Within-Host Models of High and Low Pathogenic Influenza Virus Infections: The Role of Macrophages

Measles Infection Dose Responses: Insights from Mathematical Modeling

How viral infections develop can change based on the number of viruses initially entering the body. The understanding of the impacts of infection doses remains incomplete, in part due to challenging constraints, and a lack of research. Gaining more insights is crucial regarding the measles virus (MV). The higher the MV infection dose, the earlier the peak of acute viremia, but the magnitude of the peak viremia remains almost constant. Measles is highly contagious, causes immunosuppression such as lymphopenia, and contributes substantially to childhood morbidity and mortality. This work investigated mechanisms underlying the observed wild-type measles infection dose responses in cynomolgus monkeys. We fitted longitudinal data on viremia using maximum likelihood estimation, and used the Akaike Information Criterion (AIC) to evaluate relevant biological hypotheses and their respective model parameterizations. The lowest AIC indicates a linear relationship between the infection dose, the initial viral load, and the initial number of activated MV-specific T cells. Early peak viremia is associated with high initial number of activated MV-specific T cells. Thus, when MV infection dose increases, the initial viremia and associated immune cell stimulation increase, and reduce the time it takes for T cell killing to be sufficient, thereby allowing dose-independent peaks for viremia, MV-specific T cells, and lymphocyte depletion. Together, these results suggest that the development of measles depends on virus-host interactions at the start and the efficiency of viral control by cellular immunity. These relationships are additional motivations for prevention, vaccination, and early treatment for measles.

Mathematical Modeling Suggests That Monocyte Activity May Drive Sex Disparities during Influenza Infection

In humans, females of reproductive age often experience a more severe disease during influenza A virus infection, which may be due to differences in their innate immune response. Sex-specific outcomes to influenza infection have been recapitulated in mice, enabling researchers to study viral and immune dynamics in vivo in order to identify immune mechanisms that are differently regulated between the sexes. This study is based on the hypothesis that sex-specific outcomes emerge due to differences in the rates/speeds that select immune components respond. Using publicly available sex-specific murine data, we utilized dynamic mathematical models of the innate immune response to identify candidate mechanisms that may lead to increased disease severity in female mice. We implemented a large computational screen using the Bayesian information criterion (BIC), wherein the goodness of fit of the competing model scenarios is balanced against complexity (i.e., the number of parameters). Our results suggest that having sex-specific rates for proinflammatory monocyte induction by interferon and monocyte inhibition of virus replication provides the simplest (lowest BIC) explanation for the difference observed in the male and female immune responses. Markov-chain Monte Carlo (MCMC) analysis and global sensitivity analysis of the top performing scenario were performed to provide rigorous estimates of the sex-specific parameter distributions and to provide insight into which parameters most affect innate immune responses. Simulations using the top-performing model suggest that monocyte activity could be a key target to reduce influenza disease severity in females. Overall, our Bayesian statistical and dynamic modeling approach suggests that monocyte activity and induction parameters are sex-specific and may explain sex-differences in influenza disease immune dynamics.

Computational framework to understand the clinical stages of COVID-19 and visualization of time course for various treatment strategies

Coronavirus disease 2019 is known to be regulated by multiple factors such as delayed immune response, impaired T cell activation, and elevated levels of proinflammatory cytokines. Clinical management of the disease remains challenging due to interplay of various factors as drug candidates may elicit different responses depending on the staging of the disease. In this context, we propose a computational framework which provides insights into the interaction between viral infection and immune response in lung epithelial cells, with an aim of predicting optimal treatment strategies based on infection severity. First, we formulate the model for visualizing the nonlinear dynamics during the disease progression considering the role of T cells, macrophages and proinflammatory cytokines. Here, we show that the model is capable of emulating the dynamic and static data trends of viral load, T cell, macrophage levels, interleukin (IL)-6 and TNF-α levels. Second, we demonstrate the ability of the framework to capture the dynamics corresponding to mild, moderate, severe, and critical condition. Our result shows that, at late phase (>15 days), severity of disease is directly proportional to pro-inflammatory cytokine IL6 and tumor necrosis factor (TNF)-α levels and inversely proportional to the number of T cells. Finally, the simulation framework was used to assess the effect of drug administration time as well as efficacy of single or multiple drugs on patients. The major contribution of the proposed framework is to utilize the infection progression model for clinical management and administration of drugs inhibiting virus replication and cytokine levels as well as immunosuppressant drugs at various stages of the disease.

Enhanced viral infectivity and reduced interferon production are associated with high pathogenicity for influenza viruses

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.

Utility of Human In Vitro Data in Risk Assessments of Influenza A Virus Using the Ferret Model

As influenza A viruses (IAV) continue to cross species barriers and cause human infection, the establishment of risk assessment rubrics has improved pandemic preparedness efforts. In vivo pathogenicity and transmissibility evaluations in the ferret model represent a critical component of this work. As the relative contribution of in vitro experimentation to these rubrics has not been closely examined, we sought to evaluate to what extent viral titer measurements over the course of in vitro infections are predictive or correlates of nasal wash and tissue measurements for IAV infections in vivo. We compiled data from ferrets inoculated with an extensive panel of over 50 human and zoonotic IAV (inclusive of swine-origin and high- and low-pathogenicity avian influenza viruses associated with human infection) under a consistent protocol, with all viruses concurrently tested in a human bronchial epithelial cell line (Calu-3). Viral titers in ferret nasal wash specimens and nasal turbinate tissue correlated positively with peak titer in Calu-3 cells, whereas additional phenotypic and molecular determinants of influenza virus virulence and transmissibility in ferrets varied in their association with in vitro viral titer measurements. Mathematical modeling was used to estimate more generalizable key replication kinetic parameters from raw in vitro viral titers, revealing commonalities between viral infection progression in vivo and in vitro. Meta-analyses inclusive of IAV that display a diverse range of phenotypes in ferrets, interpreted with mathematical modeling of viral kinetic parameters, can provide critical information supporting a more rigorous and appropriate contextualization of in vitro experiments toward pandemic preparedness. IMPORTANCE Both in vitro and in vivo models are employed for assessing the pandemic potential of novel and emerging influenza A viruses in laboratory settings, but systematic examinations of how well viral titer measurements obtained in vitro align with results from in vivo experimentation are not frequently performed. We show that certain viral titer measurements following infection of a human bronchial epithelial cell line are positively correlated with viral titers in specimens collected from virus-inoculated ferrets and employ mathematical modeling to identify commonalities between viral infection progression between both models. These analyses provide a necessary first step in enhanced interpretation and incorporation of in vitro-derived data in risk assessment activities and highlight the utility of employing mathematical modeling approaches to more closely examine features of virus replication not identifiable by experimental studies alone.

Prediction of transport, deposition, and resultant immune response of nasal spray vaccine droplets using a CFPD-HCD model in a 6-year-old upper airway geometry to potentially prevent COVID-19

This study focuses on the transport, deposition, and triggered immune response of intranasal vaccine droplets to the angiotensin-converting-enzyme-2-rich region, i.e., the olfactory region (OR), in the nasal cavity of a 6-year-old female to possibly prevent corona virus disease 19 (COVID-19). To investigate how administration strategy can influence nasal vaccine efficiency, a validated multi-scale model, i.e., computational fluid-particle dynamics (CFPD) and host-cell dynamics (HCD) model, was employed. Droplet deposition fraction, size change, residence time, and the area percentage of OR covered by the vaccine droplets, and triggered immune system response were predicted with different spray cone angles, initial droplet velocities, and compositions. Numerical results indicate that droplet initial velocity and composition have negligible influences on the vaccine delivery efficiency to OR. In contrast, the spray cone angle can significantly impact the vaccine delivery efficiency. The triggered immunity was not significantly influenced by the administration investigated in this study due to the low percentage of OR area covered by the droplets. To enhance the effectiveness of the intranasal vaccine to prevent COVID-19 infection, it is necessary to optimize the vaccine formulation and administration strategy so that the vaccine droplets can cover more epithelial cells in OR to minimize the number of available receptors for SARS-CoV-2.

A simple model of COVID-19 explains disease severity and the effect of treatments

Considerable effort has been made to better understand why some people suffer from severe COVID-19 while others remain asymptomatic. This has led to important clinical findings; people with severe COVID-19 generally experience persistently high levels of inflammation, slower viral load decay, display a dysregulated type-I interferon response, have less active natural killer cells and increased levels of neutrophil extracellular traps. How these findings are connected to the pathogenesis of COVID-19 remains unclear. We propose a mathematical model that sheds light on this issue by focusing on cells that trigger inflammation through molecular patterns: infected cells carrying pathogen-associated molecular patterns (PAMPs) and damaged cells producing damage-associated molecular patterns (DAMPs). The former signals the presence of pathogens while the latter signals danger such as hypoxia or lack of nutrients. Analyses show that SARS-CoV-2 infections can lead to a self-perpetuating feedback loop between DAMP expressing cells and inflammation, identifying the inability to quickly clear PAMPs and DAMPs as the main contributor to hyperinflammation. The model explains clinical findings and reveal conditions that can increase the likelihood of desired clinical outcome from treatment administration. In particular, the analysis suggest that antivirals need to be administered early during infection to have an impact on disease severity. The simplicity of the model and its high level of consistency with clinical findings motivate its use for the formulation of new treatment strategies.

COVID-19 Adaptive Humoral Immunity Models: Weakly Neutralizing Versus Antibody-Disease Enhancement Scenarios

The interplay between the virus, infected cells and immune responses to SARS-CoV-2 is still under debate. By extending the basic model of viral dynamics, we propose here a formal approach to describe neutralisation versus weak (or non-)neutralisation scenarios and compare them with the possible effects of antibody-dependent enhancement (ADE). The theoretical model is consistent with the data available in the literature; we show that both weakly neutralising antibodies and ADE can result in final viral clearance or disease progression, but that the immunodynamics are different in each case. As a significant proportion of the world’s population is already naturally immune or vaccinated, we also discuss the implications for secondary infections after vaccination or in the presence of immune system dysfunctions.

Optimizing antiviral therapy for COVID-19 with learned pathogenic model

COVID-19 together with variants have caused an unprecedented amount of mental and economic turmoil with ever increasing fatality and no proven therapies in sight. The healthcare industry is racing to find a cure with multitude of clinical trials underway to access the efficacy of repurposed antivirals, however the much needed insights into the dynamics of pathogenesis of SARS-CoV-2 and corresponding pharmacology of antivirals are lacking. This paper introduces systematic pathological model learning of COVID-19 dynamics followed by derivative free optimization based multi objective drug rescheduling. The pathological model learnt from clinical data of severe COVID-19 patients treated with remdesivir could additionally predict immune T cells response and resulted in a dramatic reduction in remdesivir dose and schedule leading to lower toxicities, however maintaining a high virological efficacy.

Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains.

COVID-19 virtual patient cohort suggests immune mechanisms driving disease outcomes

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results suggest that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8+ T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings suggest biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

COVID-19 virtual patient cohort reveals immune mechanisms driving disease outcomes

To understand the diversity of immune responses to SARS-CoV-2 and distinguish features that predispose individuals to severe COVID-19, we developed a mechanistic, within-host mathematical model and virtual patient cohort. Our results indicate that virtual patients with low production rates of infected cell derived IFN subsequently experienced highly inflammatory disease phenotypes, compared to those with early and robust IFN responses. In these in silico patients, the maximum concentration of IL-6 was also a major predictor of CD8 + T cell depletion. Our analyses predicted that individuals with severe COVID-19 also have accelerated monocyte-to-macrophage differentiation that was mediated by increased IL-6 and reduced type I IFN signalling. Together, these findings identify biomarkers driving the development of severe COVID-19 and support early interventions aimed at reducing inflammation.

AUTHOR SUMMARY

Understanding of how the immune system responds to SARS-CoV-2 infections is critical for improving diagnostic and treatment approaches. Identifying which immune mechanisms lead to divergent outcomes can be clinically difficult, and experimental models and longitudinal data are only beginning to emerge. In response, we developed a mechanistic, mathematical and computational model of the immunopathology of COVID-19 calibrated to and validated against a broad set of experimental and clinical immunological data. To study the drivers of severe COVID-19, we used our model to expand a cohort of virtual patients, each with realistic disease dynamics. Our results identify key processes that regulate the immune response to SARS-CoV-2 infection in virtual patients and suggest viable therapeutic targets, underlining the importance of a rational approach to studying novel pathogens using intra-host models.

In-host Mathematical Modelling of COVID-19 in Humans

COVID-19 pandemic has underlined the impact of emergent pathogens as a major threat to human health. The development of quantitative approaches to advance comprehension of the current outbreak is urgently needed to tackle this severe disease. Considering different starting times of infection, mathematical models are proposed to represent SARS-CoV-2 dynamics in infected patients. Based on the target cell limited model, the within-host reproductive number for SARS-CoV-2 is consistent with the broad values of human influenza infection. The best model to fit the data was including immune cell response, which suggests a slow immune response peaking between 5 to 10 days post-onset of symptoms. The model with the eclipse phase, time in a latent phase before becoming productively infected cells, was not supported. Interestingly, model simulations predict that SARS-CoV-2 may replicate very slowly in the first days after infection, and viral load could be below detection levels during the first 4 days post infection. A quantitative comprehension of SARS-CoV-2 dynamics and the estimation of standard parameters of viral infections is the key contribution of this pioneering work. These models can serve for future evaluation of control theoretical approaches to tailor new drugs against COVID-19.

Ferreting Out Influenza Virus Pathogenicity and Transmissibility: Past and Future Risk Assessments in the Ferret Model

As influenza A viruses continue to jump species barriers, data generated in the ferret model to assess influenza virus pathogenicity, transmissibility, and tropism of these novel strains continues to inform an increasing scope of public health-based applications. This review presents the suitability of ferrets as a small mammalian model for influenza viruses and describes the breadth of pathogenicity and transmissibility profiles possible in this species following inoculation with a diverse range of viruses. Adaptation of aerobiology-based techniques and analyses have furthered our understanding of data obtained from this model and provide insight into the capacity of novel and emerging influenza viruses to cause human infection and disease.

Varying Inoculum Dose to Assess the Roles of the Immune Response and Target Cell Depletion by the Pathogen in Control of Acute Viral Infections

It is difficult to determine whether an immune response or target cell depletion by the infectious agent is most responsible for the control of acute primary infection. Both mechanisms can explain the basic dynamics of an acute infection-exponential growth of the pathogen followed by control and clearance-and can also be represented by many different differential equation models. Consequently, traditional model comparison techniques using time series data can be ambiguous or inconclusive. We propose that varying the inoculum dose and measuring the subsequent infectious load can rule out target cell depletion by the pathogen as the main control mechanism. Infectious load can be any measure that is proportional to the number of infected cells, such as viraemia. We show that a twofold or greater change in infectious load is unlikely when target cell depletion controls infection, regardless of the model details. Analyzing previously published data from mice infected with influenza, we find the proportion of lung epithelial cells infected was 21-fold greater (95% confidence interval 14-32) in the highest dose group than in the lowest. This provides evidence in favor of an alternative to target cell depletion, such as innate immunity, in controlling influenza infections in this experimental system. Data from other experimental animal models of acute primary infection have a similar pattern.

Antiviral activity of iridoid glycosides extracted from Fructus Gardeniae against influenza A virus by PACT-dependent suppression of viral RNA replication

Epidemic and pandemic influenza A virus (IAV) poses a significant threat to human populations worldwide. Iridoid glycosides are principal bioactive components from the Gardenia jasminoides J. Ellis fruit that exhibit antiviral activity against several strains of IAV. In the present study, we evaluated the protective effect of Fructus Gardeniae iridoid glycoside extracts (IGEs) against IAV by cytopathogenic effect(CPE), MTT and a plaque formation assay in vitro and examined the reduction in the pulmonary index (PI), restoration of body weight, reduction in mortality and increases in survival time in vivo. As a host factor, PACT provides protection against the pathogenic influenza A virus by interacting with IAV polymerase and activating the IFN-I response. To verify the whether IGEs suppress IAV replication in a PACT-dependent manner, IAV RNA replication, expression of PACT and the phosphorylation of eIF2α in A549 cells were detected; the levels of IFNβ, PACT and PKR in mouse lung tissues were determined; and the activity of IAV polymerase was evaluated in PACT-compromised cells. The results indicated that IGEs sufficiently alleviated cell damage and suppressed IAV replication in vitro, protecting mice from IAV-induced injury and lethal IAV infection. These anti-IAV effects might be related to disrupted interplay between IVA polymerase and PACT and/or prevention of a PACT-dependent overactivated IFN-I antiviral response. Taken together, our findings reveal a new facet of the mechanisms by which IGEs fight the influenza A virus in a PACT-dependent manner.

Avian Flavivirus Infection of Monocytes/Macrophages by Extensive Subversion of Host Antiviral Innate Immune Responses

Avian Tembusu virus (TMUV) is a newly emerging avian pathogenic flavivirus in China and Southeast Asia with features of rapid spread, an expanding host range, and cross-species transmission. The mechanisms of its infection and pathogenesis remain largely unclear. Here, we investigated the tropism of this arbovirus in peripheral blood mononuclear cells of specific-pathogen-free (SPF) ducks and SPF chickens and identified monocytes/macrophages as the key targets of TMUV infection. In vivo studies in SPF ducks and SPF chickens with monocyte/macrophage clearance demonstrated that the infection of monocytes/macrophages was crucial for viral replication, transmission, and pathogenesis. Further genome-wide transcriptome analyses of TMUV-infected chicken macrophages revealed that host antiviral innate immune barriers were the major targets of TMUV in macrophages. Despite the activation of major pattern recognition receptor signaling, the inductions of alpha interferon (IFN-α) and IFN-β were blocked by TMUV infection on transcription and translation levels, respectively. Meanwhile, TMUV inhibited host redox responses by repressing the transcription of genes encoding NADPH oxidase subunits and promoting Nrf2-mediated antioxidant responses. The recovery of either of the above-mentioned innate immune barriers was sufficient to suppress TMUV infection. Collectively, we identify an essential step of TMUV infection and reveal extensive subversion of host antiviral innate immune responses.IMPORTANCE Mosquito-borne flaviviruses include a group of pathogenic viruses that cause serious diseases in humans and animals, including dengue, West Nile, and Japanese encephalitis viruses. These flaviviruses are zoonotic and use animals, including birds, as amplifying and reservoir hosts. Avian Tembusu virus (TMUV) is an emerging mosquito-borne flavivirus that is pathogenic for many avian species and can infect cells derived from mammals and humans in vitro Although not currently pathogenic for primates, the infection of duck industry workers and the potential risk of TMUV infection in immunocompromised individuals have been highlighted. Thus, the prevention of TMUV in flocks is important for both avian and mammalian health. Our study reveals the escape of TMUV from the first line of the host defense system in the arthropod-borne transmission route of arboviruses, possibly helping to extend our understanding of flavivirus infection in birds and refine the design of anti-TMUV therapeutics.

Correction: Within-Host Models of High and Low Pathogenic Influenza Virus Infections: The Role of Macrophages

[This corrects the article DOI: 10.1371/journal.pone.0150568.].

Exploring the impact of inoculum dose on host immunity and morbidity to inform model-based vaccine design

Vaccination is an effective method to protect against infectious diseases. An important consideration in any vaccine formulation is the inoculum dose, i.e., amount of antigen or live attenuated pathogen that is used. Higher levels generally lead to better stimulation of the immune response but might cause more severe side effects and allow for less population coverage in the presence of vaccine shortages. Determining the optimal amount of inoculum dose is an important component of rational vaccine design. A combination of mathematical models with experimental data can help determine the impact of the inoculum dose. We illustrate the concept of using data and models to inform inoculum dose determination for vaccines, wby fitting a mathematical model to data from influenza A virus (IAV) infection of mice and human parainfluenza virus (HPIV) infection of cotton rats at different inoculum doses. We use the model to map inoculum dose to the level of immune protection and morbidity and to explore how such a framework might be used to determine an optimal inoculum dose. We show how a framework that combines mathematical models with experimental data can be used to study the impact of inoculum dose on important outcomes such as immune protection and morbidity. Our findings illustrate that the impact of inoculum dose on immune protection and morbidity can depend on the specific pathogen and that both protection and morbidity do not necessarily increase monotonically with increasing inoculum dose. Once vaccine design goals are specified with required levels of protection and acceptable levels of morbidity, our proposed framework can help in the rational design of vaccines and determination of the optimal amount of inoculum.

An important component of vaccines is the amount of pathogen inoculum, dead or alive, that is included in the vaccine. This inoculum dose, sometimes also referred to as antigen dose, needs to be large enough to induce good protective immunity. However, one usually also wants to keep the dose low to reduce costs, maximize the number of vaccine doses available, and minimize potential vaccine side effects. The inoculum dose is currently chosen based on limited data from clinical trials. In this study, we set up a framework that combines data with mathematical models to illustrate how such a combination could lead to better and more efficient determination of an optimal inoculum dose for vaccines.

Monocytes and Macrophages as Viral Targets and Reservoirs

Viruses manipulate cell biology to utilize monocytes/macrophages as vessels for dissemination, long-term persistence within tissues and virus replication. Viruses enter cells through endocytosis, phagocytosis, macropinocytosis or membrane fusion. These processes play important roles in the mechanisms contributing to the pathogenesis of these agents and in establishing viral genome persistence and latency. Upon viral infection, monocytes respond with an elevated expression of proinflammatory signalling molecules and antiviral responses, as is shown in the case of the influenza, Chikungunya, human herpes and Zika viruses. Human immunodeficiency virus initiates acute inflammation on site during the early stages of infection but there is a shift of M1 to M2 at the later stages of infection. Cytomegalovirus creates a balance between pro- and anti-inflammatory processes by inducing a specific phenotype within the M1/M2 continuum. Despite facilitating inflammation, infected macrophages generally display abolished apoptosis and restricted cytopathic effect, which sustains the virus production. The majority of viruses discussed in this review employ monocytes/macrophages as a repository but certain viruses use these cells for productive replication. This review focuses on viral adaptations to enter monocytes/macrophages, immune escape, reprogramming of infected cells and the response of the host cells.

Enhanced Replication of Virulent Newcastle Disease Virus in Chicken Macrophages Is due to Polarized Activation of Cells by Inhibition of TLR7

Newcastle disease (ND), caused by infections with virulent strains of Newcastle disease virus (NDV), is one of the most important infectious disease affecting wild, peridomestic, and domestic birds worldwide. Vaccines constructed from live, low-virulence (lentogenic) viruses are the most accepted prevention and control strategies for combating ND in poultry across the globe. Avian macrophages are one of the first cell lines of defense against microbial infection, responding to signals in the microenvironment. Although macrophages are considered to be one of the main target cells for NDV infection in vivo, very little is known about the ability of NDV to infect chicken macrophages, and virulence mechanisms of NDV as well as the polarized activation patterns of macrophages and correlation with viral infection and replication. In the present study, a cell culture model (chicken bone marrow macrophage cell line HD11) and three different virulence and genotypes of NDV (including class II virulent NA-1, class II lentogenic LaSota, and class I lentogenic F55) were used to solve the above underlying questions. Our data indicated that all three NDV strains had similar replication rates during the early stages of infection. Virulent NDV titers were shown to increase compared to the other lentogenic strains, and this growth was associated with a strong upregulation of both pro-inflammatory M1-like markers/cytokines and anti-inflammatory M2-like markers/cytokines in chicken macrophages. Virulent NDV was found to block toll-like receptor (TLR) 7 expression, inducing higher expression of type I interferons in chicken macrophages at the late stage of viral infection. Only virulent NDV replication can be inhibited by pretreatment with TLR7 ligand. Overall, this study demonstrated that virulent NDV activates a M1-/M2-like mixed polarized activation of chicken macrophages by inhibition of TLR7, resulting in enhanced replication compared to lentogenic viruses.

Systems-based approach to examine the cytokine responses in primary mouse lung macrophages infected with low pathogenic avian Influenza virus circulating in South East Asia

Background

Influenza A virus (IAV) is a major public health concern, being responsible for the death of approximately half a million people each year. Zoonotic transmissions of the virus from swine and avian origin have occurred in the past, and can potentially lead to the emgergence of new IAV stains in future pandemics. Pulmonary macrophages have been implicated in disease severity in the lower airway, and understanding the host response of macrophages infected with avian influenza viruses should provide new therapeutic strategies.

Results

We used a systems-based approach to investigate the transcriptome response of primary murine lung macrophages (PMФ) infected with the mouse-adapted H1N1/WSN virus and low pathogenic avian influenza (LPAI) viruses H5N2 and H5N3. The results showed that the LPAI viruses H5N2 and H5N3 can infect PMФ with similar efficiency to the H1N1/WSN virus. While all viruses induced antiviral responses, the H5N3 virus infection resulted in higher expression levels of cytokines and chemokines associated with inflammatory responses.

Conclusions

The LPAI H5N2 and H5N3 viruses are able to infect murine lung macrophages. However, the H5N3 virus was associated with increased expression of pro-inflammatory mediators. Although the H5N3 virus it is capable of inducing high levels of cytokines that are associated with inflammation, this property is distinct from its inability to efficiently replicate in a mammalian host.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3803-6) contains supplementary material, which is available to authorized users.