Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity

Influenza virus infection exacerbates gene expression related to neurocognitive dysfunction in brains of old mice

BACKGROUND

Age > 65 years is a key risk factor for poor outcomes after human influenza infection. Specifically, in addition to respiratory disease, non-neurotropic influenza A virus (IAV) causes neuro-cognitive complications, e.g. new onset depression and increases the risk of dementia after hospitalization. This study aimed to identify potential mechanisms of these effects by determining differences between young and old mice in brain gene expression in a mouse model of non-neurotropic IAV infection.

METHODS

Young (12 weeks) and old (70 weeks) C57Bl/6J mice were inoculated intranasally with 200 PFU H1N1 A/PR/34/8 (PR8) or sterile PBS (mock). Gene expression in lung and brain was measured by qRT-PCR and normalized to β-actin. Findings were confirmed using the nCounter Mouse Neuroinflammation Array (NanoString) and analyzed with nSolver 4.0 and Ingenuity Pathway Analysis (IPA, Qiagen).

RESULTS

IAV PR8 did not invade the central nervous system. Young and old mice differed significantly in brain gene expression at baseline and during non-neurotropic IAV infection. Expression of brain Ifnl, Irf7, and Tnf mRNAs was upregulated over baseline control at 3 days post-infection (p.i.) only in young mice, but old mice expressed more Ifnl than young mice 7 days p.i. Gene arrays showed down-regulation of the Epigenetic Regulation, Insulin Signaling, and Neurons and Neurotransmission pathways in old mice 3 days p.i. while young mice demonstrated no change or induction of these pathways at the same time point. IPA revealed marked baseline differences between old and young mice. Gene expression related to Cognitive Impairment, Memory Deficits and Learning worsened in old mice relative to young mice during IAV infection. Aged mice demonstrate more severe changes in gene expression related to memory loss and cognitive dysfunction by IPA.

CONCLUSIONS

These data suggest the genes and pathways related to learning and cognitive performance that were worse at baseline in old mice were further worsened by IAV infection, similar to old patients. Early events in the brain triggered by IAV infection portend downstream neurocognitive pathology in old adults.

Immune response in influenza virus infection and modulation of immune injury by viral neuraminidase

Influenza A viruses cause severe respiratory illnesses in humans and animals. Overreaction of the innate immune response to influenza virus infection results in hypercytokinemia, which is responsible for mortality and morbidity. The influenza A virus surface glycoprotein neuraminidase (NA) plays a vital role in viral attachment, entry, and virion release from infected cells. NA acts as a sialidase, which cleaves sialic acids from cell surface proteins and carbohydrate side chains on nascent virions. Here, we review progress in understanding the role of NA in modulating host immune response to influenza virus infection. We also discuss recent exciting findings targeting NA protein to interrupt influenza-induced immune injury.

Metabolomics meets systems immunology

Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.

Age-Related Changes to the Immune System Exacerbate the Inflammatory Response to Pandemic H1N1 Infection

Age-induced dysregulation of the immune response is a major contributor to the morbidity and mortality related to influenza a virus infections. Experimental data have shown substantial changes to the activation and maintenance of the immune response will occur with age, but it remains unclear which of these many interrelated changes are most critical to controlling the survival of the host during infection. To ascertain which mechanisms are predominantly responsible for the increased morbidity in elderly hosts, we developed an ordinary differential equation model to simulate the immune response to pandemic H1N1 infection. We fit this model to experimental data measured in young and old macaques. We determined that the severity of the infection in the elderly hosts is caused by a dysregulation in the innate immune response. We also simulated CD8+ T cell exhaustion, a common consequence of chronic and extensive infections. Our simulations indicate that while T cell exhaustion is possible in both age groups, its effects are more severe in the elderly population, as their dysregulated immune response cannot easily compensate for the exhausted T cells. Finally, we explore a therapeutic approach to reversing T cell exhaustion through an inflammatory stimulus. A controlled increase in inflammatory signals can lead to a higher chance of surviving the infection, but excess inflammation will likely lead to septic death. These results indicate that our model captures distinctions in the predominant mechanisms controlling the immune response in younger and older hosts and allows for simulations of clinically relevant therapeutic strategies post-infection.

Antiviral Gene Expression in Young and Aged Murine Lung during H1N1 and H3N2

Influenza is a respiratory virus that alone or in combination with secondary bacterial pathogens can contribute to the development of acute pneumonia in persons >65 years of age. Host innate immune antiviral signaling early in response to influenza is essential to inhibit early viral replication and guide the initiation of adaptive immune responses. Using young adult (3 months) and aged adult mice infected with mouse adapted H1N1 or H3N2, the results of our study illustrate dysregulated and/or diminished activation of key signaling pathways in aged lung contribute to increased lung inflammation and morbidity. Specifically, within the first seven days of infection, there were significant changes in genes associated with TLR and RIG-I signaling detected in aged murine lung in response to H1N1 or H3N2. Taken together, the results of our study expand our current understanding of age-associated changes in antiviral signaling in the lung.

Estimation of viral kinetics model parameters in young and aged SARS-CoV-2 infected macaques

The SARS-CoV-2 virus disproportionately causes serious illness and death in older individuals. In order to have the greatest impact in decreasing the human toll caused by the virus, antiviral treatment should be targeted to older patients. For this, we need a better understanding of the differences in viral dynamics between SARS-CoV-2 infection in younger and older adults. In this study, we use previously published averaged viral titre measurements from the nose and throat of SARS-CoV-2 infection in young and aged cynomolgus macaques to parametrize a viral kinetics model. We find that all viral kinetics parameters differ between young and aged macaques in the nasal passages, but that there are fewer differences in parameter estimates from the throat. We further use our parametrized model to study the antiviral treatment of young and aged animals, finding that early antiviral treatment is more likely to lead to a lengthening of the infection in aged animals, but not in young animals.

Risk Factors Associated with 30-Day Mortality in Older Patients with Influenza

Background: Influenza is a common viral condition, but factors related to short-term mortality have not been fully studied in older adults. Our objective was to determine whether there is an association between geriatric factors and 30-day mortality. Methods: This was a retrospective cohort design. All patients aged 75 years and over, with a diagnosis of influenza confirmed by a positive RT-PCR, were included. The primary endpoint was death within the 30 days after diagnosis. Results: 114 patients were included; 14 (12.3%) patients died within 30 days. In multivariate analysis these patients were older (OR: 1.37 95% CI (1.05, 1.79), p = 0.021), and had a lower ADL score (OR: 0.36 95% CI (0, 17; 0.75), p = 0.006), and a higher SOFA score (OR: 2.30 95% CI (1.07, 4.94), p = 0.03). Oseltamivir treatment, initiated within the first 48 h, was independently associated with survival (OR: 0.04 95% CI (0.002, 0.78), p = 0.034). Conclusions: Identification of mortality risk factors makes it possible to consider specific secondary prevention measures such as the rapid introduction of antiviral treatment. Combined with primary prevention, these measures could help to limit the mortality associated with influenza in older patients.

A study of the effects of age on the dynamics of RSV in animal models

Respiratory syncytial virus can cause severe illness and even death, particularly in infants. The increased severity of disease in young children is thought to be due to a lack of previous exposure to the virus as well as the limited immune response in infants. While studies have examined the clinical differences in disease between infants and adults, there has been limited examination of how the viral dynamics differ as infants develop. In this study, we apply a mathematical model to data from cotton rats and ferrets of different ages to assess how viral kinetics parameters change as the animals age. We find no clear trend in the viral decay rate, infecting time, and basic reproduction number as the animals age. We discuss possible reasons for the null result including the limited data, lack of detail of the mathematical model, and the limitations of animal models.

Short Communication: Oral Administration of Heat-killed Lactobacillus brevis KB290 in Combination with Retinoic Acid Provides Protection against Influenza Virus Infection in Mice

Influenza virus type A (IAV) is a seasonal acute respiratory disease virus with severe symptoms, and an effective preventive measure is required. Despite many reports describing the potentially protective effects of lactic acid bacteria, few studies have investigated the effects of nutritional supplement combinations. This study reports the effect of the combined intake of heat-killed Lactobacillus brevis KB290 (KB290) and vitamin A (VA) on mice challenged with a sublethal dose of IAV. For 2 weeks, five groups of mice were fed either placebo, KB290, VA, or a combination of KB290 and VA (KB290+VA). After subsequent IAV challenge, bodyweight and general health were monitored for up to 2 weeks. Viral titres were determined in the lungs of animal subgroups euthanised at days 3, 7, and 14 after IAV challenge. A significant loss was observed in the bodyweights of IAV-infected animals from day 1 post-IAV challenge, whereas the mice fed KB290+VA did not lose any weight after IAV infection, indicating successful protection from the infection. Additionally, mice in the KB290+VA group showed the highest reduction in lung viral titres. In conclusion, the combination of KB290 and VA could be a useful food supplement relevant for protection against seasonal influenza virus infection in humans.

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.