@phdthesis{Boyanova2012,
  author    = {Boyanova, Desislava Veselinova},
  title     = {Systems biological analysis of the platelet proteome and applications of functional module search in proteome networks},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-72165},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2012},
  abstract  = {Recent development of proteomic approaches and generation of large-scale proteomic datasets calls for new methods for biological interpretation of the obtained results. Systems biological approaches such as integrated network analysis and functional module search have become an essential part of proteomic investigation. Proteomics is especially applied in anucleate cells such as platelets. The underlying molecular mechanisms of platelet activation and their pharmacological modulation are of immense importance for clinical research. Advances in platelet proteomics have provided a large amount of proteomic data, which has not yet been comprehensively investigated in a systems biological perspective. To this end, I assembled platelet specific data from proteomic and transcriptomic studies by detailed manual curation and worked on the generation of a comprehensive human platelet repository for systems biological analysis of platelets in the functional context of integrated networks (PlateletWeb) (http:/PlateletWeb.bioapps.biozentrum.uni-wuerzburg.de). I also added platelet-specific experimentally validated phosphorylation data and generated kinase predictions for 80\% of the newly identified platelet phosphosites. The combination of drug, disease and pathway information with phosphorylation and interaction data makes this database the first integrative platelet platform available for platelet research. PlateletWeb contains more than 5000 platelet proteins, which can also be analyzed and visualized in a network context, allowing identification of all major signaling modules involved in platelet activation and inhibition. Using the wealth of integrated data I performed a series of platelet-specific analyses regarding the platelet proteome, pathways, drug targets and novel platelet phosphorylation events involved in crucial signaling events. I analyzed the statistical enrichment of known pathways for platelet proteins and identified endocytosis as a highly represented pathway in platelets. Further results revealed that highly connected platelet proteins are more often targeted by drugs. Using integrated network analysis offered by PlateletWeb, I analyzed the crucial activation signaling pathway of adenosine diphosphate (ADP), visualizing how the signal flow from receptors to effectors is maintained. My work on integrin inside-out signaling was also based on the integrated network approach and examined new platelet-specific phosphorylation sites and their regulation using kinase predictions. I generated hypothesis on integrin signaling, by investigating the regulation of Ser269 phosphorylation site on the docking protein 1 (DOK1). This phosphorylation site may influence the inhibiting effect of DOK1 on integrin a2bb3. Extending the integrated network approach to further cell lines, I used the assembled human interactome information for the analysis of functional modules in cellular networks. The investigation was performed with a previously developed module detection algorithm, which finds maximum-scoring subgraphs in transcriptomic datasets by using assigned values to the network nodes. We extended the algorithm to qualitative proteomic datasets and enhanced the module search by adding functional information to the network edges to concentrate the solution onto modules with high functional similarity. I performed a series of analyses to validate its performance in small-sized (virus-infected gastric cells) and medium-sized networks (human lymphocytes). In both cases the algorithm extracted characteristic modules of sample proteins with high functional similarity. The functional module search is especially useful in site-specific phosphoproteomic datasets, where kinase regulation of the detected sites is often sparse or lacking. Therefore, I used the module detection algorithm in quantitative phosphoproteomic datasets. In a platelet phosphorylation dataset, I presented a pipeline for network analysis of detected phosphorylation sites. In a second approach, the functional module detecting algorithm was used on a phosphoproteome network of human embryonic stem cells, in which nodes represented the maximally changing phosphorylation sites in the experiment. Additional kinases from the human phosphoproteome in PlateletWeb were included to the network to investigate the regulation of the signal flow. Results indicated important phosphorylation sites and their upstream kinases and explained changes observed in embryonic stem cells during differentiation. This work presents novel approaches for integrated network analysis in cells and introduces for the first time a systematic biological investigation of the human platelet proteome based on the platelet-specific knowledge base PlateletWeb. The extended methods for optimized functional module detection offer an invaluable tool for exploring proteomic datasets and covering gaps in complex large-scale data analysis. By combining exact module detection approaches with functional information data between interacting proteins, characteristic functional modules with high functional resemblance can be extracted from complex datasets, thereby focusing on important changes in the observed networks.},
  subject      = {Netzwerkanalyse},
  language  = {en}
}