Huntington’s disease (HD) is an autosomal dominant inherited neurodegenerative disease that specifically affects the striatum of the human brain. HD is characterized by a chorea-like movement disorder, cognitive decline, and psychiatric symptoms. In Europe, it has a relatively high prevalence of about 2.17-7.33 per 100,000 people compared with other continents. By far, there is no cure for HD. The mean survival time of patients after the diagnosis of HD is 15 to 20 years. Although the mutant form of the Huntingtin (HTT) as the cause of HD has been confirmed for decades, the exact pathogenesis of HD is still elusive. More recently, large global genome-wide association studies (GWAS) and several other studies provided new insights for HD mechanism, by highlighting several genes involved in DNA damage repair mechanisms as modifiers of age at onset and disease severity in HD. Thus, this project focused on the investigation of DNA damage response and repair in HD in vitro models. Fused in sarcoma (FUS) was the protein of our interest, as it has been confirmed to participate in DNA damage response and repair in multiple ways. Furthermore, FUS protein was implicated to have a relationship with neurodegenerative diseases, as it was found to play a role in the pathogenesis of subtypes of amyotrophic lateral sclerosis and frontotemporal dementia. FUS was also found to co-localize with mutant huntingtin protein in intracellular aggregates in HD mice models. In this project, donor/patient-specific induced pluripotent cells (iPSCs) and its derived striatal neurons were the main materials. By immunofluorescence staining approach of γH2AX and 53BP1, DNA double-strand breaks (DSBs) damage was investigated on iPSCs-derived in vitro striatal neurons. HD neurons showed an obvious and excessive accumulation of DNA DSBs damage. Then, in order to visualize FUS protein during DNA damage response procedure, eGFP tagged endogenous wild-type FUS iPSCs were generated, and later were differentiated into striatal neurons. UVA laser micro-irradiation was applied onto both hiPSCs and their differentiated striatal neurons in vitro models, simultaneously conducting with live-cell imaging approach. FUS was found to recruit to the DNA damage site induced by laser irradiation. For studying the kinetics of wild-type FUS protein during the response to laser irradiation, a novel and robust workflow was generated. By this workflow, the kinetics of FUS protein was characterized into four phases and a real-time scale of the kinetics was offered. After comparisons, a prominent change of FUS kinetics in HD at neuron-stage but not iPSC-stage was found. Furthermore, an intriguing different performance of FUS protein was found in different types of in vitro cellular models. In iPSCs, not all the laser-irradiated cells recruited FUS at the DNA damage site. The kinetics of the FUS protein also differed in different models. In conclusion, first, our in vitro striatal neuron model recapitulated the impaired DNA damage repair phenotype that published by other models. Second, new evidence was offered that wild-type FUS was involved in the pathogenesis of HD. Third, depending on cell-type, FUS performed differently during the response to the laser irradiation-induced DNA damage. Thus, these results suggest that the impaired DNA damage response and repair would be crucial to the mechanism of HD. Furthermore, the role of FUS protein playing especially the functional part in DNA damage response and repair might be a potential target for further investigation of neurodegenerative diseases including HD.

Protein-Protein Interaktions (PPI) Netzwerke liefern ein Grundgerüst für systematische Untersuchungen der komplexen molekularen Maschinerie in der Zelle. Die Komplexität von Protein-Wechselwirkungen stellt jedoch in Bezug auf ihre Identifizierung, Validierung und Annotation eine große experimentelle und rechnerische Herausforderung dar. In dieser Arbeit analysierte ich diese Probleme und lieferte Lösungen, um die Limitierungen aktueller humanen PPI Netzwerke zu überwinden. Meine Arbeit kann in zwei Teile aufgeteilt werden: Im ersten Teil führte ich eine kritischen Vergleich von acht unabhängig konstruierten humanen PPI Netzwerke durch, um mögliche experimentellen Verzerrungen zu erkennen. Die Ergebnisse zeigten starke Tendenzen bezüglich der Selektion und Detektion von Interaktionen, die in zukünftigen Anwendungen dieser Netzwerke berücksichtigt werden sollten. Einer der wichtigsten Schlussfolgerungen dieser Studie war, dass die derzeitigen humanen Interaktions Netzwerke komplementär sind und deshalb wurde eine Datenbank mit der Bezeichnung Unified Human Interaktome (UniHI) entwickelt, die menschliche PPI Daten aus zwölf wichtigsten Quellen integriert. Im zweiten Teil dieser Forschungsarbeit benutzte ich die Daten aus der UniHI Datenbank, die genetischen Modifikatoren in einer bestimmten Krankheit, Chorea Huntington (HD) eine autosomal dominante neurodegenerative Erkrankung, zu charakterisieren. Um die Proteine zu identifizieren, die den Krankheitsverlauf modifizieren können, wurden Protein Interaktion Daten mit Genexpressionsdaten von HD-Patienten in Kombination mit einem Mehrschritt-Filterungsverfahren integriert. Mit dem neuartigen Ansatz wurde ein Nucleus caudatus-spezifische Protein-Interaktion HD (PPI)-Netzwerk vorhergesagt, das 14 potentiell dysregulierten Proteine direkt oder indirekt mit dem Huntingtin-Protein verlinkt, mit mögliche Verbindung zu Molekularen Prozessen wie z.B. Apoptose, Metabolismus, neuronale Entwicklung. / Protein interaction networks aim to provide the scaffold maps for systematic studies of the complex molecular machinery in the cell. The complexity of protein interactions poses, however, large experimental and computational challenges regarding their identification, validation and annotation. Additionally, storage and linking is demanding since new data are rapidly accumulating. In this research work, I addressed these issues and provided solutions to overcome the limitations of current human protein-protein interaction (PPI) maps. In particular, my thesis can be partitioned into two parts: In the first part, I conducted a comparative assessment of eight recently constructed human protein-protein interaction networks to identify experimental biases. Results showed strong selection and detection biases which are necessary to take into consideration in future applications of these maps. One of the important conclusions of this study was that the current human interaction networks contain complementary information; hence, a database was developed, termed as Unified Human Interactome (UniHI), integrating human PPI data from twelve major sources. Several new tools were included for querying, analyzing and visualizing human PPI networks. In the second part of this research work, UniHI dataset was applied to characterize the genetic modifiers involved in a specific disease: Chorea Huntington (HD), an autosomal dominant neurodegenerative disease. To find the modifiers, a network-based modeling approach was implemented by integrating huntingtin-specific protein interaction network with gene expression data from HD patients in multiple steps. Using this approach, a Caudate Nucleus-specific HD protein interaction (PPI) network was predicted, connecting 14 potentially dysregulated proteins directly or indirectly to the disease protein, showing a possible link to molecular processes such as pro-apoptotic pathways, cell survival, anti-apoptotic, growth, and neuronal diseases.