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A Systems Biological Approach to Parkinson's Disease

Tid: Fr 2018-04-27 kl 13.00

Plats: D2, Lindstedtsvägen 5, KTH Campus

Ämnesområde: Computer Science, Computational Biology

Respondent: Katharina F Heil , CST

Opponent: Prof. Nigel Williams

Handledare: Prof Jeanette Hellgren Kotaleski


Parkinson’s Disease (PD) is the second most common neurodegenerative disease in the Western world. Itshows a high degree of genetic and phenotypic complexity with many implicated factors, various diseasemanifestations but few clear causal links. Ongoing research has identified a growing number of molecularalterations linked to the disease.Dopaminergic neurons in the substantia nigra, specifically their synapses, are the key-affected region in PD.Therefore, this work focuses on understanding the disease effects on the synapse, aiming to identify potentialgenetic triggers and synaptic PD associated mechanisms. Currently, one of the main challenges in this area isdata quality and accessibility.In order to study PD, publicly available data were systematically retrieved and analysed. 418 PD associatedgenes could be identified, based on mutations and curated annotations. I curated an up-to-date and completesynaptic proteome map containing a total of 6,706 proteins. Region specific datasets describing thepresynapse, postsynapse and synaptosome were also delimited. These datasets were analysed, investigatingsimilarities and differences, including reproducibility and functional interpretations.The use of Protein-Protein-Interaction Network (PPIN) analysis was chosen to gain deeper knowledgeregarding specific effects of PD on the synapse. Thus I generated a customised, filtered, human specificProtein-Protein Interaction (PPI) dataset, containing 211,824 direct interactions, from four public databases.Proteomics data and PPI information allowed the construction of PPINs. These were analysed and a set oflow level statistics, including modularity, clustering coefficient and node degree, explaining the network’stopology from a mathematical point of view were obtained.Apart from low-level network statistics, high-level topology of the PPINs was studied. To identify functionalnetwork subgroups, different clustering algorithms were investigated. In the context of biological networks, theunderlying hypothesis is that proteins in a structural community are more likely to share common functions.Therefore I attempted to identify PD enriched communities of synaptic proteins. Once identified, they werecompared amongst each other. Three community clusters could be identified as containing largely overlappinggene sets. These contain 24 PD associated genes. Apart from the known disease associated genes in thesecommunities, a total of 322 genes was identified. Each of the three clusters is specifically enriched for specificbiological processes and cellular components, which include neurotransmitter secretion, positive regulation ofsynapse assembly, pre- and post-synaptic membrane, scaffolding proteins, neuromuscular junctiondevelopment and complement activation (classical pathway) amongst others.The presented approach combined a curated set of PD associated genes, filtered PPI information andsynaptic proteomes. Various small- and large-scale analytical approaches, including PPIN topology analysis,clustering algorithms and enrichment studies identified highly PD affected synaptic proteins and subregions.Specific disease associated functions confirmed known research insights and allowed me to propose a newlist of so far unknown potential disease associated genes. Due to the open design, this approach can be usedto answer similar research questions regarding other complex diseases amongst others.

The thesis in Diva