A Critical, Nonlinear Threshold Dictates Bacterial Invasion and Initial Kinetics During Influenza

Exploring the Interplay between COVID-19 and Gut Health: The Potential Role of Prebiotics and Probiotics in Immune Support

The COVID-19 pandemic has profoundly impacted global health, leading to extensive research focused on developing strategies to enhance outbreak response and mitigate the disease's severity. In the aftermath of the pandemic, attention has shifted towards understanding and addressing long-term health implications, particularly in individuals experiencing persistent symptoms, known as long COVID. Research into potential interventions to alleviate long COVID symptoms has intensified, with a focus on strategies to support immune function and mitigate inflammation. One area of interest is the gut microbiota, which plays a crucial role in regulating immune responses and maintaining overall health. Prebiotics and probiotics, known for their ability to modulate the gut microbiota, have emerged as potential therapeutic agents in bolstering immune function and reducing inflammation. This review delves into the intricate relationship between long COVID, the gut microbiota, and immune function, with a specific focus on the role of prebiotics and probiotics. We examine the immune response to long COVID, emphasizing the importance of inflammation and immune regulation in the persistence of symptoms. The potential of probiotics in modulating immune responses, including their mechanisms in combating viral infections such as COVID-19, is discussed in detail. Clinical evidence supporting the use of probiotics in managing long COVID symptoms is summarized, highlighting their role as adjunctive therapy in addressing various aspects of SARS-CoV-2 infection and its aftermath.

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.

Adjuvant effects of combination monophosphoryl lipid A and poly I:C on antigen-specific immune responses and protective efficacy of influenza vaccines

Toll-like receptor (TLR) agonists improve vaccine immunogenicity and efficacy, but they are currently unlicensed as adjuvants in influenza vaccines. This study aimed to investigate whether a combination of monophosphoryl lipid A (MPL, a TLR4 agonist) and polyriboinosinic polyribocytidylic acid (poly I:C, a TLR3 agonist) can enhance the protective efficacy of an inactivated A/Puerto Rico/8/1934 (A/PR8) H1N1 influenza vaccine against homologous influenza infection and minimize illness outcomes. Results showed that combination MPL and poly I:C adjuvanted influenza vaccination increased the production of antigen-specific antibodies, decreased the levels of cytokines and cellular infiltrates at the infection sites, and induced significant memory T and B cell responses in mice. The results of this study suggest that the combination of MPL and poly I:C can be developed into a possible adjuvant for enhancing the efficacy of influenza vaccines.

Increased virus dissemination leads to enhanced lung injury but not inflammation during influenza-associated secondary bacterial infection

Secondary bacterial infections increase influenza-related morbidity and mortality, particularly if acquired after 5-7 d from the viral onset. Synergistic host responses and direct pathogen-pathogen interactions are thought to lead to a state of hyperinflammation, but the kinetics of the lung pathology have not yet been detailed, and identifying the contribution of different mechanisms to disease is difficult because these may change over time. To address this gap, we examined host-pathogen and lung pathology dynamics following a secondary bacterial infection initiated at different time points after influenza within a murine model. We then used a mathematical approach to quantify the increased virus dissemination in the lung, coinfection time-dependent bacterial kinetics, and virus-mediated and postbacterial depletion of alveolar macrophages. The data showed that viral loads increase regardless of coinfection timing, which our mathematical model predicted and histomorphometry data confirmed was due to a robust increase in the number of infected cells. Bacterial loads were dependent on the time of coinfection and corresponded to the level of IAV-induced alveolar macrophage depletion. Our mathematical model suggested that the additional depletion of these cells following the bacterial invasion was mediated primarily by the virus. Contrary to current belief, inflammation was not enhanced and did not correlate with neutrophilia. The enhanced disease severity was correlated to inflammation, but this was due to a nonlinearity in this correlation. This study highlights the importance of dissecting nonlinearities during complex infections and demonstrated the increased dissemination of virus within the lung during bacterial coinfection and simultaneous modulation of immune responses during influenza-associated bacterial pneumonia.

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infections During SARS-CoV-2

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate infection with the SARS-CoV-2 USA-WA1/2020 strain increased the risk of pneumococcal (type 2 strain D39) coinfection in a time-dependent, but sex-independent, manner in the transgenic K18-hACE2 mouse model of COVID-19. Bacterial coinfection increased lethality when the bacteria was initiated at 5 or 7 d post-virus infection (pvi) but not at 3 d pvi. Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

Time-Dependent Increase in Susceptibility and Severity of Secondary Bacterial Infection during SARS-CoV-2 Infection

Secondary bacterial infections can exacerbate SARS-CoV-2 infection, but their prevalence and impact remain poorly understood. Here, we established that a mild to moderate SARS-CoV-2 infection increased the risk of pneumococcal coinfection in a time-dependent, but sexindependent, manner in the transgenic K18-hACE mouse model of COVID-19. Bacterial coinfection was not established at 3 d post-virus, but increased lethality was observed when the bacteria was initiated at 5 or 7 d post-virus infection (pvi). Bacterial outgrowth was accompanied by neutrophilia in the groups coinfected at 7 d pvi and reductions in B cells, T cells, IL-6, IL-15, IL-18, and LIF were present in groups coinfected at 5 d pvi. However, viral burden, lung pathology, cytokines, chemokines, and immune cell activation were largely unchanged after bacterial coinfection. Examining surviving animals more than a week after infection resolution suggested that immune cell activation remained high and was exacerbated in the lungs of coinfected animals compared with SARS-CoV-2 infection alone. These data suggest that SARS-CoV-2 increases susceptibility and pathogenicity to bacterial coinfection, and further studies are needed to understand and combat disease associated with bacterial pneumonia in COVID-19 patients.

Simulation of Scenarios of a Deep Population Crisis in a Rapidly Growing Population

Abstract-This article focuses on the modeling of crisis and threshold development of the population process during the formation of a new population in a competitive environment. As a population spreads, a deep population crisis may arise as a result an abrupt triggering of biotic countermeasures before resources for a further increase in population size are exhausted. A bottleneck occurred in the history of many populations, including humans at the time of the Neolithic crash in Europe. Invaders with high reproductive potential often exert deleterious effects on biosystems. The emergence of efficient competition can not only cause classical cyclical fluctuations, but also lead to a complete extinction of the population after a series of high peaks in its abundance. Two alternative scenarios provide classical examples of induced population crises. One was observed in Gause's experiments where an introduction of a predatory ciliate drove another ciliate species to extinction. The other scenario was observed in a series of experiments where bacteriophages were introduced into colonies of actively dividing bacteria that had a dynamically adapting antiviral mechanism. In this work, modifications to the model were proposed to describe the actual scenarios of crisis effects in population dynamics. Equations with deviating arguments in the time variable allowed a threshold effect of conditions on reproduction of the invasive species and an aggregated nature of the lagging regulation with two time factors. The computational scenarios described both completion of the process after a repeated outbreak and successful elimination of the population crisis via rapid adaptation. Deep crisis phenomena are characteristic of local population dynamics when organisms interact with viruses that are new to them.

Role of Probiotics in the Management of COVID-19: A Computational Perspective

Coronavirus disease 2019 (COVID-19) was declared a pandemic at the beginning of 2020, causing millions of deaths worldwide. Millions of vaccine doses have been administered worldwide; however, outbreaks continue. Probiotics are known to restore a stable gut microbiota by regulating innate and adaptive immunity within the gut, demonstrating the possibility that they may be used to combat COVID-19 because of several pieces of evidence suggesting that COVID-19 has an adverse impact on gut microbiota dysbiosis. Thus, probiotics and their metabolites with known antiviral properties may be used as an adjunctive treatment to combat COVID-19. Several clinical trials have revealed the efficacy of probiotics and their metabolites in treating patients with SARS-CoV-2. However, its molecular mechanism has not been unraveled. The availability of abundant data resources and computational methods has significantly changed research finding molecular insights between probiotics and COVID-19. This review highlights computational approaches involving microbiome-based approaches and ensemble-driven docking approaches, as well as a case study proving the effects of probiotic metabolites on SARS-CoV-2.

Importance of non-pharmaceutical interventions in lowering the viral inoculum to reduce susceptibility to infection by SARS-CoV-2 and potentially disease severity

Adherence to non-pharmaceutical interventions to prevent the transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been highly variable across settings, particularly in the USA. In this Personal View, we review data supporting the importance of the viral inoculum (the dose of viral particles from an infected source over time) in increasing the probability of infection in respiratory, gastrointestinal, and sexually transmitted viral infections in humans. We also review the available evidence linking the relationship of the viral inoculum to disease severity. Non-pharmaceutical interventions might reduce the susceptibility to SARS-CoV-2 infection by reducing the viral inoculum when there is exposure to an infectious source. Data from physical sciences research suggest that masks protect the wearer by filtering virus from external sources, and others by reducing expulsion of virus by the wearer. Social distancing, handwashing, and improved ventilation also reduce the exposure amount of viral particles from an infectious source. Maintaining and increasing non-pharmaceutical interventions can help to quell SARS-CoV-2 as we enter the second year of the pandemic. Finally, we argue that even as safe and effective vaccines are being rolled out, non-pharmaceutical interventions will continue to play an essential role in suppressing SARS-CoV-2 transmission until equitable and widespread vaccine administration has been completed.

Microbial co-infections in COVID-19: Associated microbiota and underlying mechanisms of pathogenesis

The novel coronavirus infectious disease-2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has traumatized the whole world with the ongoing devastating pandemic. A plethora of microbial domains including viruses (other than SARS-CoV-2), bacteria, archaea and fungi have evolved together, and interact in complex molecular pathogenesis along with SARS-CoV-2. However, the involvement of other microbial co-pathogens and underlying molecular mechanisms leading to extortionate ailment in critically ill COVID-19 patients has yet not been extensively reviewed. Although, the incidence of co-infections could be up to 94.2% in laboratory-confirmed COVID-19 cases, the fate of co-infections among SARS-CoV-2 infected hosts often depends on the balance between the host's protective immunity and immunopathology. Predominantly identified co-pathogens of SARS-CoV-2 are bacteria such as Streptococcus pneumoniae, Staphylococcus aureus, Klebsiella pneumoniae, Haemophilus influenzae, Mycoplasma pneumoniae, Acinetobacter baumannii, Legionella pneumophila and Clamydia pneumoniae followed by viruses including influenza, coronavirus, rhinovirus/enterovirus, parainfluenza, metapneumovirus, influenza B virus, and human immunodeficiency virus. The cross-talk between co-pathogens (especially lung microbiomes), SARS-CoV-2 and host is an important factor that ultimately increases the difficulty of diagnosis, treatment, and prognosis of COVID-19. Simultaneously, co-infecting microbiotas may use new strategies to escape host defense mechanisms by altering both innate and adaptive immune responses to further aggravate SARS-CoV-2 pathogenesis. Better understanding of co-infections in COVID-19 is critical for the effective patient management, treatment and containment of SARS-CoV-2. This review therefore necessitates the comprehensive investigation of commonly reported microbial co-pathogens amid COVID-19, their transmission pattern along with the possible mechanism of co-infections and outcomes. Thus, identifying the possible co-pathogens and their underlying molecular mechanisms during SARS-CoV-2 pathogenesis may shed light in developing diagnostics, appropriate curative and preventive interventions for suspected SARS-CoV-2 respiratory infections in the current pandemic.

Dynamic Pneumococcal Genetic Adaptations Support Bacterial Growth and Inflammation during Coinfection with Influenza

Streptococcus pneumoniae (pneumococcus) is one of the primary bacterial pathogens that complicates influenza virus infections. These bacterial coinfections increase influenza-associated morbidity and mortality through a number of immunological and viral-mediated mechanisms, but the specific bacterial genes that contribute to postinfluenza pathogenicity are not known. Here, we used genome-wide transposon mutagenesis (Tn-Seq) to reveal bacterial genes that confer improved fitness in influenza virus-infected hosts. The majority of the 32 genes identified are involved in bacterial metabolism, including nucleotide biosynthesis, amino acid biosynthesis, protein translation, and membrane transport. We generated mutants with single-gene deletions (SGD) of five of the genes identified, SPD1414, SPD2047 (cbiO1), SPD0058 (purD), SPD1098, and SPD0822 (proB), to investigate their effects on in vivo fitness, disease severity, and host immune responses. The growth of the SGD mutants was slightly attenuated in vitro and in vivo, but each still grew to high titers in the lungs of mock- and influenza virus-infected hosts. Despite high bacterial loads, mortality was significantly reduced or delayed with all SGD mutants. Time-dependent reductions in pulmonary neutrophils, inflammatory macrophages, and select proinflammatory cytokines and chemokines were also observed. Immunohistochemical staining further revealed altered neutrophil distribution with reduced degeneration in the lungs of influenza virus-SGD mutant-coinfected animals. These studies demonstrate a critical role for specific bacterial genes and for bacterial metabolism in driving virulence and modulating immune function during influenza-associated bacterial pneumonia.

Altered Signal Transduction in the Immune Response to Influenza Virus and S. pneumoniae or S. aureus Co-Infections

Influenza virus is a well-known respiratory pathogen, which still leads to many severe pulmonary infections in the human population every year. Morbidity and mortality rates are further increased if virus infection coincides with co-infections or superinfections caused by bacteria such as Streptococcus pneumoniae (S. pneumoniae) and Staphylococcus aureus (S. aureus). This enhanced pathogenicity is due to complex interactions between the different pathogens and the host and its immune system and is mainly governed by altered intracellular signaling processes. In this review, we summarize the recent findings regarding the innate and adaptive immune responses during co-infection with influenza virus and S. pneumoniae or S. aureus, describing the signaling pathways involved and how these interactions influence disease outcomes.

Structure and Hierarchy of SARS-CoV-2 Infection Dynamics Models Revealed by Reaction Network Analysis

This work provides a mathematical technique for analyzing and comparing infection dynamics models with respect to their potential long-term behavior, resulting in a hierarchy integrating all models. We apply our technique to coupled ordinary and partial differential equation models of SARS-CoV-2 infection dynamics operating on different scales, that is, within a single organism and between several hosts. The structure of a model is assessed by the theory of chemical organizations, not requiring quantitative kinetic information. We present the Hasse diagrams of organizations for the twelve virus models analyzed within this study. For comparing models, each organization is characterized by the types of species it contains. For this, each species is mapped to one out of four types, representing uninfected, infected, immune system, and bacterial species, respectively. Subsequently, we can integrate these results with those of our former work on Influenza-A virus resulting in a single joint hierarchy of 24 models. It appears that the SARS-CoV-2 models are simpler with respect to their long term behavior and thus display a simpler hierarchy with little dependencies compared to the Influenza-A models. Our results can support further development towards more complex SARS-CoV-2 models targeting the higher levels of the hierarchy.

Leveraging Computational Modeling to Understand Infectious Diseases

Purpose of Review

Computational and mathematical modeling have become a critical part of understanding in-host infectious disease dynamics and predicting effective treatments. In this review, we discuss recent findings pertaining to the biological mechanisms underlying infectious diseases, including etiology, pathogenesis, and the cellular interactions with infectious agents. We present advances in modeling techniques that have led to fundamental disease discoveries and impacted clinical translation.

Recent Findings

Combining mechanistic models and machine learning algorithms has led to improvements in the treatment of Shigella and tuberculosis through the development of novel compounds. Modeling of the epidemic dynamics of malaria at the within-host and between-host level has afforded the development of more effective vaccination and antimalarial therapies. Similarly, in-host and host-host models have supported the development of new HIV treatment modalities and an improved understanding of the immune involvement in influenza. In addition, large-scale transmission models of SARS-CoV-2 have furthered the understanding of coronavirus disease and allowed for rapid policy implementations on travel restrictions and contract tracing apps.

Summary

Computational modeling is now more than ever at the forefront of infectious disease research due to the COVID-19 pandemic. This review highlights how infectious diseases can be better understood by connecting scientists from medicine and molecular biology with those in computer science and applied mathematics.

Effect of Vitamin A Deficiency in Dysregulating Immune Responses to Influenza Virus and Increasing Mortality Rates After Bacterial Coinfections

BACKGROUND

Secondary bacterial coinfections are ranked as a leading cause of hospitalization and morbid conditions associated with influenza. Because vitamin A deficiency (VAD) and insufficiency are frequent in both developed and developing countries, we asked how VAD influences coinfection severity.

METHODS

VAD and control mice were infected with influenza virus for evaluation of inflammatory cytokines, cellular immune responses, and viral clearance. Influenza-infected mice were coinfected with Streptococcus pneumoniae to study weight loss and survival.

RESULTS

Naive VAD mouse lungs exhibited dysregulated immune function. Neutrophils were enhanced in frequency and there was a significant reduction in RANTES (regulated on activation of normal T cells expressed and secreted), a chemokine instrumental in T-cell homing and recruitment. After influenza virus infection, VAD mice experienced failures in CD4+ T-cell recruitment and B-cell organization into lymphoid structures in the lung. VAD mice exhibited higher viral titers than controls and slow viral clearance. There were elevated levels of inflammatory cytokines and innate cell subsets in the lungs. However, arginase, a marker of alternatively activated M2 macrophages, was rare. When influenza-infected VAD animals were exposed to bacteria, they experienced a 100% mortality rate.

CONCLUSION

Data showed that VAD dysregulated the immune response. Consequently, secondary bacterial infections were 100% lethal in influenza-infected VAD mice.

Nuclear Receptors, Ligands and the Mammalian B Cell

Questions concerning the influences of nuclear receptors and their ligands on mammalian B cells are vast in number. Here, we briefly review the effects of nuclear receptor ligands, including estrogen and vitamins, on immunoglobulin production and protection from infectious diseases. We describe nuclear receptor interactions with the B cell genome and the potential mechanisms of gene regulation. Attention to the nuclear receptor/ligand regulation of B cell function may help optimize B cell responses, improve pathogen clearance, and prevent damaging responses toward inert- and self-antigens.

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.

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.

Principles Governing Establishment versus Collapse of HIV-1 Cellular Spread

A population at low census might go extinct or instead transition into exponential growth to become firmly established. Whether this pivotal event occurs for a within-host pathogen can be the difference between health and illness. Here, we define the principles governing whether HIV-1 spread among cells fails or becomes established by coupling stochastic modeling with laboratory experiments. Following ex vivo activation of latently infected CD4 T cells without de novo infection, stochastic cell division and death contributes to high variability in the magnitude of initial virus release. Transition to exponential HIV-1 spread often fails due to release of an insufficient amount of replication-competent virus. Establishment of exponential growth occurs when virus produced from multiple infected cells exceeds a critical population size. We quantitatively define the crucial transition to exponential viral spread. Thwarting this process would prevent HIV transmission or rebound from the latent reservoir.

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.

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.

Structure and Hierarchy of Influenza Virus Models Revealed by Reaction Network Analysis

Influenza A virus is recognized today as one of the most challenging viruses that threatens both human and animal health worldwide. Understanding the control mechanisms of influenza infection and dynamics is crucial and could result in effective future treatment strategies. Many kinetic models based on differential equations have been developed in recent decades to capture viral dynamics within a host. These models differ in their complexity in terms of number of species elements and number of reactions. Here, we present a new approach to understanding the overall structure of twelve influenza A virus infection models and their relationship to each other. To this end, we apply chemical organization theory to obtain a hierarchical decomposition of the models into chemical organizations. The decomposition is based on the model structure (reaction rules) but is independent of kinetic details such as rate constants. We found different types of model structures ranging from two to eight organizations. Furthermore, the model's organizations imply a partial order among models entailing a hierarchy of model, revealing a high model diversity with respect to their long-term behavior. Our methods and results can be helpful in model development and model integration, also beyond the influenza area.

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.

Monocytes Represent One Source of Bacterial Shielding from Antibiotics following Influenza Virus Infection

Methicillin-resistant Staphylococcus aureus has emerged as a significant contributor to morbidity and mortality associated with influenza infection. In this study, we show in a mouse model that preceding influenza infection promotes S. aureus resistance to killing by antibiotics. This resistance coincides with influenza-induced accumulation of inflammatory monocytes in the lung. CCR type 2 (CCR2) is responsible for pulmonary monocyte recruitment after influenza infection. We found that antibiotic-treated Ccr2-deficient (Ccr2^-/-) mice exhibit significantly improved bacterial control and survival from influenza and methicillin-resistant S. aureus coinfection, despite a delay in viral clearance. Mechanistically, our results from in vivo studies indicate that influenza-induced monocytes serve as reservoirs for intracellular S. aureus survival, thereby promoting bacterial resistance to antibiotic treatment. Blocking CCR2 with a small molecular inhibitor (PF-04178903), in conjunction with antibiotic treatment, enhanced lung bacterial clearance and significantly improved animal survival. Collectively, our study demonstrates that inflammatory monocytes constitute an important and hitherto underappreciated mechanism of the conflicting immune requirements for viral and bacterial clearance by hosts, which subsequently leads to exacerbated outcomes of influenza and S. aureus coinfection.

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.

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.

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.

Effects of Influenza on Alveolar Macrophage Viability Are Dependent on Mouse Genetic Strain

Secondary bacterial coinfections following influenza virus pose a serious threat to human health. Therefore, it is of significant clinical relevance to understand the immunological causes of this increased susceptibility. Influenza-induced alterations in alveolar macrophages (AMs) have been shown to be a major underlying cause of the increased susceptibility to bacterial superinfection. However, the mechanisms responsible for this remain under debate, specifically in terms of whether AMs are depleted in response to influenza infection or are maintained postinfection, but with disrupted phagocytic activity. The data presented in this article resolves this issue by showing that either mechanism can differentially occur in individual mouse strains. BALB/c mice exhibited a dramatic IFN-γ-dependent reduction in levels of AMs following infection with influenza A, whereas AM levels in C57BL/6 mice were maintained throughout the course of influenza infection, although the cells displayed an altered phenotype, namely an upregulation in CD11b expression. These strain differences were observed regardless of whether infection was performed with low or high doses of influenza virus. Furthermore, infection with either the H1N1 A/California/04/2009 (CA04) or H1N1 A/PR8/1934 (PR8) virus strain yielded similar results. Regardless of AM viability, both BALB/c and C57BL/6 mice showed a high level of susceptibility to postinfluenza bacterial infection. These findings resolve the apparent inconsistencies in the literature, identify mouse strain-dependent differences in the AM response to influenza infection, and ultimately may facilitate translation of the mouse model to clinical application.

When do co-infections matter?

PURPOSE OF REVIEW

Advances in diagnostic methods mean that co-infections are increasingly being detected in clinical practice, yet their significance is not always obvious. In parallel, basic science studies are increasingly investigating interactions between pathogens to try to explain real-life observations and elucidate biological mechanisms.

RECENT FINDINGS

Co-infections may be insignificant, detrimental, or even beneficial, and these outcomes can occur through multiple levels of interactions which include modulation of the host response, altering the performance of diagnostic tests, and drug-drug interactions during treatment. The harmful effects of chronic co-infections such as tuberculosis or Hepatitis B and C in association with HIV are well established, and recent studies have focussed on strategies to mitigate these effects. However, consequences of many acute co-infections are much less certain, and recent conflicting findings simply highlight many of the challenges of studying naturally acquired infections in humans.

SUMMARY

Tackling these challenges, using animal models, or careful prospective studies in humans may prove to be worthwhile. There are already tantalizing examples where identification and treatment of relevant co-infections seems to hold promise for improved health outcomes.

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.

Heat-killed Lactobacillus casei confers broad protection against influenza A virus primary infection and develops heterosubtypic immunity against future secondary infection

Lactic acid bacteria (LAB) are the common probiotics. Here, we investigated the antiviral protective effects of heat-killed LAB strain Lactobacillus casei DK128 (DK128) on influenza viruses. Intranasal treatment of mice with DK128 conferred protection against different subtypes of influenza viruses by lessening weight loss and lowering viral loads. Protection via heat-killed DK128 was correlated with an increase in alveolar macrophage cells in the lungs and airways, early induction of virus specific antibodies, reduced levels of pro-inflammatory cytokines and innate immune cells. Importantly, the mice that were protected against primary viral infection as a result of heat-killed DK128 pretreatment developed subsequent heterosubtypic immunity against secondary virus infection. For protection against influenza virus via heat-killed DK128 pretreatment, B cells and partially CD4 T cells but not CD8 T cells were required as inferred from studies using knockout mouse models. Our study provides insight into how hosts can be equipped with innate and adaptive immunity via heat-killed DK128 treatment to protect against influenza virus, supporting that heat-killed LAB may be developed as anti-virus probiotics.

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.

Distinct Effects of Monophosphoryl Lipid A, Oligodeoxynucleotide CpG, and Combination Adjuvants on Modulating Innate and Adaptive Immune Responses to Influenza Vaccination

Monophosphoryl lipid A (MPL) and oligodeoxynucleotide CpG are toll-like receptor (TLR) 4 and 9 agonist, respectively. Here, we investigated the effects of MPL, CpG, and combination adjuvants on stimulating in vitro dendritic cells (DCs), in vivo innate and adaptive immune responses, and protective efficacy of influenza vaccination. Combination of MPL and CpG was found to exhibit distinct effects on stimulating DCs in vitro to secrete IL-12p70 and tumor necrosis factor (TNF)-α and proliferate allogeneic CD8 T cells. Prime immunization of mice with inactivated split influenza vaccine in the presence of low dose MPL+CpG adjuvants increased the induction of virus-specific IgG and IgG2a isotype antibodies. MPL and CpG adjuvants contribute to improving the efficacy of prime influenza vaccination against lethal influenza challenge as determined by body weight monitoring, lung function, viral titers, and histology. A combination of MPL and CpG adjuvants was effective in improving vaccine efficacy as well as in reducing inflammatory immune responses locally and in inducing cellular immune responses upon lethal influenza virus challenge. This study demonstrates unique adjuvant effects of MPL, CpG, and combination adjuvants on modulating innate and adaptive immune responses to influenza prime vaccination.

Quantifying the therapeutic requirements and potential for combination therapy to prevent bacterial coinfection during influenza

Secondary bacterial infections (SBIs) exacerbate influenza-associated disease and mortality. Antimicrobial agents can reduce the severity of SBIs, but many have limited efficacy or cause adverse effects. Thus, new treatment strategies are needed. Kinetic models describing the infection process can help determine optimal therapeutic targets, the time scale on which a drug will be most effective, and how infection dynamics will change under therapy. To understand how different therapies perturb the dynamics of influenza infection and bacterial coinfection and to quantify the benefit of increasing a drug's efficacy or targeting a different infection process, I analyzed data from mice treated with an antiviral, an antibiotic, or an immune modulatory agent with kinetic models. The results suggest that antivirals targeting the viral life cycle are most efficacious in the first 2 days of infection, potentially because of an improved immune response, and that increasing the clearance of infected cells is important for treatment later in the infection. For a coinfection, immunotherapy could control low bacterial loads with as little as 20 % efficacy, but more effective drugs would be necessary for high bacterial loads. Antibiotics targeting bacterial replication and administered 10 h after infection would require 100 % efficacy, which could be reduced to 40 % with prophylaxis. Combining immunotherapy with antibiotics could substantially increase treatment success. Taken together, the results suggest when and why some therapies fail, determine the efficacy needed for successful treatment, identify potential immune effects, and show how the regulation of underlying mechanisms can be used to design new therapeutic strategies.