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The massive remodeling of the heart tissue, as observed in response to pressure overload or myocardial infarction, is considered to play a causative role in the development of heart failure. Alterations in the heart architecture clearly affect the mechanical properties of the heart muscle, but they are rooted in changes at the cellular level including modulation of gene expression. Together with integrins, the transmembrane receptors linking the extracellular environment to the cytoskeleton, extracellular matrix (ECM) proteins and matricellular proteins are key components of the remodeling process in the heart. Therefore, this thesis was aimed at analysing the role of integrins in the regulation of gene expression and heart muscle performance during cardiac wound repair induced by pressure overload or myocardial infarction (MI). To investigate the contribution of integrin Beta 1, we characterised the response of mice with a conditional, cardiac-specific deletion of the integrin Beta 1 gene in an experimental model of pressure overload by aortic banding (AB). In particular, we measured physiological alterations and gene expression events in the stressed heart in the presence or absence of integrin Beta 1. Interestingly, mice containing a knock-out allele and the ventricular myocyte-specific conditional allele of the integrin Beta 1 gene were born and grew up to adulthood. Though these animals still exhibited minor amounts of integrin Beta1 in the heart (expressed by non-myocytes), these mice displayed abnormal cardiac function and were highly sensitive to AB. Whereas a compensatory hypertrophic response to pressure overload was observed in wildtype mice, the integrin Beta 1-deficient mice were not able to undergo heart tissue remodeling. Furthermore, ECM gene expression was altered and, in particular, the increased expression of the matricellular protein SPARC after AB was abolished in integrin Beta 1–deficient mice. Interestingly, we also found a transient upregulation of SPARC mRNA during heart remodeling after MI using cDNA macroarrays. Indeed, increased SPARC protein levels were observed starting at day 2 (2.55±0.21fold, p<0.01), day 7 (3.72±0.28 fold, p<0.01) and 1 month (1.9±0.16 fold, p<0.01) after MI, which could be abolished by using an integrin alpha v inhibitor in vivo. Immunofluorescence analysis of heart tissue demonstrated that the increased SPARC expression was confined to the infarcted area and occurred together with the influx of fibroblasts into the heart. In vitro, either TGF-Beta 1 or PDGF-BB stimulated SPARC expression by fibroblasts. Inhibition of integrin alpha v did not interfere with TGF-Beta1 or PDGF induced SPARC secretion as determined by ELISA assays or Western blot. However, secretion of TGF-Beta1 and PDGF-BB by cardiomyocytes was induced by vitronectin, a ligand of integrin alpha v, and this response was blocked by the integrin alpga v inhibitor. Functionally, SPARC modulated the migratory response of fibroblasts towards ECM proteins suggesting that the local deposition of SPARC following MI contributes to scar formation. Taken together, our combined in vivo and in vitro data demonstrate that several integrin subunits play critical roles during tissue remodeling in the injured heart. Integrin-dependent gene expression events such as the upregulation of SPARC following MI are critical to orchestrate the healing response. These processes appear to involve complex cross-talk between different cell types such as cardiomyocytes and fibroblasts to allow for locally confined scar formation. The elucidation of the sophisticated interplay between integrins, matricellular proteins such as SPARC, and growth factors will undoubtedly provide us with a better and clinically useful understanding of the molecular mechanisms governing heart remodeling.
DNA microarrays have become a standard technique to assess the mRNA levels for complete genomes. To identify significantly regulated genes from these large amounts of data a wealth of methods has been developed. Despite this, the functional interpretation (i.e. deducing biological hypothesis from the data) still remains a major bottleneck in microarray data analysis. Most available methods display the set of significant genes in long lists, from which common functional properties have to be extracted. This is not only a tedious and time-consuming task, which becomes less and less feasible with increasing numbers of experimental conditions, but is also prone to errors, since it is commonly done by eye. In the course of this work methods have been developed and tested, that allow for a computerbased analysis of functional properties being relevant in the given experimental setting. To this end the Gene Ontology was chosen as an appropriate source of annotation data, because it combines human-readability with computer-accessibility of the annotations term and thus allows for a statistical analysis of functional properties. Here the gene-annotations are integrated in a Correspondence Analysis which allows to visualize genes, hybridizations and functional categories in a single plot. Due to the increasing amounts of available annotations and the fact that in most settings only few functional processes are differentially regulated, several filter criteria have been developed to reduce the number of displayed annotations to a set being relevant in the given experimental setting. The applicability of the presented visualization and filtering have both been validated on datasets of varying complexity. Starting from the well studied glucose-pathway in S. cerevisiae up to the comparison of different tumor types in human. In both settings the method generated well interpretable plots, which allowed for an immediate identification of the major functional differences between the experimental conditions [90]. While the integration of annotation data like GO facilitates functional interpretation, it lacks the capability to identify key regulatory elements. To facilitate such an analysis, the occurrence of transcription factor binding sites in upstream regions of genes has been integrated to the analysis as well. Again this methodology was biologically validated on S. cerevisiae as well human cancer data sets. In both settings TFs known to exhibit central roles for the observed transcriptional changes were plotted in marked positions and thus could be immediately identified [206]. In essence, integration of supplementary information in Correspondence Analysis visualizes genes, hybridizations and annotation data in a single, well interpretable plot. This allows for an intuitive identification of relevant annotations even in complex experimental settings. The presented approach is not limited to the shown types of data, but is generalizable to account for the majority of the available annotation data.