Phosphoproteomics-Based Modeling Defines the Regulatory Mechanism Underlying Aberrant EGFR Signaling

Abstract

BackgroundMutation of the epidermal growth factor receptor (EGFR) results in a discordant cell signaling, leading to the development of various diseases. However, the mechanism underlying the alteration of downstream signaling due to such mutation has not yet been completely understood at the system level. Here, we report a phosphoproteomics-based methodology for characterizing the regulatory mechanism underlying aberrant EGFR signaling using computational network modeling.Methodology/Principal FindingsOur phosphoproteomic analysis of the mutation at tyrosine 992 (Y992), one of the multifunctional docking sites of EGFR, revealed network-wide effects of the mutation on EGF signaling in a time-resolved manner. Computational modeling based on the temporal activation profiles enabled us to not only rediscover already-known protein interactions with Y992 and internalization property of mutated EGFR but also further gain model-driven insights into the effect of cellular content and the regulation of EGFR degradation. Our kinetic model also suggested critical reactions facilitating the reconstruction of the diverse effects of the mutation on phosphoproteome dynamics.Conclusions/SignificanceOur integrative approach provided a mechanistic description of the disorders of mutated EGFR signaling networks, which could facilitate the development of a systematic strategy toward controlling disease-related cell signaling.

Analytical reduction of combinatorial complexity arising from multiple protein modification sites

“…This is functionally similar to simulated effects of a Y992F mutation (figure 3b-fourth row). These simulation results are consistent with previous experimental studies on both scenarios, in that SHP2 is needed for activation of the ERK pathway [39,41,42], and that expression of the Y992F EGFR mutant leads to sustained EGF-induced ERK activity [43]. We found that the catalytic efficiency (k cat /K m ) for SHP2 had to be on the order of 100 s 21 nM 21 to have an appreciable effect on site-specific phosphorylation levels (results in figure 3b are for 1000 s 21 nM…”

Systematic parameter estimation in data-rich environments for cell signalling dynamics

“…New proteomic methods can measure additional proteins at virtually no additional cost, and proteomic datasets are starting to provide data-rich environments with measurements of all proteins in a system. SILAC technology has recently been used for time-series measurements of 147 proteins (Tasaki et al , 2010) in NIH3T3-derived cells, and again for time-series of 534 proteins in the cytosol and 626 proteins in the nucleus in glucocorticoid-exposed myogenic cells (Reeves et al , 2009). Most proteomic studies have not been performed with time-series repeats for studying dynamics, but large-scale dynamic data will become increasingly available with the explosive growth in the number of proteomic experiments (Zhang et al , 2011).…”

Phosphoproteomics-Based Characterization of Cancer Cell Signaling Networks

“…Thus, phosphoproteomics-based approaches have first been applied to reveal the molecular mechanisms governed by tyrosine phosphorylation in response to external growth factors such as EGF [10,11,44,45], fibroblast growth factor (FGF) [46] or heregulin (HRG) [47]. Schulze et al identified interaction partners of the four members belonging to the ErbB receptor family (EGFR, ErbB2, ErbB3 and ErbB4) using the corresponding synthetic peptides as baits in an unbiased proteomic manner [45].…”