Nanobody-mediated neutralization of candidalysin prevents epithelial damage and inflammatory responses that drive vulvovaginal candidiasis pathogenesis.

Candida albicans can cause mucosal infections in humans. This includes oropharyngeal candidiasis, which is commonly observed in human immunodeficiency virus infected patients, and vulvovaginal candidiasis (VVC), which is the most frequent manifestation of candidiasis. Epithelial cell invasion by C. albicans hyphae is accompanied by the secretion of candidalysin, a peptide toxin that causes epithelial cell cytotoxicity. During vaginal infections, candidalysin-driven tissue damage triggers epithelial signaling pathways, leading to hyperinflammatory responses and immunopathology, a hallmark of VVC. Therefore, we proposed blocking candidalysin activity using nanobodies to reduce epithelial damage and inflammation as a therapeutic strategy for VVC. Anti-candidalysin nanobodies were confirmed to localize around epithelial-invading C. albicans hyphae, even within the invasion pocket where candidalysin is secreted. The nanobodies reduced candidalysin-induced damage to epithelial cells and downstream proinflammatory responses. Accordingly, the nanobodies also decreased neutrophil activation and recruitment. In silico mathematical modeling enabled the quantification of epithelial damage caused by candidalysin under various nanobody dosing strategies. Thus, nanobody-mediated neutralization of candidalysin offers a novel therapeutic approach to block immunopathogenic events during VVC and alleviate symptoms.IMPORTANCEWorldwide, vaginal infections caused by Candida albicans (VVC) annually affect millions of women, with symptoms significantly impacting quality of life. Current treatments are based on anti-fungals and probiotics that target the fungus. However, in some cases, infections are recurrent, called recurrent VVC, which often fails to respond to treatment. Vaginal mucosal tissue damage caused by the C. albicans peptide toxin candidalysin is a key driver in the induction of hyperinflammatory responses that fail to clear the infection and contribute to immunopathology and disease severity. In this pre-clinical evaluation, we show that nanobody-mediated candidalysin neutralization reduces tissue damage and thereby limits inflammation. Implementation of candidalysin-neutralizing nanobodies may prove an attractive strategy to alleviate symptoms in complicated VVC cases.

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.

Acceptance of Different Self-sampling Methods for Semiweekly SARS-CoV-2 Testing in Asymptomatic Children and Childcare Workers at German Day Care Centers: A Nonrandomized Controlled Trial.

Importance: Closure of day care centers (DCCs) to contain the COVID-19 pandemic has been associated with negative effects on children's health and well-being. Objective: To investigate the acceptance of self-sampling methods for continuous SARS-CoV-2 surveillance among asymptomatic children and childcare workers (CCWs) in DCCs. Design, Setting, and Participants: This nonrandomized pilot study included children and CCWs at 9 DCCs in Wuerzburg, Germany, from May to July 2021. Interventions: Twice weekly testing for SARS-CoV-2 was conducted by self-sampled mouth-rinsing fluid (saliva sampling [SAL], with subsequent pooled polymerase chain reaction test) plus nasal rapid antigen self-test (RAgT) (group 1), SAL only (group 2), or RAgT only (group 3) in children and CCWs. Main Outcomes and Measures: Main outcomes were rates for initial acceptance and successful (≥60% of scheduled samples) long-term participation. The probability of SARS-CoV-2 introduction into DCCs was modeled as a function of age-adjusted background incidence and DCC size. Results: Of 836 eligible children, 452 (54.1%; 95% CI, 50.7%-57.4%) participated (median [IQR] age: 4 [3-5] years; 213 [47.1%] girls), including 215 (47.6%) in group 1, 172 (38.1%) in group 2, and 65 (14.4%) in group 3. Of 190 CCWs, 139 (73.2%; 95% CI, 66.4%-79.0%) participated (median [IQR] age: 30 [25-46] years; 128 [92.1%] women), including 96 (69.1%) in group 1, 29 (20.9%) in group 2, and 14 (10.1%) in group 3. Overall, SARS-CoV-2 PCR tests on 5306 SAL samples and 2896 RAgTs were performed in children, with 1 asymptomatic child detected by PCR from SAL. Successful long-term participation was highest in group 2 (SAL only; children: 111 of 172 [64.5%]; CCWs: 18 of 29 [62.1%]). Weekly participation rates in children ranged from 54.0% to 83.8% for SAL and from 44.6% to 61.4% for RAgT. Participation rates decreased during the study course (P < .001). The probability of SARS-CoV-2 introduction into a DCC with 50 children was estimated to reach at most 5% for an age-adjusted SARS-CoV-2 incidence below 143. Conclusions and Relevance: Self-sampling for continuous SARS-CoV-2 testing was well accepted, with SAL being the preferred method. Given the high number of negative tests, thresholds for initiating continuous testing should be established based on age-adjusted SARS-CoV-2 incidence rates. Trial Registration: German Registry for Clinical Trials Identifier: DRKS00025546.

Feasibility of SARS-CoV-2 Surveillance Testing Among Children and Childcare Workers at German Day Care Centers: A Nonrandomized Controlled Trial.

Importance: Closure of day care centers has been implemented globally to contain the COVID-19 pandemic but has negative effects on children's health and psychosocial well-being. Objective: To investigate the feasibility of surveillance among children and childcare workers and to model the efficacy of surveillance on viral spread prevention. Design, Setting, and Participants: This nonrandomized controlled trial was conducted at 9 day care centers in Wuerzburg, Germany, from October 2020 to March 2021. Participants included children attending day care, childcare workers, and household members. Participating day care centers were assigned to different surveillance modules in a nonrandomized feasibility study. A mathematical model for SARS-CoV-2 spread in day care centers was developed to identify optimal surveillance. Interventions: Modules 1, 2, and 3 involved continuous surveillance of asymptomatic children and childcare workers by SARS-CoV-2 polymerase chain reaction testing of either midturbinate nasal swabs twice weekly (module 1) or once weekly (module 2) or self-sampled saliva samples twice weekly (module 3). Module 4 involved symptom-based, on-demand testing of children, childcare workers, and their household members by oropharyngeal swabs. All participants underwent SARS-CoV-2 antibody status testing before and after the sampling period. Questionnaires on attitudes and perception of the pandemic were administered in weeks 1, 6, and 12. Mathematical modeling was used to estimate SARS-CoV-2 spread in day care centers. Main Outcomes and Measures: The primary outcomes were acceptance of the respective surveillance protocols (feasibility study) and the estimated number of secondary infections (mathematical modeling). Results: Of 954 eligible individuals (772 children and 182 childcare workers), 592 (62%), including 442 children (median [IQR] age, 3 [2-4] years; 214 [48.6%] female) and 150 childcare workers (median [IQR] age, 29 [25-44] years; 129 [90.8%] female) participated in the surveillance. In total, 4755 tests for SARS-CoV-2 detected 2 infections (1 childcare worker and 1 adult household member). Acceptance for continuous surveillance was highest for biweekly saliva testing (150 of 221 eligible individuals [67.9%; 95% CI, 61.5%-73.7%]) compared with biweekly (51 of 117 individuals [43.6%; 95% CI, 35.0%-52.6%]) and weekly (44 of 128 individuals [34.4%; 95% CI, 26.7%-43.0%]) midturbinate swabbing (P < .001). Dropout rates were higher for midturbinate swabbing (biweekly, 11 of 62 participants [18%]; once weekly, 11 of 55 participants [20%]) than for saliva testing (6 of 156 participants [4%]). Mathematical modeling based on study and literature data identified biweekly testing of at least 50% of children and childcare workers as minimal requirements to limit secondary infections. Conclusions and Relevance: In this nonrandomized controlled trial, surveillance for SARS-CoV-2 in 9 German day care centers was feasible and well accepted. Mathematical modeling estimated that testing can minimize the spread of SARS-CoV-2 in day care centers. These findings enable setup of surveillance programs to maintain institutional childcare. Trial Registration: German Registry for Clinical Trials Identifier: DRKS00023721.

Ex vivo immune profiling in patient blood enables quantification of innate immune effector functions.

The assessment of a patient's immune function is critical in many clinical situations. In complex clinical immune dysfunction like sepsis, which results from a loss of immune homeostasis due to microbial infection, a plethora of pro- and anti-inflammatory stimuli may occur consecutively or simultaneously. Thus, any immunomodulatory therapy would require in-depth knowledge of an individual patient's immune status at a given time. Whereas lab-based immune profiling often relies solely on quantification of cell numbers, we used an ex vivo whole-blood infection model in combination with biomathematical modeling to quantify functional parameters of innate immune cells in blood from patients undergoing cardiac surgery. These patients experience a well-characterized inflammatory insult, which results in mitigation of the pathogen-specific response patterns towards Staphylococcus aureus and Candida albicans that are characteristic of healthy people and our patients at baseline. This not only interferes with the elimination of these pathogens from blood, but also selectively augments the escape of C. albicans from phagocytosis. In summary, our model could serve as a valuable functional immune assay for recording and evaluating innate responses to infection.

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.

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.

Quantitative Simulations Predict Treatment Strategies Against Fungal Infections in Virtual Neutropenic Patients.

The condition of neutropenia, i.e., a reduced absolute neutrophil count in blood, constitutes a major risk factor for severe infections in the affected patients. Candida albicans and Candida glabrata are opportunistic pathogens and the most prevalent fungal species in the human microbiota. In immunocompromised patients, they can become pathogenic and cause infections with high mortality rates. In this study, we use a previously established approach that combines experiments and computational models to investigate the innate immune response during blood stream infections with the two fungal pathogens C. albicans and C. glabrata. First, we determine immune-reaction rates and migration parameters under healthy conditions. Based on these findings, we simulate virtual patients and investigate the impact of neutropenic conditions on the infection outcome with the respective pathogen. Furthermore, we perform in silico treatments of these virtual patients by simulating a medical treatment that enhances neutrophil activity in terms of phagocytosis and migration. We quantify the infection outcome by comparing the response to the two fungal pathogens relative to non-neutropenic individuals. The analysis reveals that these fungal infections in neutropenic patients can be successfully cleared by cytokine treatment of the remaining neutrophils; and that this treatment is more effective for C. glabrata than for C. albicans.

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

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.

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

Microbial invaders are ubiquitously present and pose the constant risk of infections that are opposed by various defence mechanisms of the human immune system. A tight regulation of the immune response ensures clearance of microbial invaders and concomitantly limits host damage that is crucial for host viability. To investigate the counterplay of infection and inflammation, we simulated the invasion of the human-pathogenic fungus Aspergillus fumigatus in lung alveoli by evolutionary games on graphs. The layered structure of the innate immune system is represented by a sequence of games in the virtual model. We show that the inflammatory cascade of the immune response is essential for microbial clearance and that the inflammation level correlates with the infection-dose. At low infection-doses, corresponding to daily inhalation of conidia, the resident alveolar macrophages may be sufficient to clear infections, however, at higher infection-doses their primary task shifts towards recruitment of neutrophils to infection sites.

Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.

Opportunistic fungal pathogens can cause bloodstream infection and severe sepsis upon entering the blood stream of the host. The early immune response in human blood comprises the elimination of pathogens by antimicrobial peptides and innate immune cells, such as neutrophils or monocytes. Mathematical modeling is a predictive method to examine these complex processes and to quantify the dynamics of pathogen-host interactions. Since model parameters are often not directly accessible from experiment, their estimation is required by calibrating model predictions with experimental data. Depending on the complexity of the mathematical model, parameter estimation can be associated with excessively high computational costs in terms of run time and memory. We apply a strategy for reliable parameter estimation where different modeling approaches with increasing complexity are used that build on one another. This bottom-up modeling approach is applied to an experimental human whole-blood infection assay for Candida albicans. Aiming for the quantification of the relative impact of different routes of the immune response against this human-pathogenic fungus, we start from a non-spatial state-based model (SBM), because this level of model complexity allows estimating a priori unknown transition rates between various system states by the global optimization method simulated annealing. Building on the non-spatial SBM, an agent-based model (ABM) is implemented that incorporates the migration of interacting cells in three-dimensional space. The ABM takes advantage of estimated parameters from the non-spatial SBM, leading to a decreased dimensionality of the parameter space. This space can be scanned using a local optimization approach, i.e., least-squares error estimation based on an adaptive regular grid search, to predict cell migration parameters that are not accessible in experiment. In the future, spatio-temporal simulations of whole-blood samples may enable timely stratification of sepsis patients by distinguishing hyper-inflammatory from paralytic phases in immune dysregulation.

Image-based systems biology of infection.

The successful treatment of infectious diseases requires interdisciplinary studies of all aspects of infection processes. The overarching combination of experimental research and theoretical analysis in a systems biology approach can unravel mechanisms of complex interactions between pathogens and the human immune system. Taking into account spatial information is especially important in the context of infection, since the migratory behavior and spatial interactions of cells are often decisive for the outcome of the immune response. Spatial information is provided by image and video data that are acquired in microscopy experiments and that are at the heart of an image-based systems biology approach. This review demonstrates how image-based systems biology improves our understanding of infection processes. We discuss the three main steps of this approach--imaging, quantitative characterization, and modeling--and consider the application of these steps in the context of studying infection processes. After summarizing the most relevant microscopy and image analysis approaches, we discuss ways to quantify infection processes, and address a number of modeling techniques that exploit image-derived data to simulate host-pathogen interactions in silico.