Refine
Has Fulltext
- yes (3)
Is part of the Bibliography
- yes (3)
Year of publication
- 2005 (3) (remove)
Document Type
- Doctoral Thesis (3) (remove)
Language
- English (3)
Keywords
- Drosophila (3) (remove)
Institute
- Theodor-Boveri-Institut für Biowissenschaften (3) (remove)
The development of ethanol tolerance is due to changes in synaptic plasticity. Since the mechanisms mediating synaptic plasticity are probably defective in the mutant hangAE10, it was a goal of the present study to find out how HANG contributes to synaptic plasticity. In particular, it was important to clarify in which neuronal process HANG plays a role. Antibody stainings against HANG revealed that the protein is localized in all neuronal nuclei of larval and adult brains; the staining is absent in hangAE10, thus confirming that this P-element insertion stock is a protein null for HANG. Detailed analysis of the subnuclear distribution of HANG showed that HANG immunoreactivity is enriched at distinct spots in the nucleus in a speckled pattern; these speckles are found at the inside of the nuclear membrane and do not colocalize with chromatin nor with the nucleolus; thus, HANG is probably involved in the stabilization, processing or export of RNAs. As synaptic plasticity can be studied in single neurons at the larval neuromuscular junction, the morphology of the synaptic terminals of hangAE10 mutants was analyzed at muscle 6/7, segment A4. These studies revealed that hangAE10 mutants display a 40 % increase in bouton number and axonal branch length; in addition, some boutons have an abnormal hourglass-like shape, suggesting that they are arrested in a semi-separated state following the initiation of bouton division. The increase in bouton number of hang mutants is mainly due to an increase in numbers of type Ib boutons. The analysis of the distribution of several synaptic markers in hang mutants did not show abnormalities. The presynaptic expression of HANG in hang mutants rescues the increase in bouton number and axonal branch length, thus proving that the phenotypes seen in the P-element insertion hangAE10 are attributable to the lack of HANG rather than to effects of the P-element marker rosy or to a secondary hit on the same chromsome during mutagensis. This finding is further supported by the fact that postsynaptic expression of HANG does not rescue the abnormal NMJ morphology of hangAE10. Alterations in cAMP levels regulate the number of boutons; since hang mutants display an increase in bouton number, the questions was whether this morphological abnormality was due to defects in cAMP signalling. To test this hypothesis, hangAE10 NMJs were compared to those of the hypomorphic allele dnc1 that has a defective cAMP cascade. Some aspects of the NMJ phenotype (e.g. the increase in bouton number and the unaltered ratio of active zones per bouton area) are similar in hangAE10 and dnc1, other differ. Expression of a UAS-dnc transgene in hangAE10 mutants does not modify the phenotype. In summary, the results of this study indicate that nuclear protein HANG might be involved in isoform-specific splicing of genes required for synaptic plasticity at the NMJ.
In this thesis, I introduce the Virtual Brain Protocol, which facilitates applications of the Standard Brain of Drosophila melanogaster. By providing reliable and extensible tools for the handling of neuroanatomical data, this protocol simplifies and organizes the recurring tasks involved in these applications. It is demonstrated that this protocol can also be used to generate average brains, i.e. to combine recordings of several brains with the same features such that the common features are emphasized. One of the most important steps of the Virtual Insect Protocol is the aligning of newly recorded data sets with the Standard Brain. After presenting methods commonly applied in a biological or medical context to align two different recordings, it is evaluated to what extent this alignment can be automated. To that end, existing Image Processing techniques are assessed. I demonstrate that these techniques do not satisfy the requirements needed to guarantee sensible alignments between two brains. Then, I analyze what needs to be taken into account in order to formulate an algorithm which satisfies the needs of the protocol. In the last chapter, I derive such an algorithm using methods from Information Theory, which bases the technique on a solid mathematical foundation. I show how Bayesian Inference can be applied to enhance the results further. It is demonstrated that this approach yields good results on very noisy images, detecting apparent boundaries between structures. The same approach can be extended to take additional knowledge into account, e.g. the relative position of the anatomical structures and their shape. It is shown how this extension can be utilized to segment a newly recorded brain automatically.
It has been known for a long time that Drosophila can learn to discriminate not only between different odorants but also between different concentrations of the same odor. Olfactory associative learning has been described as a pairing between odorant and electric shock and since then, most of the experiments conducted in this respect have largely neglected the dual properties of odors: quality and intensity. For odorant-coupled short-term memory, a biochemical model has been proposed that mainly relies on the known cAMP signaling pathway. Mushroom bodies (MB) have been shown to be necessary and sufficient for this type of memory, and the MB-model of odor learning and short-term memory was established. Yet, theoretically, based on the MB-model, flies should not be able to learn concentrations if trained to the lower of the two concentrations in the test. In this thesis, I investigate the role of concentration-dependent learning, establishment of a concentration-dependent memory and their correlation to the standard two-odor learning as described by the MB-model. In order to highlight the difference between learning of quality and learning of intensity of the same odor I have tried to characterize the nature of the stimulus that is actually learned by the flies, leading to the conclusion that during the training flies learn all possible cues that are presented at the time. The type of the following test seems to govern the usage of the information available. This revealed a distinction between what flies learned and what is actually measured. Furthermore, I have shown that learning of concentration is associative and that it is symmetrical between high and low concentrations. I have also shown how the subjective quality perception of an odor changes with changing intensity, suggesting that one odor can have more than one scent. There is no proof that flies perceive a range of concentrations of one odorant as one (odor) quality. Flies display a certain level of concentration invariance that is limited and related to the particular concentration. Learning of concentration is relevant only to a limited range of concentrations within the boundaries of concentration invariance. Moreover, under certain conditions, two chemically distinct odorants could smell sufficiently similarly such, that they can be generalized between each other like if they would be of the same quality. Therefore, the abilities of the fly to identify the difference in quality or in intensity of the stimuli need to be distinguished. The way how the stimulus is analyzed and processed speaks in favor of a concept postulating the existence of two separated memories. To follow this concept, I have proposed a new form of memory called odor intensity memory (OIM), characterized it and compared it to other olfactory memories. OIM is independent of some members of the known cAMP signaling pathway and very likely forms the rutabaga-independent component of the standard two-odor memory. The rutabaga-dependent odor memory requires qualitatively different olfactory stimuli. OIM is revealed within the limits of concentration invariance where the memory test gives only sub-optimal performance for the concentration differences but discrimination of odor quality is not possible at all. Based on the available experimental tools, OIM seems to require the mushroom bodies the same as odor-quality memory but its properties are different. Flies can memorize the quality of several odorants at a given time but a newly formed memory of one odor interferes with the OIM stored before. In addition, the OIM lasts only 1 to 3 hours - much shorter than the odor-quality memory.