Finding reliable gene markers for accurate disease classification is very challenging due to a number of reasons, including the small sample size of typical clinical data, high noise in gene expression measurements, and the heterogeneity across patients. In fact, gene markers identified in independent studies often do not coincide with each other, suggesting that many of the predicted markers may have no biological significance and may be simply artifacts of the analyzed dataset. To nd more reliable and reproducible diagnostic markers, several studies proposed to analyze the gene expression data at the level of groups of functionally related genes, such as pathways. Given a set of known pathways, these methods estimate the activity level of each pathway by summarizing the expression values of its member genes and using the pathway activities for classification. One practical problem of the pathway-based approach is the limited coverage of genes by currently known pathways. As a result, potentially important genes that play critical roles in cancer development may be excluded. In this thesis, we first propose a probabilistic model to infer pathway/subnetwork activities. After that, we developed a novel method for identifying reliable subnetwork markers in a human protein-protein interaction (PPI) network based on probabilistic inference of subnetwork activities. We tested the proposed methods based on two independent breast cancer datasets. The proposed method can efficiently find reliable subnetwork markers that outperform the gene-based and pathway-based markers in terms of discriminative power, reproducibility and classification performance. The identified subnetwork markers are highly enriched in common GO terms, and they can more accurately classify breast cancer metastasis compared to markers found by a previous method.

Les domaines de transactivation (TAD) acides sont présents dans plusieurs protéines oncogéniques, virales et dans des facteurs de différenciation de cellules souches. Ces domaines acides contrôlent la transcription à travers une myriade d’interactions avec divers partenaires ce qui provoque l’activation de la transcription ou leur propre élimination. Cependant, dans la dernière décennie, de plus en plus de recherches ont démontré que les TAD possédaient un sous-domaine activation/dégradation (DAD) responsable pour une fonction d'activation de la transcription dépendante de la dégradation de la protéine. Un tel phénomène peut être accompli par plusieurs moyens tels que des modifications post-traductionnelles, l’association à des cofacteurs ou la formation d’un réseau d’interaction complexe en chaînes. Or, aucune preuve concrète n’a pu clairement démontrer le fonctionnement de la dépendance paradoxale entre ces deux fonctions sur un activateur de transcription. Le DAD, a été observé dans plusieurs facteurs de transcription incluant la protéine suppresseur de tumeur p53 et le facteur de différenciation érythrocyte EKLF. Un aspect particulier des DAD est que la composition de leur séquence d’acide aminé est fortement similaire à celle des domaines de liaison à l’ubiquitine (UBD) qui jouent un rôle clé dans le contrôle de la transcription à travers leur interaction non-covalente avec l’ubiquitine. Ainsi, dans ce mémoire, nous avons étudié la possibilité que les TAD acides soient capables d’agir comme UBD pour réguler leur fonction paradoxale à travers des interactions non-covalentes avec l’ubiquitine. L’analyse est faite en utilisant la résonnance magnétique nucléaire (RMN) ainsi qu’avec des essais fonctionnels de dégradation. En somme, cette étude amène une plus grande compréhension des protéines impliquées dans le contrôle des TAD et caractérise le tout premier exemple de TAD capable d’interagir avec l’ubiquitine. / Acidic transactivating domains have been shown to be potential targets for a number of different therapies but their dynamic nature and their ability to bind many interacting partners has made it difficult to fully understand their functioning mechanisms. What we do know about these domains is that they readily control transcription through a myriad of interactions capable of either activating specific aspects of their function or simply, signal for their own demise. Within the acidic TADs lies an unusual degradation/activation domain (DAD) capable of activating transcription at the cost of its degradation. In other words, DAD transcriptional activation is dependent on the degradation of the protein. Such a phenomenon could be explained by a wide variety of hypotheses like the play of post-translational modifications, co-factors, or maybe just a really sophisticated time scaled network of interactions. However, no concrete explanation of how this dual dependent functioning domain works has yet to surface. The DAD has been observed within acidic TADs of several transcription factors including the tumor suppressor p53 and the red blood cell differentiation factor EKLF. Interestingly though, the amino acid sequence composition of DADs share a strong similarity with several types of sequences from domains that bind ubiquitin (UBDs). These domains have been shown in the past to, in addition to their role in degradation, play a key role in regulating transcription through non-covalent interaction with ubiquitin. Hence, in this project, we investigated weather acidic TADs had the ability to function as UBDs and form non-covalent interactions with ubiquitin and also to determine the functional significance of this interaction in regards to the dual function of acidic TADs.

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.