A Mathematical Model for the Macrophage Response to Respiratory Viral Infection in Normal and Asthmatic Conditions

Mechanistic modelling of allergen-induced airways disease in early life

Asthma affects approximately 300 million individuals worldwide and the onset predominantly arises in childhood. Children are exposed to multiple environmental irritants, such as viruses and allergens, that are common triggers for asthma onset, whilst their immune systems are developing in early life. Understanding the impact of allergen exposures on the developing immune system and resulting alterations in lung function in early life will help prevent the onset and progression of allergic asthma in children. In this study, we developed an in silico model describing the pulmonary immune response to a common allergen, house dust mite, to investigate its downstream impact on the pathophysiology of asthma, including airway eosinophilic inflammation, remodelling, and lung function. We hypothesised that altered epithelial function following allergen exposure determines the onset of airway remodelling and abnormal lung function, which are irreversible with current asthma therapies. We calibrated the in silico model using age appropriate in vivo data from neonatal and adult mice. We validated the in silico model using in vivo data from mice on the effects of current treatment strategies. The in silico model recapitulates experimental observations and provides an interpretable in silico tool to assess airway pathology and the underlying immune responses upon allergen exposure. The in silico model simulations predict the extent of bronchial epithelial barrier damage observed when allergen sensitisation occurs and demonstrate that epithelial barrier damage and impaired immune maturation are critical determinants of reduced lung function and asthma development. The in silico model demonstrates that both epithelial barrier repair and immune maturation are potential targets for therapeutic intervention to achieve successful asthma prevention.

Modelling the continuum of macrophage phenotypes and their role in inflammation

Macrophages are a type of white blood cell that play a significant role in determining the inflammatory response associated with a wide range of medical conditions. They are highly plastic, having the capacity to adopt numerous polarisation states or 'phenotypes' with disparate pro- or anti-inflammatory roles. Many previous studies divide macrophages into two categorisations: M1 macrophages are largely pro-inflammatory in nature, while M2 macrophages are largely restorative. However, there is a growing body of evidence that the M1 and M2 classifications represent the extremes of a much broader spectrum of phenotypes, and that intermediate phenotypes can play important roles in the progression or treatment of many medical conditions. In this article, we present a model of macrophage dynamics that includes a continuous description of phenotype, and hence incorporates intermediate phenotype configurations. We describe macrophage phenotype switching via nonlinear convective flux terms that scale with background levels of generic pro- and anti-inflammatory mediators. Through numerical simulation and bifurcation analysis, we unravel the model's resulting dynamics, paying close attention to the system's multistability and the extent to which key macrophage-mediator interactions provide bifurcations that act as switches between chronic states and restoration of health. We show that interactions that promote M1-like phenotypes generally result in a greater array of stable chronic states, while interactions that promote M2-like phenotypes can promote restoration of health. Additionally, our model admits oscillatory solutions reminiscent of relapsing-remitting conditions, with macrophages being largely polarised toward anti-inflammatory activity during remission, but with intermediate phenotypes playing a role in inflammatory flare-ups. We conclude by reflecting on our observations in the context of the ongoing pursuance of novel therapeutic interventions.

A systematic evaluation of the influence of macrophage phenotype descriptions on inflammatory dynamics

Macrophages play a wide range of roles in resolving the inflammatory damage that underlies many medical conditions and have the ability to adopt different phenotypes in response to different environmental stimuli. Categorising macrophage phenotypes exactly is a difficult task, and there is disparity in the literature around the optimal nomenclature to describe these phenotypes; however, what is clear is that macrophages can exhibit both pro- and anti-inflammatory behaviours dependent upon their phenotype, rendering mathematical models of the inflammatory response potentially sensitive to their description of the macrophage populations that they incorporate. Many previous models of inflammation include a single macrophage population with both pro- and anti-inflammatory functions. Here, we build upon these existing models to include explicit descriptions of distinct macrophage phenotypes and examine the extent to which this influences the inflammatory dynamics that the models emit. We analyse our models via numerical simulation in MATLAB and dynamical systems analysis in XPPAUT, and show that models that account for distinct macrophage phenotypes separately can offer more realistic steady state solutions than precursor models do (better capturing the anti-inflammatory activity of tissue resident macrophages), as well as oscillatory dynamics not previously observed. Finally, we reflect on the conclusions of our analysis in the context of the ongoing hunt for potential new therapies for inflammatory conditions, highlighting manipulation of macrophage polarisation states as a potential therapeutic target.

Quantitatively Mapping Immune Control during Influenza

Host immune responses play a pivotal role in defending against influenza viruses. The activation of various immune components, such as interferon, macrophages, and CD8+ T cells, works to limit viral spread while maintaining lung integrity. Recent mathematical modeling studies have investigated these responses, describing their regulation, efficacy, and movement within the lung. Here, we discuss these studies and their emphasis on identifying nonlinearities and multifaceted roles of different cell phenotypes that could be responsible for spatially heterogeneous infection patterns.

Mathematical Modeling Finds Disparate Interferon Production Rates Drive Strain-Specific Immunodynamics during Deadly Influenza Infection

The timing and magnitude of the immune response (i.e., the immunodynamics) associated with the early innate immune response to viral infection display distinct trends across influenza A virus subtypes in vivo. Evidence shows that the timing of the type-I interferon response and the overall magnitude of immune cell infiltration are both correlated with more severe outcomes. However, the mechanisms driving the distinct immunodynamics between infections of different virus strains (strain-specific immunodynamics) remain unclear. Here, computational modeling and strain-specific immunologic data are used to identify the immune interactions that differ in mice infected with low-pathogenic H1N1 or high-pathogenic H5N1 influenza viruses. Computational exploration of free parameters between strains suggests that the production rate of interferon is the major driver of strain-specific immune responses observed in vivo, and points towards the relationship between the viral load and lung epithelial interferon production as the main source of variance between infection outcomes. A greater understanding of the contributors to strain-specific immunodynamics can be utilized in future efforts aimed at treatment development to improve clinical outcomes of high-pathogenic viral strains.

Mathematical Modeling of Severe Acute Respiratory Syndrome Coronavirus 2 Infection Network with Cytokine Storm, Oxidative Stress, Thrombosis, Insulin Resistance, and Nitric Oxide Pathways

Coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection is a systemic disease affecting not only the lungs but also multiple organ systems. Clinical studies implicate that SARS-CoV-2 infection causes imbalance of cellular homeostasis and immune response that trigger cytokine storm, oxidative stress, thrombosis, and insulin resistance. Mathematical modeling can offer in-depth understanding of the SARS-CoV-2 infection and illuminate how subcellular mechanisms and feedback loops underpin disease progression and multiorgan failure. We report here a mathematical model of SARS-CoV-2 infection pathway network with cytokine storm, oxidative stress, thrombosis, insulin resistance, and nitric oxide (NO) pathways. The biochemical systems theory model shows autocrine loops with positive feedback enabling excessive immune response, cytokines, transcription factors, and interferons, which can imbalance homeostasis of the system. The simulations suggest that changes in immune response led to uncontrolled release of cytokines and chemokines, including interleukin (IL)-1β, IL-6, and tumor necrosis factor α (TNFα), and affect insulin, coagulation, and NO signaling pathways. Increased production of NETs (neutrophil extracellular traps), thrombin, PAI-1 (plasminogen activator inhibitor-1), and other procoagulant factors led to thrombosis. By analyzing complex biochemical reactions, this model forecasts the key intermediates, potential biomarkers, and risk factors at different stages of COVID-19. These insights can be useful for drug discovery and development, as well as precision treatment of multiorgan implications of COVID-19 as seen in systems medicine.

Identifying important parameters in the inflammatory process with a mathematical model of immune cell influx and macrophage polarization

In an inflammatory setting, macrophages can be polarized to an inflammatory M1 phenotype or to an anti-inflammatory M2 phenotype, as well as existing on a spectrum between these two extremes. Dysfunction of this phenotypic switch can result in a population imbalance that leads to chronic wounds or disease due to unresolved inflammation. Therapeutic interventions that target macrophages have therefore been proposed and implemented in diseases that feature chronic inflammation such as diabetes mellitus and atherosclerosis. We have developed a model for the sequential influx of immune cells in the peritoneal cavity in response to a bacterial stimulus that includes macrophage polarization, with the simplifying assumption that macrophages can be classified as M1 or M2. With this model, we were able to reproduce the expected timing of sequential influx of immune cells and mediators in a general inflammatory setting. We then fit this model to in vivo experimental data obtained from a mouse peritonitis model of inflammation, which is widely used to evaluate endogenous processes in response to an inflammatory stimulus. Model robustness is explored with local structural and practical identifiability of the proposed model a posteriori. Additionally, we perform sensitivity analysis that identifies the population of apoptotic neutrophils as a key driver of the inflammatory process. Finally, we simulate a selection of proposed therapies including points of intervention in the case of delayed neutrophil apoptosis, which our model predicts will result in a sustained inflammatory response. Our model can therefore provide hypothesis testing for therapeutic interventions that target macrophage phenotype and predict outcomes to be validated by subsequent experimentation.

Using experimental data and mathematical analysis, we develop a model for the inflammatory response that includes macrophage polarization between M1 and M2 phenotypes. Dysfunction of this phenotypic switch can disrupt the timely influx and egress of immune cells during the healing process and lead to chronic wounds or disease. The modulation of macrophage population has been suggested as a strategy to dampen inflammation in diseases that feature chronic inflammation, such as diabetes and atherosclerosis. It is therefore important that we learn more about which components of the system drive the population level switch in phenotype. Our model is able to reproduce the expected timing of sequential influx of neutrophils and macrophages in response to an inflammatory stimulus. Model parameters were estimated with weighted least squares fitting to in vivo experimental data from a mouse model of peritonitis while considering identifiability of parameter sets. We perform sensitivity analysis that identifies primary drivers of the system, and predict the effects of variations in these key parameters on immune cell populations.

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.