Not the outside but the inside matters : functional analysis of Capricious and Tartan during Drosophila tracheal morphogenesis

The tubular structures, which transport essential gases, liquids, or cells from one site to another, are shared among various divergent organisms. These highly organized tubular networks include lung, kidney, vasculature and mammary gland in mammals as well as trachea and salivary gland in Drosophila melanogaster. Many questions regarding the tubular morphogenesis cannot be addressed sufficiently by investigating the mammalian organs because their structures are extremely complex and therefore, systematic analyses of genetic and cellular programs guiding the development is not possible. In contrast, the Drosophila tracheal development provides an excellent model system since many molecular markers and powerful tools for genetic manipulations are available. Two mechanisms were shown to be important for the outgrowth of tracheal cells: the FGF signaling pathway and the interaction between the tracheal cells and the surrounding mesodermal cells. The Drosophila FGF ligand encoded by branchless (bnl) is localized in groups of cells near tracheal metameres. The tracheal cells expressing the FGF receptor breathless (btl) respond to these sources of FGF ligand and extend towards them. However, this FGF signaling pathway is not sufficient for the formation of continuous dorsal trunk, the only muticellular tube in tracheal system. Recently, it was found out that single mesodermal cells called bridge-cells are essential for the formation of continuous dorsal trunk as they direct the outgrowth of dorsal trunk cells towards the correct targets. The results in this PhD thesis demonstrate that a cell adhesion molecule Capricious (Caps), which is specifically localized on the surface of bridge-cells, plays an essential role in guiding the outgrowing dorsal trunk cells towards their correct targets. When caps is lacking, some bridge-cells cannot stretch properly towards the adjacent posterior tracheal metameres and thus fail to interconnect the juxtaposing dorsal trunk cells. Consequently, discontinuous dorsal trunks containing interruptions at several positions are formed. On the other hand, when caps is ectopically expressed in the mesodermal cells through a twi-GAL4 driver, these mesodermal cells acquire a guidance function through ectopic caps and misguide the outgrowing dorsal trunk cells in abnormal directions. As a result, disconnected dorsal trunks are formed. These loss- and gain-of-function studies suggest that Caps presumably establishes the cell-to-cell contact between the bridge-cells and the tracheal cells and thereby mediates directly the guidance function of bridge-cells. The most similar protein known to Caps is another cell adhesion molecule called Tartan (Trn). Interestingly, trn is expressed in the mesodermal cells but not in the bridge-cells. When trn is lacking, the outgrowth of not only the dorsal trunks but also the lateral trunks are disrupted. However, in contrast to the ectopic expression of caps, the misexpression of trn does not affect tracheal development. Whereas Trn requires only its extracellular domain to mediate the matrix function, Caps requires both its extracellular and intracellular domains to function as a guidance molecule in the bridge-cells. These observations suggest that Trn functions differently from Caps during tracheal morphogenesis. Presumably, Trn mediates a matrix function of mesodermal cells, which support the tracheal cells to extend efficiently through the surrounding mesodermal tissue. In order to determine which domains dictate the functional specificity of Caps, two hybrid proteins CapsEdTrnId, which contains the Caps extracellular domain and the Trn intracellular domain, and TrnEdCapsId, which consists of the Trn extracellular domain and the Caps intracellular domain, were constructed. Gain of function and rescue experiments with these hybrid proteins suggest on one hand that the extracellular domains of Caps and Trn are functionally redundant and on the other hand that the intracellular domain dictates the functional specificity of Caps. In order to identify putative interactors of Caps, yeast two-hybrid screening was performed. An in vivo interaction assay in yeast suggests that Ras64B interacts specifically with the Caps intracellular domain. In addition, an in vitro binding assay reveals a direct interaction between an inactive form of Ras64B and the Caps intracellular domain. ras64B, which encodes a small GTPase, is expressed in the mesodermal cells concurrently as caps. Finally, a gain-of-function study with the constitutively active Ras64B suggests that Ras64B presumably functions downstream of Caps. All these results suggest consistently that the small GTPase Ras64B binds specifically to the Caps intracellular domain and may thereby mediate the guidance function of Caps.

Development of thermo-analytical prediction and classification models for food in thermal devices using a multi sensor system and artificial neural networks

Die thermische Verarbeitung von Lebensmitteln beeinflusst deren Qualität und ernährungsphysiologischen Eigenschaften. Im Haushalt ist die Überwachung der Temperatur innerhalb des Lebensmittels sehr schwierig. Zudem ist das Wissen über optimale Temperatur- und Zeitparameter für die verschiedenen Speisen oft unzureichend. Die optimale Steuerung der thermischen Zubereitung ist maßgeblich abhängig von der Art des Lebensmittels und der äußeren und inneren Temperatureinwirkung während des Garvorgangs. Das Ziel der Arbeiten war die Entwicklung eines automatischen Backofens, der in der Lage ist, die Art des Lebensmittels zu erkennen und die Temperatur im Inneren des Lebensmittels während des Backens zu errechnen. Die für die Temperaturberechnung benötigten Daten wurden mit mehreren Sensoren erfasst. Hierzu kam ein Infrarotthermometer, ein Infrarotabstandssensor, eine Kamera, ein Temperatursensor und ein Lambdasonde innerhalb des Ofens zum Einsatz. Ferner wurden eine Wägezelle, ein Strom- sowie Spannungs-Sensor und ein Temperatursensor außerhalb des Ofens genutzt. Die während der Aufheizphase aufgenommen Datensätze ermöglichten das Training mehrerer künstlicher neuronaler Netze, die die verschiedenen Lebensmittel in die entsprechenden Kategorien einordnen konnten, um so das optimale Backprogram auszuwählen. Zur Abschätzung der thermische Diffusivität der Nahrung, die von der Zusammensetzung (Kohlenhydrate, Fett, Protein, Wasser) abhängt, wurden mehrere künstliche neuronale Netze trainiert. Mit Ausnahme des Fettanteils der Lebensmittel konnten alle Komponenten durch verschiedene KNNs mit einem Maximum von 8 versteckten Neuronen ausreichend genau abgeschätzt werden um auf deren Grundlage die Temperatur im inneren des Lebensmittels zu berechnen. Die durchgeführte Arbeit zeigt, dass mit Hilfe verschiedenster Sensoren zur direkten beziehungsweise indirekten Messung der äußeren Eigenschaften der Lebensmittel sowie KNNs für die Kategorisierung und Abschätzung der Lebensmittelzusammensetzung die automatische Erkennung und Berechnung der inneren Temperatur von verschiedensten Lebensmitteln möglich ist.