Dataset Information

Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood.

ABSTRACT: Bloodstream infections by the human-pathogenic fungi Candida albicans and Candida glabrata increasingly occur in hospitalized patients and are associated with high mortality rates. The early immune response against these fungi in human blood comprises a concerted action of humoral and cellular components of the innate immune system. Upon entering the blood, the majority of fungal cells will be eliminated by innate immune cells, i.e., neutrophils and monocytes. However, recent studies identified a population of fungal cells that can evade the immune response and thereby may disseminate and cause organ dissemination, which is frequently observed during candidemia. In this study, we investigate the so far unresolved mechanism of fungal immune evasion in human whole blood by testing hypotheses with the help of mathematical modeling. We use a previously established state-based virtual infection model for whole-blood infection with C. albicans to quantify the immune response and identified the fungal immune-evasion mechanism. While this process was assumed to be spontaneous in the previous model, we now hypothesize that the immune-evasion process is mediated by host factors and incorporate such a mechanism in the model. In particular, we propose, based on previous studies that the fungal immune-evasion mechanism could possibly arise through modification of the fungal surface by as of yet unknown proteins that are assumed to be secreted by activated neutrophils. To validate or reject any of the immune-evasion mechanisms, we compared the simulation of both immune-evasion models for different infection scenarios, i.e., infection of whole blood with either C. albicans or C. glabrata under non-neutropenic and neutropenic conditions. We found that under non-neutropenic conditions, both immune-evasion models fit the experimental data from whole-blood infection with C. albicans and C. glabrata. However, differences between the immune-evasion models could be observed for the infection outcome under neutropenic conditions with respect to the distribution of fungal cells across the immune cells. Based on these predictions, we suggested specific experimental studies that might allow for the validation or rejection of the proposed immune-evasion mechanism.

SUBMITTER: Prauße MTE

PROVIDER: S-EPMC5871695 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood.

Prauße Maria T E MTE Lehnert Teresa T Timme Sandra S Hünniger Kerstin K Leonhardt Ines I Kurzai Oliver O Figge Marc Thilo MT

Frontiers in immunology 20180321

Bloodstream infections by the human-pathogenic fungi <i>Candida albicans</i> and <i>Candida glabrata</i> increasingly occur in hospitalized patients and are associated with high mortality rates. The early immune response against these fungi in human blood comprises a concerted action of humoral and cellular components of the innate immune system. Upon entering the blood, the majority of fungal cells will be eliminated by innate immune cells, i.e., neutrophils and monocytes. However, recent studi ...[more]

PMID: 29619027

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Project description:Computer simulations of mathematical models open up the possibility of assessing hypotheses generated by experiments on pathogen immune evasion in human whole-blood infection assays. We apply an interdisciplinary systems biology approach in which virtual infection models implemented for the dissection of specific immune mechanisms are combined with experimental studies to validate or falsify the respective hypotheses. Focusing on the assessment of mechanisms that enable pathogens to evade the immune response in the early time course of a whole-blood infection, the least-square error (LSE) as a measure for the quantitative agreement between the theoretical and experimental kinetics is combined with the Akaike information criterion (AIC) as a measure for the model quality depending on its complexity. In particular, we compare mathematical models with three different types of pathogen immune evasion as well as all their combinations: (i) spontaneous immune evasion, (ii) evasion mediated by immune cells, and (iii) pre-existence of an immune-evasive pathogen subpopulation. For example, by testing theoretical predictions in subsequent imaging experiments, we demonstrate that the simple hypothesis of having a subpopulation of pre-existing immune-evasive pathogens can be ruled out. Furthermore, in this study we extend our previous whole-blood infection assays for the two fungal pathogens Candida albicans and C. glabrata by the bacterial pathogen Staphylococcus aureus and calibrated the model predictions to the time-resolved experimental data for each pathogen. Our quantitative assessment generally reveals that models with a lower number of parameters are not only scored with better AIC values, but also exhibit lower values for the LSE. Furthermore, we describe in detail model-specific and pathogen-specific patterns in the kinetics of cell populations that may be measured in future experiments to distinguish and pinpoint the underlying immune mechanisms.

Project description:The cell wall provides a major physical interface between fungal pathogens and their mammalian host. This extracellular armor is critical for fungal cell homeostasis and survival. Fungus-specific cell wall moieties, such as β-1,3-glucan, are recognized as pathogen-associated molecular patterns (PAMPs) that activate immune-mediated clearance mechanisms. We have reported that the opportunistic human fungal pathogen Candida albicans masks β-1,3-glucan following exposure to lactate, hypoxia, or iron depletion. However, the precise mechanism(s) by which C. albicans masks β-1,3-glucan has remained obscure. Here, we identify a secreted exoglucanase, Xog1, that is induced in response to lactate or hypoxia. Xog1 functions downstream of the lactate-induced β-glucan "masking" pathway to promote β-1,3-glucan "shaving." Inactivation of XOG1 blocks most but not all β-1,3-glucan masking in response to lactate, suggesting that other activities contribute to this phenomenon. Nevertheless, XOG1 deletion attenuates the lactate-induced reductions in phagocytosis and cytokine stimulation normally observed for wild-type cells. We also demonstrate that the pharmacological inhibition of exoglucanases undermines β-glucan shaving, enhances the immune visibility of the fungus, and attenuates its virulence. Our study establishes a new mechanism underlying environmentally induced PAMP remodeling that can be manipulated pharmacologically to influence immune recognition and infection outcomes.IMPORTANCE The immune system plays a critical role in protecting us against potentially fatal fungal infections. However, some fungal pathogens have evolved evasion strategies that reduce the efficacy of our immune defenses. Previously, we reported that the fungal pathogen Candida albicans exploits specific host-derived signals (such as lactate and hypoxia) to trigger an immune evasion strategy that involves reducing the exposure of β-glucan at its cell surface. Here, we show that this phenomenon is mediated by the induction of a major secreted exoglucanase (Xog1) by the fungus in response to these host signals. Inactivating XOG1-mediated "shaving" of cell surface-exposed β-glucan enhances immune responses against the fungus. Furthermore, inhibiting exoglucanase activity pharmacologically attenuates C. albicans virulence. In addition to revealing the mechanism underlying a key immune evasion strategy in a major fungal pathogen of humans, our work highlights the potential therapeutic value of drugs that block fungal immune evasion.

Project description:Candida albicans bloodstream infection is increasingly frequent and can result in disseminated candidiasis associated with high mortality rates. To analyze the innate immune response against C. albicans, fungal cells were added to human whole-blood samples. After inoculation, C. albicans started to filament and predominantly associate with neutrophils, whereas only a minority of fungal cells became attached to monocytes. While many parameters of host-pathogen interaction were accessible to direct experimental quantification in the whole-blood infection assay, others were not. To overcome these limitations, we generated a virtual infection model that allowed detailed and quantitative predictions on the dynamics of host-pathogen interaction. Experimental time-resolved data were simulated using a state-based modeling approach combined with the Monte Carlo method of simulated annealing to obtain quantitative predictions on a priori unknown transition rates and to identify the main axis of antifungal immunity. Results clearly demonstrated a predominant role of neutrophils, mediated by phagocytosis and intracellular killing as well as the release of antifungal effector molecules upon activation, resulting in extracellular fungicidal activity. Both mechanisms together account for almost [Formula: see text] of C. albicans killing, clearly proving that beside being present in larger numbers than other leukocytes, neutrophils functionally dominate the immune response against C. albicans in human blood. A fraction of C. albicans cells escaped phagocytosis and remained extracellular and viable for up to four hours. This immune escape was independent of filamentation and fungal activity and not linked to exhaustion or inactivation of innate immune cells. The occurrence of C. albicans cells being resistant against phagocytosis may account for the high proportion of dissemination in C. albicans bloodstream infection. Taken together, iterative experiment-model-experiment cycles allowed quantitative analyses of the interplay between host and pathogen in a complex environment like human blood.

Project description:T helper (Th) cells play a major role in the immune response and pathology at the gastric mucosa during Helicobacter pylori infection. There is a limited mechanistic understanding regarding the contributions of CD4+ T cell subsets to gastritis development during H. pylori colonization. We used two computational approaches: ordinary differential equation (ODE)-based and agent-based modeling (ABM) to study the mechanisms underlying cellular immune responses to H. pylori and how CD4+ T cell subsets influenced initiation, progression and outcome of disease. To calibrate the model, in vivo experimentation was performed by infecting C57BL/6 mice intragastrically with H. pylori and assaying immune cell subsets in the stomach and gastric lymph nodes (GLN) on days 0, 7, 14, 30 and 60 post-infection. Our computational model reproduced the dynamics of effector and regulatory pathways in the gastric lamina propria (LP) in silico. Simulation results show the induction of a Th17 response and a dominant Th1 response, together with a regulatory response characterized by high levels of mucosal Treg) cells. We also investigated the potential role of peroxisome proliferator-activated receptor ? (PPAR?) activation on the modulation of host responses to H. pylori by using loss-of-function approaches. Specifically, in silico results showed a predominance of Th1 and Th17 cells in the stomach of the cell-specific PPAR? knockout system when compared to the wild-type simulation. Spatio-temporal, object-oriented ABM approaches suggested similar dynamics in induction of host responses showing analogous T cell distributions to ODE modeling and facilitated tracking lesion formation. In addition, sensitivity analysis predicted a crucial contribution of Th1 and Th17 effector responses as mediators of histopathological changes in the gastric mucosa during chronic stages of infection, which were experimentally validated in mice. These integrated immunoinformatics approaches characterized the induction of mucosal effector and regulatory pathways controlled by PPAR? during H. pylori infection affecting disease outcomes.

Dataset Information

Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood.

Publications

Predictive Virtual Infection Modeling of Fungal Immune Evasion in Human Whole Blood.

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