Deciphering the Counterplay of Aspergillus fumigatus Infection and Host Inflammation by Evolutionary Games on Graphs

Surrogate infection model predicts optimal alveolar macrophage number for clearance of Aspergillus fumigatus infections

The immune system has to fight off hundreds of microbial invaders every day, such as the human-pathogenic fungus Aspergillus fumigatus. The fungal conidia can reach the lower respiratory tract, swell and form hyphae within six hours causing life-threatening invasive aspergillosis. Invading pathogens are continuously recognized and eliminated by alveolar macrophages (AM). Their number plays an essential role, but remains controversial with measurements varying by a factor greater than ten for the human lung. We here investigate the impact of the AM number on the clearance of A. fumigatus conidia in humans and mice using analytical and numerical modeling approaches. A three-dimensional to-scale hybrid agent-based model (hABM) of the human and murine alveolus allowed us to simulate millions of virtual infection scenarios, and to gain quantitative insights into the infection dynamics for varying AM numbers and infection doses. Since hABM simulations are computationally expensive, we derived and trained an analytical surrogate infection model on the large dataset of numerical simulations. This enables reducing the number of hABM simulations while still providing (i) accurate and immediate predictions on infection progression, (ii) quantitative hypotheses on the infection dynamics under healthy and immunocompromised conditions, and (iii) optimal AM numbers for combating A. fumigatus infections in humans and mice.

Dynamic optimization reveals alveolar epithelial cells as key mediators of host defense in invasive aspergillosis

Aspergillus fumigatus is an important human fungal pathogen and its conidia are constantly inhaled by humans. In immunocompromised individuals, conidia can grow out as hyphae that damage lung epithelium. The resulting invasive aspergillosis is associated with devastating mortality rates. Since infection is a race between the innate immune system and the outgrowth of A. fumigatus conidia, we use dynamic optimization to obtain insight into the recruitment and depletion of alveolar macrophages and neutrophils. Using this model, we obtain key insights into major determinants of infection outcome on host and pathogen side. On the pathogen side, we predict in silico and confirm in vitro that germination speed is an important virulence trait of fungal pathogens due to the vulnerability of conidia against host defense. On the host side, we found that epithelial cells, which have been underappreciated, play a role in fungal clearance and are potent mediators of cytokine release. Both predictions were confirmed by in vitro experiments on established cell lines as well as primary lung cells. Further, our model affirms the importance of neutrophils in invasive aspergillosis and underlines that the role of macrophages remains elusive. We expect that our model will contribute to improvement of treatment protocols by focusing on the critical components of immune response to fungi but also fungal virulence traits.

Function and Regulation of the Epithelial Na+ Channel ENaC

The Epithelial Na⁺ Channel, ENaC, comprised of 3 subunits (αβγ, or sometimes δβγENaC), plays a critical role in regulating salt and fluid homeostasis in the body. It regulates fluid reabsorption into the blood stream from the kidney to control blood volume and pressure, fluid absorption in the lung to control alveolar fluid clearance at birth and maintenance of normal airway surface liquid throughout life, and fluid absorption in the distal colon and other epithelial tissues. Moreover, recent studies have also revealed a role for sodium movement via ENaC in nonepithelial cells/tissues, such as endothelial cells in blood vessels and neurons. Over the past 25 years, major advances have been made in our understanding of ENaC structure, function, regulation, and role in human disease. These include the recently solved three-dimensional structure of ENaC, ENaC function in various tissues, and mutations in ENaC that cause a hereditary form of hypertension (Liddle syndrome), salt-wasting hypotension (PHA1), or polymorphism in ENaC that contributes to other diseases (such as cystic fibrosis). Moreover, great strides have been made in deciphering the regulation of ENaC by hormones (e.g., the mineralocorticoid aldosterone, glucocorticoids, vasopressin), ions (e.g., Na⁺ ), proteins (e.g., the ubiquitin-protein ligase NEDD4-2, the kinases SGK1, AKT, AMPK, WNKs & mTORC2, and proteases), and posttranslational modifications [e.g., (de)ubiquitylation, glycosylation, phosphorylation, acetylation, palmitoylation]. Characterization of ENaC structure, function, regulation, and role in human disease, including using animal models, are described in this article, with a special emphasis on recent advances in the field. © 2021 American Physiological Society. Compr Physiol 11:1-29, 2021.

Hybrid Agent-Based Modeling of Aspergillus fumigatus Infection to Quantitatively Investigate the Role of Pores of Kohn in Human Alveoli

The healthy state of an organism is constantly threatened by external cues. Due to the daily inhalation of hundreds of particles and pathogens, the immune system needs to constantly accomplish the task of pathogen clearance in order to maintain this healthy state. However, infection dynamics are highly influenced by the peculiar anatomy of the human lung. Lung alveoli that are packed in alveolar sacs are interconnected by so called Pores of Kohn. Mainly due to the lack of in vivo methods, the role of Pores of Kohn in the mammalian lung is still under debate and partly contradicting hypotheses remain to be investigated. Although it was shown by electron microscopy that Pores of Kohn may serve as passageways for immune cells, their impact on the infection dynamics in the lung is still unknown under in vivo conditions. In the present study, we apply a hybrid agent-based infection model to quantitatively compare three different scenarios and discuss the importance of Pores of Kohn during infections of Aspergillus fumigatus. A. fumigatus is an airborne opportunistic fungus with rising incidences causing severe infections in immunocompromised patients that are associated with high mortality rates. Our hybrid agent-based model incorporates immune cell dynamics of alveolar macrophages – the resident phagocytes in the lung – as well as molecular dynamics of diffusing chemokines that attract alveolar macrophages to the site of infection. Consequently, this model allows a quantitative comparison of three different scenarios and to study the importance of Pores of Kohn. This enables us to demonstrate how passaging of alveolar macrophages and chemokine diffusion affect A. fumigatus infection dynamics. We show that Pores of Kohn alter important infection clearance mechanisms, such as the spatial distribution of macrophages and the effect of chemokine signaling. However, despite these differences, a lack of passageways for alveolar macrophages does impede infection clearance only to a minor extend. Furthermore, we quantify the importance of recruited macrophages in comparison to resident macrophages.

Dynamic Surface Proteomes of Allergenic Fungal Conidia

Fungal spores and hyphal fragments play an important role as allergens in respiratory diseases. In this study, we performed trypsin shaving and secretome analyses to identify the surface-exposed proteins and secreted/shed proteins of Aspergillus fumigatus conidia, respectively. We investigated the surface proteome under different conditions, including temperature variation and germination. We found that the surface proteome of resting A. fumigatus conidia is not static but instead unexpectedly dynamic, as evidenced by drastically different surface proteomes under different growth conditions. Knockouts of two abundant A. fumigatus surface proteins, ScwA and CweA, were found to function only in fine-tuning the cell wall stress response, implying that the conidial surface is very robust against perturbations. We then compared the surface proteome of A. fumigatus to other allergy-inducing molds, including Alternaria alternata, Penicillium rubens, and Cladosporium herbarum, and performed comparative proteomics on resting and swollen conidia, as well as secreted proteins from germinating conidia. We detected 125 protein ortholog groups, including 80 with putative catalytic activity, in the extracellular region of all four molds, and 42 nonorthologous proteins produced solely by A. fumigatus. Ultimately, this study highlights the dynamic nature of the A. fumigatus conidial surface and provides targets for future diagnostics and immunotherapy.

Co-infection with Staphylococcus aureus after primary influenza virus infection leads to damage of the endothelium in a human alveolus-on-a-chip model

Pneumonia is one of the most common infectious diseases worldwide. The influenza virus can cause severe epidemics, which results in significant morbidity and mortality. Beyond the virulence of the virus itself, epidemiological data suggest that bacterial co-infections are the major cause of increased mortality. In this context, Staphylococcus aureus represents a frequent causative bacterial pathogen. Currently available models have several limitations in the analysis of the pathogenesis of infections, e.g. some bacterial toxins strongly act in a species-specific manner. Human 2D mono-cell culture models often fail to maintain the differentiation of alveolus-specific functions. A detailed investigation of the underlying pathogenesis mechanisms requires a physiological interaction of alveolus-specific cell types. The aim of the present work was to establish a human in vitro alveolus model system composed of vascular and epithelial cell structures with cocultured macrophages resembling the human alveolus architecture and functions. We demonstrate that high barrier integrity maintained for up to 14 d in our model containing functional tissue-resident macrophages. We show that flow conditions and the presence of macrophages increased the barrier function. The infection of epithelial cells induced a high inflammatory response that spread to the endothelium. Although the integrity of the epithelium was not compromised by a single infection or co-infection, we demonstrated significant endothelial cell damage associated with loss of barrier function. We established a novel immune-responsive model that reflects the complex crosstalk between pathogens and host. The in vitro model allows for the monitoring of spatiotemporal spreading of the pathogens and the characterization of morphological and functional alterations attributed to infection. The alveolus-on-a-chip represents a promising platform for mechanistic studies of host-pathogen interactions and the identification of molecular and cellular targets of novel treatment strategies in pneumonia.

Trends in mathematical modeling of host-pathogen interactions

Pathogenic microorganisms entail enormous problems for humans, livestock, and crop plants. A better understanding of the different infection strategies of the pathogens enables us to derive optimal treatments to mitigate infectious diseases or develop vaccinations preventing the occurrence of infections altogether. In this review, we highlight the current trends in mathematical modeling approaches and related methods used for understanding host-pathogen interactions. Since these interactions can be described on vastly different temporal and spatial scales as well as abstraction levels, a variety of computational and mathematical approaches are presented. Particular emphasis is placed on dynamic optimization, game theory, and spatial modeling, as they are attracting more and more interest in systems biology. Furthermore, these approaches are often combined to illuminate the complexities of the interactions between pathogens and their host. We also discuss the phenomena of molecular mimicry and crypsis as well as the interplay between defense and counter defense. As a conclusion, we provide an overview of method characteristics to assist non-experts in their decision for modeling approaches and interdisciplinary understanding.

The geographical region of origin determines the phagocytic vulnerability of Lichtheimia strains

Mucormycoses are life-threatening infections that affect patients suffering from immune deficiencies. We performed phagocytosis assays confronting various strains of Lichtheimia species with alveolar macrophages, which form the first line of defence of the innate immune system. To investigate 17 strains from four different continents in a comparative fashion, transmitted light and confocal fluorescence microscopy was applied in combination with automated image analysis. This interdisciplinary approach enabled the objective and quantitative processing of the big volume of image data. Applying machine-learning supported methods, a spontaneous clustering of the strains was revealed in the space of phagocytic measures. This clustering was not driven by measures of fungal morphology but rather by the geographical origin of the fungal strains. Our study illustrates the crucial contribution of machine-learning supported automated image analysis to the qualitative discovery and quantitative comparison of major factors affecting host-pathogen interactions. We found that the phagocytic vulnerability of Lichtheimia species depends on their geographical origin, where strains within each geographic region behaved similarly, but strongly differed amongst the regions. Based on this clustering, we were able to also classify clinical isolates with regard to their potential geographical origin.

Properties of network structures, structure coefficients, and benefit-to-cost ratios

In structured populations the spatial arrangement of cooperators and defectors on the interaction graph together with the structure of the graph itself determines the game dynamics and particularly whether or not fixation of cooperation (or defection) is favored. For networks described by regular graphs and for a single cooperator (and a single defector) the question of fixation can be addressed by a single parameter, the structure coefficient. This quantity is invariant with respect to the location of the cooperator on the graph and also does not vary over different networks. We may therefore consider it to be generic for regular graphs and call it the generic structure coefficient. For two and more cooperators (or several defectors) fixation properties can also be assigned by structure coefficients. These structure coefficients, however, depend on the arrangement of cooperators and defectors which we may interpret as a configuration of the game. Moreover, the coefficients are specific for a given interaction network modeled as a regular graph, which is why we may call them specific structure coefficients. In this paper, we study how specific structure coefficients vary over interaction graphs and analyze how spectral properties of interaction networks relate to specific structure coefficients. We also discuss implications for the benefit-to-cost ratios of donation games.

Comparative Assessment of Aspergillosis by Virtual Infection Modeling in Murine and Human Lung

Aspergillus fumigatus is a ubiquitous opportunistic fungal pathogen that can cause severe infections in immunocompromised patients. Conidia that reach the lower respiratory tract are confronted with alveolar macrophages, which are the resident phagocytic cells, constituting the first line of defense. If not efficiently removed in time, A. fumigatus conidia can germinate causing severe infections associated with high mortality rates. Mice are the most extensively used model organism in research on A. fumigatus infections. However, in addition to structural differences in the lung physiology of mice and the human host, applied infection doses in animal experiments are typically orders of magnitude larger compared to the daily inhalation doses of humans. The influence of these factors, which must be taken into account in a quantitative comparison and knowledge transfer from mice to humans, is difficult to measure since in vivo live cell imaging of the infection dynamics under physiological conditions is currently not possible. In the present study, we compare A. fumigatus infection in mice and humans by virtual infection modeling using a hybrid agent-based model that accounts for the respective lung physiology and the impact of a wide range of infection doses on the spatial infection dynamics. Our computer simulations enable comparative quantification of A. fumigatus infection clearance in the two hosts to elucidate (i) the complex interplay between alveolar morphometry and the fungal burden and (ii) the dynamics of infection clearance, which for realistic fungal burdens is found to be more efficiently realized in mice compared to humans.

Validation of a simplified in vitro Transwell® model of the alveolar surface to assess host immunity induced by different morphotypes of Aspergillus fumigatus

Interactions between fungal pathogens such as Aspergillus fumigatus with host alveolar epithelium and innate immune cells are crucial in the defense against opportunistic fungal infections. In this study a simplified Transwell^® system with a confluent layer of A549 cells acted as a model for the alveolar surface. A. fumigatus and dendritic cells were added to simulate the spatial and cellular complexity in the alveolus. Fungal growth into the lower chamber was validated by galactomannan assays. Addition of moDCs to the upper chamber led to a reduced GM signal and fungal growth, indicating moDC antifungal activity. Minimal cell death was documented by analyses of lactate dehydrogenase concentrations and pro-apoptotic gene expression. Measurement of trans-epithelial dextran blue movement confirmed tightness of the epithelial barrier even in presence of A. fumigatus. Cytokine measurements in supernatants from both chambers of the Transwell^® system documented distinct response patterns during early and late stages of epithelial invasion, with A549 cells appearing to make a minimal contribution to cytokine release. Concentrations of cytokines in the lower chamber varied distinctly from the upper chamber, depending on the molecular weight of the cytokines. Low inter-assay variability of fungal biomarkers and cytokines was confirmed, highlighting that in vitro models closely mimicking conditions in the human lung can facilitate reproducible measurement of the dynamics of cytokine release and fungal penetration of host epithelia.

Bacterial Adherence and Dwelling Probability: Two Drivers of Early Alveolar Infection by Streptococcus pneumoniae Identified in Multi-Level Mathematical Modeling

Pneumococcal infection is the most frequent cause of pneumonia, and one of the most prevalent diseases worldwide. The population groups at high risk of death from bacterial pneumonia are infants, elderly and immunosuppressed people. These groups are more vulnerable because they have immature or impaired immune systems, the efficacy of their response to vaccines is lower, and antibiotic treatment often does not take place until the inflammatory response triggered is already overwhelming. The immune response to bacterial lung infections involves dynamic interactions between several types of cells whose activation is driven by intracellular molecular networks. A feasible approach to the integration of knowledge and data linking tissue, cellular and intracellular events and the construction of hypotheses in this area is the use of mathematical modeling. For this paper, we used a multi-level computational model to analyse the role of cellular and molecular interactions during the first 10 h after alveolar invasion of Streptococcus pneumoniae bacteria. By “multi-level” we mean that we simulated the interplay between different temporal and spatial scales in a single computational model. In this instance, we included the intracellular scale of processes driving lung epithelial cell activation together with the scale of cell-to-cell interactions at the alveolar tissue. In our analysis, we combined systematic model simulations with logistic regression analysis and decision trees to find genotypic-phenotypic signatures that explain differences in bacteria strain infectivity. According to our simulations, pneumococci benefit from a high dwelling probability and a high proliferation rate during the first stages of infection. In addition to this, the model predicts that during the very early phases of infection the bacterial capsule could be an impediment to the establishment of the alveolar infection because it impairs bacterial colonization.