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 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.
Sepsis is a systemic inflammatory response triggered by infection and a major cause of death worldwide [1]–[3]. In recent years, fungal pathogens have caused an increasing number of sepsis cases with high mortality rates [4], [5]. The major fungal pathogen Candida albicans is a common human commensal but can become invasive in patients with a compromised immune system and disturbance of epithelial barrier integrity or may enter the bloodstream by disseminating from biofilms on medical devices [6]–[8]. Among the different components of human immunity, neutrophils (polymorphonuclear neutrophilic granulocytes, PMN) are crucial for antifungal immune responses and neutropenia is associated with impaired prognosis in systemic candidiasis [9]. PMN possess several mechanisms that may contribute to clearing of C. albicans like phagocytosis, oxidative burst, degranulation and formation of neutrophil extracellular traps (NETs) and have been shown to respond specifically to the invasive filamentous form of C. albicans [10]. Other peripheral blood immune cells have also been implicated in the response against C. albicans, including monocytes as well as NK-cells [11], [12]. Furthermore, C. albicans has been shown to strongly activate complement while at the same time recruiting complement regulators to its surface that may protect it against antimicrobial effector functions [13]–[17]. So far little is known about the interplay of these effects in vivo. Studies using purified human immune cells or experiments performed at a molecular level provide important insights into mechanisms of immune recognition but fail to address in vivo complexity. Murine models are mainly used to address in vivo settings but peripheral blood components differ substantially from their human counterparts with regard to quantity and functional aspects [18]. To overcome some of these limitations, a human whole-blood infection model can be used to monitor host-pathogen interactions. Such models have successfully been used in identifying microbial virulence factors [19], analyzing early immune responses [20], determining the influence of genetic polymorphisms on immune response [21] and testing potential therapeutic approaches or vaccine efficacy [22]–[26]. Whole-blood assays provide time-resolved data on localization and physiological state of the pathogen and immune activation. Whereas many parameters are accessible to direct experimental quantification, others are not due to experimental limitations. However, biomathematical modeling can provide tools to overcome these experimental limitations. Here, we formulate a mathematical infection model for C. albicans in human blood and apply a state-based modeling approach to perform computer simulations that predict details on the dynamics of the immune response. The state-based model corresponds to a non-spatial agent-based model that enables decision making depending on the occurrence of specific events, such as first-time phagocytosis, and allows modeling interactions between individual cells occurring in small numbers in a stochastic fashion [27]. We demonstrate that a priori unknown transition rates between any two states can be estimated by fitting the simulation results to the experimental data using the Monte Carlo method of simulated annealing. Therefore, the state-based model allows detailed predictions on dynamics of host-pathogen interaction in human blood and, in particular, on the main course of the immune response.
To analyze early immune responses to a fungal pathogen, C. albicans was added to lepirudin-anticoagulated whole-blood of healthy volunteers at different concentrations. After inoculation of C. albicans yeasts, activation of PMN, monocytes and NK-cells but no unspecific early activation of T- and B-cells could be detected by quantification of the general activation marker CD69 (Fig. 1A). Furthermore, no cell death or decrease in host cell numbers was observed with this inoculum throughout the course of the experiment. No or only slight changes in CD69 expression levels could be observed in response to lower concentrations of C. albicans (Fig. 1A). Fungal concentrations of and more resulted in significant host cell death at later stages of infection. Therefore an inoculum of C. albicans yeasts was used in subsequent experiments. Innate immune activation by C. albicans resulted in significantly elevated plasma levels of pro-inflammatory cytokines () as well as chemokines () (Fig. 1B). As PMN have been shown to play a central role in the defense against C. albicans, we quantified activation of these cells in more detail. Early after inoculation of C. albicans a strong induction of reactive oxygen intermediates in PMN could be observed (Fig. 1C). Surface levels of receptors involved in immune recognition like CD11b and CD64 increased, whereas CD16 markedly decreased on PMN after fungal inoculation indicating cellular activation (Fig. 1C). Up-regulated surface exposure of the degranulation marker CD66b and increased plasma concentrations of myeloperoxidase, lactoferrin and elastase confirmed massive degranulation (Fig. 1D). Consequently, activation of neutrophils also resulted in the accumulation of potentially fungicidal activity in plasma [28], [29]. To analyze the distribution of the fungal pathogen in different compartments of human blood we used a C. albicans strain constitutively expressing GFP. Within of blood infection of fungal cells associated with PMN and this interaction was further increased at () and (). Whereas low association of C. albicans to monocytes (maximum association to monocytes at p. i.) could be observed, no interactions with lymphocytes were detectable (Fig. 2A). A significant proportion of C. albicans cells (at) remained extracellularly throughout the observation period and therefore escaped the cellular immune response by developing resistance against phagocytosis. The inoculation of human blood with C. albicans yeasts/ml resulted in similar fungal association patterns indicating that distribution of C. albicans in blood is largely independent of the fungus to immune cell ratio. To test, whether this distribution pattern was characteristic for C. albicans or rather strain specific, we used a set of ten clinical isolates from bloodstream infections. All strains showed similar distribution patterns with a strongly predominant association to PMN (at p. i. median association to PMN: [range, median association to monocytes: [range. For none of the strains, association to lymphocytes could be detected. Concomitant to interaction with immune cells, changes in C. albicans morphology could be observed in microscopic analyses (Fig. 2B). Intracellular organisms were predominantly found in PMN throughout the experiment and showed different morphotypes, in line with a growth arrest of filaments in PMN after phagocytosis [10]. In contrast, extracellular fungi showed small germ tubes after inoculation and mainly occurred as pseudohyphae at later time points, indicating continuous filamentation of these cells during the experiment (Fig. 2B). Plating assays demonstrated a substantial killing of C. albicans over time with only of fungal cells remaining viable four hours after inoculation (Fig. 2C). To model host-pathogen interaction in C. albicans blood infection we used a state-based model that comprises all experimentally validated C. albicans states in human blood (Fig. 3, for details see Methods section and a flow-diagram of the algorithm in Fig. S1). Alive C. albicans cells () may be extracellularly killed () and both, and may turn into cells that are resistant against phagocytosis and further killing, denoted by and, respectively. Non-resistant extracellular cells may be phagocytosed by monocytes or PMN and internalized viable fungal cells could be killed intracellularly. A proper bookkeeping of these intracellular processes in monocytes () or granulocytes () was ensured by the two indices, which refer to the numbers of internalized C. albicans cells that are alive () and killed (), respectively. Transitions between states occur with specific transition rates that determine the time-dependent simulation of the infection process and are summarized in Fig. 4. Of note, we distinguished the initial phagocytosis by PMN with rate from subsequent phagocytosis events by activated PMN that may occur with a different rate [30]. Furthermore, taking into account that the release of antimicrobial peptides by PMN induces extracellular killing, we used a time-dependent rate for extracellular killing that increases with the number of initial phagocytosis events by PMN. Initially, all immune cells occupied states and and the number of immune cells were set to average physiological numbers in blood: and. The initial number of C. albicans cells corresponded to the inocula used in the experiments and these cells were either in the -state or in the -state, while no resistant cells existed at the initial time point. A priori unknown transition rates were estimated by the method of simulated annealing based on the Metropolis Monte Carlo Scheme. Starting with a randomly chosen parameter set, the algorithm searched in the parameter space of transition rates for the global optimum from a fit to the time-resolved experimental data of the whole-blood infection assays with C. albicans (see Materials and Methods section for details). The mean values of the transition rates could be estimated with standard deviations below, indicating the high accuracy of the fitting procedure (Table 1) and the comparison of simulated and experimental data clearly showed quantitative agreement for the whole time course of infection (Fig. 5). The simulations were repeated 100 times for the normally distributed transition rates (Table 1) and the thickness of the solid lines in Fig. 5 represents the mean standard deviation due to these variations. The limiting value of the standard deviations was below for each quantity and the solid lines remained well within the experimental error bars, indicating that the simulation results are robust against variations in the transition rates. Due to experimental limitations it is impossible to quantify the contribution of single effector mechanisms to the overall elimination of C. albicans in the whole-blood infection model, whereas the virtual infection model allowed separate analyses for all killing mechanisms. In the state-based model, we assumed that the phagocytosis rates were constant in time. This assumption was experimentally justified by reinoculation of C. albicans cells into an infected blood sample after. Since we observed a similar distribution pattern for the newly added C. albicans after as in the initial experimental set-up (Fig. 6), it could be concluded that the phagocytosis rates remain fairly constant over time. According to the model, phagocytosis of C. albicans by a monocyte is less probable than uptake by PMN (). To confirm the different phagocytic capacity of PMN and monocytes we experimentally increased the total monocyte number by adding autologous isolated monocytes to blood samples. Distribution of C. albicans to the different immune cell populations in these samples was quantified after and compared to non-substituted blood samples. Despite an almost equal number of PMN and monocytes in the substituted blood samples (PMN to monocytes ratio:), the majority of C. albicans cells still associated with PMN (), clearly indicating that PMN are more efficient in taking up C. albicans than monocytes (Fig. 7). In addition, the model predicted that internalization by PMN that phagocytose for the first time is lower compared to internalization by PMN which did phagocytose more than one C. albicans cell (). We examined the robustness of the prediction by performing four restricted parameter estimations with conditions (i), (ii), (iii) and (iv). For all those conditions, the fitting errors were significantly larger than the fitting error of free parameter estimation (see Fig. S2A). This was verified by Wilcoxon rank-sum test and the variations in the corresponding parameter sets are depicted in Fig. S2B. Surprisingly, the model predicted that intracellular killing of PMN occurs with a lower transition rate than intracellular killing by monocytes (). To test the robustness of this prediction we repeated the parameter estimation procedure under the biologically motivated condition. We found that the fitting error of this conditional parameter estimation was not significantly different from the free parameter estimation, but is again significantly smaller than that of parameter estimations under conditions (i) – (iv) (see Fig. S2A). The parameter estimation with condition yielded, which was mainly due to a decrease of by more than. This was compensated by relatively small variations in all other rates (see Fig. S2B), indicating that the parameter estimation for the virtual infection model is generally robust in all the other rates. The original parameter estimation revealed that most C. albicans cells were killed within PMN (), were killed extracellularly and a small amount was killed within monocytes (). Consequently, elimination of C. albicans in human blood is mainly mediated by PMN which – apart from being present in higher numbers – release antimicrobial peptides inducing extracellular killing and are therefore more effective in eliminating C. albicans than monocytes. The virtual model allowed us to distinguish between intracellularly and extracellularly killed C. albicans cells inside monocytes and PMN. Both immune cell types bear more intracellularly killed than extracellularly killed C. albicans throughout the first of infection (PMN versus, monocytes versus, see Fig. 5). To analyze the average contribution of single PMN to elimination of C. albicans we determined the distribution of alive and killed C. albicans over PMN. The model predicted PMN to phagocytose up to five viable C. albicans cells, with most of the PMN containing one fungus (see Fig. S3A). The amount of PMN that contain viable C. albicans started to decrease after, whereas the amount of PMN containing killed C. albicans increased and reached a maximum after (see Fig. S3B). We found that PMN contained at maximum six C. albicans cells, however, the majority of cells carried only one. After, the relative amount of PMN that contained one C. albicans cell was predominantly greater than the fraction of PMN that contained more than one C. albicans cell (versus, see Fig. S3C). Similar results were obtained for the distribution of C. albicans in monocytes (Fig. S4). These predictions were experimentally verified by manually counting C. albicans cells per PMN in blood smears with quantitatively comparable results, confirming that most PMN which had phagocytosed contained a single C. albicans cell throughout the experiment (see Fig. 8). Excellent fits were achieved for and after inoculation whereas a higher degree of variation was observed at after inoculation, consistent with a higher standard deviation of the experimentally quantified concentrations around this time point (see Fig. 8). These data indicate that activation of PMN triggered by phagocytosis of C. albicans enhances extracellular killing and results into a series of secondary phagocytosis events. Therefore, the distributions of C. albicans cells in PMN and monocytes deviate from the distributions expected for simple Poisson statistics. A comparison revealed a decrease in the number of monocytes containing Candida cells, whereas the number of PMN containing two or more Candida cells was increased (see Supporting Information Text S1 and Fig. S5). These deviations are a direct result of the relationship. Experimental results had shown that a fraction of C. albicans cells remained extracellular and some fungi also survived throughout the experiment (Fig. 5B). These findings could not be explained by proliferation of C. albicans as budding could not be observed and filamentation does not lead to an increase of cell numbers. Lytic escape from phagocytes, which has been described for C. albicans [31], could be excluded as no cell death occurred throughout the experiment. In the model, this was integrated by allowing extracellular C. albicans cells to become resistant against phagocytosis and further killing (Fig. S1). This was required for fitting the virtual infection model to the experimental data as the fractions of extracellular and viable C. albicans cells were not negligible. Our model predicted that almost all C. albicans cells that remained alive had developed resistance against phagocytosis and further killing () and only few fungi remained alive in PMN () and monocytes (). Resistant fungal cells also constituted the major fraction () of extracellular C. albicans at post infection. Using a non-filamentous mutant (C. albicans efg1, cph1) we could demonstrate that development of resistance was not linked to filamentation as this mutant showed an identical distribution as the wild-type without developing filamentous forms (distribution of C. albicans, at p. i. associated to PMN, associated to monocytes and free, for all). Moreover, inoculation of killed C. albicans cells into human blood proved that killed fungal cells developed resistance against phagocytosis with identical rates as viable fungi resulting in similar amounts (for viable versus for inactivated C. albicans) of extracellular fungi (Fig. 9). The simulation results predicted that the amount of alive resistant C. albicans cells was larger than the relative number of killed resistant C. albicans cells, i. e. versus, respectively, which was in line with the observation that extracellular C. albicans showed continued filamentous growth throughout the experiment. Development of resistance was not linked to exhaustion of the host cells. In contrast, immune cells in the model infection system clearly retained their phagocytic capacity throughout the experiment. This was shown by reinoculation of an infected blood sample after, which resulted in identical uptake kinetics as primary infection (Fig. 6). To further confirm these data we added freshly drawn blood of the same donor to an infected blood sample to test whether the new immune cells were able to take up all or part of the extracellular resistant C. albicans population. As expected, no additional uptake of C. albicans cells could be observed. Taken together, the simulation results revealed that development of resistance against phagocytosis and further killing is the only way for C. albicans cells to survive immune activation in human blood.
We applied a state-based modelling approach to simulate the host-pathogen interaction for C. albicans in human blood. This approach allowed to set up a virtual infection model that captures the stochastic transitions between systems states, e. g. including all possible configurations of alive and killed C. albicans cells in monocytes and PMN as well as in the extracellular space. In contrast to deterministic models based on differential equations, the bookkeeping of discrete transitions in the state-based model enabled us to accurately model (i) the killing by secreted antimicrobial factors due to the primary phagocytosis of C. albicans cells by PMN and (ii) the dynamic distribution of killed and alive C. albicans in immune cells. This is a consequence of the fact that non-spatial agent-based models represent interactions between cells occurring in small numbers as stochastic events and allow for decision making depending on the preceding occurrence of specific events [27]. A priori unknown transition rates between any two states could be estimated by fitting the simulation results to the experimental data using the Monte Carlo method of simulated annealing. This procedure enabled us to quantify transition rates with high accuracy by identifying the set of parameters that globally minimizes the least-square error between the results of the simulation and the experiment. The current model has been fitted to results obtained with blood samples from several independent blood donors. Furthermore, we have shown that overall distribution rates are highly similar for a set of unrelated clinical bloodstream isolates. Despite this, it has to be noted that our data will most likely underestimate the biological variability of both host and pathogen as a small set of selected donors and C. albicans strains does not cover the complete biological variability of both populations. However, our approach offers an unique option to study this diversity, e. g. by using C. albicans strains that have been shown to differ in their interaction with host immunity [32]. In addition, the ability to use the whole-blood infection assay rather than purified primary immune cell populations bears several other advantages: (i) as no isolation procedure is involved all cells in the assay are completely untouched and should show minimal pre-activation [33], (ii) the whole-blood model allows communication between different effector cells and contains a functional complement system [12], [17], [34], (iii) the whole-blood model enables pharmacological intervention by blocking several arms of innate immune activation [35], [36]. Consequently, several future applications of our approach can be envisioned. These include the comparative analysis of different pathogens, investigation of clinically relevant scenarios (neutropenia) as well as studies on the influence of genetic markers on innate immune activation. The virtual infection model clearly predicts a predominant role of neutrophils in the early immune response mounted in human blood against C. albicans. Although neutrophils have mostly been considered as central in the defense against invasive C. albicans infection, their role in the clinical setting is not unambiguous. In patients with chronic granulomatous disease, a congenital disorder of NADPH oxidase which prevents oxidative burst and formation of NETs, candidemia is surprisingly rare, especially when compared to invasive mould infections like aspergillosis or zygomycosis [37], [38]. In line with this, many studies have failed to identify neutropenia as an independent risk factor for candidemia [39]. As these studies have largely been performed in ICU settings, this may however be due to the rarity of neutropenic patients in these cohorts. In cancer patients, neutropenia has been found to contribute to the risk for developing candidemia [40]–[42] and it is generally accepted that the outcome of candidemia is impaired in neutropenic patients and therefore current therapeutic guidelines recommend intensified treatment protocols for candidemia in neutropenic patients [43]. Our results suggest that neutrophils are of central importance in the immediate response against invading C. albicans and contribute to elimination in two ways. First, they effectively take up viable C. albicans cells and kill them intracellularly. This activity of neutrophils has generally been considered a major route of antifungal activity and was studied in detail using purified neutrophils [10], [44]–[46]. Second, neutrophils release antifungal effector molecules upon activation that result in extracellular killing of C. albicans. Our model predicts that both mechanisms together account for as much as of fungal killing. This clearly underlines the outstanding importance of neutrophils in mounting a protective response against invasive C. albicans infection which has been suggested by experimental in vivo studies [47]. Bloodstream infection with C. albicans frequently results in organ dissemination, which can affect many organs and anatomical sites including liver, eye, joints and even brain. In an early study, 9 of 32 patients with candidemia showed chorioretinitis compatible with Candida infection and routine performance of fundoscopy is advised for patients suffering from candidemia within one week of treatment initiation [43], [48]. Other studies also documented high rates of dissemination in candidemia, resulting in a disease entitity termed acute disseminated candidiasis [49], [50]. Interestingly, profound and prolonged neutropenia can result in a different disease entity known as chronic disseminated candidemia which is defined by a hematogenous infection of liver and spleen by Candida spp. [51]. Our virtual infection model suggests that elimination of C. albicans will be severely hampered in neutropenic blood, which could explain increased levels of dissemination in the respective patients. The ability of C. albicans to disseminate is linked to its ability to interact with endothelial cells in a way that allows invasion of tissue [52], [53]. However, to establish disseminated infection in multiple organs, it is a prerequisite that some C. albicans cells remain viable in the blood for a prolonged time period. Here, we provide clear evidence that this is indeed the case. Furthermore, of several hypotheses that could potentially explain long-term survival of C. albicans in human blood, the model clearly predicts the development of resistance against phagocytosis among an extracellular population of fungal cells to be the most favourable explanation. The molecular basis for development of resistance will have to be addressed in future studies. However, experimental testing of model-generated hypotheses has provided some important clues: (i) development of resistance against phagocytosis does not require viability of the fungus. In contrast, thimerosal-killed yeast cells were able to acquire resistance at identical rates as viable fungi. This also clearly proves that (ii) development of resistance is not linked to filamentation of C. albicans. In line with this finding, a non-filamentous, mutant of C. albicans was also able to acquire resistance at the same rate as C. albicans wild-type. (iii) Finally, the resistance phenotype does not seem to be linked to exhaustion of phagocytes at later stages of infection. This could be shown by reinoculation after two hours of initial infection, which again resulted in unimpaired phagocytosis and killing of the newly inoculated yeast cells. A range of host factors has previously been shown to bind to the fungal cell wall and some Candida proteins may even recruit several host factors at a time [16], [54]. Shielding of the fungal cell wall by host factors may be the basis for developing resistance against phagocytosis and/or killing of C. albicans as observed in our model. Although so far no study has addressed the recruitment of host factors from complex and physiological environments, the established whole-blood infection model in combination with flow-cytometry assisted cell sorting offers a unique opportunity to pursue this hypothesis in future experiments. Moreover, interpreting the experimental results in the light of the virtual infection model will enable quantitative analyses of the dynamic immune response and the relative importance of defence mechanims by iterative cycles between experiment and theoretical modeling.
Candida albicans is the most important fungal pathogen in nosocomial bloodstream infections. So far little is known about the interplay of different cellular and non-cellular immune mechanisms mediating the protective response against C. albicans in blood. The in vivo scenario of C. albicans infection can be mimicked by human whole-blood infection assays to analyze the innate immune response against this pathogen. These experiments reveal the time-evolution of certain mechanisms while leaving the values of other quantities in the dark. To shed light on quantities that are not experimentally accessible, we exploited the descriptive and predictive power of mathematical models to estimate these parameters. The combination of experiment and theory enabled us to identify and quantify the main course of the immune response against C. albicans in human blood. We quantified the central role of neutrophils in the defence against this fungal pathogen, both directly by phagocytosis and indirectly by secreting antimicrobial factors inducing extracellular killing. Other findings include the distribution of C. albicans cells in neutrophils and monocytes as well as the immune escape of C. albicans cells in the course of infection.
medicine
immune cells
immune activation
immunology
host-pathogen interaction
microbiology
fungi
immune defense
fungal diseases
infectious diseases
mycology
biology
infectious disease modeling
pathogenesis
immune response
yeast
computer science
computer modeling
immunity
innate immunity
A Virtual Infection Model Quantifies Innate Effector Mechanisms and Candida albicans Immune Escape in Human Blood