Proteine können aufgrund ihrer biochemischen Vielfalt eine Vielzahl von Interaktionen mit anderen Proteinen oder chemischen Verbindungen eingehen. Im ersten Teil dieser Arbeit wurden Protein-Protein Interaktionen mittels chemischen Quervernetzens untersucht. Das Ziel war, neue und verbesserte Methoden zu entwickeln, um Interaktionsnetzwerke zu erstellen. Im zweiten Teil wurden die Interaktionen von Proteinen mit niedermolekularen Verbindungen untersucht, um Drug Targets zu identifizieren und zu validieren. Die Untersuchung von Protein-Protein Interaktionen mittels Massenspektrometrie (MS) ist eine leistungsfähige Methode, um alle potentiellen Interaktionen eines Proteins nach einer Anreicherung (Co-IP) aus einem Zelllysat zu detektieren. Durch das zusätzliche Quervernetzen dieser Proteine und anschließender MS kann ein Interaktionsnetzwerk erstellt werden, um direkte von indirekten Interaktionen unterscheiden zu können (Topology Mapping). Zur Methodenetablierung wurden kommerzielle Crosslinker und rekombinante Proteine von bekannten Interaktionspartnern mit niedriger Komplexität verwendet. Die beiden Interaktionspartner NPL4 und UFD1 konnten mit dem Crosslinker BS3 erfolgreich quervernetzt und anhand der vernetzten Peptide identifiziert werden. Im nächsten Schritt wurde dieser Arbeitsablauf auf eine Co-IP des Mediatorkomplexes aus Hefe angewendet. Die Probenkomplexität ist hierbei 500 - 1000-fach höher als bei der Verwendung von rekombinanten Proteinen. Nach der erfolgreichen Quervernetzung konnte innerhalb des Komplexes ein Interaktionsnetzwerk erstellt werden. Diese Daten passen zu dem bereits bekannten Modell des Mediatorkomplexes. Interaktionen zu bekannten Interaktionspartnern, wie der RNA-Pol II, konnten aufgrund deren substöchiometrischen Anreicherung nicht identifiziert werden. Aufgrund der genannten Limitationen beim Quervernetzen von Proteinen wurden folgende neue und verbesserte Methoden entwickelt: 1. Verwendung des spaltbaren Crosslinkers (DSSO), der während der Messung selektiv durch niedrige Kollisionsenergie gespalten werden kann, um die Datenbanksuche zu vereinfachen. Die Funktionalität der DSSO-Strategie konnte erfolgreich am Protein Cytochrom C getestet werden. Bei der ersten Fragmentierung wird der Linker gespalten, anschließend können die getrennten Peptide separat fragmentiert werden. Die erzeugten Daten sind mit einer Standarddatenbanksuche kompatibel, was bei gemischten Spektren von zwei Peptiden nicht der Fall wäre. Beim Quervernetzen der rekombinanten Interaktionspartner UBX und p97N mit DSSO konnte der zu bestätigende Crosslink zwischen zwei Lysinen nicht identifziert werden. Grund hierfür könnte eine zu kurze Linkerlänge von DSSO sein. Diese Versuche brachten jedoch einige Limitationen des Ansatzes zum Vorschein, wie die Beschränkung auf die Protease Trypsin, aufgrund der positiven Ladung am C-Terminus und die Notwendigkeit von großen Proteinmengen, da das Spalten des Linkers einen zusätzlichen Intensitätsverlust für die folgende Identifizierung der Peptide mit sich bringt. 2. Da die niedrige Abundanz von quervernetzten Peptiden das Hauptproblem bei deren Identifizierung ist, wurde eine Methode entwickelt, um während der Messung direkt nach diesen niedrig abundanten Spezies zu suchen. Entscheidendes Kriterium hierfür war, dass quervernetzte Peptide zwei C-Termini haben. Diese wurden zur Hälfte enzymatisch mit 18O bzw. 16O markiert und wieder vereinigt. Der resultierende Massenunterschied von 8 Da (4 x 18O) kommt ausschließlich bei zwei quervernetzten Peptiden vor und kann während der Messung direkt gesucht werden. Die vollständige Markierung von Peptiden mit 18O wurde zunächst am Protein Beta-Galaktosidase getestet. Bereits hier stellte sich heraus, dass der enzymatische Rücktausch von 18O zu 16O ein Problem darstellt und die Markierungseffizienz von Aminosäuren beeinflusst wird, die sich C-terminal nach der Spaltstelle befinden. Mit dieser Strategie ließ sich somit keine vollständige Markierung für alle Peptide erreichen, was für diese Strategie essentiell gewesen wäre. 3. Um alle Probleme zu umgehen, die bei der Identifizierung von quervernetzten Peptiden auftreten, wurde eine Methode entwickelt, um quervernetzte Proteine anhand von Profilen nach einer Auftrennung im Polyacrylamidgel (SDS-PAGE) zu identifizieren. Durch das Quervernetzen von Proteinen entstehen zusätzliche Proteinbanden nach einer SDSPAGE, die im Gel nach oben verschoben sind. Alle Proteine in diesen neu erzeugten Bereichen stellen somit potentielle Interaktionspartner dar. Als Modellsystem wurde der Mediatorkomplex verwendet. Er wurde aus einem Zelllysat mittels Co-IP angereichert und anschließend quervernetzt. Aus den mittels LC-MS/MS gemessenen Gelfraktionen wurden Proteinprofile erstellt und miteinander verglichen. Die Intensitätsmaxima der Proteine des Mediatorkomplexes konnten in bestimmten zusätzlichen Fraktionen gefunden werden, was den indirekten Nachweis für eine Interaktion darstellt. Die Funktionalität der Strategie konnte somit bestätigt werden. Ein verbleibender Nachteil ist jedoch die zu geringe Trennleistung von Polyacrylamidgelen. Befinden sich mehr als 50 Proteine in einer Fraktion, können potentielle Interaktionspartner nicht eindeutig zu einer Untereinheit eines Komplexes zugeordnet werden. Im zweiten Teil der Arbeit wurde im Rahmen der Klinischen Forschergruppe 216 (CRU216) Interaktionen von Proteinen mit verschiedenen niedermolekularen Verbindungen massenspektrometrisch untersucht, um potentielle Drug Targets zu identifizieren. Diese Versuche sind vergleichbar mit Co-IP Experimenten, da sich der Arbeitsablauf nur durch die Anreicherung mittels chemischer Verbindung unterscheidet. Hierzu wurden biotinylierte Verbindungen immobilisiert und potentielle Drug Targets aus einem komplexen Zelllysat angereichert. Die Identifzierung der echten Bindungspartner wurde über quantitive Massenspektrometrie erreicht. Dabei wurden die angereicherten Proteine, die an die niedermolekularen Substanzen binden mit einer geeigneten Kontrollanreicherung verglichen. Mit den getesteten α-acyl Aminocarboxamiden konnten verschiedene Proteinkomplexe und interagierende Proteine spezifisch angereichert werden. Hierbei waren die vier Kinasen DNA-PK, ATM, ATR und mTOR besonders interessant, da sie mit onkogenem Signalling und Überlebensmechanismen wie der Hitzeschockantwort in Zellen des Multiplen Myeloms (MM) in Verbidnung stehen. Die Inhibition der DNA-PK, ATM, ATR und mTOR mit α- acyl Aminocarboxamiden stellt somit einen möglichen Therapieansatz dar, wenn er zusammen mit hitzestressauslösenden Inhibitoren verwendet wird. Weiterhin konnte gezeigt werden, dass die Armadillodomäne innerhalb der potentiellen Drug targets signifkant angereichert wurde. Sie stellt damit eine potentielle Bindestelle der α-acyl Aminocarboxamide dar. Abschließend wurden Proteine mit biotinylierten Naphtylisochinolinen aus einem MMZelllysat angereichert, deren Vorläufersubstanzen eine Wirkung auf Tumorzellen und den Malariaparasit Plasmodium falciparum gezeigt hatten. Hierbei konnten vor allem RNAbindende- und mRNA-Splicing Proteine identifiziert werden, die zum Teil essentiell für das Spleißen in-vivo sind. Hierzu gehören mehrere Untereinheiten der Splicing Factoren 3A und 3B. Die Veränderung der transkriptionellen Regulation und der resultierende Effekt auf Krebszellen konnte bereits in anderen Studien mit dem Inhibitor Spliceostatin A gezeigt werden, der das Spleißen beeinflusst. / Based on the huge biochemical variety of proteins they can interact in many different ways with other proteins or small molecules. The first part of this work is about protein-Protein interactions which were investigated with chemical crosslinking. The aim of this work was the development of new and improved methods for the construction of interaction networks. The second part is about protein-small molecule interactions to identify new drug targets with subsequent validation. Mass spectrometry (MS) is a powerful tool to investigate all protein-protein interactions after enrichment from a cell lysate (Co-IP). By adding crosslinking to Co-IP experiments it is possible to create a topology map which is important to differenciate between direct and indirect interactions. To validate the crosslinking procedure, a commercial available crosslinker and recombinant proteins of known interaction partners with low sample complexity were used. The interaction partners NPL4 and UFD1 were successfully crosslinked with BS3 and crosslinked peptides were identified with MS. Next step was to apply this workflow on the Co-IP of the yeast mediator complex. Here the sample complexity is 500 to 1000 times higher than with recombinant proteins. After successfully crosslinking, a topology map of the subunits of the mediator complex was created. The result matches the latest model of the complex. Crosslinks to known interation partners, like the RNA-Pol II, were not identified because of their substoichiometric enrichment. Because of the known limitations of protein crosslinking, new and improved methods were developed: 1. Application of the MS-cleavable crosslinker DSSO, which could be cleaved with low collision energy, to improve the database search of crosslinked peptides. Proof of concept was done by crosslinking Cytochrom C. In the first fragmentation step only DSSO is cleaved. Fragmentation of the separated peptides is done in the next step with higher energy. Peptid fragments are compatible with a standard database search. The application of this method on the interacting proteins UBX and p97N failed because the predicted crosslink could not be identified. This could be due to the lack of linker length of DSSO. But more important, this experiment revealed the drawbacks of the DSSO approach. Trypsin is needed because this leads to a positive charge on the C-Terminus. Furthermore large amounts of protein are required to reach the needed intensity for all fragmentation steps. 2. Since the low abundancy of crosslinked peptides is the main issue for their identification, a new method was developed to directly search for them during the MS measurement. The defining criterion for this search was the occurrence of two C-termini in crosslinked peptides. Samples were splitted, labeled with 18O or 16O, and mixed again to generate a mass shift of 8 Da which is unique for crosslinked peptides. This mass shift can be used to directly search for these peptides during the measurement. Proof of concept was tested by labeling beta-galactosidase. Complete labeling could not be reached because of enzymatic back-exchange from 18O with 16O. Furthermore the neighbouring amino acids at the C-termini influence the labeling efficiency. Due to the incomplete labeling, the method could not be used for identifying crosslinked peptides. 3. To circumvent all problems which occur with the identification of crosslinked peptides, another method was developed to identifiy crosslinked proteins by their mass shift after separation on a polyacrylamide gel (SDS-PAGE). By crosslinking proteins additional protein bands are generated which appear in the upper part of the gel. All proteins in this region are potential interation partners. Proof of concept was done by enrichment (Co-IP) of the yeast mediator complex from a cell lysate with subsequent crosslinking. From the LC-MS/MS data, profiles for every protein of the complex were generated. The additionally generated crosslink intensity peaks of interacting subunits overlapped with each other, which is the indirect proof of the functionality of this approach. The main problem with this strategy is the low separation performance. When about 50 proteins are located in one fraction, it is not possible to unambiguously match a protein to a specific subunit of the protein complex. In the second part of this work, the interactions of proteins with small molecules were investigated by mass spectrometry, to identify potential drug targets. Biotinylated versions of active compounds were immobilized to magnetic streptavidin beads and incubated with INA6 cell lysate. By quantitiative analysis of proteins, which bind to the control beads and proteins, which bind to the inhibitor beads, potential drug targets were identified. With the used α-acyl aminocarboxamides several protein complexes and interacting proteins were specifically enriched. These included the four kinases DNA-PK, ATM, ATR and mTOR, which are involved in oncogenic signaling and survival mechanisms. Moreover it is known, that the DNA-PK interacts with HSF1 and therefore regulates the HSF1-mediated heat shock response. The inhibition of DNA-PK, ATM, ATR and mTOR with α-acyl aminocarboxamides is a new therapeutic opportunity when it is combined with other substances which trigger the heat shock response. A potential binding site of the α-acyl aminocarboxamides is the armadillo domain, because it was significantly enriched among the potential drug targets. Several naphtylisochinolines were also biotinylated, immobilized and incubated with INA6 cell lysate. Precursors of these substances showed activity against multiple myeloma cells and the malaria pathogen Plasmodium falciparum. Here we could enrich proteins which are associated with RNA-binding and mRNA splicing, including several subunits of the splicing factors 3A and 3B, which are essential for splicing. The change of the transcriptional regulation and the resulting effect on cancer cells by influencing mRNA splicing is already known and was shown by other inhibitors like Spliceostatin A.

Microarray technologies enable scientists to identify co-expressed genes at large scale. However, the gene expression analysis does not show functional relationships between co-expressed genes. There is a demand for effective approaches to analyse gene expression data to enable biological discoveries that can lead to identification of markers or therapeutic targets of many diseases. In cancer research, a number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue such as Pancreatic Ductal Adenocarcinoma (PDAC). PDAC carries very poor prognosis, it eludes early detection and is characterised by its aggressiveness and resistance to currently available therapies. To identify molecular markers and suitable targets, there exist a research effort that maps differentially expressed genes to protein interactions to gain an understanding at systems level. Such interaction networks have a complex interconnected structure, whose the understanding of which is not a trivial task. Several formal approaches use simulation to support the investigation of such networks. These approaches suffer from the missing knowledge concerning biological systems. Reasoning in the other hand has the advantage of dealing with incomplete and partial information of the network knowledge. The initial approach adopted was to provide an algorithm that utilises a network-centric approach to pancreatic cancer, by re-constructing networks from known interactions and predicting novel protein interactions from structural templates. This method was applied to a data set of co-expressed PDAC genes. To this end, structural domains for the gene products are identified by using threading which is a 3D structure prediction technique. Next, the Protein Structure Interaction Database (SCOPPI), a database that classifies and annotates domain interactions derived from all known protein structures, is used to find templates of structurally interacting domains. Moreover, a network of related biological pathways for the PDAC data was constructed. In order to reason over molecular networks that are affected by dysregulation of gene expression, BioRevise was implemented. It is a belief revision system where the inhibition behaviour of reactions is modelled using extended logic programming. The system computes a minimal set of enzymes whose malfunction explains the abnormal expression levels of observed metabolites or enzymes. As a result of this research, two complementary approaches for the analysis of pancreatic cancer gene expression data are presented. Using the first approach, the pathways found to be largely affected in pancreatic cancer are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. The analysis indicates that the alteration of the calcium pathway plays an important role in pancreas specific tumorigenesis. Furthermore, the structural prediction method reveals ~ 700 potential protein-protein interactions from the PDAC microarray data, among them, 81 novel interactions such as: serine/threonine kinase CDC2L1 interacting with cyclin-dependent kinase inhibitor CDKN3 and the tissue factor pathway inhibitor 2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). These resulting genes were further investigated and some were found to be potential therapeutic markers for PDAC. Since TMPRSS4 is involved in metastasis formation, it is hypothesised that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. The predicted protein-protein network inspired the analysis of the data from two other perspectives. The resulting protein-protein interaction network highlighted the importance of the co-expression of KLK6 and KLK10 as prognostic factors for survival in PDAC as well as the construction of a PDAC specific apoptosis pathway to study different effects of multiple gene silencing in order to reactivate apoptosis in PDAC. Using the second approach, the behaviour of biological interaction networks using computational logic formalism was modelled, reasoning over the networks is enabled and the abnormal behaviour of its components is explained. The usability of the BioRevise system is demonstrated through two examples, a metabolic disorder disease and a deficiency in a pancreatic cancer associated pathway. The system successfully identified the inhibition of the enzyme glucose-6-phosphatase as responsible for the Glycogen storage disease type I, which according to literature is known to be the main reason for this disease. Furthermore, BioRevise was used to model reaction inhibition in the Glycolysis pathway which is known to be affected by Pancreatic cancer.

Die Untersuchung von Proteininteraktionen ist unerlässlich um die komplexen Mechanismen der epigenetischen Kontrolle von Zugänglichkeit zum Chromatin und dessen Struktur zu verstehen. Zellspezifizierung während der Entwicklung von Organismen kann nur durch strikte Regulation von Chromatin gewährleistet werden, was auch für den Schutz von Zellenidentitäten im späteren Lebensverlauf wichtig ist. Die Modifizierung von Histon-Proteinen, welche integrale Komponenten des Chromatins sind, fördert entweder positive oder negative Genregulation. Eine Vielzahl von Chromatin regulierenden Proteinen hat jedoch keine enzymatische Aktivität für Histon- Modifikationen, so dass sie nur such Interaktionen mit anderen Proteinen regulatorisch einwirken können. Der Nematode Caenorhabditis elegans eignet sich als ein in vivo System, um die Schutzmechanismen der Zellen basierend auf Chromatinfaktoren zu untersuchen, indem systematisch Protein-Interaktionsnetzwerke bestimmt werden. Diese Dissertation beschriebt zunächst die Etablierung eines optimierten Verfahrens für die quantitative Analyse ohne Markierung von Proteinen in C. elegans, die mittels CRISPR mit einem Epitop fusioniert wurden. Mit Hilfe dieses Verfahrens wurden fünf Chromatin regulierende Proteine, die eine wichtige Rolle beim Schutz von Zellidentitäten spielen, charakterisiert. Es wurden in vivo Proteininteraktions-Netzwerke erstellt und dabei neue funktionsrelevante Interaktionspartner identifiziert. Darüber hinaus wurde eine vertiefende Analyse der Interaktionen des Chromatinfaktors MRG- 1 durchgeführt, das homolog zum humanen MRG15 ist. MRG-1 besitzt eine sogenannte Chromodomäne, um an methylierte Histone zu binden. Diese Studie zeigt, dass die Untersuchung der Proteininteraktionen von epigenetischen Faktoren in einem in vivo System ein bedeutendes Verfahren ist, um wichtige biologische Mechanismen der Schutzfunktion von Zellen zu entschlüsseln. / Elucidating protein-protein interactions has been instrumental to understand the complex mechanisms underlying epigenetic regulations to control chromatin accessibility and structure. Proper development and cell fate specification are established under strict chromatin regulation to safeguard cellular identities throughout an organism's life. Modifications of histone proteins as an integral component of chromatin can promote either positive or negative gene regulation. However, many chromatin-regulation proteins lack enzymatic activity and depend on protein-protein interaction to cooperate with other factors to regulate chromatin through histone modifications. The nematode Caenorhabditis elegans can be used as an in vivo system to study chromatin regulators that safeguard cell identity and offers an attractive model system for mapping in vivo protein interactions. The presented thesis includes establishment of an optimized protocol for a quantitative approach based on label-free interaction proteomics to accurately identify interactions of chromatin-regulating proteins, which were epitope-tagged using CRISPR in C. elegans. This protocol was utilized to reveal the interaction partners of five bait proteins involved in essential chromatin regulation mechanisms during cell fate maintenance. The present study generated an in vivo protein interaction network identifying new interactions of high functional relevance. Moreover, in-depth protein-protein interaction analysis of the chromodomain protein MRG-1, homolog of human MRG15, detected a strong association with the Small Ubiquitin-like Modifier (SUMO), besides previously described and novel interactions with other proteins. In summary, in vivo interactome mapping of epigenetic regulators is a powerful approach that can reveal crucial biological insights into how cell fate decisions are regulated.

Post-transcriptional regulatory mechanisms are widely used to influence cell fate decisions in germ cells, early embryos, and neurons. Many conserved cytoplasmic RNA regulatory proteins associate with each other and assemble on target mRNAs, forming ribonucleoprotein (RNP) complexes, to control the mRNAs translational output. How these RNA regulatory networks are orchestrated during development to regulate cell fate decisions remains elusive. We addressed this problem by focusing on Caenorhabditis elegans germline development, an exemplar of post-transcriptional control mechanisms. Here, we report the discovery of GLS-1, a new factor required for many aspects of germline development, including the oocyte cell fate in hermaphrodites and germline survival. We find that GLS-1 is a cytoplasmic protein that localizes in germ cells dynamically to germplasm (P) granules. Furthermore, its functions depend on its ability to form a protein complex with the RNA-binding Bicaudal-C ortholog GLD-3, a translational activator and P granule component important for similar germ cell fate decisions. Based on genetic epistasis experiments and in vitro competition experiments, we suggest that GLS-1 releases FBF/Pumilio from GLD-3 repression. This facilitates the sperm-to-oocyte switch, as liberated FBF represses the translation of mRNAs encoding spermatogenesis-promoting factors. Our proposed molecular mechanism is based on the GLS-1 protein acting as a molecular mimic of FBF/Pumilio. Furthermore, we suggest that a maternal GLS-1/GLD-3 complex in early embryos promotes the expression of mRNAs encoding germline survival factors. Our work identifies GLS-1 as a fundamental regulator of germline development. GLS-1 directs germ cell fate decisions by modulating the availability and activity of a single translational network component, GLD-3. Hence, the elucidation of the mechanisms underlying GLS-1 functions provides a new example of how conserved machinery can be developmentally manipulated to influence cell fate decisions and tissue development.

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.

No description available.

Post-transcriptional regulatory mechanisms are widely used to influence cell fate decisions in germ cells, early embryos, and neurons. Many conserved cytoplasmic RNA regulatory proteins associate with each other and assemble on target mRNAs, forming ribonucleoprotein (RNP) complexes, to control the mRNAs translational output. How these RNA regulatory networks are orchestrated during development to regulate cell fate decisions remains elusive. We addressed this problem by focusing on Caenorhabditis elegans germline development, an exemplar of post-transcriptional control mechanisms. Here, we report the discovery of GLS-1, a new factor required for many aspects of germline development, including the oocyte cell fate in hermaphrodites and germline survival. We find that GLS-1 is a cytoplasmic protein that localizes in germ cells dynamically to germplasm (P) granules. Furthermore, its functions depend on its ability to form a protein complex with the RNA-binding Bicaudal-C ortholog GLD-3, a translational activator and P granule component important for similar germ cell fate decisions. Based on genetic epistasis experiments and in vitro competition experiments, we suggest that GLS-1 releases FBF/Pumilio from GLD-3 repression. This facilitates the sperm-to-oocyte switch, as liberated FBF represses the translation of mRNAs encoding spermatogenesis-promoting factors. Our proposed molecular mechanism is based on the GLS-1 protein acting as a molecular mimic of FBF/Pumilio. Furthermore, we suggest that a maternal GLS-1/GLD-3 complex in early embryos promotes the expression of mRNAs encoding germline survival factors. Our work identifies GLS-1 as a fundamental regulator of germline development. GLS-1 directs germ cell fate decisions by modulating the availability and activity of a single translational network component, GLD-3. Hence, the elucidation of the mechanisms underlying GLS-1 functions provides a new example of how conserved machinery can be developmentally manipulated to influence cell fate decisions and tissue development.

Microarray technologies enable scientists to identify co-expressed genes at large scale. However, the gene expression analysis does not show functional relationships between co-expressed genes. There is a demand for effective approaches to analyse gene expression data to enable biological discoveries that can lead to identification of markers or therapeutic targets of many diseases. In cancer research, a number of gene expression screens have been carried out to identify genes differentially expressed in cancerous tissue such as Pancreatic Ductal Adenocarcinoma (PDAC). PDAC carries very poor prognosis, it eludes early detection and is characterised by its aggressiveness and resistance to currently available therapies. To identify molecular markers and suitable targets, there exist a research effort that maps differentially expressed genes to protein interactions to gain an understanding at systems level. Such interaction networks have a complex interconnected structure, whose the understanding of which is not a trivial task. Several formal approaches use simulation to support the investigation of such networks. These approaches suffer from the missing knowledge concerning biological systems. Reasoning in the other hand has the advantage of dealing with incomplete and partial information of the network knowledge. The initial approach adopted was to provide an algorithm that utilises a network-centric approach to pancreatic cancer, by re-constructing networks from known interactions and predicting novel protein interactions from structural templates. This method was applied to a data set of co-expressed PDAC genes. To this end, structural domains for the gene products are identified by using threading which is a 3D structure prediction technique. Next, the Protein Structure Interaction Database (SCOPPI), a database that classifies and annotates domain interactions derived from all known protein structures, is used to find templates of structurally interacting domains. Moreover, a network of related biological pathways for the PDAC data was constructed. In order to reason over molecular networks that are affected by dysregulation of gene expression, BioRevise was implemented. It is a belief revision system where the inhibition behaviour of reactions is modelled using extended logic programming. The system computes a minimal set of enzymes whose malfunction explains the abnormal expression levels of observed metabolites or enzymes. As a result of this research, two complementary approaches for the analysis of pancreatic cancer gene expression data are presented. Using the first approach, the pathways found to be largely affected in pancreatic cancer are signal transduction, actin cytoskeleton regulation, cell growth and cell communication. The analysis indicates that the alteration of the calcium pathway plays an important role in pancreas specific tumorigenesis. Furthermore, the structural prediction method reveals ~ 700 potential protein-protein interactions from the PDAC microarray data, among them, 81 novel interactions such as: serine/threonine kinase CDC2L1 interacting with cyclin-dependent kinase inhibitor CDKN3 and the tissue factor pathway inhibitor 2 (TFPI2) interacting with the transmembrane protease serine 4 (TMPRSS4). These resulting genes were further investigated and some were found to be potential therapeutic markers for PDAC. Since TMPRSS4 is involved in metastasis formation, it is hypothesised that the upregulation of TMPRSS4 and the downregulation of its predicted inhibitor TFPI2 plays an important role in this process. The predicted protein-protein network inspired the analysis of the data from two other perspectives. The resulting protein-protein interaction network highlighted the importance of the co-expression of KLK6 and KLK10 as prognostic factors for survival in PDAC as well as the construction of a PDAC specific apoptosis pathway to study different effects of multiple gene silencing in order to reactivate apoptosis in PDAC. Using the second approach, the behaviour of biological interaction networks using computational logic formalism was modelled, reasoning over the networks is enabled and the abnormal behaviour of its components is explained. The usability of the BioRevise system is demonstrated through two examples, a metabolic disorder disease and a deficiency in a pancreatic cancer associated pathway. The system successfully identified the inhibition of the enzyme glucose-6-phosphatase as responsible for the Glycogen storage disease type I, which according to literature is known to be the main reason for this disease. Furthermore, BioRevise was used to model reaction inhibition in the Glycolysis pathway which is known to be affected by Pancreatic cancer.

A biological function is rarely accomplished by a single gene. More often, proteins come together in complexes, and it is their collaboration within a complex that enables the associated biological function. However, the current map of the interactome is incomplete, meaning we have not observed all the interactions occurring in the cell yet. Gold standard experimental methods for the determination of all the protein-protein interactions (PPIs) in human interactome are time-consuming, expensive and may not even be feasible considering the vast number of protein pairs that need to be tested. For decades, scientists and engineers dedicated their efforts to forecasting protein interactions, predominantly relying on network topology methods. However, the emergence of AlphaFold2 intelligence has redefined the computational biology field by harnessing 3D molecular structural data to predict interacting protein in complexes, offering a promising alternative to traditional laboratory experiments. It is in this context that we introduce an innovative concept known as Network Shape Intelligence (NSI). It is the intelligence displayed by any topological network automata to perform valid connectivity predictions without training, but only processing the input knowledge associated to the local topological network organization. NSI transcends conventional link prediction methods by weaving together principles inspired by brain network science. It achieves this by minimizing external links within local communities, a strategy founded on local topology and plasticity principles initially developed for brain networks but subsequently extended to diverse complex networks. In addition to the incompleteness of the PPI network, the question of the reliability of the existing wealth of information through observed physical links also arises. Therefore, to evaluate the performance of a predictor we must make sure that the tested positive and negative interactions are reliable. We introduce the Bona Fide Evaluation Methodology (BFEM). The rigor of protein interaction predictions is ensured through a balanced classification scenario, meticulously constructed using the well-studied yeast protein interactome. Our methodology focuses on creating a golden standard set of true and false interactions, enhancing the reliability of our evaluations. We show that by using only local network information and without the need for training, these network automata designed for modelling and predicting network connectivity can outperform AlphaFold2 intelligence in vanilla protein interactions prediction. We find that the set of interactions mispredicted by AlphaFold2 predominantly consists of proteins whose amino acids exhibit higher probability of being associated with intrinsically disordered regions. Finally, we suggest that the future advancements in AlphaFold intelligence could integrate principles of NSI to further enhance the modelling and structural prediction of protein interactions.

Gegenstand der vorliegenden Arbeit ist das Kerngen CBS2 aus Saccharomyces cerevisiae. Cbs2p wird gemeinsam mit Cbs1p spezifisch für die Translation der Cytochrom b (COB)-mRNA in den Mitochondrien benötigt. Die Untersuchungen konzentrierten sich auf die Charakterisierung funktionell wichtiger Bereiche im N- und C-terminalen Bereich des Proteins, den Nachweis von Protein-Protein Wechelwirkungen, die Assoziation von Cbs2p mit mitochondrialen Ribosomen und die Bedeutung des N-terminalen Bereiches für den Import von Cbs2p in die Mitochondrien. Die aminoterminalen 35 Aminosäuren (As) von Cbs2p genügen, um das Reporterprotein Gfp vollständig in die Mitochondrien zu rekrutieren. Dieses Ergebnis zeigt, dass der N-Terminus von Cbs2p den Import von Proteinen in die Mitochondrien vermitteln kann. Da ein N-terminal um 35 As verkürztes Cbs2-Protein ebenfalls noch in den Mitochondrien nachweisbar ist, besitzt Cbs2p wenigstens ein weiteres, vom N-Terminus unabhängiges, internes oder C-terminal lokalisiertes Importsignal für die Mitochondrien. Aufgrund genetischer Daten ist Abc1p ein potenzieller Interaktionspartner von Cbs2p. Es konnte gezeigt werden, dass eine physikalische Wechselwirkung zwischen beiden Proteinen stattfindet. Mittels Blauer Nativ-Gelelektrophorese wurde Cbs2p in einem höher molekularen Proteinkomplex nachgewiesen. Da Cbs2p in der Lage ist, in vitro Homomere zu bilden, sprechen die Daten dafür, das Cbs2p als Multimer in diesem Komplex vorliegt. Es konnte weiterhin gezeigt werden, dass der N-Terminus von Cbs2p eine essentielle Rolle bei der Homomerisierung des Proteins spielt. Die vorliegenden Ergebnisse erweitern das von Michaelis et al. (1991) entwickelte Modell der Wirkungsweise der Translationsaktivatoren Cbs1p und Cbs2p wie folgt: Cbs1p und Cbs2p sind mit der inneren Membran assoziiert. Beide Proteine könnten die COB-mRNA an die mitochondriale Innenmembran führen. Der Kontakt zwischen der COB-mRNA und der mitochondrialen Translationsmaschinerie könnte durch die Assoziation von Cbs2p mit mitochondrialen Ribosomen hergestellt werden. Nur an der Membran erlaubt die Prozessierungs- und Translationsmaschinerie die Reifung von COB-mRNA und die Synthese und Assemblierung von Cytochrom b. Das Vorliegen von Cbs2p in einem hoch molekularen Komplex und die physikalische Wechselwirkung mit Abc1p könnten ein Hinweis dafür sein, dass die Translation in räumlicher Nähe des Assemblierungsortes von Cytochrom b, dem bc1-Komplex, stattfindet.