Abstract (Expand)

Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1, CD66a) is a receptor for Candida albicans. It is crucial for the immune response of intestinal epithelial cells to this opportunistic pathogen. Moreover, CEACAM1 is of importance for the mucosal colonization by different bacterial pathogens. We therefore studied the influence of the human CEACAM1 receptor in human CEACAM1-transgenic mice on the C. albicans colonization and infection utilizing a colonization/dissemination and a systemic infection mouse model. Our results showed no alterations in the host response between the transgenic mice and the wild-type littermates to the C. albicans infections. Both mouse strains showed comparable C. albicans colonization and mycobiota, similar fungal burdens in various organs, and a similar survival in the systemic infection model. Interestingly, some of the mice treated with anti-bacterial antibiotics (to prepare them for C. albicans colonization via oral infection) also showed a strong reduction in endogenous fungi instead of the normally observed increase in fungal numbers. This was independent of the expression of human CEACAM1. In the systemic infection model, the human CEACAM1 expression was differentially regulated in the kidneys and livers of Candida-infected transgenic mice. Notably, in the kidneys, a total loss of the largest human CEACAM1 isoform was observed. However, the overwhelming immune response induced in the systemic infection model likely covered any CEACAM1-specific effects in the transgenic animals. In vitro studies using bone marrow-derived neutrophils from both mouse strains also revealed no differences in their reaction to C. albicans. In conclusion, in contrast to bacterial pathogens interacting with CEACAM1 on different mucosal surfaces, the human CEACAM1-transgenic mice did not reveal a role of human CEACAM1 in the in vivo candidiasis models used here. Further studies and different approaches will be needed to reveal a putative role of CEACAM1 in the host response to C. albicans.

Authors: Esther Klaile, M. M. Muller, C. Zubiria-Barrera, S. Brehme, Tilman Klassert, M. Stock, A. Durotin, T. D. Nguyen, S. Feer, B. B. Singer, Peter Zipfel, Sven Rudolphi, Ilse Jacobsen, Hortense Slevogt

Date Published: 19th Dec 2019

Journal: Front Microbiol

Abstract (Expand)

Automated microscopy has given researchers access to great amounts of live cell imaging data from in vitro and in vivo experiments. Much focus has been put on extracting cell tracks from such data using a plethora of segmentation and tracking algorithms, but further analysis is normally required to draw biologically relevant conclusions. Such relevant conclusions may be whether the migration is directed or not, whether the population has homogeneous or heterogeneous migration patterns. This review focuses on the analysis of cell migration data that are extracted from time lapse images. We discuss a range of measures and models used to analyze cell tracks independent of the biological system or the way the tracks were obtained. For single-cell migration, we focus on measures and models giving examples of biological systems where they have been applied, for example, migration of bacteria, fibroblasts, and immune cells. For collective migration, we describe the model systems wound healing, neural crest migration, and Drosophila gastrulation and discuss methods for cell migration within these systems. We also discuss the role of the extracellular matrix and subsequent differences between track analysis in vitro and in vivo. Besides methods and measures, we are putting special focus on the need for openly available data and code, as well as a lack of common vocabulary in cell track analysis. (c) 2017 International Society for Advancement of Cytometry.

Authors: Carl-Magnus Svensson, A. Medyukhina, I. Belyaev, N. Al-Zaben, Marc Thilo Figge

Date Published: 5th Oct 2017

Journal: Cytometry A

Abstract (Expand)

In systems biology, researchers aim to understand complex biological systems as a whole, which is often achieved by mathematical modelling and the analyses of high-throughput data. In this review, we give an overview of medical applications of systems biology approaches with special focus on host-pathogen interactions. After introducing general ideas of systems biology, we focus on (1) the detection of putative biomarkers for improved diagnosis and support of therapeutic decisions; (2) network modelling for the identification of regulatory interactions between cellular molecules to reveal putative drug targets; (3) module discovery for the detection of phenotype-specific modules in molecular interaction networks. Biomarker detection applies supervised machine learning methods utilising high-throughput data (e.g. SNP detection, RNA-seq, proteomics) and clinical data. We demonstrate structural analysis of molecular networks, especially by identification of disease modules as novel strategy, and discuss possible applications to host-pathogen interactions. Pioneering work was done to predict molecular host-pathogen interactions networks based on dual RNA-seq data. However, currently this network modelling is restricted to a small number of genes. With increasing number and quality of databases and data repositories, also the prediction of large-scale networks will be feasible that can used for multi-dimensional diagnosis and decision support for prevention and therapy of diseases. Finally, we outline further perspective issues such as support of personalised medicine with high-throughput data and generation of multi-scale host-pathogen interaction models.

Authors: Andreas Dix, S. Vlaic, Reinhard Guthke, Jörg Linde

Date Published: 27th Apr 2016

Journal: Clin Microbiol Infect

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