Abstract (Expand)

Candida albicans colonizes human mucosa, including the gastrointestinal tract, as a commensal. In immunocompromised patients, C. albicans can breach the intestinal epithelial barrier and cause fatal invasive infections. Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1; CD66a), CEACAM5 (CEA), and CEACAM6 (CD66c) are immunomodulatory receptors expressed on human mucosa and are recruited by bacterial and viral pathogens. Here we show for the first time that a fungal pathogen (i.e., C. albicans) also binds directly to the extracellular domain of human CEACAM1, CEACAM3, CEACAM5, and CEACAM6. Binding was specific for human CEACAMs and mediated by the N-terminal IgV-like domain. In enterocytic C2BBe1 cells, C. albicans caused a transient tyrosine phosphorylation of CEACAM1 and induced higher expression of membrane-bound CEACAM1 and soluble CEACAM6. Lack of the CEACAM1 receptor after short hairpin RNA (shRNA) knockdown abolished CXCL8 (interleukin-8) secretion by C2BBe1 cells in response to C. albicans In CEACAM1-competent cells, the addition of recombinant soluble CEACAM6 reduced the C. albicans-induced CXCL8 secretion.IMPORTANCE The present study demonstrates for the first time that fungal pathogens can be recognized by at least four members of the immunomodulatory CEACAM receptor family: CEACAM1, -3, -5, and -6. Three of the four receptors (i.e., CEACAM1, -5, and -6) are expressed in mucosal cells of the intestinal tract, where they are implicated in immunomodulation and control of tissue homeostasis. Importantly, the interaction of the major fungal pathogen in humans Candida albicans with CEACAM1 and CEACAM6 resulted in an altered epithelial immune response. With respect to the broad impact of CEACAM receptors on various aspects of the innate and the adaptive immune responses, in particular epithelial, neutrophil, and T cell behavior, understanding the role of CEACAMs in the host response to fungal pathogens might help to improve management of superficial and systemic fungal infections.

Authors: E. Klaile, M. M. Muller, M. R. Schafer, A. K. Clauder, S. Feer, K. A. Heyl, M. Stock, T. E. Klassert, P. F. Zipfel, B. B. Singer, H. Slevogt

Date Published: 16th Mar 2017

Journal: MBio

Abstract (Expand)

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.

Authors: T. Lehnert, Sandra Timme, J. Pollmacher, Kerstin Hünniger, Oliver Kurzai, Marc Thilo Figge

Date Published: 19th Jun 2015

Journal: Front Microbiol

Abstract (Expand)

Sepsis is a clinical syndrome that can be caused by bacteria or fungi. Early knowledge on the nature of the causative agent is a prerequisite for targeted anti-microbial therapy. Besides currently used detection methods like blood culture and PCR-based assays, the analysis of the transcriptional response of the host to infecting organisms holds great promise. In this study, we aim to examine the transcriptional footprint of infections caused by the bacterial pathogens Staphylococcus aureus and Escherichia coli and the fungal pathogens Candida albicans and Aspergillus fumigatus in a human whole-blood model. Moreover, we use the expression information to build a random forest classifier to classify if a sample contains a bacterial, fungal, or mock-infection. After normalizing the transcription intensities using stably expressed reference genes, we filtered the gene set for biomarkers of bacterial or fungal blood infections. This selection is based on differential expression and an additional gene relevance measure. In this way, we identified 38 biomarker genes, including IL6, SOCS3, and IRG1 which were already associated to sepsis by other studies. Using these genes, we trained the classifier and assessed its performance. It yielded a 96% accuracy (sensitivities >93%, specificities >97%) for a 10-fold stratified cross-validation and a 92% accuracy (sensitivities and specificities >83%) for an additional test dataset comprising Cryptococcus neoformans infections. Furthermore, the classifier is robust to Gaussian noise, indicating correct class predictions on datasets of new species. In conclusion, this genome-wide approach demonstrates an effective feature selection process in combination with the construction of a well-performing classification model. Further analyses of genes with pathogen-dependent expression patterns can provide insights into the systemic host responses, which may lead to new anti-microbial therapeutic advances.

Authors: Andreas Dix, Kerstin Hünniger, M. Weber, Reinhard Guthke, Oliver Kurzai, Jörg Linde

Date Published: 11th Mar 2015

Journal: Front Microbiol

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