Topology and dynamics of a protein interaction network for the control of cell growth
Zusammenfassung der Projektergebnisse
In the funded project a novel workflow for quantitative mass spectrometry was developed that allows profiling of biological samples over many conditions/replicates without using isotopical labels. This label-free workflow is currently used in various projects, with a focus on protein-interaction studies. Owing to its conceptual simplicity, this label free approach has since been widely used within the lab of Prof. Ruedi Aebersold and in other labs which is reflected by altogether more than 60 citations until July/2009. In the biological application part of the funded project we aimed for systematical characterization of the organization of the central and evolutionary conserved cell growth regulating Insulin Receptor/Target of rapamycine (InR/TOR) pathway in Drosophila melanogaster cells. Here we identified reproducibly 112 specific protein-protein interactions linked to drosophila InR/TOR network, which showed a remarkable overlap to known orthologues protein interaction information and thereby proofs the robustness of our mass spectrometric strategy to obtain reliable protein interaction data. Furthermore, we performed label-free quantification to analyze sub-complex architectures and to investigate the dynamic reorganization of protein interactions upon Insulin stimulation on a whole network level. Both, the sub-complex mapping approach as well as the analysis of dynamic protein complex assembly was successful to confirm previously established knowledge. Importantly, the data obtained by our label-free quantification strategy provides new insights into the organization of the TOR kinase complex and the growth hormone induced reorganization of protein interactions within the InR/TOR network.
Projektbezogene Publikationen (Auswahl)
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A high-quality catalog of the Drosophila melanogaster proteome. Nature biotechnology 25, 576-583 (2007)
Brunner, E. et al.
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An integrated mass spectrometric and computational framework for the analysis of protein interaction networks. Nature biotechnology 25, 345-352 (2007)
Rinner, O. et al.
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PhosphoPep-a phosphoproteome resource for systems biology research in Drosophila Kc167 cells. Molecular systems biology 3, 139 (2007)
Bodenmiller, B. et al.
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SuperHirn - a novel tool for high resolution LC-MS-based peptide/protein profiling. Proteomics 7, 3470-3480 (2007)
Mueller, L.N. et al.
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Identification of cross-linked peptides from large sequence databases. Nature methods 5, 315-318 (2008)
Rinner, O. et al.
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Quantitative proteomic analysis of protein complexes: concurrent identification of interactors and their state of phosphorylation. Mol Cell Proteomics 7, 326-346 (2008)
Pflieger, D. et al.
