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- Krebs <Medizin> (2) (remove)
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.
Cancer immune therapy represents a promising alternative to conventional anti tumour therapy like radiation, surgical excision of the tumour or classical chemotherapy. The biggest advantage of cancer immune therapy is specificity, achieved by targeting tumour-associated antigens with the effector arms of the host immune system. This is believed to result in less adverse effects than standard therapy and reaches presumably also metastatic lesions at distant sites from the primary tumour. However, cancer immune therapy by vaccination against tumour antigens failed to translate into clinical success, yet. Furthermore, despite tremendous clinical efforts malignant disease still results in high mortalities giving rise to the need for novel vaccination-based therapies against cancer. An interesting approach in this respect is the use of bacteria like attenuated salmonellae as carriers for heterologous cancer antigens. In numerous preclinical studies Salmonella-based vaccines could elicit cell mediated immune responses of the CD4+ and CD8+ type against own and heterologous antigens which make them ideally suited for anti tumour therapy. Special delivery systems in Salmonella carriers like surface display or secretion of antigens were shown to be advantageous for the immunological outcome. This work focussed on developing novel Salmonella carriers for immune therapy against cancer. In a first project, TolC, a multifunctional outer membrane protein of E. coli was utilized as membrane anchor for 3 heterologous antigens. Respective TolC fusion proteins encoded on plasmids were analysed for expression, functionality and plasmid stability in different engineered Salmonella strains. The amount of membrane localized recombinant TolC was enhanced in tolC-deficient strains. Furthermore, fusion proteins were functional and plasmid stability was very high in vitro and in vivo. Disappointingly, neither specific CD4+/CD8+ T-cell responses against the model antigen ovalbumin nor CD8+ responses against the cancer antigen BRAFV600E were detectable in murine model systems. However, mice immunized with Salmonella strains displaying an immunodominant epitope of the cancer related prostate specific antigen (PSA) were partially protected from subsequent tumour challenge with a PSA expressing melanoma cell line. Tumour growth in mice immunized with the respective strain was significantly decelerated compared to controls, thus indicating that this surface display system confers protective immunity against tumours. In a second study, the approved typhoid vaccine strain Salmonella enterica serovar Typhi Ty21a (Ty21a) was improved for the hemolysin type I secretion system of E. coli. This secretion system is widely used for heterologous antigen delivery in live bacterial vaccines. It was demonstrated throughout this work that a mutation of rpoS in Ty21a correlated with decreased ability for hemolysin secretion compared to other Salmonella strains. Complementation with rpoS or the presumed downstream target of rpoS, rfaH resulted in enhanced expression and secretion of heterologous hemolysin in Ty21a. Presumably by raising the amount of free antigen, rfaHcomplemented Ty21a elicited higher antibody titres against heterologous hemolysin in immunized mice than controls and even rpoS-positive Ty21a. Therefore, rfaHcomplemented Ty21a could form the basis of a novel generation of vaccines for human use based on (cancer) antigen secretion.