Browsing by Person "Gilbert, Max"

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Prediction of protein-protein complexes by combining size exclusion chromatography and mass spectrometric analysis
(2021) Gilbert, Max; Schulze, Waltraud
Two major objectives were pursued and met in this study. First, the goal was to add to the scientific toolbox a diligent method for uncovering PPi dynamics on a proteomic scale, with a focus on plant membranes. There are large-scale or high-throughput approaches, but they rely on genetically modified proteins or heterologous expression systems to describe PPi outside of their natural context. Similarly, those methods are incapable of describing the dynamics of protein interactions. In course of this study, a co-elution based approach was combined with modern mass spectrometric label free quantification in order to investigate PPi and interaction dynamics on a proteomic scale. A rigorous data processing pipeline was developed to not only address known fallacies of using co-elution based methods (such as for example random co elution), but also to access and utilize meta-information in form of protein abundance and protein network connectivity to draw conclusions not only on proteomic scale, but also for individual proteins. In total, 6.928 individual proteins extracted from Arabidopsis thaliana root membranes were detected under different nutritional conditions (full nutrition, nitrogen starvation and nitrogen resupply). The data processing pipeline described in this study was used to predict and discover connectivity information for at least 2.058 of these proteins. Each step in data processing was validated by comparison to database confirmed interactions to improve filtering criteria. Protein abundance was evaluated through a unique ranking system, allowing a seamless integration as network attributes for each condition. From the suggested interaction data, an interactome network of the various nutritional conditions was reconstructed. Using different network parameters from graph theory, protein significance and dynamic conditional changes were described. Second, this study applied the aforementioned approach to identify relevant proteins involved in nitrogen signaling in Arabidopsis thaliana root membranes. Through correlation analysis and network reconstruction, receptor kinase AT5G49770 was identified as a component of the nitrogen signaling network that collaborates with co-receptor QSK1, BAK1, the nitrogen transporter NRT2.1 and proton pump AHA2. In response to nitrogen deficiency, the network parameters of AT5G49770 reacted strongly and its involvement was demonstrated by a phenotypic similarity to knock-out lines of NRT2.1, NRT1.1 and AHA2 during a root growth assay of Arabidopsis seedlings. The interaction between QSK1 and BAK1 was further confirmed using FRET/FLIM microscopy and pulldown assays. These findings show that combining a co-elution based approach with a rigorous data processing pipeline and network analysis is suitable to study the protein interaction environment and signal response dynamics in plant root membranes. The modular experimental design allows for a simple adaptation to study different stimuli and the unbiased proteomic approach yields results for proteins regardless of the individual scientific focus. Meta-information such as protein abundance and network connectivity parameters can be used to prospect and identify important proteins involved in stress response dynamics. The author of this study is confident that the proteomic data produced can be utilized in further research and contributes to the understanding of nitrogen signaling in plant root membranes. Through integration of the data processing pipeline and adaptation to different scientific scenarios, valuable information beyond protein interaction is gained. Thus, this work makes an important contribution to the advancement of proteomic analysis and data interpretation methodology.