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Abstract

The cellular adaptive immune response plays a key role in resolving influenza infection. It can provide cross-protection between subtypes of influenza A which share epitopes; thus, the strength of the immune response to a given strain is dependent upon the individual's infection history. We model cross-reactive cellular adaptive immune responses induced by multiple infections, and show how the formation and re-activation of memory T cells explains observed shortening of a second infection when cross-reactivity is present. We include three possible mechanisms which determine the strength of the cross-reactive immune response. Our model of cross-reactivity contributes to understanding how repeated exposures change an individual's immune profile over a lifetime.

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... Naïve CD8 + T cells (E 0 ) initiate proliferation and differentiate into effector cells E 1 on stimulation via antigen-presentation at a rate γ E V/(V + E 50 ), where γ E is the maximal stimulation rate, and E 50 is a half saturation level at which half of the stimulation rate is obtained (as shown in Equation (9)). Effector cells E 1 perform programmed proliferation to E i where i denotes proliferation stages (Equations (10) and (11)) for τ E days, experience through n E stages [39], finally becoming mature effector cytotoxic T lymphocytes (E) at a rate φ E at the final stage. The decay rate of E is δ E , as shown in Equation (12). ...
... Naïve B cells (B 0 ) start to proliferate and differentiate into plasma cells (B 1 ) once stimulated by virus at a rate γ B V/(V + B 50 ), where γ B is the maximal stimulation rate and B 50 is a half-saturation level, as shown in Equation (13). Equations (14) and (15) capture how plasma cells (B 1 ) undergo programmed proliferation through n B stages into B i , where i denotes proliferation stages, for τ B days [39]. Finally, mature plasma cells P (Equation (16)) are produced at a rate φ B and decay at a rate δ p . ...
... In detail, our model has 10 parameters to estimate, and the parameter space is denoted as Φ = (ε 1 , β, δ I , p, δ V , s, δ M , ε 2 , κ M , φ). Upon calibrating the IR model, we fixed all parameters of the adaptive immune responses (e.g., all parameters in Equations (9)-(18)) to previous estimated values in the literature [27,39]. We fixed the parameters because estimating the immunological effects of adaptive immunity is not a focus of this study, and [24] does not provide sufficient data for estimation of these parameters. ...
Article
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MUC1 belongs to the family of cell surface (cs-) mucins. Experimental evidence indicates that its presence reduces in vivo influenza viral infection severity. However, the mechanisms by which MUC1 influences viral dynamics and the host immune response are not yet well understood, limiting our ability to predict the efficacy of potential treatments that target MUC1. To address this limitation, we use available in vivo kinetic data for both virus and macrophage populations in wildtype and MUC1 knockout mice. We apply two mathematical models of within-host influenza dynamics to this data. The models differ in how they categorise the mechanisms of viral control. Both models provide evidence that MUC1 reduces the susceptibility of epithelial cells to influenza virus and regulates macrophage recruitment. Furthermore, we predict and compare some key infection-related quantities between the two mice groups. We find that MUC1 significantly reduces the basic reproduction number of viral replication as well as the number of cumulative macrophages but has little impact on the cumulative viral load. Our analyses suggest that the viral replication rate in the early stages of infection influences the kinetics of the host immune response, with consequences for infection outcomes, such as severity. We also show that MUC1 plays a strong anti-inflammatory role in the regulation of the host immune response. This study improves our understanding of the dynamic role of MUC1 against influenza infection and may support the development of novel antiviral treatments and immunomodulators that target MUC1.
... Viral interference between antigenically related influenza A viruses occurred at short intervals, as well as longer intervals of >1 week [25]. We have developed influenza viral dynamics models that explain these observations in terms of a nonspecific innate immune response [26] and cross-reactive adaptive immune responses [27]. ...
... This pattern is different from that observed in animals infected and then challenged with antigenically unrelated viruses, such as influenza A and B virus, where shedding of the challenge virus was delayed, compared with controls, but not rapidly cleared [25]. The delay of shedding of the challenge virus suggests that the primary infection, 3 days prior, activates innate host antiviral genes and mediators that limit subsequent virus replication and infection, and our extensive mathematical modeling supports this hypothesis [27]. We assessed the cytokine and chemokine mRNA profile in the nasal wash specimens of ferrets following infection with all the influenza B viruses used in this study. ...
... Our data indicate that cross-reactive cellular immunity is induced and can be detected 10 days following infection with influenza B virus, suggesting it can prevent or limit infection with virus of the other influenza B virus lineage. We observed these patterns when 10 and 28 days separated primary infection and challenge, respectively, suggesting that this effect is mediated by T lymphocytes, with further support for this mechanism provided by our modeling analysis [27] and our assessment of cross-protection between influenza A virus subtypes [42] The observation of antigen-specificity in the ELISpot studies also suggests the action of T lymphocytes, rather than natural killer (NK) cells and NKT cells, which also produce IFN-γ [43]. Cross-reactive B lymphocytes (producing neutralizing, nonneutralizing, or anti-NA antibodies) may also contribute to protection as seen on days 10 and 28, but cross-reactive antibodies to the viral HAs were minimal by the HI assay. ...
Article
Background: Two influenza B virus lineages, B/Victoria and B/Yamagata, cocirculate in the human population. While the lineages are serologically distinct, cross-reactive responses to both lineages have been detected. Viral interference describes the situation whereby infection with one virus limits infection and replication of a second virus. We investigated the potential for viral interference between the influenza B virus lineages. Methods: Ferrets were infected and then challenged 3, 10, or 28 days later with pairs of influenza B/Victoria and B/Yamagata viruses. Results: Viral interference occurred at challenge intervals of 3 and 10 days and occasionally at 28 days. At the longer interval, shedding of challenge virus was reduced, and this correlated with cross-reactive interferon γ responses from lymph nodes from virus-infected animals. Viruses from both lineages could prevent or significantly limit subsequent infection with a virus from the other lineage. Coinfections were rare, indicating the potential for reassortment between lineages is limited. Conclusions: These data suggest that innate and cross-reactive immunity mediate viral interference and that this may contribute to the dominance of a specific influenza B virus lineage in any given influenza season. Furthermore, infection with one influenza B virus lineage may be beneficial in protecting against subsequent infection with either influenza B virus lineage.
... Affinity matg serological data is difficult duration for B and T cells takes several weeks 30,31 , meaning that a typical primary infection is mostly resolved by the innate immune system (non-strain-specific cells and antibodies). However, any subsequent exposures to the same or antigenically similar antigens will result in a recall and boost of strain-specific memory cells leading to faster and better adaptive immune response 4,32 . ...
... It should be noted that this model is based on the scientific community's still incomplete understanding of immune correlates of protection against influenza which could explain some discrepancies between simulation and observations. Our model is highly sensitive to humoral and cellular immunity levels against historical strains, aligning with empirical and theoretical studies 4,24,32,62 . In our seasonal simulations, we kept prior immunity constant, focusing on variations in the main circulating subtype and its AgD from the vaccine strain. ...
Article
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Influenza vaccine effectiveness (VE) varies seasonally due to host, virus and vaccine characteristics. To investigate how antigenic matching and dosage impact VE, we developed a mechanistic knowledge-based mathematical model. Immunization with a split vaccine is modeled for exposure to A/H1N1 or A/H3N2 virus strains. The model accounts for cross-reactivity of immune cells elicited during previous immunizations with new antigens. We simulated vaccine effectiveness (sVE) of high dose (HD) versus standard dose (SD) vaccines in the older population, from 2011 to 2022. We find that sVE is highly dependent on antigenic matching and that higher dosage improves immunogenicity, activation and memory formation of immune cells. In alignment with clinical observations, the HD vaccine performs better than the SD vaccine in all simulations, supporting the use of the HD vaccine in the older population. This model could be adapted to predict the impact of alternative virus strain selection on clinical outcomes in future influenza seasons.
... Regarding adaptive responses, antibodies that were produced following 415/11 infection were not able to neutralize the variant 212/13 virus, which confirmed an antigenic distance between both viruses, probably in line with mutations in H1 antigenic sites ( Figure S1), as immunodominant epitopes that are targeted by the humoral adaptive immune response are present on HA surface protein [24]. On the contrary, the same quantities of IFN-Gsecretory cells were obtained, regardless of the strain used to stimulate PBMC from infected animals, indicating that T cells recognized epitopes common to both viruses. ...
... On the contrary, the same quantities of IFN-Gsecretory cells were obtained, regardless of the strain used to stimulate PBMC from infected animals, indicating that T cells recognized epitopes common to both viruses. Such immunodominant epitopes that are linked to cellular responses are typically found in internal viral proteins-matrix 1 (M1), nucleoprotein (NP), polymerase acidic subunit (PA), and polymerase basic subunit 1 (PB1)-which are quite conserved between swine influenza A viruses of same viral origin, allowing for T cells to cross-react with antigenic variants within the same swIAV subtype and, to a lesser extent, with other swIAV subtypes [10,[24][25][26]. In silico comparison of whole genome sequences of 415/11 and 212/13 strains showed mutations on NP (D112E, I232T, E292G), PB1 (V591I), M2 (T11I, R18K, D21G, I28T et I39L), and PB2 (T559N) in the variant strain, at sites that are described as epitopes for T cells (www.iedb.org), ...
Article
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The surveillance of swine influenza A viruses in France revealed the emergence of an antigenic variant following deletions and mutations that are fixed in the HA-encoding gene of the European human-like reassortant swine H1N2 lineage. In this study, we compared the outcomes of the parental (H1huN2) and variant (H1huN2Δ14–147) virus infections in experimentally-inoculated piglets. Moreover, we assessed and compared the protection that was conferred by an inactivated vaccine currently licensed in Europe. Three groups of five unvaccinated or vaccinated piglets were inoculated with H1huN2 or H1huN2Δ14–147 or mock-inoculated, respectively. In unvaccinated piglets, the variant strain induced greater clinical signs than the parental virus, in relation to a higher inflammatory response that involves TNF-α production and a huge afflux of granulocytes into the lung. However, both infections led to similar levels of virus excretion and adaptive (humoral and cellular) immune responses in blood. The vaccinated animals were clinically protected from both infectious challenges and did not exhibit any inflammatory responses, regardless the inoculated virus. However, whereas vaccination prevented virus shedding in H1huN2-infected animals, it did not completely inhibit the multiplication of the variant strain, since live virus particles were detected in nasal secretions that were taken from H1huN2Δ14–147-inoculated vaccinated piglets. This difference in the level of vaccine protection was probably related to the poorer ability of the post-vaccine antibodies to neutralize the variant virus than the parental virus, even though post-vaccine cellular immunity appeared to be equally effective against both viruses. These results suggest that vaccine antigens would potentially need to be updated if this variant becomes established in Europe.
... The model also does not include an immune response, either innate or adaptive. While some of the effect of the immune response can be implicitly modeled by changing parameter values (a larger value of viral clearance can account for the effect of antibodies), more realistic viral dynamics models that include an immune response are available and can be considered (Dobrovolny et al. 2013;Cao and McCaw 2017;Yan et al. 2017). Implications of these simplifications are considered in the discussion. ...
... Finally, our model does not explicitly include an immune response. Many models have been proposed, at least for influenza, that contain various elements of the innate (primarily interferon) response and adaptive (Cytotoxic T lymphocytes and antibodies) response (Yan et al. 2017;Cao and McCaw 2017;Handel et al. 2018;Dobrovolny et al. 2013). While some of the effect of the immune response is implicitly contained in parameter estimates, inclusion of an explicit immune response in the model would allow comparison of reduced immune response and reduced advection as mechanisms causing LRI. ...
Article
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Respiratory syncytial virus can lead to serious lower respiratory infection (LRI), particularly in children and the elderly. LRI can cause longer infections, lingering respiratory problems, and higher incidence of hospitalization. In this paper, we use a simplified ordinary differential equation model of viral dynamics to study the role of transport mechanisms in the occurrence of LRI. Our model uses two compartments to simulate the upper respiratory tract and the lower respiratory tract (LRT) and assumes two distinct types of viral transfer between the two compartments: diffusion and advection. We find that a range of diffusion and advection values lead to long-lasting infections in the LRT, elucidating a possible mechanism for the severe LRI infections observed in humans.
... Lee et al. [10] proposed a more complete within-host model containing dendritic cells, CD4 T cells, B cells, and PC et al. that quantifies the relations between viral replication and adaptive immunity. Ada et al. [16] investigated a within-host model that considers the memory of the adaptive immune responses. We adapt these models to study the effect of the immune responses of B cells and PC, naïve CD8 T cells and effector CD8 T cells, and memory B cells, respectively. ...
Article
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The adaptive immune system has two types of plasma cells (PC), long-lived plasma cells (LLPC) and short-lived plasma cells (SLPC), that differ in their lifespan. In this paper, we propose that LLPC is crucial to the clearance of viral particles in addition to reducing the viral basic reproduction number in secondary infections. We use a sequence of within-host mathematical models to show that, CD8 T cells, SLPC and memory B cells cannot achieve full viral clearance, and the viral load will reach a low positive equilibrium level because of a continuous replenishment of target cells. However, the presence of LLPC is crucial for viral clearance.
... Our model predicted the contribution of macrophages to viral clearance (among all the modelled mechanisms for viral clearance) is small in both HP and LP infections of H1N1 ( Our study has some limitations. Rather than explicitly modelling the dynamics of CD8 + T cells and antibodies [35,48], we used Hill functions to capture their dynamics. We assumed the adaptive immune response dominates infected cell or viral clearance at day 5 post-infection regardless of macrophage dynamics, as shown in [49]. ...
Article
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Epidemiological and clinical evidence indicates that humans infected with the 1918 pandemic H1N1 influenza virus and highly pathogenic avian H5N1 influenza viruses often displayed severe lung pathology. High viral load and extensive infiltration of macrophages are the hallmarks of highly pathogenic (HP) influenza viral infections. However, it remains unclear what biological mechanisms primarily determine the observed difference in the kinetics of viral load and macrophages between HP and low pathogenic (LP) viral infections, and how the mechanistic differences are associated with viral pathogenicity. In this study, we develop a mathematical model of viral dynamics that includes the dynamics of different macrophage populations and interferon. We fit the model to in vivo kinetic data of viral load and macrophage level from BALB/c mice infected with an HP or LP strain of H1N1/H5N1 virus to estimate model parameters using Bayesian inference. Our primary finding is that HP viruses have a higher viral infection rate, a lower interferon production rate and a lower macrophage recruitment rate compared to LP viruses, which are strongly associated with more severe tissue damage (quantified by a higher percentage of epithelial cell loss). We also quantify the relative contribution of macrophages to viral clearance and find that macrophages do not play a dominant role in the direct clearance of free viruses although their role in mediating immune responses such as interferon production is crucial. Our work provides new insight into the mechanisms that convey the observed difference in viral and macrophage kinetics between HP and LP infections and establishes an improved model-fitting framework to enhance the analysis of new data on viral pathogenicity.
... In-host infection dynamics capture the interplay between virus and host. Models describing the dynamics of the immune response [47] in the presence of an infectious disease have been proposed for influenza [96], [141], [239], [246] and generic viral infections [160]. Very recently an immunological description for Covid-19 has been provided [154] and has enabled the characterization of virus-host dynamics for SARS-CoV-2 [2], [110]. ...
Article
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This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to epidemic analysis, such as data science, epidemiology, and systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address three different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, such as Covid-19, as well as their potential application to future epidemics.
... In-host infection dynamics capture the interplay between virus and host. Models describing the dynamics of the immune response [46] in the presence of an infectious disease have been proposed for influenza [92], [135], [232], [239] and generic viral infections [154]. Very recently an immunological description for Covid-19 has been provided [148] and has enabled the characterization of virus-host dynamics for SARS-CoV-2 [2], [105]. ...
Preprint
Full-text available
This survey analyses the role of data-driven methodologies for pandemic modelling and control. We provide a roadmap from the access to epidemiological data sources to the control of epidemic phenomena. We review the available methodologies and discuss the challenges in the development of data-driven strategies to combat the spreading of infectious diseases. Our aim is to bring together several different disciplines required to provide a holistic approach to the epidemic, such as data science, epidemiology, or systems-and-control theory. A 3M-analysis is presented, whose three pillars are: Monitoring, Modelling and Managing. The focus is on the potential of data-driven schemes to address different challenges raised by a pandemic: (i) monitoring the epidemic evolution and assessing the effectiveness of the adopted countermeasures; (ii) modelling and forecasting the spread of the epidemic; (iii) making timely decisions to manage, mitigate and suppress the contagion. For each step of this roadmap, we review consolidated theoretical approaches (including data-driven methodologies that have been shown to be successful in other contexts) and discuss their application to past or present epidemics, as well as their potential application to future epidemics.
... Mathematical models have incorporated the effect of IFN in a variety of ways, starting with Ref. Baccam et al. (2006) where an interferon-dependent model of influenza was used to explain the occurrence of bimodal virus titer curves. More recent models have also examined the effect of IFN on the viral titer either on its own or ( Pawelek et al., 2012;Handel et al., 2010;Saenz et al., 2010;Leviyang and Griva, 2018 ) in conjunction with other immune responses ( Bocharov and Romanyukha, 1994;Hancioglu et al., 2007;Handel et al., 2018;Yan et al., 2017;Price et al., 2015 ). Dehttps://doi.org/10.1016/j.jtbi.2020.110266 ...
Article
The analysis of viral kinetics models is mostly achieved by numerical methods. We present an approach via a Magnus expansion that allows us to give an approximate solution to the interferon-dependent viral infection model of influenza which is compared with numerical results. The time of peak viral load is calculated from the approximation and stays within 10% in the studied range of interferon (IFN) efficacy ϵ ∈ [0, 1000]. We utilize our solution to interpret the effect of varying IFN efficacy, suggesting a competition between virions and interferon that can cause an additional peak in the usually exponential increase in the viral load.
... This assumption is supported by two arguments. Previous modelling studies have shown that memory T-cells are needed in order for a shortened infectious period to be experienced [7,24]. ...
Article
Generally, vaccines are designed to provide protection against infection (susceptibility), disease (symptoms and transmissibility), and/or complications. In a recent study of influenza vaccination, it was observed that vaccinated yet infected individuals experienced increased transmission levels. In this paper, using a mathematical model of infection and transmission, we study the impact of vaccine-modified effects, including susceptibility and infectivity, on important epidemiological outcomes of an immunization program. The balance between vaccine-modified susceptibility, infectivity and recovery needed in preventing an influenza outbreak, or in mitigating the health outcomes of the outbreak is studied using the SIRV-type of disease transmission model. We also investigate the impact of influenza vaccination program on the infection risk of vaccinated and non-vaccinated individuals.
... Within host mathematical models of influenza have previously been used to study many aspects of antiviral treatment including extracting of drug efficacy parameters (Beauchemin et al., 2008;Brown et al., 2011;Beggs and Dobrovolny, 2015;Liao et al., 2017), treatment of severe influenza (Dobrovolny et al., 2010(Dobrovolny et al., , 2011Deecke and Dobrovolny, 2018), emergence of drug resistance (Handel et al., 2007;Perelson et al., 2012;Hur et al., 2013;Canini et al., 2014;Dobrovolny and Beauchemin, 2017;Deecke and Dobrovolny, 2018), and to optimize antiviral treatments (Perelson et al., 2012;Heldt et al., 2013;Hur et al., 2013;Canini et al., 2014). While there are some mathematical models that attempt to model infections in patients by including an immune response (Dobrovolny et al., 2013;Cao and McCaw, 2015;Price et al., 2015;Zarnitsyna et al., 2016;Yan et al., 2017), the lack of appropriate human data for parameterizing and validating these models limits their predictive ability (Dobrovolny et al., 2013;Boianelli et al., 2015). However, simpler mathematical models can successfully reproduce in vitro dynamics (Beauchemin and Handel, 2011;Pinilla et al., 2012), and since mathematical models can quickly and efficiently simulate hundreds of combinations of doses, they are ideally suited as preliminary studies to ascertain whether combination therapy is effective and, if so, which combinations of doses produce the best results. ...
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Combination therapy for influenza can have several benefits, from reducing the emergence of drug resistant virus strains to decreasing the cost of antivirals. However, there are currently only two classes of antivirals approved for use against influenza, limiting the possible combinations that can be considered for treatment. However, new antivirals are being developed that target different parts of the viral replication cycle, and their potential for use in combination therapy should be considered. The role of antiviral mechanism of action in the effectiveness of combination therapy has not yet been systematically investigated to determine whether certain antiviral mechanisms of action pair well in combination. Here, we use a mathematical model of influenza to model combination treatment with antivirals having different mechanisms of action to measure peak viral load, infection duration, and synergy of different drug combinations. We find that antivirals that lower the infection rate and antivirals that increase the duration of the eclipse phase perform poorly in combination with other antivirals.
... Notably, this effect depends on the virus combinations and the order and timing of sequential infections [10,11,26]. We have established complementary influenza viral dynamics models that explain these observations via the innate immune response [27] and cross-reactive adaptive immune responses [28]. ...
Article
Epidemiological studies have observed that the seasonal peak incidence of influenza virus infection is sometimes separate from the peak incidence of human respiratory syncytial virus (hRSV) infection, with the peak incidence of hRSV infection delayed. This is proposed to be due to viral interference, whereby infection with one virus prevents or delays infection with a different virus. We investigated viral interference between hRSV and 2009 pandemic influenza A(H1N1) virus (A[H1N1]pdm09) in the ferret model. Infection with A(H1N1)pdm09 prevented subsequent infection with hRSV. Infection with hRSV reduced morbidity attributed to infection with A(H1N1)pdm09 but not infection, even when an increased inoculum dose of hRSV was used. Notably, infection with A(H1N1)pdm09 induced higher levels of proinflammatory cytokines, chemokines, and immune mediators in the ferret than hRSV. Minimal cross-reactive serological responses or interferon γ-expressing cells were induced by either virus ≥14 days after infection. These data indicate that antigen-independent mechanisms may drive viral interference between unrelated respiratory viruses that can limit subsequent infection or disease.
... The model presented here is relatively comprehensive (see some recent mathematical modelling efforts of CD8 + T cell responses [8,14]), yet it fails to include the role of TCR specificity or that of cytokines. Thus, improvement of the current model will require the consideration of the "signal strength" hypothesis [64] and cross-reactivity [65]. This will be essential to decipher the role of individual T cell clonotypes, with different TCR affinities, in the dynamics of a human immune response. ...
Article
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Complete understanding of the mechanisms regulating the proliferation and differentiation that takes place during human immune CD8+ T cell responses is still lacking. Human clinical data is usually limited to blood cell counts, yet the initiation of these responses occurs in the draining lymph nodes; antigen-specific effector and memory CD8+ T cells generated in the lymph nodes migrate to those tissues where they are required. We use approximate Bayesian computation with deterministic mathematical models of CD8+ T cell populations (naive, central memory, effector memory and effector) and yellow fever virus vaccine data to infer the dynamics of these CD8+ T cell populations in three spatial compartments: draining lymph nodes, circulation and skin. We have made use of the literature to obtain rates of division and death for human CD8+ T cell population subsets and thymic export rates. Under the decreasing potential hypothesis for differentiation during an immune response, we find that, as the number of T cell clonotypes driven to an immune response increases, there is a reduction in the number of divisions required to differentiate from a naive to an effector CD8+ T cell, supporting the "division of labour" hypothesis observed in murine studies. We have also considered the reverse differentiation scenario, the increasing potential hypothesis. The decreasing potential model is better supported by the yellow fever virus vaccine data.
... The viral dynamics model is based on a model we previously published [72]. It incorporates three major components of the immune response -innate, humoral adaptive and cellular adaptive. ...
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Laboratory models are often used to understand the interaction of related pathogens via host immunity. For example, recent experiments where ferrets were exposed to two influenza strains within a short period of time have shown how the effects of cross-immunity vary with the time between exposures and the specific strains used. On the other hand, studies of the workings of different arms of the immune response, and their relative importance, typically use experiments involving a single infection. However, inferring the relative importance of different immune components from this type of data is challenging. Using simulations and mathematical modelling, here we investigate whether the sequential infection experiment design can be used not only to determine immune components contributing to cross-protection, but also to gain insight into the immune response during a single infection. We show that virological data from sequential infection experiments can be used to accurately extract the timing and extent of cross-protection. Moreover, the broad immune components responsible for such cross-protection can be determined. Such data can also be used to infer the timing and strength of some immune components in controlling a primary infection, even in the absence of serological data. By contrast, single infection data cannot be used to reliably recover this information. Hence, sequential infection data enhances our understanding of the mechanisms underlying the control and resolution of infection, and generates new insight into how previous exposure influences the time course of a subsequent infection.
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Vaccination is the most effective means of protecting people from influenza virus infection. The effectiveness of existing vaccines is very limited due to antigenic drift of the influenza virus. Therefore, there is a requirement to develop a universal vaccine that provides broad and long-lasting protection against influenza. CD8+ T-cell response played a vital role in controlling influenza virus infection, reducing viral load, and less clinical syndrome. In this study, we optimized the HA sequences of human seasonal influenza viruses (H1N1, H3N2, Victoria, and Yamagata) by designing multivalent vaccine antigen sets using a mosaic vaccine design strategy and genetic algorithms, and designed an HA mosaic cocktail containing the most potential CTL epitopes of seasonal influenza viruses. We then tested the recombinant mosaic antigen, which has a significant number of potential T-cell epitopes. Results from genetic evolutionary analyses and 3D structural simulations demonstrated its potential to be an effective immunogen. In addition, we have modified an existing neutralizing antibody-based seasonal influenza virus vaccine to include a component that activates cross-protective T cells, which would provide an attractive strategy for improving human protection against seasonal influenza virus drift and mutation and provide an idea for the development of a rationally designed influenza vaccine targeting T lymphocyte immunity.
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Epidemiological and clinical evidence indicates that humans infected with the 1918 pandemic influenza virus and highly pathogenic avian H5N1 influenza viruses often displayed severe lung pathology. High viral load and extensive infiltration of macrophages are the hallmarks of highly pathogenic (HP) influenza viral infections. However, it remains unclear what biological mechanisms primarily determine the observed difference in the kinetics of viral load and macrophages between HP and low pathogenic (LP) viral infections, and how the mechanistic differences are associated with viral pathogenicity. In this study, we develop a mathematical model of viral dynamics that includes the dynamics of different macrophage populations and interferon. We fit the model to in vivo kinetic data of viral load and macrophage level from BALB/c mice infected with an HP or LP strain of H1N1/H5N1 virus using Bayesian inference. Our primary finding is that HP viruses has a higher viral infection rate, a lower interferon production rate and a lower macrophage recruitment rate compared to LP viruses, which are strongly associated with more severe tissue damage (quantified by a higher percentage of epithelial cell loss). We also quantify the relative contribution of macrophages to viral clearance and find that macrophages do not play a dominant role in direct clearance of free virus although their role in mediating immune responses such as interferon production is crucial. Our work provides new insight into the mechanisms that convey the observed difference in viral and macrophage kinetics between HP and LP infections and establishes an improved model fitting framework to enhance the analysis of new data on viral pathogenicity. Author Summary Infections with highly pathogenic (HP) influenza virus (e.g., the 1918 pandemic virus) often lead to serious morbidity and mortality. HP influenza virus infection is characterised by rapid viral growth rate, high viral load and excessive infiltration of macrophages to the lungs. Despite extensive study, we do not yet fully understand what biological processes leading to the observed viral and macrophage dynamics and therefore viral pathogenicity. Experimental studies have previously suggested that bot viral factors (e.g., viral proteins) and host factors (e.g., the host immune response) play a role to enhance viral pathogenicity. Here, we utilise in vivo kinetic data of viral load and macrophages and fit a viral dynamic model the data. Our model allow us to explore the biological mechanisms that contribute to the difference viral and macrophage dynamics between HP and LP infections. This study improves our understanding of the role of interferon on distinguishing immunodynamics between HP and LP infections. Our findings may contribute to the development of next-generation treatment which rely upon an understanding of the host different immunological response to HP influenza viruses.
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The cellular adaptive immune response to influenza has been analyzed through several recent mathematical models. In particular, Zarnitsyna et al. (Front Immunol 7:1-9, 2016) show how central memory CD8+ T cells reach a plateau after repeated infections, and analyze their role in the immune response to further challenges. In this paper, we further investigate the theoretical features of that model by extracting from the infection dynamics a discrete map that describes the build-up of memory cells. Furthermore, we show how the model by Zarnitsyna et al. (Front Immunol 7:1-9, 2016) can be viewed as a fast-scale approximation of a model allowing for recruitment of target epithelial cells. Finally, we analyze which components of the model are essential to understand the progressive build-up of immune memory. This is performed through the analysis of simplified versions of the model that include some components only of immune response. The analysis performed may also provide a theoretical framework for understanding the conditions under which two-dose vaccination strategies can be helpful.
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The efficacy of antibody-dependent vaccines relies on both virus replication and neutralisation, but their quantitative relationship is unknown. To bridge this gap, we investigated the growth of avian and human influenza viruses, and the virus neutralisation by antibodies in vitro. A one-dimensional deterministic model accurately predicted the growth of avian and human influenza in cell culture, and neutralisation of seasonal influenza viruses determined using focus reduction assay. According to this model, at a specific interval of antibody concentration, viruses can either survive or die due to bistability, where small viral inocula are eliminated but not large virus inocula; this is caused by saturated virus neutralization or antibody consumption. Our finding highlights the importance of inoculum size even when virus-antibody pair is well-matched and provides a possible mechanism for high influenza re-infections and low vaccine efficacy, thereby facilitating the formulation of strategies to enhance the efficacy of influenza vaccines and antiviral treatments.
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Mathematical modelling of influenza A virus infection has seen increased use over the last several years. Models applied to both in vitro and in vivo data have provided important new understanding of the kinetics of the virus, the role of different components of the immune response, the importance of non-infectious influenza A virus particles, the issue of drug treatment and resistance, and the interaction mechanisms during bacterial co-infections. We review these contributions by mathematical models, with a focus on studies performed in the last several years. For continued progress, we emphasize robust data and parameter estimation approaches.
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Influenza virus infections are a leading cause of morbidity and mortality worldwide. This is due in part to the continual emergence of new viral variants and to synergistic interactions with other viruses and bacteria. There is a lack of understanding about how host responses work to control the infection and how other pathogens capitalize on the altered immune state. The complexity of multi‐pathogen infections makes dissecting contributing mechanisms, which may be non‐linear and occur on different time scales, challenging. Fortunately, mathematical models have been able to uncover infection control mechanisms, establish regulatory feedbacks, connect mechanisms across time scales, and determine the processes that dictate different disease outcomes. These models have tested existing hypotheses and generated new hypotheses, some of which have been subsequently tested and validated in the laboratory. They have been particularly a key in studying influenza‐bacteria coinfections and will be undoubtedly be useful in examining the interplay between influenza virus and other viruses. Here, I review recent advances in modeling influenza‐related infections, the novel biological insight that has been gained through modeling, the importance of model‐driven experimental design, and future directions of the field.
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Evidence is mounting that influenza virus interacts with other pathogens colonising or infecting the human respiratory tract. Taking into account interactions with other pathogens may be critical to determining the real influenza burden and the full impact of public health policies targeting influenza. This is particularly true for mathematical modelling studies, which have become critical in public health decision-making. Yet models usually focus on influenza virus acquisition and infection alone, thereby making broad oversimplifications of pathogen ecology. Herein, we report evidence of influenza virus interactions with bacteria and viruses and systematically review the modelling studies that have incorporated interactions. Despite the many studies examining possible associations between influenza and Streptococcus pneumoniae, Staphylococcus aureus, Haemophilus influenzae, Neisseria meningitidis, respiratory syncytial virus (RSV), human rhinoviruses, human parainfluenza viruses, etc., very few mathematical models have integrated other pathogens alongside influenza. The notable exception is the pneumococcus–influenza interaction, for which several recent modelling studies demonstrate the power of dynamic modelling as an approach to test biological hypotheses on interaction mechanisms and estimate the strength of those interactions. We explore how different interference mechanisms may lead to unexpected incidence trends and possible misinterpretation, and we illustrate the impact of interactions on public health surveillance using simple transmission models. We demonstrate that the development of multipathogen models is essential to assessing the true public health burden of influenza and that it is needed to help improve planning and evaluation of control measures. Finally, we identify the public health, surveillance, modelling, and biological challenges and propose avenues of research for the coming years.
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Myriad experiments have identified an important role for CD8⁺ T cell response mechanisms in determining recovery from influenza A virus infection. Animal models of influenza infection further implicate multiple elements of the immune response in defining the dynamical characteristics of viral infection. To date, influenza virus models, while capturing particular aspects of the natural infection history, have been unable to reproduce the full gamut of observed viral kinetic behavior in a single coherent framework. Here, we introduce a mathematical model of influenza viral dynamics incorporating innate, humoral, and cellular immune components and explore its properties with a particular emphasis on the role of cellular immunity. Calibrated against a range of murine data, our model is capable of recapitulating observed viral kinetics from a multitude of experiments. Importantly, the model predicts a robust exponential relationship between the level of effector CD8⁺ T cells and recovery time, whereby recovery time rapidly decreases to a fixed minimum recovery time with an increasing level of effector CD8⁺ T cells. We find support for this relationship in recent clinical data from influenza A (H7N9) hospitalized patients. The exponential relationship implies that people with a lower level of naive CD8⁺ T cells may receive significantly more benefit from induction of additional effector CD8⁺ T cells arising from immunological memory, itself established through either previous viral infection or T cell-based vaccines.
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Significance Compromised CD8 ⁺ T-cell immunity is associated with significant morbidity and mortality in the elderly. Whereas the number of naïve CD8 ⁺ T cells declines with age, the drivers of loss and consequences for clonal composition are unclear. We show that aging disproportionately impacts small naïve CD8 ⁺ T-cell populations. For one CD8 ⁺ T-cell population, loss of diversity was minimally attributable to expansion but rather was associated with diminished cell number and selective retention of cells exhibiting markers of heightened self, but not foreign, recognition. Thus, vaccine formulations for the elderly may benefit from targeting naïve antigen-specific populations with relatively high precursor frequency and self-reactivity, and retention of high-quality T cells may be achieved through repeated low-level T-cell receptor stimulation.
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Not every exposure to virus establishes infection in the host; instead, the small amount of initial virus could become extinct due to stochastic events. Different diseases and routes of transmission have a different average number of exposures required to establish an infection. Furthermore, the host immune response and antiviral treatment affect not only the time course of the viral load provided infection occurs, but can prevent infection altogether by increasing the extinction probability. We show that the extinction probability when there is a time-dependent immune response depends on the chosen form of the model-specifically, on the presence or absence of a delay between infection of a cell and production of virus, and the distribution of latent and infectious periods of an infected cell. We hypothesise that experimentally measuring the extinction probability when the virus is introduced at different stages of the immune response, alongside the viral load which is usually measured, will improve parameter estimates and determine the most suitable mathematical form of the model.
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Importance: Both influenza A and B viruses co-circulate in the human population and annual influenza seasons are typically dominated by an influenza A subtype or an influenza B lineage. Surveillance data indicates that the burden of disease is higher in some seasons, yet it is unclear whether this is due to specific virus strains or to other factors, such as cross reactive immunity or clinical definitions of influenza. We directly compared disease and the localised inflammatory response to different seasonal influenza strains, including the 2009 pandemic strain, in healthy naïve ferrets. We have found that disease, as well as the cytokine and chemokine responses, were similar irrespective of the seasonal strain or the location of the infection in the respiratory tract. This suggests that factors other than the immune response to a particular virus (sub)type contribute to the variable impact of influenza infection in a population.
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T cell vaccines against Mycobacterium tuberculosis (Mtb) and other pathogens are based on the principle that memory T cells rapidly generate effector responses upon challenge, leading to pathogen clearance. Despite eliciting a robust memory CD8+ T cell response to the immunodominant Mtb antigen TB10.4 (EsxH), we find the increased frequency of TB10.4-specific CD8+ T cells conferred by vaccination to be short-lived after Mtb challenge. To compare memory and naïve CD8+ T cell function during their response to Mtb, we track their expansions using TB10.4-specific retrogenic CD8+ T cells. We find that the primary (naïve) response outnumbers the secondary (memory) response during Mtb challenge, an effect moderated by increased TCR affinity. To determine whether the expansion of polyclonal memory T cells is restrained following Mtb challenge, we used TCRβ deep sequencing to track TB10.4-specific CD8+ T cells after vaccination and subsequent challenge in intact mice. Successful memory T cells, defined by their clonal expansion after Mtb challenge, express similar CDR3β sequences suggesting TCR selection by antigen. Thus, both TCR-dependent and -independent factors affect the fitness of memory CD8+ responses. The impaired expansion of the majority of memory T cell clonotypes may explain why some TB vaccines have not provided better protection.
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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.
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Influenza is an infectious disease that primarily attacks the respiratory system. Innate immunity provides both a very early defense to influenza virus invasion and an effective control of viral growth. Previous modelling studies of virus-innate immune response interactions have focused on infection with a single virus and, while improving our understanding of viral and immune dynamics, have been unable to effectively evaluate the relative feasibility of different hypothesised mechanisms of antiviral immunity. In recent experiments, we have applied consecutive exposures to different virus strains in a ferret model, and demonstrated that viruses differed in their ability to induce a state of temporary immunity or viral interference capable of modifying the infection kinetics of the subsequent exposure. These results imply that virus-induced early immune responses may be responsible for the observed viral hierarchy. Here we introduce and analyse a family of within-host models of re-infection viral kinetics which allow for different viruses to stimulate the innate immune response to different degrees. The proposed models differ in their hypothesised mechanisms of action of the non-specific innate immune response. We compare these alternative models in terms of their abilities to reproduce the re-exposure data. Our results show that 1) a model with viral control mediated solely by a virus-resistant state, as commonly considered in the literature, is not able to reproduce the observed viral hierarchy; 2) the synchronised and desynchronised behaviour of consecutive virus infections is highly dependent upon the interval between primary virus and challenge virus exposures and is consistent with virus-dependent stimulation of the innate immune response. Our study provides the first mechanistic explanation for the recently observed influenza viral hierarchies and demonstrates the importance of understanding the host response to multi-strain viral infections. Re-exposure experiments provide a new paradigm in which to study the immune response to influenza and its role in viral control.
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Background: Epidemiological studies suggest that, following infection with influenza virus, there is a short period during which a host experiences a lower susceptibility to infection with other influenza viruses. This viral interference appears to be independent of any antigenic similarities between the viruses. We used the ferret model of human influenza to systematically investigate viral interference. Methods: Ferrets were first infected then challenged 1-14 days later with pairs of influenza A(H1N1)pdm09, influenza A(H3N2), and influenza B viruses circulating in 2009 and 2010. Results: Viral interference was observed when the interval between initiation of primary infection and subsequent challenge was <1 week. This effect was virus specific and occurred between antigenically related and unrelated viruses. Coinfections occurred when 1 or 3 days separated infections. Ongoing shedding from the primary virus infection was associated with viral interference after the secondary challenge. Conclusions: The interval between infections and the sequential combination of viruses were important determinants of viral interference. The influenza viruses in this study appear to have an ordered hierarchy according to their ability to block or delay infection, which may contribute to the dominance of different viruses often seen in an influenza season.
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A hallmark of immunological memory is the ability of previously primed T cells to undergo rapid recall responses upon antigen reencounter. Classic work has suggested that memory T cells proliferate in response to lower doses of antigen than naive T cells and with reduced requirements for co-stimulation. In contrast to this premise, we observed that naive but not memory T cells proliferate in vivo in response to limited antigen presentation. To reconcile these observations, we tested the antigen threshold requirement for cell cycle entry in naive and central memory CD8(+) T cells. Although both naive and memory T cells detect low dose antigen, only naive T cells activate cell cycle effectors. Direct comparison of TCR signaling on a single cell basis indicated that central memory T cells do not activate Zap70, induce cMyc expression, or degrade p27 in response to antigen levels that activate these functions in naive T cells. The reduced sensitivity of memory T cells may result from both decreased surface TCR expression and increased expression of protein tyrosine phosphatases as compared with naive T cells. Our data describe a novel aspect of memory T cell antigen threshold sensitivity that may critically regulate recall expansion.
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The role of T cells in mediating heterosubtypic protection against natural influenza illness in humans is uncertain. The 2009 H1N1 pandemic (pH1N1) provided a unique natural experiment to determine whether crossreactive cellular immunity limits symptomatic illness in antibody-naive individuals. We followed 342 healthy adults through the UK pandemic waves and correlated the responses of pre-existing T cells to the pH1N1 virus and conserved core protein epitopes with clinical outcomes after incident pH1N1 infection. Higher frequencies of pre-existing T cells to conserved CD8 epitopes were found in individuals who developed less severe illness, with total symptom score having the strongest inverse correlation with the frequency of interferon-γ (IFN-γ)(+) interleukin-2 (IL-2)(-) CD8(+) T cells (r = -0.6, P = 0.004). Within this functional CD8(+)IFN-γ(+)IL-2(-) population, cells with the CD45RA(+) chemokine (C-C) receptor 7 (CCR7)(-) phenotype inversely correlated with symptom score and had lung-homing and cytotoxic potential. In the absence of crossreactive neutralizing antibodies, CD8(+) T cells specific to conserved viral epitopes correlated with crossprotection against symptomatic influenza. This protective immune correlate could guide universal influenza vaccine development.
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Tissues such as the genital tract, skin, and lung act as barriers against invading pathogens. To protect the host, incoming microbes must be quickly and efficiently controlled by the immune system at the portal of entry. Memory is a hallmark of the adaptive immune system, which confers long-term protection and is the basis for efficacious vaccines. While the majority of existing vaccines rely on circulating antibody for protection, struggles to develop antibody-based vaccines against infections such as herpes simplex virus (HSV) and human immunodeficiency virus (HIV) have underscored the need to generate memory T cells for robust antiviral control. The circulating memory T-cell population is generally divided into two subsets: effector memory (TEM ) and central memory (TCM ). These two subsets can be distinguished by their localization, as TCM home to secondary lymphoid organs and TEM circulate through non-lymphoid tissues. More recently, studies have identified a third subset, called tissue-resident memory (TRM ) cells, based on its migratory properties. This subset is found in peripheral tissues that require expression of specific chemoattractants and homing receptors for T-cell recruitment and retention, including barrier sites such as the skin and genital tract. In this review, we categorize different tissues in the body based on patterns of memory T-cell migration and tissue residency. This review also describes the rules for TRM generation and the properties that distinguish them from circulating TEM and TCM cells. Finally, based on the failure of recent T-cell-based vaccines to provide optimal protection, we also discuss the potential role of TRM cells in vaccine design against microbes that invade through the peripheral tissues and highlight new vaccination strategies that take advantage of this newly described memory T-cell subset.
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Influenza A and B viruses form different genera, which were originally distinguished by antigenic differences in their nucleoproteins and matrix 1 proteins. Cross-protection between these two genera has not been observed in animal experiments, which is consistent with the low homology in viral proteins common to both viruses except for one of three polymerase proteins, polymerase basic 1 (PB1). Recently, however, antibody and CD4+ T cell epitopes conserved between the two genera were identified in humans. A protective antibody epitope was located in the stalk region of the surface glycoprotein, hemagglutinin, and a CD4+ T cell epitope was located in the fusion peptide of the hemagglutinin. The fusion peptide was also found to contain antibody epitopes in humans and animals. A short stretch of well-conserved peptide was also identified in the other surface glycoprotein, neuraminidase, and antibodies binding to this peptide were generated by peptide immunization in rabbits. Although PB1, the only protein which has relatively high overall sequence homology between influenza A and B viruses, is not considered an immunodominant protein in the T cell responses to influenza A virus infection, amino acid sequence comparisons show that a considerable number of previously identified T cell epitopes in the PB1 of influenza A viruses are conserved in the PB1 of influenza B viruses. These data indicate that B and T cell cross-reactivity exists between influenza A and B viruses, which may have modulatory effects on the disease process and recovery. Although the antibody titers and the specific T cell frequencies induced by natural infection or standard vaccination may not be high enough to provide cross protection in humans, it might be possible to develop immunization strategies to induce these cross-reactive responses more efficiently.
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It is well accepted that Ag-induced B cell differentiation often results in the generation of exceptionally long-lived plasma cells. Much of the work supporting this viewpoint stems from studies focused on germinal center-derived plasma cells secreting high-affinity isotype-switched Abs in mice immunized with T cell-dependent Ags. In contrast, less attention has been devoted to understanding Ab responses to T cell-independent Ags and pathogens. In this study, we review recent work showing that T cell-independent Ags consisting of either polysaccharides or LPSs also induce the formation of long-lived plasma cells, despite their general inability to sustain germinal center responses. This new information provides a framework for more fully understanding the forces underlying immunity to pathogens that resist T cell recognition and the extracellular cues governing plasma cell longevity.
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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.
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The role of the host immune response in determining the severity and duration of an influenza infection is still unclear. In order to identify severity factors and more accurately predict the course of an influenza infection within a human host, an understanding of the impact of host factors on the infection process is required. Despite the lack of sufficiently diverse experimental data describing the time course of the various immune response components, published mathematical models were constructed from limited human or animal data using various strategies and simplifying assumptions. To assess the validity of these models, we assemble previously published experimental data of the dynamics and role of cytotoxic T lymphocytes, antibodies, and interferon and determined qualitative key features of their effect that should be captured by mathematical models. We test these existing models by confronting them with experimental data and find that no single model agrees completely with the variety of influenza viral kinetics responses observed experimentally when various immune response components are suppressed. Our analysis highlights the strong and weak points of each mathematical model and highlights areas where additional experimental data could elucidate specific mechanisms, constrain model design, and complete our understanding of the immune response to influenza.
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T cell responses are characterized by the phenomenon of immunodominance (ID), whereby peptide-specific T cells are elicited in a reproducible hierarchy of dominant and subdominant responses. However, the mechanisms that give rise to ID are not well understood. We investigated the effect of viral dose on primary CD8(+) T cell (T(CD8+)) ID by injecting mice i.p. with various doses of influenza A virus and assessing the primary T(CD8+) response to five dominant and subdominant peptides. Increasing viral dose enhanced the overall strength of the T(CD8+) response, and it altered the ID hierarchy: specifically, NP(366-374) T(CD8+) were dominant at low viral doses but were supplanted by PA(224-233) T(CD8+) at high doses. To understand the basis for this reversal, we mathematically modeled these T(CD8+) responses and used Bayesian statistics to obtain estimates for Ag presentation, T(CD8+) precursor numbers, and avidity. Interestingly, at low viral doses, Ag presentation most critically shaped ID hierarchy, enabling T(CD8+) specific to the more abundantly presented NP(366-374) to dominate. By comparison, at high viral doses, T(CD8+) avidity and precursor numbers appeared to be the major influences on ID hierarchy, resulting in PA(224-233) T(CD8+) usurping NP(366-374) cells as the result of higher avidity and precursor numbers. These results demonstrate that the nature of primary T(CD8+) responses to influenza A virus is highly influenced by Ag dose, which, in turn, determines the relative importance of Ag presentation, T(CD8+) avidity, and precursor numbers in shaping the ID hierarchy. These findings provide valuable insights for future T(CD8+)-based vaccination strategies.
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Influenza virus infection remains a public health problem worldwide. The mechanisms underlying viral control during an uncomplicated influenza virus infection are not fully understood. Here, we developed a mathematical model including both innate and adaptive immune responses to study the within-host dynamics of equine influenza virus infection in horses. By comparing modeling predictions with both interferon and viral kinetic data, we examined the relative roles of target cell availability, and innate and adaptive immune responses in controlling the virus. Our results show that the rapid and substantial viral decline (about 2 to 4 logs within 1 day) after the peak can be explained by the killing of infected cells mediated by interferon activated cells, such as natural killer cells, during the innate immune response. After the viral load declines to a lower level, the loss of interferon-induced antiviral effect and an increased availability of target cells due to loss of the antiviral state can explain the observed short phase of viral plateau in which the viral level remains unchanged or even experiences a minor second peak in some animals. An adaptive immune response is needed in our model to explain the eventual viral clearance. This study provides a quantitative understanding of the biological factors that can explain the viral and interferon kinetics during a typical influenza virus infection.
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CD4⁺T cells are crucial in achieving a regulated effective immune response to pathogens. Naive CD4⁺T cells are activated after interaction with antigen-MHC complex and differentiate into specific subtypes depending mainly on the cytokine milieu of the microenvironment. Besides the classical T-helper 1 and T-helper 2, other subsets have been identified, including T-helper 17, regulatory T cell, follicular helper T cell, and T-helper 9, each with a characteristic cytokine profile. For a particular phenotype to be differentiated, a set of cytokine signaling pathways coupled with activation of lineage-specific transcription factors and epigenetic modifications at appropriate genes are required. The effector functions of these cells are mediated by the cytokines secreted by the differentiated cells. This paper will focus on the cytokine-signaling and the network of transcription factors responsible for the differentiation of naive CD4⁺T cells.
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CD4 effectors generated in vitro can promote survival against a highly pathogenic influenza virus via an antibody-independent mechanism involving class II-restricted, perforin-mediated cytotoxicity. However, it is not known whether CD4 cells activated during influenza virus infection can acquire cytolytic activity that contributes to protection against lethal challenge. CD4 cells isolated from the lungs of infected mice were able to confer protection against a lethal dose of H1N1 influenza virus A/Puerto Rico 8/34 (PR8). Infection of BALB/c mice with PR8 induced a multifunctional CD4 population with proliferative capacity and ability to secrete interleukin-2 (IL-2) and tumor necrosis factor alpha (TNF-α) in the draining lymph node (DLN) and gamma interferon (IFN-γ) and IL-10 in the lung. IFN-γ-deficient CD4 cells produced larger amounts of IL-17 and similar levels of TNF-α, IL-10, and IL-2 compared to wild-type (WT) CD4 cells. Both WT and IFN-γ(-/-) CD4 cells exhibit influenza virus-specific cytotoxicity; however, IFN-γ-deficient CD4 cells did not promote recovery after lethal infection as effectively as WT CD4 cells. PR8 infection induced a population of cytolytic CD4 effectors that resided in the lung but not the DLN. These cells expressed granzyme B (GrB) and required perforin to lyse peptide-pulsed targets. Lethally infected mice given influenza virus-specific CD4 cells deficient in perforin showed greater weight loss and a slower time to recovery than mice given WT influenza virus-specific CD4 cells. Taken together, these data strengthen the concept that CD4 T cell effectors are broadly multifunctional with direct roles in promoting protection against lethal influenza virus infection.
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Memory CD8 T cells are an important component of protective immunity against viral infections, and understanding their development will aid in the design of optimal vaccines. Recent work has shed light on the complex differentiation process that occurs during a CD8 T-cell response to viral infection. Dramatic cellular changes occur as T cells transition through the three characteristic phases of an antiviral response, initial activation and expansion, the death phase, and the formation of memory T cells. Each of these three phases of the T-cell response is accompanied by extensive transcriptional and functional changes that result in naive T cells expanding and gaining effector functions, survival of 5 to 10% of the effectors through the death phase, and the gradual acquisition of memory properties by the surviving virus-specific T cells. This review will discuss our current understanding of how functional and protective CD8 T-cell responses are generated and maintained following different types of infections. Viral infections can be largely divided into two types: (i) acute infections, where virus is eliminated; and (ii) chronic infections, where virus persists. This second type of infection may be further classified into latent infections and those in which there is persistent viral replication. While acute infections usually result in effective antiviral immune responses, chronic infections can be associated with suboptimal T-cell function. We will first focus on acute infections and on recent work that has led to our current understanding of the CD8 T-cell differentiation program that occurs when antigen is eliminated following initial infection and then discuss how CD8 T-cell responses can be altered and impaired during chronic infections when virus persists.
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The extent to which the progeny of one primary memory CD8 T cell differs from the progeny of one naive CD8 T cell of the same specificity remains an unresolved question. To explore cell-autonomous functional differences between naive and memory CD8 T cells that are not influenced by differences in the priming environment, an experimental model has been developed in which physiological numbers of both populations of cells were cotransferred into naive hosts before Ag stimulation. Interestingly, naive CD8 T cells undergo greater expansion in numbers than do primary memory CD8 T cells after various infections or immunizations. The intrinsic ability of one naive CD8 T cell to give rise to more effector CD8 T cells than one memory CD8 T cell is independent of the number and quality of primary memory CD8 T cells present in vivo. The sustained proliferation of newly activated naive CD8 T cells contributed to their greater magnitude of expansion. Additionally, longitudinal analyses of primary and secondary CD8 T cell responses revealed that on a per-cell basis naive CD8 T cells generate higher numbers of long-lived memory cells than do primary memory CD8 T cells. This enhanced "memory generation potential" of responding naive CD8 T cells occurred despite the delayed contraction of secondary CD8 T cell responses. Taken together, the data in this study revealed previously unappreciated differences between naive and memory CD8 T cells and will help further define the functional potential for both cell types.
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Ordinary differential equations (ODE) are a powerful tool for modeling dynamic processes with wide applications in a variety of scientific fields. Over the last 2 decades, ODEs have also emerged as a prevailing tool in various biomedical research fields, especially in infectious disease modeling. In practice, it is important and necessary to determine unknown parameters in ODE models based on experimental data. Identifiability analysis is the first step in determing unknown parameters in ODE models and such analysis techniques for nonlinear ODE models are still under development. In this article, we review identifiability analysis methodologies for nonlinear ODE models developed in the past one to two decades, including structural identifiability analysis, practical identifiability analysis and sensitivity-based identifiability analysis. Some advanced topics and ongoing research are also briefly reviewed. Finally, some examples from modeling viral dynamics of HIV, influenza and hepatitis viruses are given to illustrate how to apply these identifiability analysis methods in practice.
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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.
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Infection with seasonal influenza viruses induces a certain extent of protective immunity against potentially pandemic viruses of novel subtypes, also known as heterosubtypic immunity. Here we demonstrate that infection with a recent influenza A/H3N2 virus strain induces robust protection in ferrets against infection with a highly pathogenic avian influenza virus of the H5N1 subtype. Prior H3N2 virus infection reduced H5N1 virus replication in the upper respiratory tract, as well as clinical signs, mortality, and histopathological changes associated with virus replication in the brain. This protective immunity correlated with the induction of T cells that cross-reacted with H5N1 viral antigen. We also demonstrated that prior vaccination against influenza A/H3N2 virus reduced the induction of heterosubtypic immunity otherwise induced by infection with the influenza A/H3N2 virus. The implications of these findings are discussed in the context of vaccination strategies and vaccine development aiming at the induction of immunity to pandemic influenza.
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We use mathematical models to investigate the relationship between viral characteristics and virus load under the following immune responses: (a) CTL-mediated lysis, (b) CTL-mediated inhibition of virus entry into target cells, (c) CTL-mediated inhibition of virion production and (d) antibody responses. We find that the rate of virus entry into target cells may generally only have a weak influence on virus load. The rate of virion production by infected cells only has a weak effect on the equilibrium number of infected cells while strongly influencing the number of free virus particles. On the other hand, viral cytopathogenicity may be a major determinant of virus load under certain types of immune responses. If there is no immune response, or if immune mediators inhibit infection of target cells, non-cytopathic viruses may attain significantly higher abundances than cytopathic ones. On the other hand, immune mediators acting on infected cells control both types of viruses with similar efficiencies. These results are used to interpret data on perforin-knockout experiments in LCMV infection and provide the basis for understanding the suppression and rise of non-syncytium (NSI) and syncytium inducing (SI) HIV phenotypes during the disease process.
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Seasonal and pandemic influenza A virus (IAV) continues to be a public health threat. However, we lack a detailed and quantitative understanding of the immune response kinetics to IAV infection and which biological parameters most strongly influence infection outcomes. To address these issues, we use modeling approaches combined with experimental data to quantitatively investigate the innate and adaptive immune responses to primary IAV infection. Mathematical models were developed to describe the dynamic interactions between target (epithelial) cells, influenza virus, cytotoxic T lymphocytes (CTLs), and virus-specific IgG and IgM. IAV and immune kinetic parameters were estimated by fitting models to a large data set obtained from primary H3N2 IAV infection of 340 mice. Prior to a detectable virus-specific immune response (before day 5), the estimated half-life of infected epithelial cells is approximately 1.2 days, and the half-life of free infectious IAV is approximately 4 h. During the adaptive immune response (after day 5), the average half-life of infected epithelial cells is approximately 0.5 days, and the average half-life of free infectious virus is approximately 1.8 min. During the adaptive phase, model fitting confirms that CD8(+) CTLs are crucial for limiting infected cells, while virus-specific IgM regulates free IAV levels. This may imply that CD4 T cells and class-switched IgG antibodies are more relevant for generating IAV-specific memory and preventing future infection via a more rapid secondary immune response. Also, simulation studies were performed to understand the relative contributions of biological parameters to IAV clearance. This study provides a basis to better understand and predict influenza virus immunity.
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Novel swine-origin influenza viruses of the H1N1 subtype were first detected in humans in April 2009. As of 12 August 2009, 180,000 cases had been reported globally. Despite the fact that they are of the same antigenic subtype as seasonal influenza viruses circulating in humans since 1977, these viruses continue to spread and have caused the first influenza pandemic since 1968. Here we show that a pandemic H1N1 strain replicates in and transmits among guinea pigs with similar efficiency to that of a seasonal H3N2 influenza virus. This transmission was, however, partially disrupted when guinea pigs had preexisting immunity to recent human isolates of either the H1N1 or H3N2 subtype and was fully blocked through daily intranasal administration of interferon to either inoculated or exposed animals. Our results suggest that partial immunity resulting from prior exposure to conventional human strains may blunt the impact of pandemic H1N1 viruses in the human population. In addition, the use of interferon as an antiviral prophylaxis may be an effective way to limit spread in at-risk populations.
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The present approach to the mathematical modelling of infectious diseases is based upon the idea that specific immune mechanisms play a leading role in development, course, and outcome of infectious disease. The model describing the reaction of the immune system to infectious agent invasion is constructed on the bases of Burnet's clonal selection theory and the co-recognition principle. The mathematical model of antiviral immune response is formulated by a system of ten non-linear delay-differential equations. The delayed argument terms in the right-hand part are used for the description of lymphocyte division, multiplication and differentiation processes into effector cells. The analysis of clinical and experimental data allows one to construct the generalized picture of the acute form of viral hepatitis B. The concept of the generalized picture includes a quantitative description of dynamics of the principal immunological, virological and clinical characteristics of the disease. Data of immunological experiments in vitro and experiments on animals are used to obtain estimates of permissible values of model parameters. This analysis forms the bases for the solution of the parameter identification problem for the mathematical model of antiviral immune response which will be the topic of the following paper (Marchuk et al., 1991, J. theor. Biol. 15).
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Naive CD8(+) T cells give rise to cytotoxic T lymphocytes (CTLs), which promote the effective eradication of viruses and tumours. Although the past decades have seen enormous advances in cellular immunology, a precise understanding of the key elements that determine the specificity and magnitude of primary CTL responses has been lacking. However, recent technological advances have allowed us to more accurately identify, characterize and quantitate key determinants that define the specificity and magnitude of CD8(+) T cell-mediated immunity. This Review discusses the technical and conceptual advances that have markedly changed our understanding of the determinants of primary CTL responses.
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Using a variety of techniques, including limiting dilution assays (LDA), intracellular IFNγ assays, and Db-IgG1 MHC dimer staining to measure viral peptide-specific T cell number and function, we show here that heterologous virus infections quantitatively delete and qualitatively alter the memory pool of T cells specific to a previously encountered virus. We also show that a prior history of a virus infection can alter the hierarchy of the immunodominant peptide response to a second virus and that virus infections selectively reactivate memory T cells with distinct specificities to earlier viruses. These results are consistent with a model for the immune system that accommodates memory T cell populations for multiple pathogens over the course of a lifetime.
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Repetitive antigen stimulation by prime-boost vaccination or pathogen reencounter increases memory CD8(+) T cell numbers, but the impact on memory CD8(+) T cell differentiation is unknown. Here we showed that repetitive antigen stimulations induced accumulation of memory CD8(+) T cells with uniform effector memory characteristics. However, genome-wide microarray analyses revealed that each additional antigen challenge resulted in the differential regulation of several hundred new genes in the ensuing memory CD8(+) T cell populations and, therefore, in stepwise diversification of CD8(+) T cell transcriptomes. Thus, primary and repeatedly stimulated (secondary, tertiary, and quaternary) memory CD8(+) T cells differed substantially in their molecular signature while sharing expression of a small group of genes and biological pathways, which may constitute a core signature of memory differentiation. These results reveal the complex regulation of memory CD8(+) T cell differentiation and identify potential new molecular targets to dissect the function of memory cells generated by repeated antigen stimulation.
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Influenza virus causes an acute, mostly self-limited, infection of the upper respiratory tract. Yearly epidemics of influenza infect up to 20% of the population, and in the US cause an average of 36,000 deaths every year. Because influenza is a short-term infection lasting 4 to 7 d in most cases, studying the dynamics of the virus and the immune response in vivo is difficult. Here we review the most recent attempts at mathematical modeling of influenza dynamics within the host to better understand the kinetics of the virus and associated immune responses. These models have been developed based on very successful kinetic studies of chronic infections, such as human immunodeficiency and hepatitis C viruses. We briefly review the approach taken for these infections before discussing the results obtained in the case of influenza. The dynamics of the latter have been studied both in vitro and in vivo. It was shown that the virus turnover is very fast, which helps to explain the accumulation of diversity. Moreover, initial attempts have been made at modeling the immune response to influenza, but these are still incipient and further studies, both experimental and theoretical, are needed to better elucidate the interplay of the virus and the immune response.
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After an infection, T cells that carry the CD8 marker are activated and undergo a characteristic kinetic sequence of rapid expansion, subsequent contraction and formation of memory cells. The pool of naive T-cell clones is diverse and contains cells bearing T-cell antigen receptors (TCRs) that differ in their affinity for the same antigen. How these differences in affinity affect the function and the response kinetics of individual T-cell clones was previously unknown. Here we show that during the in vivo response to microbial infection, even very weak TCR-ligand interactions are sufficient to activate naive T cells, induce rapid initial proliferation and generate effector and memory cells. The strength of the TCR-ligand interaction critically affects when expansion stops, when the cells exit lymphoid organs and when contraction begins; that is, strongly stimulated T cells contract and exit lymphoid organs later than weakly stimulated cells. Our data challenge the prevailing view that strong TCR ligation is a prerequisite for CD8(+) T-cell activation. Instead, very weak interactions are sufficient for activation, but strong TCR ligation is required to sustain T-cell expansion. We propose that in response to microbial challenge, T-cell clones with a broad range of avidities for foreign ligands are initially recruited, and that the pool of T cells subsequently matures in affinity owing to the more prolonged expansion of high-affinity T-cell clones.
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Influenza A virus (IAV) strains are denoted by the subtype of their hemagglutinin (HA) and neuraminidase (NA) virion surface proteins. Major changes in HA subtype among strains circulating in humans are referred to as "antigenic shift". Antigenic shift can occur by two means: direct transmission of a zoonotic strain to humans or through reshuffling of the segmented genome in cells co-infected with animal and human strains. The lack of circulating anti-HA antibodies in human populations to a novel IAV results in extremely high frequency of illness and the potential for severe morbidity and mortality on a world-wide basis; the dreaded pandemic. Such pandemics could be partially controlled by developing a vaccine that generates effective heterosubtypic immunity (HSI) based on immune recognition of IAV antigens conserved across all viral strains. While it has long been known that T cells exhibit such broad cross-reactive specificity that could provide effective HSI, recent animal studies suggest a potential role for antibodies as well. Here we review current knowledge of the mechanisms contributing to HSI to influenza and speculate on the potential for this approach to contribute to public health.
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Athymic (nude) mice and their normal littermates were intranasally inoculated with graded doses of A/WSN influenza virus. At a dose of 103 EID50, all mice survived the infection. In contrast, at a dose of 5 × 104 EID50, all mice died by 7 days. At intermediate doses of 5 × 103 and 104 EID50, the nude mice were less resistant to the infection than their normal littermates, so that a higher proportion always died. Given a dose of 5 × 103 EID50, lung virus levels in both groups reached similar high levels by Day 5. Thereafter, virus levels in the normal mice rapidly fell so that no infectious virus could be detected by Day 18. In nude mice, the levels fell very slowly so that relatively high levels were still present at Day 18 in the surviving mice. At the height of the infection, high levels of cytotoxic T-cell activity was detected in the lungs of normal but not nude mice. Transfer to the nude mice of specific immune T cells raised from infected normal littermates enhanced survival of the nude mice and reduced the lung virus levels. Nude mice consistently showed a greater degree of lung consolidation than their normal littermates. Microscopically, the nude mouse lungs showed greater respiratory epithelial hyperplasia with minimal inflammatory cell infiltration in the foci of consolidation compared with their infected normal littermates. Under the conditions of these experiments, influenza-immune T cells seemed to inhibit rather than contribute to the generation of virus-mediated pulmonary pathology. The findings strongly suggest that T cells play an important positive role in the process of recovery from murine influenza infection.
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To investigate the defensive roles and production of interferon and antibodies, C3H/He mice were subjected to various immunosuppressive treatments and infected with influenza virus. In infected normal control mice the pattern of pulmonary viral growth can be divided into three phases. The first phase is characterized by an exponential increase of virus titer, the second by a rapid decrease, and the third by a moderate decrease. At the time of transition from the first phase to the second in pulmonary virus growth, interferon could be detected in the tracheobronchial washings of infected mice, but neutralizing antibodies could not. In infected B cell-deprived mice and infected anti-µ-treated mice, the transition from the first phase to the second occurred without any detectable antibody production, and interferon could be induced in the early stage of infection. However, the pulmonary virus in these mice increased again exponentially until the death of the mice. In infected T cell-deprived mice which could not induce interferon, but produced IgM-neutralizing antibodies, the second phase was not observed after the first phase, but a transient plateau phase could be demonstrated, and then the pulmonary virus increased again exponentially until the death of the mice. In anti-γ-treated infected mice, pulmonary virus growth and production of interferon and neutralizing antibody were almost similar to those of infected normal control mice except for the absence of IgG neutralizing antibody production. Although anti-α-treated infected mice produced interferon and no IgA antibody, the transition from the first exponential increase of pulmonary virus to the second rapid decrease was seen, but then the virus increased exponentially again until the death of the mice. These results suggest that interferon plays an important role in the transition from the first phase to the second, and that T cells are required for interferon induction in mice infected with influenza virus. These data also suggest that IgA antibodies play an important role in the inhibition of virus propagation in the lungs after the disappearance of interferon. Moreover, infected T cell-deprived mice could produce only IgM neutralizing antibodies, but not IgG and IgA antibodies. Therefore, T cells are required for the production of IgG and IgA antibodies and eventually for defense functions in mice infected primarily with influenza virus.
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The role and interdependence of CD8+ and CD4+ alpha beta-T cells in the acute response after respiratory infection with the murine parainfluenza type 1 virus, Sendai virus, has been analyzed for H-2b mice. Enrichment of CD8+ virus-specific CTL effectors in the lungs of immunologically intact C57BL/6 animals coincided with the clearance of the virus from this site by day 10 after infection. Removal of the CD4+ T cells by in vivo mAb treatment did not affect appreciably either the recruitment of CD8+ T cells to the infected lung, or their development into virus-specific cytotoxic effectors. In contrast, depletion of the CD8+ subset delayed virus clearance, although most mice survived the infection. Transgenic H-2b F3 mice homozygous (-/-) for a beta 2 microglobulin (beta 2-m) gene disruption, which lack both class I MHC glycoproteins and mature CD8+ alpha beta-T cells, showed a comparable, delayed clearance of Sendai virus from the lung. Virus-specific, class II MHC-restricted CTL were demonstrated in both freshly isolated bronchoalveolar lavage populations and cultured lymph node and spleen tissue from the beta 2-m (-/-) transgenics. Treatment of the beta 2-m (-/-) mice with the mAb to CD4 led to delayed virus clearance and death, which was also the case for normal mice that were depleted simultaneously of the CD4+ and CD8+ subsets. These results indicate that, although classical class I MHC-restricted CD8+ cytotoxic T cells normally play a dominant role in the recovery of mice acutely infected with Sendai virus, alternative mechanisms involving CD4+ T cells exist and can compensate, in time, for the loss of CD8+ T cell function.
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After intravenous infection of mice with 10(3) infectious units (IU) the WE strain lymphocytic choriomeningitis (LCM) virus multiplied in the spleens (as in all other major organs), reaching more than 10(8) IU/g of tissue on days 4 to 5. Subsequently, the virus was quickly eliminated, being below detectability usually by day 10. During the time of virus clearance, the mononuclear phagocytes (MNP) of the spleen were activated as revealed by suppression of growth of Listeria monocytogenes and increase of cell-associated hydrolytic enzymes. In athymic nude mice, in whom the MNP system is assumed to be permanently activated, the virus replicated slightly but reproducibly less than in their euthymic counterparts. However, when the MNP were activated by Corynebacterium parvum, virus in spleens attained higher concentrations than in mice not so treated, and the rate of elimination was not altered. In mice whose MNP had been damaged by injection of dextran sulfate 500, the spleen virus titers were also increased, but the subsequent immune elimination was slightly delayed. Activation of spleen MNP was not evident at the time virus was rapidly cleared as a result of transfusion of LCM-immune T lymphocytes. Adoptive immunization was as successful in mice that had been pretreated with gamma-rays or cyclophosphamide, suggesting that replicating cells or their descendants, in particular monocytes, did not participate measurably in the process of elimination. Pretreatments of recipients with dextran sulfate 500 reduced the efficacy of transfused LCM-immune T lymphocytes, but this compound probably directly affected the cells. We interpret these findings to mean that the LCM virus in the mouse's spleen is controlled by a mechanism in which MNP do not play an essential role.
Article
BALB/c normal and nude mice were infected with a non-lethal mouse-passaged A/PC/1/73 (H3N2) influenza virus in order to assess the role of T cells on the course of disease of the nose, trachea and lung. The tracheal epithelium of both mouse strains was desquamated by 3 days after infection. Although normal regeneration began, nude mice never completed that regeneration whereas normal mice had fully regenerated tracheas by Day 14. This failure to complete the recovery was also evident from the continued virus shedding by the nude mouse. In order to assess the role of serum antibody on recovery from infection, ferret, goat or mouse antibody to H3N2 influenza virus was passively administered to nude mice after infection. It resulted in a transient decrease in virus shedding from the nose, trachea and lung, and complete but temporary regeneration of the tracheal epithelium. However, later in the course of the infection, when serum antibody levels were no longer detectable, the tracheal epithelium of these animals redesquamated and large amounts of virus were again shed from nose, trachea and lungs. We conclude that: (i) desquamation of the ciliated epithelium of the trachea is not T-cell dependent; and (ii) serum antibody can contribute to temporary recovery from infection, but by itself is insufficient for permanent recovery of the nose, trachea or lung.