Hierarchical effects of pro-inflammatory cytokines on the post-influenza susceptibility to pneumococcal coinfection

Invariant set theory for predicting potential failure of antibiotic cycling

Collateral sensitivity, where resistance to one drug confers heightened sensitivity to another, offers a promising strategy for combating antimicrobial resistance, yet predicting resultant evolutionary dynamics remains a significant challenge. We propose here a mathematical model that integrates fitness trade-offs and adaptive landscapes to predict the evolution of collateral sensitivity pathways, providing insights into optimizing sequential drug therapies. Our approach embeds collateral information into a network of switched systems, allowing us to abstract the effects of sequential antibiotic exposure on antimicrobial resistance. We analyze the system stability at disease-free equilibrium and employ set-control theory to tailor therapeutic windows. Consequently, we propose a computational algorithm to identify effective sequential therapies to counter antibiotic resistance. By leveraging our theory with data on collateral sensivity interactions, we predict scenarios that may prevent bacterial escape for chronic Pseudomonas aeruginosa infections.

Co-infection in unvaccinated infants with acute pertussis in Western China (2018-2019): pathogen distribution and impact on disease severity

BACKGROUND

Co-infections in pertussis patients are common, but there has been limited research on the distribution of co-infecting pathogens and their impact on disease severity in infant patients remaining unvaccinated against pertussis. This study aims to investigate the pathogen distribution in unvaccinated infants with acute pertussis and explore how the number and type of co-infecting pathogens influence disease severity.

METHOD

This cross-sectional study analyzed clinical data from 302 unvaccinated infants diagnosed with acute pertussis in western China. We compared clinical variables across different co-infection groups (bacteria, viruses, bacterial-viral combinations) and by the number of co-infecting pathogens (0, 1, ≥ 2).

RESULTS

Of the 302 patients, 121 (40.1%) were infected solely with Bordetella pertussis, while 181 (59.9%) had co-infections with other pathogens. The most common co-infections were bacterial (93 of 139 cases), particularly Gram-negative bacteria, followed by viral co-infections, mainly parainfluenza virus type-3 (PIV-3), in 71.3% of viral cases. The number of co-infecting pathogens was positively associated with longer hospital stays, more severe pneumonia, and higher incidence of respiratory failure (P < 0.05). Notably, bacterial co-infections were associated with more severe clinical outcomes than viral co-infections, with significant differences in hospitalization duration, as well as in peak white blood cell and lymphocyte counts (P < 0.05). No significant differences were observed in co-infection types or pathogen numbers across different age groups.

CONCLUSION

Co-infections are prevalent among unvaccinated infants with acute pertussis in western China. Bacterial and viral pathogens are the most common co-infecting agents, and disease severity increases with the number of co-infecting pathogens. Bacterial co-infections may lead to more severe outcomes compared to viral co-infections, underscoring the need for targeted diagnostic and therapeutic strategies.

Mathematical Modeling of the Lethal Synergism of Coinfecting Pathogens in Respiratory Viral Infections: A Review

Influenza A virus (IAV) infections represent a substantial global health challenge and are often accompanied by coinfections involving secondary viruses or bacteria, resulting in increased morbidity and mortality. The clinical impact of coinfections remains poorly understood, with conflicting findings regarding fatality. Isolating the impact of each pathogen and mechanisms of pathogen synergy during coinfections is challenging and further complicated by host and pathogen variability and experimental conditions. Factors such as cytokine dysregulation, immune cell function alterations, mucociliary dysfunction, and changes to the respiratory tract epithelium have been identified as contributors to increased lethality. The relative significance of these factors depends on variables such as pathogen types, infection timing, sequence, and inoculum size. Mathematical biological modeling can play a pivotal role in shedding light on the mechanisms of coinfections. Mathematical modeling enables the quantification of aspects of the intra-host immune response that are difficult to assess experimentally. In this narrative review, we highlight important mechanisms of IAV coinfection with bacterial and viral pathogens and survey mathematical models of coinfection and the insights gained from them. We discuss current challenges and limitations facing coinfection modeling, as well as current trends and future directions toward a complete understanding of coinfection using mathematical modeling and computer simulation.

Antiviral epithelial-macrophage crosstalk permits secondary bacterial infections

IMPORTANCE

Miscommunication of antiviral and antibacterial immune signals drives worsened morbidity and mortality during respiratory viral-bacterial coinfections. Extracellular vesicles (EVs) are a form of intercellular communication with broad implications during infection, and here we show that epithelium-derived EVs released during the antiviral response impair the antibacterial activity of macrophages, an innate immune cell crucial for bacterial control in the airway. Macrophages exposed to antiviral EVs display reduced clearance of Staphylococcus aureus as well as altered inflammatory signaling and anti-inflammatory metabolic reprogramming, thus revealing EVs as a source of dysregulated epithelium-macrophage crosstalk during coinfection. As effective epithelium-macrophage communication is critical in mounting an appropriate immune response, this novel observation of epithelium-macrophage crosstalk shaping macrophage metabolism and antimicrobial function provides exciting new insight and improves our understanding of immune dysfunction during respiratory coinfections.

Predicting Influenza A Virus Infection in the Lung from Hematological Data with Machine Learning

The tracking of pathogen burden and host responses with minimally invasive methods during respiratory infections is central for monitoring disease development and guiding treatment decisions. Utilizing a standardized murine model of respiratory influenza A virus (IAV) infection, we developed and tested different supervised machine learning models to predict viral burden and immune response markers, i.e., cytokines and leukocytes in the lung, from hematological data. We performed independently in vivo infection experiments to acquire extensive data for training and testing of the models. We show here that lung viral load, neutrophil counts, cytokines (such as gamma interferon [IFN-γ] and interleukin 6 [IL-6]), and other lung infection markers can be predicted from hematological data. Furthermore, feature analysis of the models showed that blood granulocytes and platelets play a crucial role in prediction and are highly involved in the immune response against IAV. The proposed in silico tools pave the path toward improved tracking and monitoring of influenza virus infections and possibly other respiratory infections based on minimally invasively obtained hematological parameters. IMPORTANCE During the course of respiratory infections such as influenza, we do have a very limited view of immunological indicators to objectively and quantitatively evaluate the outcome of a host. Methods for monitoring immunological markers in a host's lungs are invasive and expensive, and some of them are not feasible to perform. Using machine learning algorithms, we show for the first time that minimally invasively acquired hematological parameters can be used to infer lung viral burden, leukocytes, and cytokines following influenza virus infection in mice. The potential of the framework proposed here consists of a new qualitative vision of the disease processes in the lung compartment as a noninvasive tool.

Human C1q Regulates Influenza A Virus Infection and Inflammatory Response via Its Globular Domain

The Influenza A virus (IAV) is a severe respiratory pathogen. C1q is the first subcomponent of the complement system's classical pathway. C1q is composed of 18 polypeptide chains. Each of these chains contains a collagen-like region located at the N terminus, and a C-terminal globular head region organized as a heterotrimeric structure (ghA, ghB and ghC). This study was aimed at investigating the complement activation-independent modulation by C1q and its individual recombinant globular heads against IAV infection. The interaction of C1q and its recombinant globular heads with IAV and its purified glycoproteins was examined using direct ELISA and far-Western blotting analysis. The effect of the complement proteins on IAV replication kinetics and immune modulation was assessed by qPCR. The IAV entry inhibitory properties of C1q and its recombinant globular heads were confirmed using cell binding and luciferase reporter assays. C1q bound IAV virions via HA, NA and M1 IAV proteins, and suppressed replication in H1N1, while promoting replication in H3N2-infected A549 cells. C1q treatment further triggered an anti-inflammatory response in H1N1 and pro-inflammatory response in H3N2-infected cells as evident from differential expression of TNF-α, NF-κB, IFN-α, IFN-β, IL-6, IL-12 and RANTES. Furthermore, C1q treatment was found to reduce luciferase reporter activity of MDCK cells transfected with H1N1 pseudotyped lentiviral particles, indicative of an entry inhibitory role of C1q against infectivity of IAV. These data appear to demonstrate the complement-independent subtype specific modulation of IAV infection by locally produced C1q.

Secondary infection with Streptococcus pneumoniae decreases influenza virus replication and is linked to severe disease

Secondary bacterial infection is a common complication in severe influenza virus infections. During the H1N1 pandemic of 2009, increased mortality was observed among healthy young adults due to secondary bacterial pneumonia, one of the most frequent bacterial species being Streptococcus pneumoniae (Spn). Previous studies in mice and ferrets have suggested a synergistic relationship between Spn and influenza viruses. In this study, the ferret model was used to examine whether secondary Spn infection (strains BHN97 and D39) influence replication and airborne transmission of the 2009 pandemic H1N1 virus (H1N1pdm09). Secondary infection with Spn after H1N1pdm09 infection consistently resulted in a significant decrease in viral titers in the ferret nasal washes. While secondary Spn infection appeared to negatively impact influenza virus replication, animals precolonized with Spn were equally susceptible to H1N1pdm09 airborne transmission. In line with previous work, ferrets with preceding H1N1pdm09 and secondary Spn infection had increased bacterial loads and more severe clinical symptoms as compared to animals infected with H1N1pdm09 or Spn alone. Interestingly, the donor animals that displayed the most severe clinical symptoms had reduced airborne transmission of H1N1pdm09. Based on these data, we propose an asymmetrical relationship between these two pathogens, rather than a synergistic one, since secondary bacterial infection enhances Spn colonization and pathogenesis but decreases viral titers.

Computational simulations to dissect the cell immune response dynamics for severe and critical cases of SARS-CoV-2 infection

BACKGROUND

COVID-19 is a global pandemic leading to high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe, and critical cases. In particular, studies have highlighted the relationship between lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. However, a quantitative understanding of the immune responses in COVID-19 patients is still missing.

OBJECTIVES

In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical cases. The dynamics of different immune cells are taken into account in mechanistic models to elucidate those that contribute to the worsening of the disease.

METHODS

Several mathematical models based on ordinary differential equations are proposed to represent data sets of different immune response cells dynamics such as CD8+ T cells, NK cells, and also CD4+ T cells in patients with SARS-CoV-2 infection. Parameter fitting is performed using the differential evolution algorithm. Non-parametric bootstrap approach is introduced to abstract the stochastic environment of the infection.

RESULTS

The mathematical model that represents the data more appropriately is considering CD8+ T cell dynamics. This model had a good fit to reported experimental data, and in accordance with values found in the literature. The NK cells and CD4+ T cells did not contribute enough to explain the dynamics of the immune responses.

CONCLUSIONS

Our computational results highlight that a low viral clearance rate by CD8+ T cells could lead to the severity of the disease. This deregulated clearance suggests that it is necessary immunomodulatory strategies during the course of the infection to avoid critical states in COVID-19 patients.

Influenza A virus infection in turkeys induces respiratory and enteric bacterial dysbiosis correlating with cytokine gene expression

Turkey respiratory and gut microbiota play important roles in promoting health and production performance. Loss of microbiota homeostasis due to pathogen infection can worsen the disease or predispose the bird to infection by other pathogens. While turkeys are highly susceptible to influenza viruses of different origins, the impact of influenza virus infection on turkey gut and respiratory microbiota has not been demonstrated. In this study, we investigated the relationships between low pathogenicity avian influenza (LPAI) virus replication, cytokine gene expression, and respiratory and gut microbiota disruption in specific-pathogen-free turkeys. Differential replication of two LPAI H5N2 viruses paralleled the levels of clinical signs and cytokine gene expression. During active virus shedding, there was significant increase of ileal and nasal bacterial contents, which inversely corresponded with bacterial species diversity. Spearman’s correlation tests between bacterial abundance and local viral titers revealed that LPAI virus-induced dysbiosis was strongest in the nasal cavity followed by trachea, and weakest in the gut. Significant correlations were also observed between cytokine gene expression levels and relative abundances of several bacteria in tracheas of infected turkeys. For example, interferon γ/λ and interleukin-6 gene expression levels were correlated positively with Staphylococcus and Pseudomonas abundances, and negatively with Lactobacillus abundance. Overall, our data suggest a potential relationship where bacterial community diversity and enrichment or depletion of several bacterial genera in the gut and respiratory tract are dependent on the level of LPAI virus replication. Further work is needed to establish whether respiratory and enteric dysbiosis in LPAI virus-infected turkeys is a result of host immunological responses or other causes such as changes in nutritional uptake.

Cytokine expression patterns in hospitalized children with Bordetella pertussis, Rhinovirus or co-infection

Mechanisms of interaction between Bordetella pertussis and other viral agents are yet to be fully explored. We studied the inflammatory cytokine expression patterns among children with both viral-bacterial infections. Nasopharyngeal aspirate (NPA) samples were taken from children, aged < 1 year, positive for Rhinovirus, Bordetella pertussis and for Rhinovirus and Bordetella pertussis. Forty cytokines were evaluated in NPA by using human cytokine protein arrays and a quantitative analysis was performed on significantly altered cytokines. Forty cytokines were evaluated in NPA by using human cytokine protein arrays and a quantitative analysis was performed on significantly altered cytokines. Our results show that co-infections display a different inflammatory pattern compared to single infections, suggesting that a chronic inflammation caused by one of the two pathogens could be the trigger for exacerbation in co-infections.

Transkingdom Interactions Important for the Pathogenesis of Human Viruses

The bacterial, fungal, and helminthic species that comprise the microbiome of the mammalian host have profound effects on health and disease. Pathogenic viruses must contend with the microbiome during infection and likely have evolved to exploit or evade the microbiome. Both direct interactions between the virions and the microbiota and immunomodulation and tissue remodeling caused by the microbiome alter viral pathogenesis in either host- or virus-beneficial ways. Recent insights from in vitro and murine models of viral pathogenesis have highlighted synergistic and antagonistic, direct and indirect interactions between the microbiome and pathogenic viruses. This review will focus on the transkingdom interactions between human gastrointestinal and respiratory viruses and the constituent microbiome of those tissues.

Resolved Influenza A Virus Infection Has Extended Effects on Lung Homeostasis and Attenuates Allergic Airway Inflammation in a Mouse Model

Allergic airway inflammation (AAI) involves T helper cell type 2 (Th2) and pro-inflammatory responses to aeroallergens and many predisposing factors remain elusive. Influenza A virus (IAV) is a major human pathogen that causes acute respiratory infections and induces specific immune responses essential for viral clearance and resolution of the infection. Beyond acute infection, IAV has been shown to persistently affect lung homeostasis and respiratory immunity. Here we asked how resolved IAV infection affects subsequently induced AAI. Mice infected with a sublethal dose of IAV were sensitized and challenged in an ovalbumin mediated mouse model for AAI after resolution of the acute viral infection. Histological changes, respiratory leukocytes, cytokines and airway hyperreactivity were analyzed in resolved IAV infection alone and in AAI with and without previous IAV infection. More than five weeks after infection, we detected persistent pneumonia with increased activated CD4⁺ and CD8⁺ lymphocytes as well as dendritic cells and MHCII expressing macrophages in the lung. Resolved IAV infection significantly affected subsequently induced AAI on different levels including morphological changes, respiratory leukocytes and lymphocytes as well as the pro-inflammatory cytokine responses, which was clearly diminished. We conclude that IAV has exceptional persisting effects on respiratory immunity with substantial consequences for subsequently induced AAI.

Flu Virus Attenuates Memory Clearance of Pneumococcus via IFN-γ-Dependent Th17 and Independent Antibody Mechanisms

Bacterial coinfection is a major cause of influenza-associated mortality. Most people have experienced infections with bacterial pathogens commonly associated with influenza A virus (IAV) coinfection before IAV exposure; however, bacterial clearance through the immunological memory response (IMR) in coinfected patients is inefficient, suggesting that the IMR to bacteria is impaired during IAV infection. Adoptive transfer of CD4⁺ T cells from mice that had experienced bacterial infection into IAV-infected mice revealed that memory protection against bacteria was weakened in the latter. Additionally, memory Th17 cell responses were impaired due to an IFN-γ-dependent reduction in Th17 cell proliferation and delayed migration of CD4⁺ T cells into the lungs. A bacterium-specific antibody-mediated memory response was also substantially reduced in coinfected mice, independently of IFN-γ. These findings provide additional perspectives on the pathogenesis of coinfection and suggest additional strategies for the treatment of defective antibacterial immunity and the design of bacterial vaccines against coinfection.

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Memory protection against bacteria was impaired in coinfection

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Memory Th17 response to bacteria was reduced by IAV-induced IFN-γ

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The Th17 reduction was caused by impeded Th17 proliferation and migration

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Bacteria-specific antibody was reduced in coinfection independent of IFN-γ

Topological data analysis to model the shape of immune responses during co-infections

Co-infections by multiple pathogens have important implications in many aspects of health, epidemiology and evolution. However, how to disentangle the non-linear dynamics of the immune response when two infections take place at the same time is largely unexplored. Using data sets of the immune response during influenza-pneumococcal co-infection in mice, we employ here topological data analysis to simplify and visualise high dimensional data sets. We identified persistent shapes of the simplicial complexes of the data in the three infection scenarios: single viral infection, single bacterial infection, and co-infection. The immune response was found to be distinct for each of the infection scenarios and we uncovered that the immune response during the co-infection has three phases and two transition points. During the first phase, its dynamics is inherited from its response to the primary (viral) infection. The immune response has an early shift (few hours post co-infection) and then modulates its response to react against the secondary (bacterial) infection. Between 18 and 26 h post co-infection the nature of the immune response changes again and does no longer resembles either of the single infection scenarios.

Double-Edged Role of Interleukin 17A in Streptococcus pneumoniae Pathogenesis During Influenza Virus Coinfection

BACKGROUND

We sought to determine the role of host interleukin 17A (IL-17A) response against colonizing Streptococcus pneumoniae, and its transition to a pathogen during coinfection with an influenza virus, influenza A H1N1 A/Puerto Rico/8/1934 (PR8).

METHOD

Wild-type (WT) C57BL/6 mice were intranasally inoculated with S. pneumoniae serotype 6A to establish colonization and later infected with the influenza strain, PR8, resulting in invasive S. pneumoniae disease. The role of the IL-17A response in colonization and coinfection was investigated in WT, RoRγt-/- and RAG1-/- mice with antibody-mediated depletion of IL-17A (WT) and CD90 cells (RAG1-/-).

RESULTS

RAG1-/- mice did not clear colonization and IL-17A neutralization impaired 6A clearance in WT mice. RoRγt-/- mice also had reduced clearance. S. pneumoniae-PR8 coinfection elicited a robust IL-17A response in the nasopharynx; IL-17A neutralization reduced S. pneumoniae invasive disease. RoRγt-/- mice also had reduced S. pneumoniae disease in a coinfection model. Depletion of CD90+ cells suppressed the IL-17A response and reduced S. pneumoniae invasion in RAG1-/- mice.

CONCLUSION

Our data show that although IL-17A reduces S. pneumoniae colonization, coinfection with influenza virus elicits a robust innate IL-17A response that promotes inflammation and S. pneumoniae disease in the nasopharynx.

Influenza species and subtypes circulation among hospitalized patients in Laleh hospital during two influenza seasonal (2016-2017 and 2017-2018) using a multiplex Real Time-Polymerase Chain Reaction

The introduction of polymerase chain reaction (PCR) techniques has improved the detection of respiratory viruses, particularly with the use of multiplex real-time technique with the capability of simultaneous detection of various pathogens in a single reaction. The aim of this study was to apply the above technology for the diagnosis of influenza infections and at the same time to differentiate between common flu species between hospitalized patients in Laleh hospital (Iran) between two flu seasons (2016-2017 and 2017-2018). Different respiratory specimens were collected from 540 patients from a period of December 2016 to May 2018 and were sent to the laboratory for molecular diagnosis. RNAs were extracted and subsequently, a multiplex real time PCR identifying flu A, flu B and typing flu A (H1N1) was carried out. The mean age of patients was 47.54±23.96. 216 (40%) and 321 (60%) of subjects were male and female, respectively. 219 out of 540 (40.5%) were positive for influenza infection including flu A (n=97, 44.3%), flu A (H1N1) (n=45, 20.7%) and flu B (n=77, 35%). Flu A was the dominant species on 2016-2017 and flu B was the major species on 2017-2018. Flu A (H1N1) was comparable in both time periods. Flu infections were most frequently diagnosed in age groups 21-40. Flu-positive patients suffered more from body pain and sore throat than flunegative patients with significant statistical difference (P values <0.001). The mean duration of hospitalization was shorter for flu-positive patients (P value = 0.016). Application of multiplex real time PCR could facilitate the influenza diagnosis in a short period of time, benefiting patients from exclusion of bacterial infections and avoiding unnecessary antibiotic therapy. Influenza diagnosis was not achieved in up to 60% of flu-like respiratory infections, suggesting the potential benefit of adopting the same methodology for assessing the involvement of other viral or/and bacterial pathogens in those patients.

Complement-Independent Modulation of Influenza A Virus Infection by Factor H

The complement system is an ancient innate immune defense mechanism that can recognize molecular patterns on the invading pathogens. Factor H, as an inhibitor of the alternative pathway, down-regulates complement activation on the host cell surface. Locally synthesized factor H at the site of infection/injury, including lungs, can act as a pattern recognition molecule without involving complement activation. Here, we report that factor H, a sialic acid binder, interacts with influenza A virus (IAV) and modulates IAV entry, as evident from down-regulation of matrix protein 1 (M1) in H1N1 subtype-infected cells and up-regulation of M1 expression in H3N2-infected A549 cells. Far-western blot revealed that factor H binds hemagglutinin (HA, ~70 kDa), neuraminidase (NA, ~60 kDa), and M1 (~25 kDa). IAV-induced transcriptional levels of IFN-α, TNF-α, IL-12, IL-6, IFN-α, and RANTES were reduced following factor H treatment for the H1N1 subtype at 6 h post-infection. However, for the H3N2 subtype, mRNA levels of these pro-inflammatory cytokines were enhanced. A recombinant form of vaccinia virus complement control protein (VCP), which like factor H, contains CCP modules and has complement-regulatory activity, mirrored the results obtained with factor H. Both factor H (25%), and VCP (45%) were found to reduce luciferase reporter activity in MDCK cells transduced with H1N1 pseudotyped lentiviral particles. Factor H (50%) and VCP (30%) enhanced the luciferase reporter activity for H3N2, suggesting an entry inhibitory role of factor H and VCP against H1N1, but not H3N2. Thus, factor H can modulate IAV infection and inflammatory responses, independent of its complement-related functions.

The annexin A1/FPR2 signaling axis expands alveolar macrophages, limits viral replication, and attenuates pathogenesis in the murine influenza A virus infection model

Pattern recognition receptors (PRRs) are key elements in the innate immune response. Formyl peptide receptor (FPR) 2 is a PRR that, in addition to proinflammatory, pathogen-derived compounds, also recognizes the anti-inflammatory endogenous ligand annexin A1 (AnxA1). Because the contribution of this signaling axis in viral infections is undefined, we investigated AnxA1-mediated FPR2 activation on influenza A virus (IAV) infection in the murine model. AnxA1-treated mice displayed significantly attenuated pathology upon a subsequent IAV infection with significantly improved survival, impaired viral replication in the respiratory tract, and less severe lung damage. The AnxA1-mediated protection against IAV infection was not caused by priming of the type I IFN response but was associated with an increase in the number of alveolar macrophages (AMs) and enhanced pulmonary expression of the AM-regulating cytokine granulocyte-M-CSF (GM-CSF). Both AnxA1-mediated increase in AM levels and GM-CSF production were abrogated when mouse (m)FPR2 signaling was antagonized but remained up-regulated in mice genetically deleted for mFPR1, an mFPR2 isoform also serving as AnxA1 receptor. Our results indicate a novel protective function of the AnxA1-FPR2 signaling axis in IAV pathology via GM-CSF–associated maintenance of AMs, expanding knowledge on the potential use of proresolving mediators in host defense against pathogens.—Schloer, S., Hübel, N., Masemann, D., Pajonczyk, D., Brunotte, L., Ehrhardt, C., Brandenburg, L.-O., Ludwig, S., Gerke, V., Rescher, U. The annexin A1/FPR2 signaling axis expands alveolar macrophages, limits viral replication, and attenuates pathogenesis in the murine influenza A virus infection model.

In vivo Neutralization of Pro-inflammatory Cytokines During Secondary Streptococcus pneumoniae Infection Post Influenza A Virus Infection

An overt pro-inflammatory immune response is a key factor contributing to lethal pneumococcal infection in an influenza pre-infected host and represents a potential target for therapeutic intervention. However, there is a paucity of knowledge about the level of contribution of individual cytokines. Based on the predictions of our previous mathematical modeling approach, the potential benefit of IFN-γ- and/or IL-6-specific antibody-mediated cytokine neutralization was explored in C57BL/6 mice infected with the influenza A/PR/8/34 strain, which were subsequently infected with the Streptococcus pneumoniae strain TIGR4 on day 7 post influenza. While single IL-6 neutralization had no effect on respiratory bacterial clearance, single IFN-γ neutralization enhanced local bacterial clearance in the lungs. Concomitant neutralization of IFN-γ and IL-6 significantly reduced the degree of pneumonia as well as bacteremia compared to the control group, indicating a positive effect for the host during secondary bacterial infection. The results of our model-driven experimental study reveal that the predicted therapeutic value of IFN-γ and IL-6 neutralization in secondary pneumococcal infection following influenza infection is tightly dependent on the experimental protocol while at the same time paving the way toward the development of effective immune therapies.

Allergic Airway Disease Prevents Lethal Synergy of Influenza A Virus-Streptococcus pneumoniae Coinfection

Asthma has become one of the most common chronic diseases and has been identified as a risk factor for developing influenza. However, the impact of asthma on postinfluenza secondary bacterial infection is currently not known. Here, we developed a novel triple-challenge model of allergic airway disease, primary influenza infection, and secondary Streptococcus pneumoniae infection to investigate the impact of asthma on susceptibility to viral-bacterial coinfections. We report for the first time that mice recovering from acute allergic airway disease are highly resistant to influenza-pneumococcal coinfection and that this resistance is due to inhibition of influenza virus-mediated impairment of bacterial clearance. Further characterization of allergic airway disease-associated resistance against postinfluenza secondary bacterial infection may aid in the development of prophylactic and/or therapeutic treatment against coinfection.

Fatal outcomes following influenza infection are often associated with secondary bacterial infections. Allergic airway disease (AAD) is known to influence severe complications from respiratory infections, and yet the mechanistic effect of AAD on influenza virus-Streptococcus pneumoniae coinfection has not been investigated previously. We examined the impact of AAD on host susceptibility to viral-bacterial coinfections. We report that AAD improved survival during coinfection when viral-bacterial challenge occurred 1 week after AAD. Counterintuitively, mice with AAD had significantly deceased proinflammatory responses during infection. Specifically, both CD4⁺ and CD8⁺ T cell interferon gamma (IFN-γ) responses were suppressed following AAD. Resistance to coinfection was also associated with strong transforming growth factor β1 (TGF-β1) expression and increased bacterial clearance. Treatment of AAD mice with IFN-γ or genetic deletion of TGF-β receptor II expression reversed the protective effects of AAD. Using a novel triple-challenge model system, we show for the first time that AAD can provide protection against influenza virus-S. pneumoniae coinfection through the production of TGF-β that suppresses the influenza virus-induced IFN-γ response, thereby preserving antibacterial immunity.

Immunobiochemical Reconstruction of Influenza Lung Infection-Melanoma Skin Cancer Interactions

It was recently reported that acute influenza infection of the lung promoted distal melanoma growth in the dermis of mice. Melanoma-specific CD8+ T cells were shunted to the lung in the presence of the infection, where they expressed high levels of inflammation-induced cell-activation blocker PD-1, and became incapable of migrating back to the tumor site. At the same time, co-infection virus-specific CD8+ T cells remained functional while the infection was cleared. It was also unexpectedly found that PD-1 blockade immunotherapy reversed this effect. Here, we proceed to ground the experimental observations in a mechanistic immunobiochemical model that incorporates T cell pathways that control PD-1 expression. A core component of our model is a kinetic motif, which we call a PD-1 Double Incoherent Feed-Forward Loop (DIFFL), and which reflects known interactions between IRF4, Blimp-1, and Bcl-6. The different activity levels of the PD-1 DIFFL components, as a function of the cognate antigen levels and the given inflammation context, manifest themselves in phenotypically distinct outcomes. Collectively, the model allowed us to put forward a few working hypotheses as follows: (i) the melanoma-specific CD8+ T cells re-circulating with the blood flow enter the lung where they express high levels of inflammation-induced cell-activation blocker PD-1 in the presence of infection; (ii) when PD-1 receptors interact with abundant PD-L1, constitutively expressed in the lung, T cells loose motility; (iii) at the same time, virus-specific cells adapt to strong stimulation by their cognate antigen by lowering the transiently-elevated expression of PD-1, remaining functional and mobile in the inflamed lung, while the infection is cleared. The role that T cell receptor (TCR) activation and feedback loops play in the underlying processes are also highlighted and discussed. We hope that the results reported in our study could potentially contribute to the advancement of immunological approaches to cancer treatment and, as well, to a better understanding of a broader complexity of fundamental interactions between pathogens and tumors.

Investigating Functional Roles for Positive Feedback and Cellular Heterogeneity in the Type I Interferon Response to Viral Infection

Secretion of type I interferons (IFN) by infected cells mediates protection against many viruses, but prolonged or excessive type I IFN secretion can lead to immune pathology. A proper type I IFN response must therefore maintain a balance between protection and excessive IFN secretion. It has been widely noted that the type I IFN response is driven by positive feedback and is heterogeneous, with only a fraction of infected cells upregulating IFN expression even in clonal cell lines, but the functional roles of feedback and heterogeneity in balancing protection and excessive IFN secretion are not clear. To investigate the functional roles for feedback and heterogeneity, we constructed a mathematical model coupling IFN and viral dynamics that extends existing mathematical models by accounting for feedback and heterogeneity. We fit our model to five existing datasets, reflecting different experimental systems. Fitting across datasets allowed us to compare the IFN response across the systems and suggested different signatures of feedback and heterogeneity in the different systems. Further, through numerical experiments, we generated hypotheses of functional roles for IFN feedback and heterogeneity consistent with our mathematical model. We hypothesize an inherent tradeoff in the IFN response: a positive feedback loop prevents excessive IFN secretion, but also makes the IFN response vulnerable to viral antagonism. We hypothesize that cellular heterogeneity of the IFN response functions to protect the feedback loop from viral antagonism. Verification of our hypotheses will require further experimental studies. Our work provides a basis for analyzing the type I IFN response across systems.

Host-pathogen kinetics during influenza infection and coinfection: insights from predictive modeling

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.

Respiratory Influenza A Virus Infection Triggers Local and Systemic Natural Killer Cell Activation via Toll-Like Receptor 7

The innate immune system senses influenza A virus (IAV) through different pathogen-recognition receptors including Toll-like receptor 7 (TLR7). Downstream of viral recognition natural killer (NK) cells are activated as part of the anti-IAV immune response. Despite the known decisive role of TLR7 for NK cell activation by therapeutic immunostimulatory RNAs, the contribution of TLR7 to the NK cell response following IAV infection has not been addressed. We have analyzed lung cytokine responses as well as the activation, interferon (IFN)-γ production, and cytotoxicity of lung and splenic NK cells following sublethal respiratory IAV infection in wild-type and TLR7ko mice. Early airway IFN-γ levels as well as the induction of lung NK cell CD69 expression and IFN-γ production in response to IAV infection were significantly attenuated in TLR7-deficient hosts. Strikingly, respiratory IAV infection also primed splenic NK cells for IFN-γ production, degranulation, and target cell lysis, all of which were fully dependent on TLR7. At the same time, lung type I IFN levels were significantly reduced in TLR7ko mice early following IAV infection, displaying a potential upstream mechanism of the attenuated NK cell activation observed. Taken together, our data clearly demonstrate a specific role for TLR7 signaling in local and systemic NK cell activation following respiratory IAV infection despite the presence of redundant innate IAV-recognition pathways.

Parameter Estimation in Mathematical Models of Viral Infections Using R

In recent years, mathematical modeling approaches have played a central role in understanding and quantifying mechanisms in different viral infectious diseases. In this approach, biology-based hypotheses are expressed via mathematical relations and then tested based on empirical data. The simulation results can be used to either identify underlying mechanisms and provide predictions of infection outcomes or to evaluate the efficacy of a treatment.Conducting parameter estimation for mathematical models is not an easy task. Here we detail an approach to conduct parameter estimation and to evaluate the results using the free software R. The method is applicable to influenza virus dynamics at different complexity levels, widening experimentalists' capabilities in understanding their data. The parameter estimation approach presented here can be also applied to other viral infections or biological applications.

Polymeric Nanoparticles Induce NLRP3 Inflammasome Activation and Promote Breast Cancer Metastasis

Polymeric nanoparticles gain enormous interests in cancer therapy. Polyethylenimine (PEI) 25 kD is well known for its high transfection efficiency and cytotoxicity. PEI-CyD (PC) was previously synthesized by conjugating low molecular PEI (M _w 600) with β-cyclodextrin (β-CyD), which is shown to induce lower cytotoxicity than PEI 25 kD. In the current study, the in vivo immune response of branched PEI 25 kD and PC is investigated. Compared to PC/pDNA, exposure of PEI 25kD/pDNA induces higher level of immune-stimulation evidenced by the increased spleen weight, phagocytic capacity of peritoneal macrophage, and proinflammatory cytokines in serum and liver. Importantly, administration of PEI 25 kD can greatly promote breast cancer metastasis in liver and lung tissues, which correlates with its ability to induce high oxidative stress and NLRP3-inflammasome activation. These results suggest that polymeric nanocarriers have the potential to induce immune-stimulation and cancer metastasis, which may affect their efficiency for cancer therapy.

Influenza A Virus as a Predisposing Factor for Cryptococcosis

Influenza A virus (IAV) infects millions of people annually and predisposes to secondary bacterial infections. Inhalation of fungi within the Cryptococcus complex causes pulmonary disease with secondary meningo-encephalitis. Underlying pulmonary disease is a strong risk factor for development of C. gattii cryptococcosis though the effect of concurrent infection with IAV has not been studied. We developed an in vivo model of Influenza A H1N1 and C. gattii co-infection. Co-infection resulted in a major increase in morbidity and mortality, with severe lung damage and a high brain fungal burden when mice were infected in the acute phase of influenza multiplication. Furthermore, IAV alters the host response to C. gattii, leading to recruitment of significantly more neutrophils and macrophages into the lungs. Moreover, IAV induced the production of type 1 interferons (IFN-α4/β) and the levels of IFN-γ were significantly reduced, which can be associated with impairment of the immune response to Cryptococcus during co-infection. Phagocytosis, killing of cryptococci and production of reactive oxygen species (ROS) by IAV-infected macrophages were reduced, independent of previous IFN-γ stimulation, leading to increased proliferation of the fungus within macrophages. In conclusion, IAV infection is a predisposing factor for severe disease and adverse outcomes in mice co-infected with C. gattii.

Windows of opportunity for Ebola virus infection treatment and vaccination

Ebola virus (EBOV) infection causes a high death toll, killing a high proportion of EBOV-infected patients within 7 days. Comprehensive data on EBOV infection are fragmented, hampering efforts in developing therapeutics and vaccines against EBOV. Under this circumstance, mathematical models become valuable resources to explore potential controlling strategies. In this paper, we employed experimental data of EBOV-infected nonhuman primates (NHPs) to construct a mathematical framework for determining windows of opportunity for treatment and vaccination. Considering a prophylactic vaccine based on recombinant vesicular stomatitis virus expressing the EBOV glycoprotein (rVSV-EBOV), vaccination could be protective if a subject is vaccinated during a period from one week to four months before infection. For the case of a therapeutic vaccine based on monoclonal antibodies (mAbs), a single dose might resolve the invasive EBOV replication even if it was administrated as late as four days after infection. Our mathematical models can be used as building blocks for evaluating therapeutic and vaccine modalities as well as for evaluating public health intervention strategies in outbreaks. Future laboratory experiments will help to validate and refine the estimates of the windows of opportunity proposed here.

Analysis of Practical Identifiability of a Viral Infection Model

Mathematical modelling approaches have granted a significant contribution to life sciences and beyond to understand experimental results. However, incomplete and inadequate assessments in parameter estimation practices hamper the parameter reliability, and consequently the insights that ultimately could arise from a mathematical model. To keep the diligent works in modelling biological systems from being mistrusted, potential sources of error must be acknowledged. Employing a popular mathematical model in viral infection research, existing means and practices in parameter estimation are exemplified. Numerical results show that poor experimental data is a main source that can lead to erroneous parameter estimates despite the use of innovative parameter estimation algorithms. Arbitrary choices of initial conditions as well as data asynchrony distort the parameter estimates but are often overlooked in modelling studies. This work stresses the existence of several sources of error buried in reports of modelling biological systems, voicing the need for assessing the sources of error, consolidating efforts in solving the immediate difficulties, and possibly reconsidering the use of mathematical modelling to quantify experimental data.