death. Additionally, the H1N1 viruses of swine origin replicated less efficiently at the temperature of the human proximal airways (33°C). We also observed that the 2009 H1N1 viruses replicated to significantly higher titers than seasonal H1N1 virus in polarized colon epithelial cells. These studies reveal that in comparison to seasonal influenza virus, H1N1 viruses of swine origin poorly activate multiple aspects of the human innate response, which may contribute to the virulence of these viruses. In addition, their less efficient replication at human upper airway temperatures has implications for the understanding of pandemic H1N1 virus adaptation to humans.