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In this thesis, the development of a phylogenetic DNA microarray, the analysis of several gene expression microarray datasets and new approaches for improved data analysis and interpretation are described. In the first publication, the development and analysis of a phylogenetic microarray is presented. I could show that species detection with phylogenetic DNA microarrays can be significantly improved when the microarray data is analyzed with a linear regression modeling approach. Standard methods have so far relied on pure signal intensities of the array spots and a simple cutoff criterion was applied to call a species present or absent. This procedure is not applicable to very closely related species with high sequence similarity because cross-hybridization of non-target DNA renders species detection impossible based on signal intensities alone. By modeling hybridization and cross-hybridization with linear regression, as I have presented in this thesis, even species with a sequence similarity of 97% in the marker gene can be detected and distinguished from related species. Another advantage of the modeling approach over existing methods is that the model also performs well on mixtures of different species. In principle, also quantitative predictions can be made. To make better use of the large amounts of microarray data stored in public databases, meta-analysis approaches need to be developed. In the second publication, an explorative meta-analysis exemplified on Arabidopsis thaliana gene expression datasets is presented. Integrating datasets studying effects such as the influence of plant hormones, pathogens and different mutations on gene expression levels, clusters of similarly treated datasets could be found. From the clusters of pathogen-treated and indole-3-acetic acid (IAA) treated datasets, representative genes were selected which pointed to functions which had been associated with pathogen attack or IAA effects previously. Additionally, hypotheses about the functions of so far uncharacterized genes could be set up. Thus, this kind of meta-analysis could be used to propose gene functions and their regulation under different conditions. In this work, also primary data analysis of Arabidopsis thaliana datasets is presented. In the third publication, an experiment which was conducted to find out if microwave irradiation has an effect on the gene expression of a plant cell culture is described. During the first steps, the data analysis was carried out blinded and exploratory analysis methods were applied to find out if the irradiation had an effect on gene expression of plant cells. Small but statistically significant changes in a few genes were found and could be experimentally confirmed. From the functions of the regulated genes and a meta-analysis with publicly available microarray data, it could be suspected that the plant cell culture somehow perceived the irradiation as energy, similar to perceiving light rays. The fourth publication describes the functional analysis of another Arabidopsis thaliana gene expression dataset. The gene expression data of the plant tumor dataset pointed to a switch from a mainly aerobic, auxotrophic to an anaerobic and heterotrophic metabolism in the plant tumor. Genes involved in photosynthesis were found to be repressed in tumors; genes of amino acid and lipid metabolism, cell wall and solute transporters were regulated in a way that sustains tumor growth and development. Furthermore, in the fifth publication, GEPAT (Genome Expression Pathway Analysis Tool), a tool for the analysis and integration of microarray data with other data types, is described. It consists of a web application and database which allows comfortable data upload and data analysis. In later chapters of this thesis (publication 6 and publication 7), GEPAT is used to analyze human microarray datasets and to integrate results from gene expression analysis with other datatypes. Gene expression and comparative genomic hybridization data from 71 Mantle Cell Lymphoma (MCL) patients was analyzed and allowed proposing a seven gene predictor which facilitates survival predictions for patients compared to existing predictors. In this study, it was shown that CGH data can be used for survival predictions. For the dataset of Diffuse Large B-cell lymphoma (DLBCL) patients, an improved survival predictor could be found based on the gene expression data. From the genes differentially expressed between long and short surviving MCL patients as well as for regulated genes of DLBCL patients, interaction networks could be set up. They point to differences in regulation for cell cycle and proliferation genes between patients with good and bad prognosis.
Myelinmutationen des zentralen und peripheren Nervensystems verursachen erheblich behindernde und bislang nicht heilbare Erkrankungen. In dieser Arbeit verwendeten wir transgene PLP überexprimierende Mäuse (PLPtg) als Modell für zentrale Myelinopathien und heterozygot P0 defiziente (P0+/-) Mäuse als Modell für hereditäre Neuropathien des peripheren Nervensystems. Beide Modelle zeigen eine niedriggradige Inflammation des Nervengewebes. Durch Verpaarung mit immundefizienten Mausstämmen konnten wir die Relevanz von Makrophagen und T- Lymphozyten in der Entstehung der Myelinpathologie zeigen. Nachdem wir beweisen konnten, dass CD8+ T- Lymphozyten maßgeblich zur Pathologie in PLPtg Mäusen beitragen untersuchten wir den Einfluss eines wichtigen zytotoxischen Moleküls, Granzym B, auf den neuralen Schaden. Durch Generierung von Granzym B defizienten PLPtg Knochenmarkschimären konnten wir eine deutliche Reduktion des glialen Schadens und der Oligodendrozytenapoptose nachweisen. Granzym B ist also zumindest teilweise verantwortlich für die Schädigung, die durch T- Lymphozyten hervorgerufen wird. Um die zusätzliche Informationen über die Rolle der Immunmodulation in unseren Modellen zu gewinnen, untersuchten wir das koinhibitorische Molekül PD-1, einen CD-28 verwandten Rezeptor, der auf B- und T- Lymphozyten exprimiert wird. Bei der Untersuchung von Myelinmutanten des ZNS und PNS (PLPtg und P0+/-), die zusätzlich PD-1 defizient waren, konnten wir einen signifikanten Anstieg von CD8+ T- Lymphozyten und eine deutliche Verschlechterung des glialen Schadens beobachten. In PLPtg Mäusen induzierte die Abwesenheit von PD-1 verstärkte Oligodendrozytenapoptose und klonale Expansion. Außerdem neigen ZNS- Lymphozyten aber nicht periphere CD8+ T- Zellen zur verstärkten Sekretion von proinflammatorischen Zytokinen. In P0+/- Mäusen führt Abwesenheit von PD-1 zu moderaten motorischen und sensorischen Störungen, was die wichtige Rolle von PD-1 in immunologischen Regulationsmechanismen unterstreicht. Zusammenfassend kann man festhalten, daß Granzym B ein wichtiges Effektormolekül zytotoxischer T- Zellen in PLPtg Mäusen ist. PD-1 spielt eine wichtige Rolle in der Regulation von Effektorzellen in unseren Modellen für zentrale und periphere Myelinopathien. Veränderungen dieser Regulation können deutliche Neuroinflammation mit starker Myelinpathologie hervorrufen. Diese Ergebnisse können dazu beitragen, die starke klinische Variabilität von polygenen und sogar monogenen neurologischen Erkrankungen zu erklären.
Past experience contributes to behavioural organization mainly via learning: Animals learn otherwise ordinary cues as predictors for biologically significant events. This thesis studies such predictive, associative learning, using the fruit fly Drosophila melanogaster. I ask two main questions, which complement each other: One deals with the processing of those cues that are to be learned as predictors for an important event; the other one deals with the processing of the important event itself, which is to be predicted. Do fruit flies learn about combinations of olfactory and visual cues? I probe larval as well as adult fruit flies for the learning about combinations of olfactory and visual cues, using a so called ‘biconditional discrimination’ task: During training, one odour is paired with reinforcement only in light, but not in darkness; the other odour in turn is reinforced only in darkness, but not in light. Thus, neither the odours nor the visual conditions alone predict reinforcement, only combinations of both do. I find no evidence that either larval or adult fruit flies were to solve such task, speaking against a cross-talk between olfactory and visual modalities. Previous studies however suggest such cross-talk. To reconcile these results, I suggest classifying different kinds of interaction between sensory modalities, according to their site along the sensory-motor continuum: I consider an interaction ‘truly’ cross-modal, if it is between the specific features of the stimuli. I consider an interaction ’amodal’ if it instead engages the behavioural tendencies or ‘values’ elicited by each stimulus. Such reasoning brings me to conclude that different behavioural tasks require different kinds of interaction between sensory modalities; whether a given kind of interaction will be found depends on the neuronal infrastructure, which is a function of the species and the developmental stage. Predictive learning of pain-relief in fruit flies Fruit flies build two opposing kinds of memory, based on an experience with electric shock: Those odours that precede shock during training are learned as predictors for punishment and are subsequently avoided; those odours that follow shock during training on the other hand are learned as signals for relief and are subsequently approached. I focus on such relief learning. I start with a detailed parametric analysis of relief learning, testing for reproducibility as well as effects of gender, repetition of training, odour identity, odour concentration and shock intensity. I also characterize how relief memories, once formed, decay. In addition, concerning the psychological mechanisms of relief learning, first, I show that relief learning establishes genuinely associative conditioned approach behaviour and second, I report that it is most likely not mediated by context associations. These results enable the following neurobiological analysis of relief learning; further, they will form in the future the basis for a mathematical model; finally, they will guide the researchers aiming at uncovering relief learning in other experimental systems. Next, I embark upon neurogenetic analysis of relief learning. First, I report that fruit flies mutant for the so called white gene build overall more ‘negative’ memories about an experience with electric shock. That is, in the white mutants, learning about the painful onset of shock is enhanced, whereas learning about the relieving offset of shock is diminished. As they are coherently affected, these two kinds of learning should be in a balance. The molecular mechanism of the effect of white on this balance remains unresolved. Finally, as a first step towards a neuronal circuit analysis of relief learning, I compare it to reward learning and punishment learning. I find that relief learning is distinct from both in terms of the requirement for biogenic amine signaling: Reward and punishment are respectively signalled by octopamine and dopamine, for relief learning, either of these seem dispensible. Further, I find no evidence for roles for two other biogenic amines, tyramine and serotonin in relief learning. Based on these findings I give directions for further research.