Virtual infection modeling for Aspergillus fumigatus in human and murine alveoli

Der Der filamentöse pathogene Pilz Aspergillus fumigatus kann schwere Infektionen wie die invasive pulmonale Aspergillose in immungeschwächten Patienten verursachen. Verbunden mit einer hohen Mortalität und einer steigenden Inzidenz der letzten Jahrzehnte bezeugt dies die Notwendigkeit zur Erforschung seines opportunistischen Verhaltens sowie zur Entwicklung effizienter Behandlungsstrategien, um Menschenleben zu retten. Da die Lunge, als primäres Ziel von A. fumigatus Infektionen, nur begrenzt experimentell in vivo studiert werden kann, verfolgt diese Arbeit den Ansatz agenten-basierter Simulation. Die kumulative Dissertation basiert auf 4 veröffentlichten Manuskripten. Untersucht wurden dabei die Vergleichbarkeit von natürlichen Infektionen im Menschen und künstlichen Infektionen im etablierten Mausmodell. Eine zweite Veröffentlichung untersucht den Einfluss von Kohn'schen Poren auf die Dynamik der Immunabwehr gegen Aspergillus fumigatus. Eine dritte Veröffentlichung untersucht die Anwendbarkeit von dynamischen Kugeloberflächenfunktionen - Spherical Harmonics - als Werkzeug der Klassifikation und Beschreibung von beweglichen Zellen. Die vierte Veröffentlichung präsentiert erstmals einen Aspergillose Chip auf Mikrofluidikchips. Dies erlaubt es, die Pathogen-Wirt-Beziehungen unter realistischen Bedingungen zu untersuchen sowie das Wachstum der Pilzhyphen zu quantifizieren

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

Dynamic spherical harmonics approach for shape classification of migrating cells

Cell migration involves dynamic changes in cell shape. Intricate patterns of cell shape can be analyzed and classified using advanced shape descriptors, including spherical harmonics (SPHARM). Though SPHARM have been used to analyze and classify migrating cells, such classification did not exploit SPHARM spectra in their dynamics. Here, we examine whether additional information from dynamic SPHARM improves classification of cell migration patterns. We combine the static and dynamic SPHARM approach with a support-vector-machine classifier and compare their classification accuracies. We demonstrate that the dynamic SPHARM analysis classifies cell migration patterns more accurately than the static one for both synthetic and experimental data. Furthermore, by comparing the computed accuracies with that of a naive classifier, we can identify the experimental conditions and model parameters that significantly affect cell shape. This capability should – in the future – help to pinpoint factors that play an essential role in cell migration