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

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.

Generation of "OP7 chimera" defective interfering influenza A particle preparations free of infectious virus that show antiviral efficacy in mice

Influenza A virus (IAV) defective interfering particles (DIPs) are considered as new promising antiviral agents. Conventional DIPs (cDIPs) contain a deletion in the genome and can only replicate upon co-infection with infectious standard virus (STV), during which they suppress STV replication. We previously discovered a new type of IAV DIP "OP7" that entails genomic point mutations and displays higher antiviral efficacy than cDIPs. To avoid safety concerns for the medical use of OP7 preparations, we developed a production system that does not depend on infectious IAV. We reconstituted a mixture of DIPs consisting of cDIPs and OP7 chimera DIPs, in which both harbor a deletion in their genome. To complement the defect, the deleted viral protein is expressed by the suspension cell line used for production in shake flasks. Here, DIP preparations harvested are not contaminated with infectious virions, and the fraction of OP7 chimera DIPs depended on the multiplicity of infection. Intranasal administration of OP7 chimera DIP material was well tolerated in mice. A rescue from an otherwise lethal IAV infection and no signs of disease upon OP7 chimera DIP co-infection demonstrated the remarkable antiviral efficacy. The clinical development of this new class of broad-spectrum antiviral may contribute to pandemic preparedness.

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

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

NLRP6 Induces Lung Injury and Inflammation Early in Brucella and Influenza Coinfection

(1) Background: With the resurgence of brucellosis epidemics in China in recent years, the chances of a brucella coinfection with other common respiratory pathogens, such as the influenza virus, have increased dramatically. However, little is known about the pathogenicity or the mechanisms of brucella and influenza coinfections. (2) Methods: To clarify the interventions in the early stages of lung damage due to brucella and influenza coinfections, we evaluated the effect of the coinfection on disease progression and mortality using a coinfection model in WT mice and NLRP6^-/- mice, and we verified the function of NLRP6 in infection and proinflammation. (3) Results: The coinfection induced significant respiratory symptoms, weight loss, and a high mortality rate in WT mice. Influenza in the coinfection group significantly increased brucella proliferation in a synergistic manner. Meanwhile, a histological examination showed severe lung tissue destruction and excessive inflammatory responses in coinfected WT animals, and the expression of NLRP6 and IL-18 was dramatically increased in the lung tissues. Furthermore, NLRP6 deletion attenuated lung injuries and inflammation, a reduced bacterial load, and decreased IL-18 protein expression. (4) Conclusions: Our findings indicated that NLRP6 plays a critical role and might be a promising potential therapeutic target for brucella-influenza coinfections.

Pathogenesis of Respiratory Viral and Fungal Coinfections

Individuals suffering from severe viral respiratory tract infections have recently emerged as "at risk" groups for developing invasive fungal infections. Influenza virus is one of the most common causes of acute lower respiratory tract infections worldwide. Fungal infections complicating influenza pneumonia are associated with increased disease severity and mortality, with invasive pulmonary aspergillosis being the most common manifestation. Strikingly, similar observations have been made during the current coronavirus disease 2019 (COVID-19) pandemic. The copathogenesis of respiratory viral and fungal coinfections is complex and involves a dynamic interplay between the host immune defenses and the virulence of the microbes involved that often results in failure to return to homeostasis. In this review, we discuss the main mechanisms underlying susceptibility to invasive fungal disease following respiratory viral infections. A comprehensive understanding of these interactions will aid the development of therapeutic modalities against newly identified targets to prevent and treat these emerging coinfections.

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

BACKGROUND

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

OBJECTIVES

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

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

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

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

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

In-host Mathematical Modelling of COVID-19 in Humans

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

Influenza sequelae: from immune modulation to persistent alveolitis

Acute influenza virus infections are a global public health concern accounting for millions of illnesses worldwide ranging from mild to severe with, at time, severe complications. Once an individual is infected, the immune system is triggered in response to the pathogen. This immune response can be beneficial ultimately leading to the clearance of the viral infection and establishment of immune memory mechanisms. However, it can be detrimental by increasing susceptibility to secondary bacterial infections and resulting in permanent changes to the lung architecture, in the form of fibrotic sequelae. Here, we review influenza associated bacterial super-infection, the formation of T-cell memory, and persistent lung injury resulting from influenza infection.

Beneficial Effects of Inflammatory Cytokine-Targeting Aptamers in an Animal Model of Chronic Prostatitis

Non-bacterial prostatitis is an inflammatory disease that is difficult to treat. Oligonucleotide aptamers are well known for their stability and flexibility in conjugating various inflammatory molecules. In this study, we investigated the effects of inflammatory cytokine-targeting aptamers (ICTA), putative neutralizers of TNF-alpha and IL-1 beta activation, on local carrageenan-induced prostate inflammation, allodynia, and hyperalgesia in rats. In vitro evaluation confirmed the binding capability of ICTA. Intraprostatic injection of carrageenan or control vehicle was performed in six-week-old rats, and ICTA (150 µg) or vehicle was administered in the prostate along with carrageenan injection. The von Frey filament test was performed to determine mechanical allodynia, and prostate inflammation was examined seven days after drug administration. Local carrageenan administration resulted in a reduction of the tactile threshold. The levels of mononuclear cell infiltration, pro-inflammatory cytokine interleukin-1 beta (b), caspase-1 (casp-1), and Nucleotide-binding oligomerization domain, Leucine rich Repeat and Pyrin domain containing proteins 1 and 3 (NALP1 and NALP3) in the prostate of rats were increased seven days after carrageenan injection. Treatment with ICTA significantly attenuated the carrageenan-induced hyperalgesia and reduced the elevated levels of proteins including TNF-a and IL-1b in the rats. Apoptosis markers, B-cell lymphoma 2-associated X protein (Bax) and caspase-3, were elevated in ICTA-treated Chronic pelvic pain syndrome (CPPS) rats. These results suggest that ICTA provides protection against local carrageenan-induced enhanced pain sensitivity, and that the neutralization of proinflammatory cytokines may result in inflammatory cell apoptosis.

Nasal Microbiota in RSV Bronchiolitis

Respiratory Syncytial Virus (RSV) is the leading cause of bronchiolitis, and the severity may be influenced by the bacterial ecosystem. Our aim was to analyze the nasal microbiota from 48 infants affected by bronchiolitis from RSV virus and 28 infants with bronchiolitis but negative for the virus. Results showed a significantly lower biodiversity in the RSV-positive group with respect to the RSV-negative group, a specific microbial profile associated with the RSV-positive group different from that observed in the negative group, and significant modifications in the relative abundance of taxa in the RSV-positive group, as well as in the RSV-A group, with respect to the negative group. Furthermore, microbial network analyses evidenced, in all studied groups, the presence of two predominant sub-networks characterized by peculiar inter- and intra-group correlation patterns as well as a general loss of connectivity among microbes in the RSV-positive group, particularly in the RSV-A group. Our results indicated that infants with more severe bronchiolitis disease, caused by RSV-A infection, present significant perturbations of both the nasal microbiota structure and the microbial relationships. Patients with a milder bronchiolitis course (RSV-B-infected and patients who have cleared the virus) presented less severe alterations.