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Analysis of the regulation of biological networks using quantitative proteomics Kristensen, Anders Riis

Abstract

A protein network can be thought of as a graph with nodes and edges, where nodes represent proteins and edges represent protein-protein interactions. Neither proteins nor their interactions are stable constituents in the cell; they are constantly changing in response to external stimulation or internal programming. Changes in protein expression are regulated by transcription, translation and protein degradation, whereas protein interaction changes have been shown in focused studies to be regulated by post translational modification. To investigate the processes influencing the regulation of protein expression and interaction changes, mass spectrometry based proteomics was applied because it has two key advantages for the study of protein networks: 1) it directly detects peptides from the proteins, and so does not rely on antibodies or the generation of fusion proteins; 2) by combining mass spectrometry based proteomics with quantitative techniques, such as stable isotope labeling by amino acids in cell culture (SILAC), it is possible to quantify thousands of proteins in a single experiment. Here a systems biology approach was applied to investigate protein expression change, synthesis and degradation of proteins during cellular differentiation in two different cell lines. This allowed observing that protein expression during cellular differentiation is largely controlled by changes in the relative synthesis rate, whereas the relative degradation rate of the majority of proteins changed little. By comparing the data with previously published data of mRNA levels, there could be provide strong evidence that the generally poor correlation observed between transcript and protein levels can be explained once the protein synthesis and degradation rates are taken into account. To study how protein interactions change in response to perturbation, a novel approach was developed combining size exclusion chromatography (SEC) and protein correlation profiling (PCP)-SILAC. Stimulation with epidermal growth factor (EGF) caused 351 proteins to alter their interactions with other proteins and, interestingly, when compared to previously published phosphorylation data, these proteins tended to also have altered phosphorylation under similar experimental conditions. This approach allowed identification of protein interactions in numbers comparable to other high throughput techniques, but also enabled quantification of protein stoichiometry between proteins participating in multiple complexes.

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