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Integrins are transmembrane receptors transmitting mechanical signals from the extracellular matrix (ECM) to the cytoskeleton (outside-in-signaling). Many molecular defects in the link between cytoskeleton and ECM are known to induce cardiomyopathies. alpha v integrin appears to play a major role in several processes relevant to remodeling, such as binding and activation of matrix metalloproteinases as well as regulation of cell proliferation, migration, and differentiation. We hypothesized that alpha v integrin-mediated signaling is required for the compensatory hypertrophy after aortic banding (AB) and associated with the modulation of ECM protein expression. Mice were treated in vivo with a specific integrin alpha v inhibitor or vehicle via osmotic minipumps starting 1 day prior to aortic banding (AB). At day 2 and day 7 following AB or sham-operation, the mice were examined by echocardiography and hemodynamic analyses were performed. Treatment of alpha v Integrin inhibitor led to a dilated cardiomyopathy and congestive heart failure in AB mice (dilated left ventricle, depressed LV function, and pulmonary congestion), but not to hypertrophy as observed in mice without inhibitor treatment. Investigation of downstream signaling revealed significant activation of the p38 Mitogen-Activated Protein Kinase (MAPK), the Extracellular signal-Regulated Kinases 1 and 2 (Erk 1/2), Focal Adhesion Kinase (FAK) and tyrosine-phosphorylation of c-Src in mice 7 days after AB. This response was blunted in mice treated with integrin alpha v inhibitor. Microarrays probing for a total of 96 cell adhesion and ECM genes identified various genomic targets of integrin alpha v mediated signalling. 7 days after AB 18 ECM genes were up-regulated more than 2-fold (n=6), e.g. collagen (8.11 ± 2.2), fibronectin (2.32 ± 0.94), secreted protein, acidic and rich in cysteine (SPARC, 3.78 ± 0.12), A disintegrin-like and metalloprotease (reprolysin type) with trombospondin type 1 (Adamts-1, 3.51 ± 0.81) and Tissue inhibitor of metalloproteinase 2 (TIMP2, 2.23 ± 0.98), whereas this up-regulation was abolished in mice that were treatd by integrin alpha v inhibitor via mini pumps. We conclude that signaling downstream of integrin alpha v is mediated by the MAPK, FAK and c-Src pathways leading to an up-regulation of extracelluar matrix components necessary for the compensatory response of the heart under a condition of pressure overload.
Die Messung der Genexpression ist für viele Bereiche der Biologie und Medizin wichtig geworden und unterstützt Studien über Behandlung, Krankheiten und Entwicklungsstadien. Microarrays können verwendet werden, um die Expression von tausenden mRNA-Molekülen gleichzeitig zu messen und ermöglichen so einen Einblick und einen Vergleich der verschiedenen zellulären Bedingungen. Die Daten, die durch Microarray-Experimente gewonnen werden, sind hochdimensional und verrauscht, eine Interpretation der Daten ist deswegen nicht einfach. Obwohl Programme für die statistische Auswertung von Microarraydaten existieren, fehlt vielen eine Integration der Analyseergebnisse mit einer automatischen Interpretationsmöglichkeit. In dieser Arbeit wurde GEPAT, Genome Expression Pathway Analysis Tool, entwickelt, das eine Analyse der Genexpression unter dem Gesichtspunkten der Genomik, Proteomik und Metabolik ermöglicht. GEPAT integriert statistische Methoden zum Datenimport und -analyse mit biologischer Interpretation für Genmengen oder einzelne Gene, die auf dem Microarray gemessen werden. Verschiedene Typen von Oligonukleotid- und cDNAMicroarrays können importiert werden, unterschiedliche Normalisierungsmethoden können auf diese Daten angewandt werden, anschließend wird eine Datenannotation durchgeführt. Nach dem Import können mit GEPAT verschiedene statische Datenanalysemethoden wie hierarchisches, k-means und PCA-Clustern, ein auf einem linearen Modell basierender t-Test, oder ein Vergleich chromosomaler Profile durchgeführt werden. Die Ergebnisse der Analysen können auf Häufungen biologischer Begriffe und Vorkommen in Stoffwechselwegen oder Interaktionsnetzwerken untersucht werden. Verschiedene biologische Datenbanken wurden integriert, um zu jeder Gensonde auf dem Array Informationen zur Verfügung stellen zu können. GEPAT bietet keinen linearen Arbeitsablauf, sondern erlaubt die Benutzung von beliebigen Teilmengen von Genen oder biologischen Proben als Startpunkt einer neuen Analyse oder Interpretation. Dabei verlässt es sich auf bewährte Datenanalyse-Pakete, bietet einen modularen Ansatz zur einfachen Erweiterung und kann auf einem verteilten Computernetzwerk installiert werden, um eine große Zahl an Benutzern zu unterstützen. Es ist unter der LGPL Open-Source Lizenz frei verfügbar und kann unter http://gepat.sourceforge.net heruntergeladen werden.
Background: The frequency of the most observed cancer, Non Hodgkin Lymphoma (NHL), is further rising. Diffuse large B-cell lymphoma (DLBCL) is the most common of the NHLs. There are two subgroups of DLBCL with different gene expression patterns: ABC (“Activated B-like DLBCL”) and GCB (“Germinal Center B-like DLBCL”). Without therapy the patients often die within a few months, the ABC type exhibits the more aggressive behaviour. A further B-cell lymphoma is the Mantle cell lymphoma (MCL). It is rare and shows very poor prognosis. There is no cure yet. Methods: In this project these B-cell lymphomas were examined with methods from bioinformatics, to find new characteristics or undiscovered events on the molecular level. This would improve understanding and therapy of lymphomas. For this purpose we used survival, gene expression and comparative genomic hybridization (CGH) data. In some clinical studies, you get large data sets, from which one can reveal yet unknown trends. Results (MCL): The published proliferation signature correlates directly with survival. Exploratory analyses of gene expression and CGH data of MCL samples (n=71) revealed a valid grouping according to the median of the proliferation signature values. The second axis of correspondence analysis distinguishes between good and bad prognosis. Statistical testing (moderate t-test, Wilcoxon rank-sum test) showed differences in the cell cycle and delivered a network of kinases, which are responsible for the difference between good and bad prognosis. A set of seven genes (CENPE, CDC20, HPRT1, CDC2, BIRC5, ASPM, IGF2BP3) predicted, similarly well, survival patterns as proliferation signature with 20 genes. Furthermore, some bands could be associated with prognosis in the explorative analysis (chromosome 9: 9p24, 9p23, 9p22, 9p21, 9q33 and 9q34). Results (DLBCL): New normalization of gene expression data of DLBCL patients revealed better separation of risk groups by the 2002 published signature based predictor. We could achieve, similarly well, a separation with six genes. Exploratory analysis of gene expression data could confirm the subgroups ABC and GCB. We recognized a clear difference in early and late cell cycle stages of cell cycle genes, which can separate ABC and GCB. Classical lymphoma and best separating genes form a network, which can classify and explain the ABC and GCB groups. Together with gene sets which identify ABC and GCB we get a network, which can classify and explain the ABC and GCB groups (ASB13, BCL2, BCL6, BCL7A, CCND2, COL3A1, CTGF, FN1, FOXP1, IGHM, IRF4, LMO2, LRMP, MAPK10, MME, MYBL1, NEIL1 and SH3BP5; Altogether these findings are useful for diagnosis, prognosis and therapy (cytostatic drugs).