Im Mittelpunkt dieser Arbeit standen Signaltransduktionsprozesse in den Strukturen der Kraftübertragung quergestreifter Muskelzellen, d. h. in den Costameren (Zell-Matrix-Kontakten) und den Glanzstreifen (Zell-Zell-Kontakten der Kardiomyozyten).<br><br>Es ließ sich zeigen, dass sich die Morphologie der Zell-Matrix-Kontakte während der Differenzierung von Skelettmuskelzellen dramatisch ändert, was mit einer veränderten Proteinzusammensetzung einhergeht. Immunfluoreszenz-Analysen von Skelettmuskelzellen verschiedener Differenzierungsstadien implizieren, dass die Signalwege, welche die Dynamik der Fokalkontakte in Nichtmuskelzellen bestimmen, nur für frühe Stadien der Muskeldifferenzierung Relevanz haben können. Ausgehend von diesem Befund wurde begonnen, noch unbekannte Signalwege zu identifizieren, welche die Ausbildung von Costameren kontrollieren: In den Vorläuferstrukturen der Costamere gelang es, eine transiente Interaktion der Proteine Paxillin und Ponsin zu identifizieren. Biochemische Untersuchungen legen nahe, dass Ponsin über eine Skelettmuskel-spezifische Insertion im Carboxyterminus das Adapterprotein Nck2 in diesen Komplex rekrutiert. Es wird vorgeschlagen, dass die drei Proteine einen ternären Signalkomplex bilden, der die Umbauvorgänge der Zell-Matrix-Kontakte kontrolliert und dessen Aktivität von mitogen activated protein kinases (MAPK) reguliert wird.<br><br>Die Anpassungsvorgänge der Strukturen der Kraftübertragung an pathologische Situtation (Kardiomyopathien) in der adulten quergestreiften Muskulatur wurden ausgehend von einem zweiten Protein, dem muscle LIM protein (MLP), untersucht. Es konnte gezeigt werden, dass ein mutiertes MLP-Protein, das im Menschen eine hypertrophe Kardiomyopathie (HCM) auslöst, strukturelle Defekte aufweist und weniger stabil ist. Weiterhin zeigte dieses mutierte Protein eine verringerte Bindungsfähigkeit an die beiden Liganden N-RAP und alpha-Actinin. Die molekulare Grundlage der HCM-verursachenden Mutationen im MLP-Gen könnte folglich eine Veränderung der Homöostase im ternären Komplex MLP &ndash; N-RAP &ndash; alpha-Actinin sein. Die Expressionsdaten eines neu generierten monoklonalen MLP-Antikörpers deuten darauf hin, dass die Funktionen des MLP nicht nur für die Integrität des Myokards, sondern auch für die der Skelettmuskulatur notwendig sind. / The cell-matrix-contacts (costameres) and cell-cell-contacts (intercalated discs of cardiomyocytes) of cross-striated muscle cells transmit mechanical forces to the exterior. On top of this mechanical function, both structures have been implied to be involved in signal transduction processes.<br><br>Dramatic morphological changes in the overall structure of cell-matrix-contacts of skeletal muscle cells were revealed during differentiation. Moreover, this reorganisation was accompanied by alterations in protein composition. Immunofluorescence microscopy indicated that signalling pathways which control the dynamics of focal contacts in non-muscle cells seem to be important only for early differentiation stages of skeletal muscle cells. To explore novel signalling pathways involved in regulating the formation of costameres, signalling molecules engaged were identified. Thus, paxillin and ponsin transiently interact at the precursors of costameres during muscle development. In addition, biochemical data indicate that a skeletal muscle specific module in the carboxyterminal part of ponsin can recruit the adapter protein Nck2 to this complex. Hence, the three proteins might form a ternary signalling complex involved in controlling the reorganisation of cell-matrix-contacts. Apparently, the activity of this signalling complex is regulated by mitogen activated protein kinases (MAPK).<br><br>A second approach has focussed on adaptational processes of the same structures observed in pathological situations. In particular, the role of muscle LIM protein (MLP) in hypertrophic cardiomyopathy (HCM) was investigated. It was shown that a HCM-causing mutant MLP protein fails to fold properly and that the consequent loss of stability is reflected in altered binding properties: the mutant MLP protein shows decreased binding to both N-RAP and alpha-actinin. Hence, the molecular basis for HCM-causing mutations in the MLP gene might be an altered homeostasis of the ternary complex MLP &ndash; N-RAP &ndash; alpha-actinin. Increasing evidence indicates that the functions of MLP are required not only for the integrity of the myocardium. In addition, MLP seems to have regulatory functions in skeletal muscle tissues.

No description available.

Proteins and their interactions are fundamental to cellular life. Disruption of protein-protein, protein-RNA, or protein-DNA interactions can lead to disease, by affecting the function of protein complexes or by affecting gene regulation. A better understanding of these interactions on the molecular level gives rise to new methods to predict protein interaction, and is critical for the rational design of new therapeutic agents that disrupt disease-causing interactions. This thesis consists of three parts that focus on various aspects of protein interactions and their prediction in the context of disease. In the first part of this thesis, we classify interfaces of protein-protein interactions. We do so by systematically computing all binding sites between protein domains in protein complex structures solved by X-ray crystallography. The result is SCOPPI, the Structural Classification of Protein Protein Interfaces. Clustering and classification of geometrically similar interfaces reveals interesting examples comprising viral mimicry of human interface binding sites, gene fusion events, conservation of interface residues, and diversity of interface localisations. We then develop a novel method to predict protein interactions which is based on these structural interface templates from SCOPPI. The method is applied in three use cases covering osteoclast differentiation, which is relevant for osteoporosis, the microtubule-associated network in meiosis, and proteins found deregulated in pancreatic cancer. As a result, we are able to reconstruct many interactions known to the expert molecular biologist, and predict novel high confidence interactions backed up by structural or experimental evidence. These predictions can facilitate the generation of hypotheses, and provide knowledge on binding sites of promising disease-relevant candidates for targeted drug development. In the second part, we present a novel algorithm to search for protein binding sites in RNA sequences. The algorithm combines RNA structure prediction with sequence motif scanning and evolutionary conservation to identify binding sites on candidate messenger RNAs. It is used to search for binding sites of the PTBP1 protein, an important regulator of glucose secretion in the pancreatic beta cell. First, applied to a benchmark set of mRNAs known to be regulated by PTBP1, the algorithm successfully finds significant binding sites in all benchmark mRNAs. Second, collaborators carried out a screen to identify changes in the proteome of beta cells upon glucose stimulation while inhibiting gene expression. Analysing this set of post-transcriptionally controlled candidate mRNAs for PTBP1 binding, the algorithm produced a ranked list of 11 high confident potential PTBP1 binding sites. Experimental validation of predicted targets is ongoing. Overall, identifying targets of PTBP1 and hence regulators of insulin secretion may contribute to the treatment of diabetes by providing novel protein drug targets or by aiding in the design of novel RNA-binding therapeutics. The third part of this thesis deals with gene regulation in disease. One of the great challenges in medicine is to correlate genotypic data, such as gene expression measurements, and other covariates, such as age or gender, to a variety of phenotypic data from the patient. Here, we address the problem of survival prediction based on microarray data in cancer patients. To this end, a computational approach was devised to find genes in human cancer tissue samples whose expression is predictive for the survival outcome of the patient. The central idea of the approach is the incorporation of background knowledge information in form of a network, and the use of an algorithm similar to Google s PageRank. Applied to pancreas cancer, it identifies a set of eight genes that allows to predict whether a patient has a poor or good prognosis. The approach shows an accuracy comparable to studies that were performed in breast cancer or lymphatic malignancies. Yet, no such study was done for pancreatic cancer. Regulatory networks contain information of transcription factors that bind to DNA in order to regulate genes. We find that including background knowledge in form of such regulatory networks gives highest improvement on prediction accuracy compared to including protein interaction or co-expression networks. Currently, our collaborators test the eight identified genes for their predictive power for survival in an independent group of 150 patients. Under a therapeutic perspective, reliable survival prediction greatly improves the correct choice of therapy. Whereas the live expectancy of some patients might benefit from extensive therapy such as surgery and chemotherapy, for other patients this may only be a burden. Instead, for this group, a less aggressive or different treatment could result in better quality of the remaining lifetime. Conclusively, this thesis contributes novel analytical tools that provide insight into disease-relevant interactions of proteins. Furthermore, this thesis work contributes a novel algorithm to deal with noisy microarray measurements, which allows to considerably improve prediction of survival of cancer patients from gene expression data.

Proteinmethylierung spielt eine immer größere Rolle in der Regulierung zellulärer Prozesse. Die Entwicklung effizienter proteomweiter Methoden zur Detektion von Methylierung auf Proteinen ist limitiert und technisch schwierig. In dieser Arbeit haben wir einen neuen Hefe-Zwei-Hybrid-Ansatz (Y2H) entwickelt, der Proteine, die miteinander wechselwirken, mit Hilfe von Sequenzierungen der zweiten Generation identifiziert (Y2H-Seq). Der neue Y2H-Seq-Ansatz wurde systematisch mit dem Y2H-Seq-Ansatz verglichen. Dafür wurde ein Bait-Set von 8 Protein-Arginin-Methyltransferasen, 17 Protein-Lysin-Methyltransferasen und 10 Demethylasen gegen 14,268 Prey-Proteine getestet. Der Y2H-Seq-Ansatz ist weniger arbeitsintensiv, hat eine höhere Sensitivität als der Standard Y2H-Matrix-Ansatz und ist deshalb besonders geeignet, um schwache Interaktionen zwischen Substraten und Protein-Methyltransferasen zu detektieren. Insgesamt wurden 523 Wechselwirkungen zwischen 22 Bait-Proteinen und 324 Prey-Pr oteinen etabliert, darunter 11 bekannte Methyltransferasen-Substrate. Netzwerkanalysen zeigen, dass Methyltransferasen bevorzugt mit Transkriptionsregulatoren, DNA- und RNA-Bindeproteinen wechselwirken. Diese Daten repräsentieren das erste proteomweite Wechselwirkungsnetzwerk über Protein-Methyltransferasen und dienen als Ressource für neue potentielle Methylierungssubstrate. In einem in vitro Methylierungsassay wurden exemplarisch mit Hilfe massenspektrometrischer Analysen die methylierten Aminosäurereste einiger Kandidatenproteine bestimmt. Von neun getesteten Proteinen waren sieben methyliert, zu denen gehören SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP und WDR42A. Wahrscheinlich sind viele Methylierungssubstrate im Netzwerk vorhanden. Das vorgestellte Protein-Protein-Wechselwirkungsnetzwerk zeigt, dass Proteinmethylierung sehr unterschiedliche zelluläre Prozesse beeinflusst und ermöglicht die Aufstellung neuer Hypothesen über die Regulierung Molekularer Mechanismen durch Methylierung. / Protein methylation on arginine and lysine residues is a largely unexplored posttranslational modification which regulates diverse cellular processes. The development of efficient proteome-wide approaches for detecting protein methylation is limited and technically challenging. We developed a novel workload reduced yeast-two hybrid (Y2H) approach to detect protein-protein interactions utilizing second generation sequencing. The novel Y2H-seq approach was systematically evaluated against our state of the art Y2H-matrix screening approach and used to screen 8 protein arginine methyltransferases, 17 protein lysine methyltransferases and 10 demethylases against a set of 14,268 proteins. Comparison of the two approaches revealed a higher sensitivity of the new Y2H-seq approach. The increased sampling rate of the Y2H-seq approach is advantageous when assaying transient interactions between substrates and methyltransferases. Overall 523 interactions between 22 bait proteins and 324 prey proteins were identified including 11 proteins known to be methylated. Network analysis revealed enrichment of transcription regulator activity, DNA- and RNA-binding function of proteins interacting with protein methyltransferases. The dataset represents the first proteome-wide interaction network of enzymes involved in methylation and provides a comprehensively annotated resource of potential new methylation substrates. An in vitro methylation assay coupled to mass spectrometry revealed amino acid methylation of candidate proteins. Seven of nine proteins tested were methylated including SPIN2B, DNAJA3, QKI, SAMD3, OFCC1, SYNCRIP and WDR42A indicating that the interaction network is likely to contain many putative methyltransferase substrate pairs. The presented protein-protein interaction network demonstrates that protein methylation is involved in diverse cellular processes and can inform hypothesis driven investigation into molecular mechanisms regulated through methylation.

No description available.