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ATP dependent chromatin remodeling complexes are multifactorial complexes that utilize the energy of ATP to rearrange the chromatin structure. The changes in chromatin structure lead to either increased or decreased DNA accessibility. SWI/SNF is one of such complex. The SWI/SNF complex is involved in both transcription activation and transcription repression. The ATPase subunit of SWI/SNF is called SWI2/SNF2 in yeast and Brahma, Brm, in Drosophila melanogaster. In mammals there are two paralogs of the ATPase subunit, Brm and Brg1. Recent studies have shown that the human Brm is involved in the regulation of alternative splicing. The aim of this study was to investigate the role of Brm in pre-mRNA processing. The model systems used were Chironomus tentans, well suited for in situ studies and D. melanogaster, known for its full genome information. Immunofluorescent staining of the polytene chromosome indicated that Brm protein of C. tentans, ctBrm, is associated with several gene loci including the Balbiani ring (BR) puffs. Mapping the distribution of ctBrm along the BR genes by both immuno-electron microscopy and chromatin immunoprecipitation showed that ctBrm is widely distributed along the BR genes. The results also show that a fraction of ctBrm is associated with the nascent BR pre-mRNP. Biochemical fractionation experiments confirmed the association of Brm with the RNP fractions, not only in C. tentans but also in D. melanogaster and in HeLa cells. Microarray hybridization experiments performed on S2 cells depleted of either dBrm or other SWI/SNF subunits show that Brm affects alternative splicing and 3´ end formation. These results indicated that BRM affects pre-mRNA processing as a component of SWI/SNF complexes. 1
Precise control of progression through mitosis is essential to maintain genomic stability and to prevent aneuploidy. The DREAM complex is an important regulator of mitotic gene expression. Depletion of Lin9, one core-subunit of DREAM, leads to reduced expression of G2/M genes and impaired proliferation. In conditional mouse knockout cells (MEFs) Lin9 deletion causes defects in mitosis and cytokinesis and cells undergo premature senescence in order to prevent further proliferation. In this work it could be shown that the senescence phenotype in Lin9 knockout MEFs is independently mediated by the two tumor suppressor pathways p53-p21 and p16-pRB. Studies using the conditional Lin9 knockout mouse model demonstrated an important function of Lin9 in the regulation of mitotic gene expression and proliferation in vivo. Deletion of Lin9 caused reduced proliferation in the intestinal crypts resulting in atrophy of the intestinal epithelium and in rapid death of the animals. In the second part of this work, the pathways leading to p53 mediated G1 arrest after failed cytokinesis were analyzed by using a chemical inhibitor of the mitotic kinase Aurora B. In a high throughput siRNA screen the MAP kinase MAP3K4 was identified as an upstream activator of p53. It could be shown that MAP3K4 activates the downstream stress kinase p38b to induce the p53 mediated cell cycle arrest of tetraploid cells. p38b was required for the transcriptional activation of the p53 target gene p21 in response to Aurora B inhibition. In contrast, phosphorylation, stabilization and recruitment of p53 to the p21 promoter occured independently of p38 signaling. Partial inhibition of Aurora B demonstrated that chromosome missegregation also activates the MAP3K4-p38-p53 pathway, suggesting that subtle defects in mitosis are sufficient for inducing this stress signaling pathway. Although p38 was required for the G1 cell cycle arrest after mitotic failures, long-term co-inhibition of p38 and Aurora B resulted in reduced proliferation probably due to increased apoptosis. Presumably, MAP3K4-p38-p53 signaling is a common pathway that is activated after errors in mitosis or cytokinesis to arrest cells in G1 and to prevent chromosomal instability.
Computer Science approaches (software, database, management systems) are powerful tools to boost research. Here they are applied to metabolic modelling in infections as well as health care management. Starting from a comparative analysis this thesis shows own steps and examples towards improvement in metabolic modelling software and health data management. In section 2, new experimental data on metabolites and enzymes induce high interest in metabolic modelling including metabolic flux calculations. Data analysis of metabolites, calculation of metabolic fluxes, pathways and their condition-specific strengths is now possible by an advantageous combination of specific software. How can available software for metabolic modelling be improved from a computational point of view? A number of available and well established software solutions are first discussed individually. This includes information on software origin, capabilities, development and used methodology. Performance information is obtained for the compared software using provided example data sets. A feature based comparison shows limitations and advantages of the compared software for specific tasks in metabolic modeling. Often found limitations include third party software dependence, no comprehensive database management and no standard format for data input and output. Graphical visualization can be improved for complex data visualization and at the web based graphical interface. Other areas for development are platform independency, product line architecture, data standardization, open source movement and new methodologies. The comparison shows clearly space for further software application development including steps towards an optimal user friendly graphical user interface, platform independence, database management system and third party independence especially in the case of desktop applications. The found limitations are not limited to the software compared and are of course also actively tackled in some of the most recent developments. Other improvements should aim at generality and standard data input formats, improved visualization of not only the input data set but also analyzed results. We hope, with the implementation of these suggestions, metabolic software applications will become more professional, cheap, reliable and attractive for the user. Nevertheless, keeping these inherent limitations in mind, we are confident that the tools compared can be recommended for metabolic modeling for instance to model metabolic fluxes in bacteria or metabolic data analysis and studies in infection biology. ...