Project B4 focuses on the analysis, interpretation and modelling of the image data acquired within the CRC/Transregio. This will be accomplished by a tailored systems biology approach that aims at generating predictions to initiate new experiments thus stimulating the iterative cycle between experiment and theory. Image-based systems biology is an innovative approach that will be applied to extract the spatio-temporal information contained in the image data with regard to the morphological, functional and dynamical aspects of host-pathogen interactions at a quantitative level. It comprises the following three steps: (i) image analysis in an automated way, (ii) statistical quantification of characteristic features, and (iii) mathematical modelling based on the acquired spatio-temporal information. The enormous amount of image data needs to be analysed in an automated manner to realise high-throughput screening with regard to segmentation and classification of cellular image objects. This is a challenging task, especially, because fungi can exhibit time-dependent morphological changes making a high-content screening of the images necessary. In the second step, characteristic features – e.g. phagocytosis and adhesion rates, hyphal length growth or the number of hyphal invasion events – will be evaluated by statistical analyses that translate the image data into characteristic quantities of the host-pathogen interaction. Beyond the evaluation of the image data, in the third step, the acquired spatio-temporal information will be integrated in agent-based models, which assign agents to the pathogen and host cells, i.e. individually migrating and interacting objects with dynamic properties. This method allows performing predictive computer simulations of infection processes in their spatial environment and, in the long-term run, is the most suited approach that also enables the integration of gene expression data within a single computational framework.
Microscopy experiments including life-cell imaging, such as phagocytosis assays and host-cell invasion assays, will be performed by partner projects of the CRC/Transregio. The generated image data form the data basis of this project. Various scenarios will be investigated in comparative studies of Aspergillus fumigatus and Candida albicans, ranging from the screening and analysis of mutants differently acting on environmental variations, to elucidate aspects of fungal adaptation, to the interaction of the fungi with the host at a quantitative level.
Prof. Dr. Marc Thilo Figge
Research Group Applied Systems Biology
Leibniz Institute for Natural Product Research and Infection Biology – Hans Knöll Institute
Hünniger K, Lehnert T, Bieber K, Martin R, Figge MT, Kurzai O
A virtual infection model quantifies innate effector mechanisms and Candida albicans immune escape in human blood.
|PLoS Comput Biol 10: e1003479||PubMed|
|Hünniger K, Bieber K, Martin R, Lehnert T, Figge MT, Löffler J, Guo RF, Riedemann NC, Kurzai O||2015||
A second stimulus required for enhanced antifungal activity of human neutrophils in blood is provided by anaphylatoxin C5a.
|J Immunol 194: 1199-210||PubMed|
|Brandes S, Mokhtari Z, Essig F, Hünniger K, Kurzai O, Figge MT||2015||Automated segmentation and tracking of non-rigid objects in time-lapse microscopy videos of polymorphonuclear neutrophils.||Med Image Anal 20: 34-51||PubMed|
|Duggan S, Essig F, Hünniger K, Mokhtari Z, Bauer L, Lehnert T, Brandes S, Häder A, Jacobsen ID, Martin R, Figge MT, Kurzai O||2015||Neutrophil activation by Candida glabrata but not Candida albicans promotes fungal uptake by monocytes.||Cell Microbiol 17: 1259-76||PuMed|
|Lehnert T, Timme S, Pollmächer J, Hünniger K, Kurzai O, Figge MT||2015||Bottom-up modeling approach for the quantitative estimation of parameters in pathogen-host interactions.||Front Microbiol 6: 608||PubMed|
|Medyukhina A, Timme S, Mokhtari Z, Figge MT||2015||Image-based systems biology of infection.||Cytometry A 87: 462-70||PubMed|
|Kraibooj K, Park HR, Dahse HM, Skerka C, Voigt K, Figge MT||2014||Virulent strain of Lichtheimia corymbifera shows increased phagocytosis by macrophages as revealed by automated microscopy image analysis.||Mycoses 57 Suppl 3: 56-66||PubMed|
|Mech F, Wilson D, Lehnert T, Hube B, Figge MT||2014||
Epithelial invasion outcompetes hypha development during Candida albicans infection as revealed by an image-based systems biology approach.
|Cytometry A 85: 126-39||PubMed|
|Kraibooj K, Schoeler H, Svensson CM, Brakhage AA, Figge MT||2015||Automated quantification of the phagocytosis of Aspergillus fumigatus conidia by a novel image analysis algorithm.||Front Microbiol 6: 549||PubMed|
|Mattern DJ, Schoeler H, Weber J, Novohradská S, Kraibooj K, Dahse HM, Hillmann F, Valiante V, Figge MT, Brakhage AA||2015||Identification of the antiphagocytic trypacidin gene cluster in the human-pathogenic fungus Aspergillus fumigatus.||Appl Microbiol Biotechnol 99: 10151-61||PubMed|
|Pollmächer J, Figge MT||2015||Deciphering chemokine properties by a hybrid agent-based model of Aspergillus fumigatus infection in human alveoli.||Front Microbiol 6: 503||PubMed|
|Pollmächer J, Timme S, Schuster S, Brakhage AA, Zipfel PF, Figge MT||2016||Deciphering the counterplay of Aspergillus fumigatus infection and host inflammation by evolutionary games on graphs.||
Sci Rep 6: 27807
|Buhlmann D, Eberhardt HU, Medyukhina A, Prodinger WM, Figge MT, Zipfel PF, Skerka C||2016||Complement factor H related protein 3 (FHR3) blocks C3d-mediated co-activation of human B cells.||J Immunol 197: 620-9||PubMed|