Kinetics of coinfection with influenza A virus and Streptococcus pneumoniae

Risks and features of secondary infections in severe and critical ill COVID-19 patients

Objectives Severe or critical COVID-19 is associated with intensive care unit admission, increased secondary infection rate, and would lead to significant worsened prognosis. Risks and characteristics relating to secondary infections in severe COVID-19 have not been described. Methods Severe and critical COVID-19 patients from Shanghai were included. We collected lower respiratory, urine, catheters, and blood samples according to clinical necessity and culture and mNGS were performed. Clinical and laboratory data were archived. Results We found 57.89% (22/38) patients developed secondary infections. The patient receiving invasive mechanical ventilation or in critical state has a higher chance of secondary infections (P<0.0001). The most common infections were respiratory, blood-stream and urinary infections, and in respiratory infections, the most detected pathogens were gram-negative bacteria (26, 50.00%), following by gram-positive bacteria (14, 26.92%), virus (6, 11.54%), fungi (4, 7.69%), and others (2, 3.85%). Respiratory Infection rate post high flow, tracheal intubation, and tracheotomy were 12.90% (4/31), 30.43% (7/23), and 92.31% (12/13) respectively. Secondary infections would lead to lower discharge rate and higher mortality rate. Conclusion Our study originally illustrated secondary infection proportion in severe and critical COVID-19 patients. Culture accompanied with metagenomics sequencing increased pathogen diagnostic rate. Secondary infections risks increased after receiving invasive respiratory ventilations and intravascular devices, and would lead to a lower discharge rate and a higher mortality rate.

From virus to inflammation, how influenza promotes lung damage

Despite seasonal vaccines, influenza-related hospitalization and death rates have remained unchanged over the past 5 years. Influenza pathogenesis has 2 crucial clinical components; first, influenza causes acute lung injury that may require hospitalization. Second, acute injury promotes secondary bacterial pneumonia, a leading cause of hospitalization and disease burden in the United States and globally. Therefore, developing an effective therapeutic regimen against influenza requires a comprehensive understanding of the damage-associated immune-mechanisms to identify therapeutic targets for interventions to mitigate inflammation/tissue-damage, improve antiviral immunity, and prevent influenza-associated secondary bacterial diseases. In this review, the pathogenic immune mechanisms implicated in acute lung injury and the possibility of using lung inflammation and barrier crosstalk for developing therapeutics against influenza are highlighted.

Bacterial co-infections with SARS-CoV-2

The pandemic coronavirus disease 2019 (COVID‐19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS‐CoV‐2), has affected millions of people worldwide. To date, there are no proven effective therapies for this virus. Efforts made to develop antiviral strategies for the treatment of COVID‐19 are underway. Respiratory viral infections, such as influenza, predispose patients to co‐infections and these lead to increased disease severity and mortality. Numerous types of antibiotics such as azithromycin have been employed for the prevention and treatment of bacterial co‐infection and secondary bacterial infections in patients with a viral respiratory infection (e.g., SARS‐CoV‐2). Although antibiotics do not directly affect SARS‐CoV‐2, viral respiratory infections often result in bacterial pneumonia. It is possible that some patients die from bacterial co‐infection rather than virus itself. To date, a considerable number of bacterial strains have been resistant to various antibiotics such as azithromycin, and the overuse could render those or other antibiotics even less effective. Therefore, bacterial co‐infection and secondary bacterial infection are considered critical risk factors for the severity and mortality rates of COVID‐19. Also, the antibiotic‐resistant as a result of overusing must be considered. In this review, we will summarize the bacterial co‐infection and secondary bacterial infection in some featured respiratory viral infections, especially COVID‐19.

What came first, the virus or the egg: Innate immunity during viral coinfections

Infections with any pathogen can be severe and present with numerous complications caused by the pathogen or the host immune response to the invading microbe. However, coinfections, also called polymicrobial infections or secondary infections, can further exacerbate disease. Coinfections are more common than is often appreciated. In this review, we focus specifically on coinfections between viruses and other viruses, bacteria, parasites, or fungi. Importantly, innate immune signaling and innate immune cells that facilitate clearance of the initial viral infection can affect host susceptibility to coinfections. Understanding these immune imbalances may facilitate better diagnosis, prevention, and treatment of such coinfections.

Characterizing Chemotherapy-Induced Neutropenia and Monocytopenia Through Mathematical Modelling

In spite of the recent focus on the development of novel targeted drugs to treat cancer, cytotoxic chemotherapy remains the standard treatment for the vast majority of patients. Unfortunately, chemotherapy is associated with high hematopoietic toxicity that may limit its efficacy. We have previously established potential strategies to mitigate chemotherapy-induced neutropenia (a lack of circulating neutrophils) using a mechanistic model of granulopoiesis to predict the interactions defining the neutrophil response to chemotherapy and to define optimal strategies for concurrent chemotherapy/prophylactic granulocyte colony-stimulating factor (G-CSF). Here, we extend our analyses to include monocyte production by constructing and parameterizing a model of monocytopoiesis. Using data for neutrophil and monocyte concentrations during chemotherapy in a large cohort of childhood acute lymphoblastic leukemia patients, we leveraged our model to determine the relationship between the monocyte and neutrophil nadirs during cyclic chemotherapy. We show that monocytopenia precedes neutropenia by 3 days, and rationalize the use of G-CSF during chemotherapy by establishing that the onset of monocytopenia can be used as a clinical marker for G-CSF dosing post-chemotherapy. This work therefore has important clinical applications as a comprehensive approach to understanding the relationship between monocyte and neutrophils after cyclic chemotherapy with or without G-CSF support.

Infectious Disease Research in the Era of Big Data

Infectious disease research spans scales from the molecular to the global—from specific mechanisms of pathogen drug resistance, virulence, and replication to the movement of people, animals, and pathogens around the world. All of these research areas have been impacted by the recent growth of large-scale data sources and data analytics. Some of these advances rely on data or analytic methods that are common to most biomedical data science, while others leverage the unique nature of infectious disease, namely its communicability. This review outlines major research progress in the past few years and highlights some remaining opportunities, focusing on data or methodological approaches particular to infectious disease.

Prevalence and clinical impact of VIral Respiratory tract infections in patients hospitalized for Community-Acquired Pneumonia: the VIRCAP study

Prevalence and clinical impact of viral respiratory tract infections (VRTIs) on community-acquired pneumonia (CAP) has not been well defined so far. The aims of this study were to investigate the prevalence and the clinical impact of VRTIs in patients with CAP. Prospective study involving adult patients consecutively admitted at medical wards for CAP and tested for VRTIs by real-time PCR on pharyngeal swab. Patients' features were evaluated with regard to the presence of VRTI and aetiology of CAP. Clinical failure was a composite endpoint defined by worsening of signs and symptoms requiring escalation of antibiotic treatment or ICU admission or death within 30 days. 91 patients were enrolled, mean age 65.7 ± 10.6 years, 50.5% female. 62 patients (68.2%) had no viral co-infection while in 29 patients (31.8%) a VRTI was detected; influenza virus was the most frequently identified (41.9%). The two groups were similar in terms of baseline features. In presence of a VRTI, pneumonia severity index (PSI) was more frequently higher than 91 and patients had received less frequently pre-admission antibiotic therapy (adjusted OR 2.689, 95% CI 1.017-7.111, p = 0.046; adjusted OR 0.143, 95% CI 0.030-0.670, p = 0.014). Clinical failure and antibiotic therapy duration were similar with regards to the presence of VRTI and the aetiology of CAP. VRTIs can be detected in almost a third of adults with CAP; influenza virus is the most relevant one. VRTI was associated with higher PSI at admission, but it does not affect patients' outcome.

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.

Modeling Favipiravir Antiviral Efficacy Against Emerging Viruses: From Animal Studies to Clinical Trials

In 2014, our research network was involved in the evaluation of favipiravir, an anti-influenza polymerase inhibitor, against Ebola virus. In this review, we discuss how mathematical modeling was used, first to propose a relevant dosing regimen in humans, and then to optimize its antiviral efficacy in a nonhuman primate (NHP) model. The data collected in NHPs were finally used to develop a model of Ebola pathogenesis integrating the interactions among the virus, the innate and adaptive immune response, and the action of favipiravir. We conclude the review of this work by discussing how these results are of relevance for future human studies in the context of Ebola virus, but also for other emerging viral diseases for which no therapeutics are available.

Application of Optimal Control to Influenza Pneumonia Coinfection with Antiviral Resistance

Influenza and pneumonia independently lead to high morbidity and mortality annually among the human population globally; however, a glaring fact is that influenza pneumonia coinfection is more vicious and it is a threat to public health. Emergence of antiviral resistance is a major impediment in the control of the coinfection. In this paper, a deterministic mathematical model illustrating the transmission dynamics of influenza pneumonia coinfection is formulated having incorporated antiviral resistance. Optimal control theory is then applied to investigate optimal strategies for controlling the coinfection using prevalence reduction and treatment as the system control variables. Pontryagin's maximum principle is used to characterize the optimal control. The derived optimality system is solved numerically using the Runge-Kutta-based forward-backward sweep method. Simulation results reveal that implementation of prevention measures is sufficient to eradicate influenza pneumonia coinfection from a given population. The prevention measures could be social distancing, vaccination, curbing mutation and reassortment, and curbing interspecies movement of the influenza virus.

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.

Assessment of efficacy of Oseltamivir-Azithromycin combination therapy in prevention of Influenza-A (H1N1)pdm09 infection complications and rapidity of symptoms relief

Objectives: This study aimed to assess the efficacy of oseltamivir-Azithromycin combination therapy for prevention of Influenza-A (H1N1)pdm09 infection associated complications and early relief of influenza symptoms.Methods: In a retrospective observational cohort study, Influenza-A (H1N1)pdm09 infection hospitalized patients were identified and divided into two groups based on the initial therapy. Group-AV patients were initiated on Oseltamivir without any antibiotic in treatment regimen while Group-AV+AZ patients were initiated on Oseltamivir and Azithromycin combination therapy for at least 3-5 days. Patients were evaluated for different clinical outcomes.Results: A total of 227 and 102 patients were identified for Group-AV and Group-AV+AZ respectively. Multivariate regression analysis showed that incidences of secondary bacterial infections were significantly less frequent (23.4% vs 10.4%; P-value = 0.019) in Group-AV+AZ patients. Group-AV+AZ patients were associated with shorter length of hospitalization (6.58 vs 5.09 days; P-value = <0.0001) and less frequent incidences of respiratory support (38.3% vs 17.6%; P-value = 0.016). Overall influenza symptom severity score was statistically significant less for Group-AV+AZ patients on Day-5 (10.68 ± 2.09; P-value = 0.001) of hospitalization.Conclusion: Oseltamivir-Azithromycin combination therapy was found to be more efficacious as compared to oseltamivir alone in rapid recovery and prevention of Influenza associated complications especially in high risk patients.

Model Averaging in Viral Dynamic Models

The paucity of experimental data makes both inference and prediction particularly challenging in viral dynamic models. In the presence of several candidate models, a common strategy is model selection (MS), in which models are fitted to the data but only results obtained with the "best model" are presented. However, this approach ignores model uncertainty, which may lead to inaccurate predictions. When several models provide a good fit to the data, another approach is model averaging (MA) that weights the predictions of each model according to its consistency to the data. Here, we evaluated by simulations in a nonlinear mixed-effect model framework the performances of MS and MA in two realistic cases of acute viral infection, i.e., (1) inference in the presence of poorly identifiable parameters, namely, initial viral inoculum and eclipse phase duration, (2) uncertainty on the mechanisms of action of the immune response. MS was associated in some scenarios with a large rate of false selection. This led to a coverage rate lower than the nominal coverage rate of 0.95 in the majority of cases and below 0.50 in some scenarios. In contrast, MA provided better estimation of parameter uncertainty, with coverage rates ranging from 0.72 to 0.98 and mostly comprised within the nominal coverage rate. Finally, MA provided similar predictions than those obtained with MS. In conclusion, parameter estimates obtained with MS should be taken with caution, especially when several models well describe the data. In this situation, MA has better performances and could be performed to account for model uncertainty.

State-of-the-art in the pneumococcal field: Proceedings of the 11th International Symposium on Pneumococci and Pneumococcal Diseases (ISPPD-11)

The International Symposium on Pneumococci and Pneumococcal Diseases (ISPPD) is the premier global scientific symposium dedicated to the exchange, advancement and dissemination of the latest research on the pneumococcus, one of the world's deadliest bacterial pathogens. Since the first ISPPD was held in 1998, substantial progress has been made to control pneumococcal disease, for instance, more than half of surviving infants (78.6 million) from 143 countries now have access to the life-saving pneumococcal conjugate vaccine (PCV). The 11th ISPPD (ISPPD-11) was held in Melbourne, Australia in April 2018 and the proceedings of the symposium are captured in this report. Twenty years on from the first ISPPD, there remain many challenges and unanswered questions such as the continued disparity in disease incidence in Indigenous populations, the slow roll-out of PCV in some regions such as Asia, the persisting burden of disease in adults, serotype replacement and diagnosis of pneumococcal pneumonia. ISPPD-11 also put the spotlight on cutting-edge science including metagenomic, transcriptomic, microscopy, medical imaging and mathematical modelling approaches. ISPPD-11 was highly diverse, bringing together 1184 delegates from 86 countries, representing various fields including academia, primary healthcare, pharmaceuticals, biotechnology, policymakers and public health.

Lower respiratory tract delivery, airway clearance, and preclinical efficacy of inhaled GM-CSF in a postinfluenza pneumococcal pneumonia model

Inhaled granulocyte/macrophage colony-stimulating factor (GM-CSF) shows promise as a therapeutic to treat viral and bacterial pneumonia, but no mouse model of inhaled GM-CSF has been described. We sought to 1) develop a mouse model of aerosolized recombinant mouse GM-CSF administration and 2) investigate the protection conferred by inhaled GM-CSF during influenza A virus (IAV) infection against secondary bacterial infection with pneumococcus. To assess lower respiratory tract delivery of aerosolized therapeutics, mice were exposed to aerosolized fluorescein (FITC)-labeled dextran noninvasively via an aerosolization tower or invasively using a rodent ventilator. The efficiency of delivery to the lower respiratory tracts of mice was 0.01% noninvasively compared with 0.3% invasively. The airway pharmacokinetics of inhaled GM-CSF fit a two-compartment model with a terminal phase half-life of 1.3 h. To test if lower respiratory tract levels were sufficient for biological effect, mice were infected intranasally with IAV, treated with aerosolized recombinant mouse GM-CSF, and then secondarily infected with Streptococcus pneumoniae. Inhaled GM-CSF conferred a significant survival benefit to mice against secondary challenge with S. pneumoniae (P < 0.05). Inhaled GM-CSF did not reduce airway or lung parenchymal bacterial growth but significantly reduced the incidence of S. pneumoniae bacteremia (P < 0.01). However, GM-CSF overexpression during influenza virus infection did not affect lung epithelial permeability to FITC-dextran ingress into the bloodstream. Therefore, the mechanism of protection conferred by inhaled GM-CSF appears to be locally mediated improved lung antibacterial resistance to systemic bacteremia during IAV infection.

Allergic inflammation alters the lung microbiome and hinders synergistic co-infection with H1N1 influenza virus and Streptococcus pneumoniae in C57BL/6 mice

Asthma is a chronic airways condition that can be exacerbated during respiratory infections. Our previous work, together with epidemiologic findings that asthmatics were less likely to suffer from severe influenza during the 2009 pandemic, suggest that additional complications of influenza such as increased susceptibility to bacterial superinfection, may be mitigated in allergic hosts. To test this hypothesis, we developed a murine model of 'triple-disease' in which mice rendered allergic to Aspergillus fumigatus were co-infected with influenza A virus and Streptococcus pneumoniae seven days apart. Significant alterations to known synergistic effects of co-infection were noted in the allergic mice including reduced morbidity and mortality, bacterial burden, maintenance of alveolar macrophages, and reduced lung inflammation and damage. The lung microbiome of allergic mice differed from that of non-allergic mice during co-infection and antibiotic-induced perturbation to the microbiome rendered allergic animals susceptible to severe morbidity. Our data suggest that responses to co-infection in allergic hosts likely depends on the immune and microbiome states and that antibiotics should be used with caution in individuals with underlying chronic lung disease.

Simulation modelling for immunologists

The immune system is inordinately complex with many interacting components determining overall outcomes. Mathematical and computational modelling provides a useful way in which the various contributions of different immunological components can be probed in an integrated manner. Here, we provide an introductory overview and review of mechanistic simulation models. We start out by briefly defining these types of models and contrasting them to other model types that are relevant to the field of immunology. We follow with a few specific examples and then review the different ways one can use such models to answer immunological questions. While our examples focus on immune responses to infection, the overall ideas and descriptions of model uses can be applied to any area of immunology.

Co-infection of H9N2 influenza virus and Pseudomonas aeruginosa contributes to the development of hemorrhagic pneumonia in mink

Influenza A virus (IAV) and bacteria co-infection can influence the host clinical conditions. Both H9N2 IAV and Pseudomonas aeruginosa (P. aeruginosa) are potential pathogens of respiratory diseases in mink. In this study, to clarify the effects of H9N2 IAV and P. aeruginosa co-infections on hemorrhagic pneumonia in mink, we carried out to establish the mink models of the two-pathogen co-infections in different orders. Compared with the single infections with H9N2 IAV or P. aeruginosa, the mink co-infected with H9N2 IAV and P. aeruginosa showed severe respiratory diseases, and exacerbated histopathological lesions and more obvious apoptosis in the lung tissues. H9N2 IAV shedding and viral loads in the lungs of the mink co-infected with H9N2 IAV and P. aeruginosa were higher than those in the mink with single H9N2 IAV infection. Furthermore, the clearance of P. aeruginosa in the co-infected mink lungs was delayed. In addition, the anti-H9N2 antibody titers in mink with P. aeruginosa co-infection following H9N2 IAV infection were significantly higher than those of the other groups. This implied that H9N2 IAV and P. aeruginosa co-infection contributed to the development of hemorrhagic pneumonia in mink, and that P. aeruginosa should play a major role in the disease. The exact interaction mechanism among H9N2 IAV, P. aeruginosa and the host needs to be further investigated.

The Potential of Influenza HA-Specific Immunity in Mitigating Lethality of Postinfluenza Pneumococcal Infections

Influenza virus infections pre-dispose an individual to secondary pneumococcal infections, which represent a serious public health concern. Matching influenza vaccination was demonstrated helpful in preventing postinfluenza bacterial infections and associated illnesses in humans. Yet, the impact of influenza hemagglutinin (HA)-specific immunity alone in this dual-infection scenario remains elusive. In the present study, we assessed the protective effect of neutralizing and non-neutralizing anti-hemagglutinin immunity in a BALB/c influenza-pneumococcus superinfection model. Our immunogens were insect cell-expressed hemagglutinin-Gag virus-like particles that had been differentially-treated for the inactivation of bioprocess-related baculovirus impurities. We evaluated the potential of several formulations to restrain the primary infection with vaccine-matched or -mismatched influenza strains and secondary bacterial replication. In addition, we investigated the effect of anti-HA immunity on the interferon status in mouse lungs prior to bacterial challenge. In our experimental setup, neutralizing anti-HA immunity provided significant but incomplete protection from postinfluenza bacterial superinfection, despite effective control of viral replication. In view of this, it was surprising to observe a survival advantage with non-neutralizing adaptive immunity when using a heterologous viral challenge strain. Our findings suggest that both neutralizing and non-neutralizing anti-HA immunity can reduce disease and mortality caused by postinfluenza pneumococcal infections.

Contribution of Host Immune Responses Against Influenza D Virus Infection Toward Secondary Bacterial Infection in a Mouse Model

Influenza D viruses (IDV) are known to co-circulate with viral and bacterial pathogens in cattle and other ruminants. Currently, there is limited knowledge regarding host responses to IDV infection and whether IDV infection affects host susceptibility to secondary bacterial infections. To begin to address this gap in knowledge, the current study utilized a combination of in vivo and in vitro approaches to evaluate host cellular responses against primary IDV infection and secondary bacterial infection with Staphylococcus aureus (S. aureus). Primary IDV infection in mice did not result in clinical signs of disease and it did not enhance the susceptibility to secondary S. aureus infection. Rather, IDV infection appeared to protect mice from the usual clinical features of secondary bacterial infection, as demonstrated by improved weight loss, survival, and recovery when compared to S. aureus infection alone. We found a notable increase in IFN-β expression following IDV infection while utilizing human alveolar epithelial A549 cells to analyze early anti-viral responses to IDV infection. These results demonstrate for the first time that IDV infection does not increase the susceptibility to secondary bacterial infection with S. aureus, with evidence that anti-viral immune responses during IDV infection might protect the host against these potentially deadly outcomes.

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.

Viral Coinfection Replaces Effects of Suilysin on Streptococcus suis Adherence to and Invasion of Respiratory Epithelial Cells Grown under Air-Liquid Interface Conditions

Streptococcus suis is an important zoonotic pathogen which can infect humans and pigs worldwide, posing a potential risk to global public health. Suilysin, a pore-forming cholesterol-dependent cytolysin, is considered to play an important role in the pathogenesis of S. suis infections. It is known that infection with influenza A viruses may favor susceptibility to secondary bacterial infection, resulting in more severe disease and increased mortality. However, the molecular mechanisms underlying these coinfections are incompletely understood. Applying highly differentiated primary porcine respiratory epithelial cells grown under air-liquid interface (ALI) conditions, we analyzed the contribution of swine influenza viruses (SIV) to the virulence of S. suis, with a special focus on its cytolytic toxin, suilysin. We found that during secondary bacterial infection, suilysin of S. suis contributed to the damage of well-differentiated respiratory epithelial cells in the early stage of infection, whereas the cytotoxic effects induced by SIV became prominent at later stages of infection. Prior infection by SIV enhanced the adherence to and colonization of porcine airway epithelial cells by a wild-type (wt) S. suis strain and a suilysin-negative S. suis mutant in a sialic acid-dependent manner. A striking difference was observed with respect to bacterial invasion. After bacterial monoinfection, only the wt S. suis strain showed an invasive phenotype, whereas the mutant remained adherent. When the epithelial cells were preinfected with SIV, the suilysin-negative mutant also showed an invasion capacity. Therefore, we propose that coinfection with SIV may compensate for the lack of suilysin in the adherence and invasion process of suilysin-negative S. suis.

PARAMETER AND UNCERTAINTY ESTIMATION FOR DYNAMICAL SYSTEMS USING SURROGATE STOCHASTIC PROCESSES

Inference on unknown quantities in dynamical systems via observational data is essential for providing meaningful insight, furnishing accurate predictions, enabling robust control, and establishing appropriate designs for future experiments. Merging mathematical theory with empirical measurements in a statistically coherent way is critical and challenges abound, e.g., ill-posedness of the parameter estimation problem, proper regularization and incorporation of prior knowledge, and computational limitations. To address these issues, we propose a new method for learning parameterized dynamical systems from data. We first customize and fit a surrogate stochastic process directly to observational data, front-loading with statistical learning to respect prior knowledge (e.g., smoothness), cope with challenging data features like heteroskedasticity, heavy tails, and censoring. Then, samples of the stochastic process are used as "surrogate data" and point estimates are computed via ordinary point estimation methods in a modular fashion. Attractive features of this two-step approach include modularity and trivial parallelizability. We demonstrate its advantages on a predator-prey simulation study and on a real-world application involving within-host influenza virus infection data paired with a viral kinetic model, with comparisons to a more conventional Markov chain Monte Carlo (MCMC) based Bayesian approach.

Respiratory viral infection: a potential "missing link" in the pathogenesis of COPD

Chronic obstructive pulmonary disease (COPD) is currently the third most common cause of global mortality. Acute exacerbations of COPD frequently necessitate hospital admission to enable more intensive therapy, incurring significant healthcare costs. COPD exacerbations are also associated with accelerated lung function decline and increased risk of mortality. Until recently, bacterial pathogens were believed to be responsible for the majority of disease exacerbations. However, with the advent of culture-independent molecular diagnostic techniques it is now estimated that viruses are detected during half of all COPD exacerbations and are associated with poorer clinical outcomes. Human rhinovirus, respiratory syncytial virus and influenza are the most commonly detected viruses during exacerbation. The role of persistent viral infection (adenovirus) has also been postulated as a potential pathogenic mechanism in COPD. Viral pathogens may play an important role in driving COPD progression by acting as triggers for exacerbation and subsequent lung function decline whilst the role of chronic viral infection remains a plausible hypothesis that requires further evaluation. There are currently no effective antiviral strategies for patients with COPD. Herein, we focus on the current understanding of the cellular and molecular mechanisms of respiratory viral infection in COPD.

Enhanced IL-1β production is mediated by a TLR2-MYD88-NLRP3 signaling axis during coinfection with influenza A virus and Streptococcus pneumoniae

Viral-bacterial coinfections, such as with influenza A virus and Streptococcus pneumoniae (S.p.), are known to cause severe pneumonia. It is well known that the host response has an important role in disease. Interleukin-1β (IL-1β) is an important immune signaling cytokine responsible for inflammation and has been previously shown to contribute to disease severity in numerous infections. Other studies in mice indicate that IL-1β levels are dramatically elevated during IAV-S.p. coinfection. However, the regulation of IL-1β during coinfection is unknown. Here, we report the NLRP3 inflammasome is the major inflammasome regulating IL-1β activation during coinfection. Furthermore, elevated IL-1β mRNA expression is due to enhanced TLR2-MYD88 signaling, which increases the amount of pro-IL-1β substrate for the inflammasome to process. Finally, NLRP3 and high IL-1β levels were associated with increased bacterial load in the brain. Our results show the NLRP3 inflammasome is not protective during IAV-S.p. coinfection.

Persistent spatial patterns of interacting contagions

The spread of infectious diseases, rumors, fashions, and innovations are complex contagion processes, embedded in network and spatial contexts. While the studies in the former context are intensively expanded, the latter remains largely unexplored. In this paper, we investigate the pattern formation of an interacting contagion, where two infections, A and B, interact with each other and diffuse simultaneously in space. The contagion process for each follows the classical susceptible-infected-susceptible kinetics, and their interaction introduces a potential change in the secondary infection propensity compared to the baseline reproduction number R_{0}. We show that the nontrivial spatial infection patterns arise when the susceptible individuals move faster than the infected and the interaction between the two infections is neither too competitive nor too cooperative. Interestingly, the system exhibits pattern hysteresis phenomena, i.e., quite different parameter regions for patterns exist in the direction of increasing or decreasing R_{0}. Decreasing R_{0} reveals remarkable enhancement in contagion prevalence, meaning that the eradication becomes difficult compared to the single-infection or coinfection without space. Linearization analysis supports our observations, and we have identified the required elements and dynamical mechanism, which suggests that these patterns are essentially Turing patterns. Our work thus reveals new complexities in interacting contagions and paves the way for further investigation because of its relevance to both biological and social contexts.

Superinfection and cell regeneration can lead to chronic viral coinfections

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Clinical researchers have found that coinfection of the respiratory tract can cause distinct disease outcome, sometimes leading to long-lasting infection, compared to single viral infection.

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The impact of coinfections in human respiratory tract have not yet been evaluated in either theoretical or experimental studies on a large scale.

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A few experiments confirm that different respiratory viruses can infect the same cell (superinfection).

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Superinfection alone cannot cause long-lasting viral coinfections.

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The combined mechanism of superinfection and cell regeneration provides a plausible mechanism for chronic viral coinfections.

Molecular diagnostic techniques have revealed that approximately 43% of the patients hospitalized with influenza-like illness are infected by more than one viral pathogen, sometimes leading to long-lasting infections. It is not clear how the heterologous viruses interact within the respiratory tract of the infected host to lengthen the duration of what are usually short, self-limiting infections. We develop a mathematical model which allows for single cells to be infected simultaneously with two different respiratory viruses (superinfection) to investigate the possibility of chronic coinfections. We find that a model with superinfection and cell regeneration has a stable chronic coinfection fixed point, while superinfection without cell regeneration produces only acute infections. This analysis suggests that both superinfection and cell regeneration are required to sustain chronic coinfection via this mechanism since coinfection is maintained by superinfected cells that allow slow-growing infections a chance to infect cells and continue replicating. This model provides a possible mechanism for chronic coinfection independent of any viral interactions via the immune response.

Community-acquired bacterial co-infection predicts severity and mortality in influenza-associated pneumonia admitted patients

BACKGROUND

Influenza is frequently complicated by bacterial co-infection, causing additional hospitalization and mortality. We determined the incidence, risk factors and outcomes of patients with influenza-associated community-acquired bacterial co-infection.

METHOD

This was a retrospective, observational study. Influenza was diagnosed using the polymerase chain reaction. Co-infection had to be confirmed using standard bacteriological tests. The primary endpoint was presence of community-acquired co-infection, and the secondary endpoint was in-hospital mortality.

RESULTS

During the 8 influenza seasons from 2010 to 2018, of the 209 influenza-associated pneumonia admitted patients, 41 (19.6%) were identified with community-acquired bacterial co-infections and Staphylococcus aureus was the predominant strain. Compared with patients without co-infection, patients with co-infection had similar demographic characteristics and co-morbidities, obtained a higher APACHE II score and a higher SOFA score, and had higher ratio of sepsis shock, invasive mechanical ventilation, and ICU requirement. In-hospital mortality independently associated with bacterial co-infection (adjusted hazard ratio (aHR) 2.619; 95%CI 1.252-5.480; p = 0.011); in subgroup S. aureus (aHR 6.267; 95%CI 2.679-14.662; p < 0.001) and other pathogens (aHR 2.964; 95%CI 1.160-7.577; p = 0.023); and in subgroup positive findings in bloodstream (aHR 7.420; 95%CI 2.712-20.302; p < 0.001) and positive findings in other site (aHR 3.427; 95%CI 1.514-7.757; p = 0.003).

CONCLUSION

Community-acquired bacterial co-infection was frequent in influenza-associated pneumonia, without risk factor identified yet. Bacterial co-infection was likely to predict severity, and was an independent risk factor for in-hospital mortality. Co-infection of Staphylococcus aureus with influenza was identified as a lethal synergism, and should be targeted when developing clinical antibiotic strategies.

Validated Models of Immune Response to Virus Infection

Viruses are a main cause of disease worldwide and many are without effective therapeutics or vaccines. A lack of understanding about how host responses work to control viral spread is one factor limiting effective management. How different immune components regulate infection dynamics is beginning to be better understood with the help of mathematical models. These models have been key in discriminating between hypotheses and in identifying rates of virus growth and clearance, dynamical control by different host factors and antivirals, and synergistic interactions during multi-pathogen infections. A recent focus in evaluating model predictions in the laboratory and clinic has illuminate the accuracy of models for a variety of viruses and highlighted the critical nature of theoretical approaches in virology. Here, I discuss recent model-driven exploration of host-pathogen interactions that have illustrated the importance of model validation in establishing the model's predictive capability and in defining new biology.

A review of inflammatory mechanism in airway diseases

BACKGROUND

Inflammation in the lung is the body's natural response to injury. It acts to remove harmful stimuli such as pathogens, irritants, and damaged cells and initiate the healing process. Acute and chronic pulmonary inflammation are seen in different respiratory diseases such as; acute respiratory distress syndrome, chronic obstructive pulmonary disease (COPD), asthma, and cystic fibrosis (CF).

FINDINGS

In this review, we found that inflammatory response in COPD is determined by the activation of epithelial cells and macrophages in the respiratory tract. Epithelial cells and macrophages discharge transforming growth factor-β (TGF-β), which trigger fibroblast proliferation and tissue remodeling. Asthma leads to airway hyper-responsiveness, obstruction, mucus hyper-production, and airway-wall remodeling. Cytokines, allergens, chemokines, and infectious agents are the main stimuli that activate signaling pathways in epithelial cells in asthma. Mutation of the CF transmembrane conductance regulator (CFTR) gene results in CF. Mutations in CFTR influence the lung epithelial innate immune function that leads to exaggerated and ineffective airway inflammation that fails to abolish pulmonary pathogens. We present mechanistic computational models (based on ordinary differential equations, partial differential equations and agent-based models) that have been applied in studying the complex physiological and pathological mechanisms of chronic inflammation in different airway diseases.

CONCLUSION

The scope of the present review is to explore the inflammatory mechanism in airway diseases and highlight the influence of aging on airways' inflammation mechanism. The main goal of this review is to encourage research collaborations between experimentalist and modelers to promote our understanding of the physiological and pathological mechanisms that control inflammation in different airway diseases.

Back to the Future: Lessons Learned From the 1918 Influenza Pandemic

2018 marks the 100-year anniversary of the 1918 influenza pandemic, which killed ~50 million people worldwide. The severity of this pandemic resulted from a complex interplay between viral, host, and societal factors. Here, we review the viral, genetic and immune factors that contributed to the severity of the 1918 pandemic and discuss the implications for modern pandemic preparedness. We address unresolved questions of why the 1918 influenza H1N1 virus was more virulent than other influenza pandemics and why some people survived the 1918 pandemic and others succumbed to the infection. While current studies suggest that viral factors such as haemagglutinin and polymerase gene segments most likely contributed to a potent, dysregulated pro-inflammatory cytokine storm in victims of the pandemic, a shift in case-fatality for the 1918 pandemic toward young adults was most likely associated with the host's immune status. Lack of pre-existing virus-specific and/or cross-reactive antibodies and cellular immunity in children and young adults likely contributed to the high attack rate and rapid spread of the 1918 H1N1 virus. In contrast, lower mortality rate in in the older (>30 years) adult population points toward the beneficial effects of pre-existing cross-reactive immunity. In addition to the role of humoral and cellular immunity, there is a growing body of evidence to suggest that individual genetic differences, especially involving single-nucleotide polymorphisms (SNPs), contribute to differences in the severity of influenza virus infections. Co-infections with bacterial pathogens, and possibly measles and malaria, co-morbidities, malnutrition or obesity are also known to affect the severity of influenza disease, and likely influenced 1918 H1N1 disease severity and outcomes. Additionally, we also discuss the new challenges, such as changing population demographics, antibiotic resistance and climate change, which we will face in the context of any future influenza virus pandemic. In the last decade there has been a dramatic increase in the number of severe influenza virus strains entering the human population from animal reservoirs (including highly pathogenic H7N9 and H5N1 viruses). An understanding of past influenza virus pandemics and the lessons that we have learnt from them has therefore never been more pertinent.

Role of Inflammatory Risk Factors in the Pathogenesis of Streptococcus pneumoniae

Streptococcus pneumoniae (Spn) is a colonizer of the human nasopharynx (NP), causing a variety of infections in humans including otitis media, pneumonia, sepsis, and meningitis. The NP is an immune permissive site which allows for the persistence of commensal bacteria. Acute or chronic respiratory airway inflammation constitutes a significant risk factor for the manifestation of Spn infections. The inflammatory conditions caused by an upper respiratory viral infection or respiratory conditions such as allergic asthma and chronic obstructive pulmonary disorders (COPDs) are implicated in the dysregulation of airway inflammation and tissue damage, which compromise the respiratory barrier integrity. These immune events promote bacterial outgrowth leading to Spn dissemination and invasion into the bloodstream. Therefore, suppression of inflammation and restoration of respiratory barrier integrity could contain Spn infections manifesting in the backdrop of an inflammatory disease condition. The gained knowledge could be harnessed in the design of novel therapeutic interventions to circumvent Spn bacterial infections.

Impact of microbial Aetiology on mortality in severe community-acquired pneumonia

BACKGROUND

The impact of different classes of microbial pathogens on mortality in severe community-acquired pneumonia is not well elucidated. Previous studies have shown significant variation in the incidence of viral, bacterial and mixed infections, with conflicting risk associations for mortality. We aimed to determine the risk association of microbial aetiologies with hospital mortality in severe CAP, utilising a diagnostic strategy incorporating molecular testing. Our primary hypothesis was that respiratory viruses were important causative pathogens in severe CAP and was associated with increased mortality when present with bacterial pathogens in mixed viral-bacterial co-infections.

METHODS

A retrospective cohort study from January 2014 to July 2015 was conducted in a tertiary hospital medical intensive care unit in eastern Singapore, which has a tropical climate. All patients diagnosed with severe community-acquired pneumonia were included.

RESULTS

A total of 117 patients were in the study. Microbial pathogens were identified in 84 (71.8%) patients. Mixed viral-bacterial co-infections occurred in 18 (15.4%) of patients. Isolated viral infections were present in 32 patients (27.4%); isolated bacterial infections were detected in 34 patients (29.1%). Hospital mortality occurred in 16 (13.7%) patients. The most common bacteria isolated was Streptococcus pneumoniae and the most common virus isolated was Influenza A. Univariate and multivariate logistic regression showed that serum procalcitonin, APACHE II severity score and mixed viral-bacterial infection were associated with increased risk of hospital mortality. Mixed viral-bacterial co-infections were associated with an adjusted odds ratio of 13.99 (95% CI 1.30-151.05, p = 0.03) for hospital mortality.

CONCLUSIONS

Respiratory viruses are common organisms isolated in severe community-acquired pneumonia. Mixed viral-bacterial infections may be associated with an increased risk of mortality.

Mathematical modeling of within-host Zika virus dynamics

Recent Zika virus outbreaks have been associated with severe outcomes, especially during pregnancy. A great deal of effort has been put toward understanding this virus, particularly the immune mechanisms responsible for rapid viral control in the majority of infections. Identifying and understanding the key mechanisms of immune control will provide the foundation for the development of effective vaccines and antiviral therapy. Here, we outline a mathematical modeling approach for analyzing the within-host dynamics of Zika virus, and we describe how these models can be used to understand key aspects of the viral life cycle and to predict antiviral efficacy.

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.

Immunogenicity and protective efficacy of monovalent PCVs containing 22F and 33F polysaccharides in mouse models of colonization and co-infection

BACKGROUND

In the current transmission, we studied the immunogenicity and protective efficacy of serotypes 22F and 33F in the prevention of colonization and of invasive Streptococcus pneumoniae (Spn) pathogenesis during an influenza co-infection. Serotypes 22F and 33F are emerging Spn serotypes, which are not part of currently administered pneumococcal conjugate vaccine formulations (PCVs). Spn serotype 6A is an ingredient in the currently administered PCV13 vaccine and was therefore included in the study as a control.

METHODS

Adult (six weeks) and infant (two weeks) C57BL/6 mice were intranasally infected in the nasopharynx (NP) with Spn serotypes 22F, 33F, or 6A. Influenza A H1N1 A/Puerto Rico/8/193 virus (PR8) was introduced one day after the NP Spn colonization. In an immunization challenge study, mice were vaccinated with monovalent 22F, 33F, or 6A polysaccharide conjugated to the CRM₁₉₇ antigen. The immunized mice were colonized or co-infected to study the vaccines efficacy.

RESULTS

All three Spn serotypes established colonization in adult and infant mice. The co-infected mice showed an increase in Spn NP density. Invasive Spn infection (bacteremia) was observed following the co-infection with serotypes 22F and 6A but not 33F in adult mice, whereas infant mice developed bacteremia following co-infection with all three Spn serotypes. The vaccinations led to robust serum antibody responses to capsular polysaccharides 22F, 6A, and less for 33F. The vaccinations resulted in reductions of Spn NP colonization density for all three serotypes, prevention of bacteremia, and increased survival with Spn serotypes 22F and 6A. Passive transfer of antisera was associated with a reduction of Spn colonization densities in infant mice.

CONCLUSION

Vaccinations with monovalent 22F, 33F, or 6A formulations protect against Spn colonization, and the efficacy of the 22F vaccination was comparable to the 6A vaccination in preventing an invasive Spn bacterial infection during an influenza co-infection.

Impact of viral upper respiratory tract infection on the concentration of nasopharyngeal pneumococcal carriage among Kenyan children

Viral upper respiratory tract infection (URTI) predisposes to bacterial pneumonia possibly by facilitating growth of bacteria such as Streptococcus pneumoniae colonising the nasopharynx. We investigated whether viral URTI is temporally associated with an increase in nasopharyngeal pneumococcal concentration. Episodes of symptomatic RSV or rhinovirus URTI among children <5 years were identified from a longitudinal household study in rural Kenya. lytA and alu PCR were performed on nasopharyngeal samples collected twice-weekly, to measure the pneumococcal concentration adjusted for the concentration of human DNA present. Pneumococcal concentration increased with a fold-change of 3.80 (95%CI 1.95-7.40), with acquisition of RSV or rhinovirus, during 51 URTI episodes among 42 children. In repeated swabs from the baseline period, in the two weeks before URTI developed, within-episode variation was broad; within +/-112-fold range of the geometric mean. We observed only a small increase in nasopharyngeal pneumococcal concentration during RSV or rhinovirus URTI, relative to natural variation. Other factors, such as host response to viral infection, may be more important than nasopharyngeal pneumococcal concentration in determining risk of invasive disease.

Influenza Virus Infection Model With Density Dependence Supports Biphasic Viral Decay

Mathematical models that describe infection kinetics help elucidate the time scales, effectiveness, and mechanisms underlying viral growth and infection resolution. For influenza A virus (IAV) infections, the standard viral kinetic model has been used to investigate the effect of different IAV proteins, immune mechanisms, antiviral actions, and bacterial coinfection, among others. We sought to further define the kinetics of IAV infections by infecting mice with influenza A/PR8 and measuring viral loads with high frequency and precision over the course of infection. The data highlighted dynamics that were not previously noted, including viral titers that remain elevated for several days during mid-infection and a sharp 4–5 log₁₀ decline in virus within 1 day as the infection resolves. The standard viral kinetic model, which has been widely used within the field, could not capture these dynamics. Thus, we developed a new model that could simultaneously quantify the different phases of viral growth and decay with high accuracy. The model suggests that the slow and fast phases of virus decay are due to the infected cell clearance rate changing as the density of infected cells changes. To characterize this model, we fit the model to the viral load data, examined the parameter behavior, and connected the results and parameters to linear regression estimates. The resulting parameters and model dynamics revealed that the rate of viral clearance during resolution occurs 25 times faster than the clearance during mid-infection and that small decreases to this rate can significantly prolong the infection. This likely reflects the high efficiency of the adaptive immune response. The new model provides a well-characterized representation of IAV infection dynamics, is useful for analyzing and interpreting viral load dynamics in the absence of immunological data, and gives further insight into the regulation of viral control.

Best practice assessment of disease modelling for infectious disease outbreaks

During emerging disease outbreaks, public health, emergency management officials and decision-makers increasingly rely on epidemiological models to forecast outbreak progression and determine the best response to health crisis needs. Outbreak response strategies derived from such modelling may include pharmaceutical distribution, immunisation campaigns, social distancing, prophylactic pharmaceuticals, medical care, bed surge, security and other requirements. Infectious disease modelling estimates are unavoidably subject to multiple interpretations, and full understanding of a model's limitations may be lost when provided from the disease modeller to public health practitioner to government policymaker. We review epidemiological models created for diseases which are of greatest concern for public health protection. Such diseases, whether transmitted from person-to-person (Ebola, influenza, smallpox), via direct exposure (anthrax), or food and waterborne exposure (cholera, typhoid) may cause severe illness and death in a large population. We examine disease-specific models to determine best practices characterising infectious disease outbreaks and facilitating emergency response and implementation of public health policy and disease control measures.

Metabolic conversion of CI-1040 turns a cellular MEK-inhibitor into an antibacterial compound

Influenza virus (IV) infections cause severe respiratory illnesses that can be complicated by bacterial super-infections. Previously, we identified the cellular Raf-MEK-ERK cascade as a promising antiviral target. Inhibitors of MEK, such as CI-1040, showed potent antiviral activity. However, it remained unclear if this inhibitor and its active form, ATR-002, might sensitize host cells to either IV or secondary bacterial infections. To address these questions, we studied the anti-pathogen activity of ATR-002 in comparison to CI-1040, particularly, its impact on Staphylococcus aureus (S. aureus), which is a major cause of IV super-infections. We analysed IV and S. aureus titres in vitro during super-infection in the presence and absence of the drugs and characterized the direct impact of ATR-002 on bacterial growth and phenotypic changes. Importantly, neither CI-1040 nor ATR-002 treatment led to increased bacterial titres during super-infection, indicating that the drug does not sensitize cells for bacterial infection. In contrast, we rather observed reduced bacterial titres in presence of ATR-002. Surprisingly, ATR-002 also led to reduced bacterial growth in suspension cultures, reduced stress- and antibiotic tolerance without resistance induction. Our data identified for the first time that a particular MEK-inhibitor metabolite exhibits direct antibacterial activity, which is likely due to interference with the bacterial PknB kinase/Stp phosphatase signalling system.

Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling

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.

Critical Role of HAX-1 in Promoting Avian Influenza Virus Replication in Lung Epithelial Cells

The PB1-F2 protein of influenza A virus has been considered a virulence factor, but its function in inducing apoptosis may be of disadvantage to viral replication. Host mechanisms to regulate PB1-F2-induced apoptosis remain unknown. We generated a PB1-F2-deficient avian influenza virus (AIV) H9N2 and found that the mutant virus replicated less efficiently in human lung epithelial cells. The PB1-F2-deficient virus produced less apoptotic cells, indicating that PB1-F2 of the H9N2 virus promotes apoptosis, occurring at the early stage of infection, in the lung epithelial cells. To understand how host cells regulate PB1-F2-induced apoptosis, we explored to identify cellular proteins interacting with PB1-F2 and found that HCLS1-associated protein X-1 (HAX-1), located mainly in the mitochondria as an apoptotic inhibitor, interacted with PB1-F2. Increased procaspase-9 activations, induced by PB1-F2, could be suppressed by HAX-1. In HAX-1 knockdown A549 cells, the replication of AIV H9N2 was suppressed in parallel to the activation of caspase-3 activation, which increased at the early stage of infection. We hypothesize that HAX-1 promotes AIV replication by interacting with PB1-F2, resulting in the suppression of apoptosis, prolonged cell survival, and enhancement of viral replication. Our data suggest that HAX-1 may be a promoting factor for AIV H9N2 replication through desensitizing PB1-F2 from its apoptotic induction in human lung epithelial cells.

Staphylococcus aureus triggers a shift from influenza virus-induced apoptosis to necrotic cell death

Superinfections with Staphylococcus aureus are a major complication of influenza disease, causing excessive inflammation and tissue damage. This enhanced cell-damaging effect is also observed in superinfected tissue cultures, leading to a strong decrease in overall cell viability. In our analysis of the underlying molecular mechanisms, we observed that, despite enhanced cell damage in superinfection, S. aureus did not increase but rather inhibited influenza virus (IV)-induced apoptosis in cells on the level of procaspase-8 activation. This apparent contradiction was solved when we observed that S. aureus mediated a switch from apoptosis to necrotic cell death of IV-infected cells, a mechanism that was dependent on the bacterial accessory gene regulator ( agr) locus that promotes bacterial survival and spread. This so far unknown action may be a bacterial strategy to enhance dissemination of intracellular S. aureus and may thereby contribute to increased tissue damage and severity of disease.-Van Krüchten, A., Wilden, J. J., Niemann, S., Peters, G., Löffler, B., Ludwig, S., Ehrhardt, C. Staphylococcus aureus triggers a shift from influenza virus-induced apoptosis to necrotic cell death.

IL-1 Signaling Prevents Alveolar Macrophage Depletion during Influenza and Streptococcus pneumoniae Coinfection

Influenza and bacterial coinfection is a significant cause of hospitalization and death in humans during influenza epidemics and pandemics. However, the fundamental protective and pathogenic mechanisms involved in this complex virus-host-bacterium interaction remain incompletely understood. In this study, we have developed mild to lethal influenza and Streptococcus pneumoniae coinfection models for comparative analyses of disease pathogenesis. Specifically, wild-type and IL-1R type 1-deficient (Il1r1^-/- ) mice were infected with influenza virus and then superchallenged with noninvasive S. pneumoniae serotype 14 (Spn14) or S. pneumoniae serotype 19A (Spn19A). The coinfections were followed by comparative analyses of inflammatory responses and animal protection. We found that resident alveolar macrophages are efficient in the clearance of both pneumococcal serotypes in the absence of influenza infection; in contrast, they are essential for airway control of Spn14 infection but not Spn19A infection. In agreement, TNF-α and neutrophils play a compensatory protective role in secondary bacterial infection associated with Spn19A; however, the essential requirement for alveolar macrophage-mediated clearance significantly enhances the virulence of Spn14 during postinfluenza pneumococcal infection. Furthermore, we show that, although IL-1 signaling is not required for host defense against pneumococcal infection alone, it is essential for sustaining antibacterial immunity during postinfluenza pneumococcal infection, as evidenced by significantly aggravated bacterial burden and animal mortality in Il1r1^-/- mice. Mechanistically, we show that through preventing alveolar macrophage depletion, inflammatory cytokine IL-1 signaling is critically involved in host resistance to influenza and pneumococcal coinfection.

Capsules of virulent pneumococcal serotypes enhance formation of neutrophil extracellular traps during in vivo pathogenesis of pneumonia

Neutrophil extracellular traps (NETs) are released by activated neutrophils to ensnare and kill microorganisms. NETs have been implicated in tissue injury since they carry cytotoxic components of the activated neutrophils. We have previously demonstrated the generation of NETs in infected murine lungs during both primary pneumococcal pneumonia and secondary pneumococcal pneumonia after primary influenza. In this study, we assessed the correlation of pneumococcal capsule size with pulmonary NETs formation and disease severity. We compared NETs formation in the lungs of mice infected with three pneumococcal strains of varying virulence namely serotypes 3, 4 and 19F, as well as a capsule-deficient mutant of serotype 4. In primary pneumonia, NETs generation was strongly associated with the pneumococcal capsule thickness, and was proportional to the disease severity. Interestingly, during secondary pneumonia after primary influenza infection, intense pulmonary NETs generation together with elevated myeloperoxidase activity and cytokine dysregulation determined the disease severity. These findings highlight the crucial role played by the size of pneumococcal capsule in determining the extent of innate immune responses such as NETs formation that may contribute to the severity of pneumonia.

The Unexpected Impact of Vaccines on Secondary Bacterial Infections Following Influenza

Influenza virus infections remain a significant health burden worldwide, despite available vaccines. Factors that contribute to this include a lack of broad coverage by current vaccines and continual emergence of novel virus strains. Further complicating matters, when influenza viruses infect a host, severe infections can develop when bacterial pathogens invade. Secondary bacterial infections (SBIs) contribute to a significant proportion of influenza-related mortality, with Streptococcus pneumoniae, Staphylococcus aureus, Streptococcus pyogenes, and Haemophilus influenzae as major coinfecting pathogens. Vaccines against bacterial pathogens can reduce coinfection incidence and severity, but few vaccines are available and those that are, may have decreased efficacy in influenza virus-infected hosts. While some studies indicate a benefit of vaccine-induced immunity in providing protection against SBIs, a comprehensive understanding is lacking. In this review, we discuss the current knowledge of viral and bacterial vaccine availability, the generation of protective immunity from these vaccines, and the effectiveness in limiting influenza-associated bacterial infections.

What Controls the Acute Viral Infection Following Yellow Fever Vaccination?

Does target cell depletion, innate immunity, or adaptive immunity play the dominant role in controlling primary acute viral infections? Why do some individuals have higher peak virus titers than others? Answering these questions is a basic problem in immunology and can be particularly difficult in humans due to limited data, heterogeneity in responses in different individuals, and limited ability for experimental manipulation. We address these questions for infections following vaccination with the live attenuated yellow fever virus (YFV-17D) by analyzing viral load data from 80 volunteers. Using a mixed effects modeling approach, we find that target cell depletion models do not fit the data as well as innate or adaptive immunity models. Examination of the fits of the innate and adaptive immunity models to the data allows us to select a minimal model that gives improved fits by widely used model selection criteria (AICc and BIC) and explains why it is hard to distinguish between the innate and adaptive immunity models. We then ask why some individuals have over 1000-fold higher virus titers than others and find that most of the variation arises from differences in the initial/maximum growth rate of the virus in different individuals.

EVALUATING THE IMPACTS OF COINFECTION ON IMMUNE SYSTEM FUNCTION OF THE DEER MOUSE ( PEROMYSCUS MANICULATUS) USING SIN NOMBRE VIRUS AND BARTONELLA AS MODEL PATHOGEN SYSTEMS

:  Simultaneous infections with multiple pathogens can alter the function of the host's immune system, often resulting in additive or synergistic morbidity. We examined how coinfection with the common pathogens Sin Nombre virus (SNV) and Bartonella sp. affected aspects of the adaptive and innate immune responses of wild deer mice ( Peromyscus maniculatus). Adaptive immunity was assessed by measuring SNV antibody production; innate immunity was determined by measuring levels of C-reactive protein (CRP) in blood and the complement activity of plasma. Coinfected mice had reduced plasma complement activity and higher levels of CRP compared to mice infected with either SNV or Bartonella. However, antibody titers of deer mice infected with SNV were more than double those of coinfected mice. Plasma complement activity and CRP levels did not differ between uninfected deer mice and those infected with only Bartonella, suggesting that comorbid SNV and Bartonella infections act synergistically, altering the innate immune response. Collectively, our results indicated that the immune response of deer mice coinfected with both SNV and Bartonella differed substantially from individuals infected with only one of these pathogens. Results of our study provided unique, albeit preliminary, insight into the impacts of coinfection on immune system function in wild animal hosts and underscore the complexity of the immune pathways that exist in coinfected hosts.