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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.
NFAT transcription factors play critical roles in gene transcription during immune responses. Besides regulation of lymphokine promoters in T lymphocytes, NFAT factors are also expressed in other cell types and regulate the activity of numerous genes that control the generation of cardiac septa and valves in embryonic heart, the formation of blood vessels, the outgrowth of neuronal axons and the differentiation of osteoclasts during bone formation [10, 24]. Here we show that the induction of NFATc/αA in effector T cells is controlled by a strong inducible promoter, P1. It results in splicing of exon 1 to exon 3 transcripts and, in concert with the activity of a poly A site downstream of exon 9, leads to the massive synthesis of NFATc/αA in effector Th1 cells. A second, weak promoter, P2, lies in front of exon 2 and directs the synthesis of longer NFAT β isoforms. Both P1 and P2 direct the synthesis of three different RNAs: αA, αB, αC and βA, βB, βC correspondingly. The B and C isoforms arise from alternative splicing and poly A addition at the distal site pA2. P1 but not P2 activity is autoregulated by NFAT factors which bind to two tandemly arranged NFAT sites within P1 and enhance its induction. In resting T cells, the NFATc1/β RNAs are the most prominent nfatc1 transcripts and their synthesis is reduced upon T-cell activation. However, following activation in primary effector T cells or in T-cell lines of human or murine origin, a 15–20-fold induction of NFATc1/αA RNA was detected, whereas only a 2–5-fold increase was observed for the NFATc1/αB or NFATc1/αC RNAs. Optimal induction of P1 promoter require involving of a persistent increase in free cytosolic Ca2+ induced by ionomycin, which stimulates the nuclear translocation and transcriptional activation of all NFATc factors and phorbol esters, which activate protein kinase C and other protein kinase pathways in T cells. This suggests that both TCR and co-receptor signals contribute to give full P1 nfatc1 induction. Because NFATc1/αA induction is unaffected in NFATc2+c3 double-deficient T cells, NFATc1 autoregulates its own synthesis by controlling P1 activity and NFATc1/αA induction. P1 promoter contains tandemly arranged NFAT core binding motif TGGAAA to witch bind monomeric NFATc1 proteins and numerous conservative binding sites of other transcriptional factors like CREB, Fos, ATF-2, Sp1, NF-kB and GATA suggesting complex multi-factor regulation of NFATc1 gene. We also highlight that initial phase of nfatc1 transcription in naive CD4+ T cells is controlled by the promoter P2 which is constitutively active in resting T cells. The activation of resting T cells results in a decrease of P2 and the induction of P1 activity and, under optimal conditions, in the predominant synthesis of NFATc1/αA in effector T cells. In addition to the high concentrations of poly A factors required for optimal pA1 function, the levels of transcription factors, in particular NFATs, must also increase for P1 induction. That could be explained by achievement of certain threshold levels for transcriptional activation. Finally, the altered transactivation potential of NFATc1/αA suggests a specific role for this NFATc1 protein in gene control, such as in Th1 effector cells where NFATc1/αA is synthesized at high concentrations.