Modelling the host-pathogen interactions of macrophages and Candida albicans using Game Theory and dynamic optimization

Studying mixed-species biofilms of Candida albicans and Staphylococcus aureus using evolutionary game theory

Mixed-species biofilms of Candida albicans and Staphylococcus aureus pose a significant clinical challenge due to their resistance to the human immune system and antimicrobial therapy. Using evolutionary game theory and nonlinear dynamics, we analyse the complex interactions between these organisms to understand their coexistence in the human host. We determine the Nash equilibria and evolutionary stable strategies of the game between C. albicans and S. aureus and point out different states of the mixed-species biofilm. Using replicator equations we study the fungal-bacterial interactions on a population level. Our focus is on the influence of available nutrients and the quorum sensing molecule farnesol, including the potential therapeutic use of artificially added farnesol. We also investigate the impact of the suggested scavenging of C. albicans hyphae by S. aureus. Contrary to common assumptions, we confirm the hypothesis that under certain conditions, mixed-species biofilms are not universally beneficial. Instead, different Nash equilibria occur depending on encountered conditions (i.e. varying farnesol levels, either produced by C. albicans or artificially added), including antagonism. We further show that the suggested scavenging of C. albicans' hyphae by S. aureus does not influence the overall outcome of the game. Moreover, artificially added farnesol strongly affects the dynamics of the game, although its use as a medical adjuvant (add-on medication) may pose challenges.

Macrophage states: there's a method in the madness

Single-cell approaches have shone a spotlight on discrete context-specific tissue macrophage states, deconstructed to their most minute details. Machine-learning (ML) approaches have recently challenged that dogma by revealing a context-agnostic continuum of states shared across tissues. Both approaches agree that 'brake' and 'accelerator' macrophage subpopulations must be balanced to achieve homeostasis. Both approaches also highlight the importance of ensemble fluidity as subpopulations switch between wide ranges of accelerator and brake phenotypes to mount the most optimal wholistic response to any threat. A full comprehension of the rules that govern these brake and accelerator states is a promising avenue because it can help formulate precise macrophage re-education therapeutic strategies that might selectively boost or suppress disease-associated states and phenotypes across various tissues.

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.

Modeling the energy metabolism in immune cells

In this review, we summarize and briefly discuss various approaches to modeling the metabolism in human immune cells, with a focus on energy metabolism. These approaches include metabolic reconstruction, elementary modes, and flux balance analysis, which are often subsumed under constraint-based modeling. Further approaches are evolutionary game theory and kinetic modeling. Many immune cells such as macrophages show the Warburg effect, meaning that glycolysis is upregulated upon activation. We outline a minimal model for explaining that effect using optimization. The effect of a confrontation with pathogen cells on immunometabolism is highlighted. Models describing the differences between M1 and M2 macrophages, ROS production in neutrophils, and tryptophan metabolism are discussed. Obstacles and future prospects are outlined.

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.

Playing 'hide-and-seek' with factor H: game-theoretical analysis of a single nucleotide polymorphism

As a part of the complement system, factor H regulates phagocytosis and helps differentiate between a body's own and foreign cells. Owing to mimicry efforts, some pathogenic microorganisms such as Candida albicans are able to bind factor H on their cell surfaces and, thus, become similar to host cells. This implies that the decision between self and foreign is not clear-cut, which leads to a classification problem for the immune system. Here, two different alleles determining the binding affinity of factor H are relevant. Those alleles differ in the SNP Y402H; they are known to be associated with susceptibility to certain diseases. Interestingly, the fraction of both alleles differs in ethnic groups. The game-theoretical model proposed in this article explains the coexistence of both alleles by a battle of the sexes game and investigates the trade-off between pathogen detection and protection of host cells. Further, we discuss the ethnicity-dependent frequencies of the alleles. Moreover, the model elucidates the mimicry efforts by pathogenic microorganisms.

Bottom-Up Approaches to Synthetic Cooperation in Microbial Communities

Microbial cooperation pervades ecological scales, from single-species populations to host-associated microbiomes. Understanding the mechanisms promoting the stability of cooperation against potential threats by cheaters is a major question that only recently has been approached experimentally. Synthetic biology has helped to uncover some of these basic mechanisms, which were to some extent anticipated by theoretical predictions. Moreover, synthetic cooperation is a promising lead towards the engineering of novel functions and enhanced productivity of microbial communities. Here, we review recent progress on engineered cooperation in microbial ecosystems. We focus on bottom-up approaches that help to better understand cooperation at the population level, progressively addressing the challenges of tackling higher degrees of complexity: spatial structure, multispecies communities, and host-associated microbiomes. We envisage cooperation as a key ingredient in engineering complex microbial ecosystems.

Molecular crypsis by pathogenic fungi using human factor H. A numerical model

Molecular mimicry is the formation of specific molecules by microbial pathogens to avoid recognition and attack by the immune system of the host. Several pathogenic Ascomycota and Zygomycota show such a behaviour by utilizing human complement factor H to hide in the blood stream. We call this type of mimicry molecular crypsis. Such a crypsis can reach a point where the immune system can no longer clearly distinguish between self and non-self cells. Thus, a trade-off between attacking disguised pathogens and erroneously attacking host cells has to be made. Based on signalling theory and protein-interaction modelling, we here present a mathematical model of molecular crypsis of pathogenic fungi using the example of Candida albicans. We tackle the question whether perfect crypsis is feasible, which would imply that protection of human cells by complement factors would be useless. The model identifies pathogen abundance relative to host cell abundance as the predominant factor influencing successful or unsuccessful molecular crypsis. If pathogen cells gain a (locally) quantitative advantage over host cells, even autoreactivity may occur. Our new model enables insights into the mechanisms of candidiasis-induced sepsis and complement-associated autoimmune diseases.

Graphene oxide nanosheets increase Candida albicans killing by pro-inflammatory and reparative peritoneal macrophages

Graphene oxide (GO) is a new nanomaterial with different potential biomedical applications due to its excellent physicochemical properties and ease of surface functionalization. Macrophages play key roles in the control of fungal infections preventing invasive candidiasis by both limiting the growth of the opportunistic fungal pathogen Candida albicans and activating other immune effector cells. In order to know if macrophages maintain their immunocompetence against this microorganism after GO uptake, we have evaluated the interactions at the interface of GO nanosheets, macrophages and Candida albicans. Poly (ethylene glycol-amine)-derivatized GO nanosheets labelled with fluorescein isothiocyanate (FITC-PEG-GO), were efficiently taken up by peritoneal macrophages inducing a significant increase of C. albicans phagocytosis by both pro-inflammatory macrophages (M1/stimulated with LPS/IFN-γ)  and reparative macrophages (M2/stimulated with IL-4). On the other hand, after FITC-PEG-GO treatment and C. albicans infection, the percentages of GO⁺ macrophages diminished when Candida uptake increased in every condition (macrophages with no stimuli, M1 and M2 macrophages), thus suggesting the exocytosis of this nanomaterial as a dynamic mechanism favoring fungal phagocytosis. For the first time, we have analyzed the effects of PEG-GO nanosheets on Candida albicans killing by unstimulated, M1 and M2 macrophages, evidencing that intracellular GO modulates the macrophage candidacidal activity in a multiplicity of infection (MOI) dependent manner. At MOI 1, the high intracellular GO levels increase the fungicidal activity of basal and stimulated macrophages. At MOI 5, as intracellular GO decreases, the previous pro-inflammatory or reparative stimulus predefines the killing ability of macrophages. In summary, GO treatment enhances classical M1 macrophage activation, important for pathogen eradication, and diminishes alternative activation of M2 macrophages, thus decreasing fungal persistence and avoiding chronic infectious diseases.

Identification of Antifungal Targets Based on Computer Modeling

Aspergillus fumigatus is a saprophytic, cosmopolitan fungus that attacks patients with a weak immune system. A rational solution against fungal infection aims to manipulate fungal metabolism or to block enzymes essential for Aspergillus survival. Here we discuss and compare different bioinformatics approaches to analyze possible targeting strategies on fungal-unique pathways. For instance, phylogenetic analysis reveals fungal targets, while domain analysis allows us to spot minor differences in protein composition between the host and fungi. Moreover, protein networks between host and fungi can be systematically compared by looking at orthologs and exploiting information from host⁻pathogen interaction databases. Further data—such as knowledge of a three-dimensional structure, gene expression data, or information from calculated metabolic fluxes—refine the search and rapidly put a focus on the best targets for antimycotics. We analyzed several of the best targets for application to structure-based drug design. Finally, we discuss general advantages and limitations in identification of unique fungal pathways and protein targets when applying bioinformatics tools.