462614 Integrative Meta-Modeling Ranks RTK Signaling and Identifies Connection Between Nuclear Translocation and Extracellular Ligand Concentrations

Monday, November 14, 2016
Grand Ballroom B (Hilton San Francisco Union Square)
Jared Weddell and Princess Imoukhuede, University of Illinois at Urbana-Champaign, Urbana, IL

Integrative meta-modeling ranks signaling strength across eight RTKs and finds that RTK signaling is dependent on the extracellular ligand concentration



Current therapeutics seeking tyrosine kinase receptor (RTK) control are ineffective and met with drug resistance [1]; however, controlling RTK signal transduction would allow treatment of many pathologies, including cancers [2] and vascular diseases [3]. This challenge underlies a continuing need to increase our understanding of RTK signaling to cell response. Indeed, while signal transduction pathways have been established for many membrane receptors [4]–[7], these pathways are often established for individual RTKs, accounting for mechanisms specific to a particular receptor. As such, insights gained for a specific RTK have never been compared to other RTKs. In order to achieve signal transduction control for any RTK, a systematic analysis identifying how cell physiology generally mediates RTK signaling is needed. Moreover, this systematic analysis delineating how cell physiology mediates signal transduction, in general, would serve as a “signaling template” of signaling fundamentals that could be tuned to account for receptor-specific spatiotemporal  dynamics [9-10]. Such an analysis would also improve and enhance knowledge of signal mapping and facilitate research on receptor-based signaling control, both of which are critical in ultimately being able to treat pathological conditions. Here, we engineer such a signaling template and used it to meta-analyze signaling of eight receptor tyrosine kinases: EGFR, FGFR1, IGFR1, PDGFRα, PDGFRβ, VEGFR1, VEGFR2, and Tie2 (Table 1). This computational meta-analysis delineates the complex endocytosis mechanisms underlying intracellular RTK signaling in general, by examining how cell (compartment volume, trafficking kinetics and pH) and ligand-receptor physiology (ligand/receptor concentration and interaction kinetics) determine signaling.

Materials and Methods: We analyze RTK signaling by computationally modeling ligand-receptor interactions and subsequent receptor internalization and trafficking using MATLAB Simbiology. While undergoing trafficking, compartment pH dynamically regulates ligand-receptor interaction kinetics. Using this understanding, we quantitated receptor phosphorylation, a post-translational modification, associated with each endocytic compartment, to weigh the signaling contribution from each intracellular compartment. We take a data-driven approach towards model development, mining ligand-receptor kinetics via surface plasmon resonance, receptor concentrations via qFlow (quantitative) cytometry and western blot, ligand concentrations via ELISA, and compartment volumes and trafficking kinetics via microscopy. We also account for pH-dependent ligand-receptor interaction kinetics across the compartments. With this computational model, we rank order signaling across RTKs and examine how RTK parameters (Table 1) direct the receptor signaling strength. We also conduct a correlation analysis, assuming a lognormal fit, between the physiological RTK parameter values given by each RTK (Table 1) to membrane-based and nuclear-based RTK signaling in Origin. Here we focus on membrane- and nuclear-based RTK signaling, as these highlight the initial (membrane) and final (nucleus) compartments that receptors are trafficked throughout.

Results and Discussion: The extent of absolute signaling is dependent on the RTK. With this computational signaling template, we quantify the integrated response, the total receptor phosphorylation over time, at each compartment. We find that receptor signaling primarily occurs within endocytic vesicles, comprising > 43% of total receptor signaling within the cell for these eight RTKs. Conversely, we found that membrane signaling is relatively low, providing < 1% of total receptor signaling within the cell across these eight RTKs, indicating that nearly all receptor phosphorylation throughout the cell occurs intracellularly. While these eight RTKs follow the trend of receptor signaling primarily occurring within endocytic vesicles with low membrane-based receptor signaling, we found that absolute receptor signaling is highly variable across the eight RTKs. Indeed, among the eight RTKs, PDGFRβ has the largest absolute membrane signaling at a level 3.1·103-fold greater than FGFR1, which has the lowest absolute membrane signaling (Table 1). By analyzing the three RTK-specific parameters - receptor concentration [R], ligand concentration [L], and ligand-receptor dissociation constant [Kd] (Note [Kd] = koff/kon) - we analyze the complex concentration, [R][L]/[Kd], across several RTKs. Indeed, FGFR1 has the lowest complex concentration, while PDGFRβ has the highest—due to its very high [R]. Overall, our meta-modeling ranks RTK signaling strength: PDGFRβ > IGFR1 > EGFR > PDGFRα > VEGFR1 > VEGFR2 > Tie2 > FGFR1.

Table 1. Experimentally obtained parameters (receptor concentration, interaction kinetics, and extracellular ligand concentration) were pulled from literature for the eight meta-analyzed RTKs.




















kon (M-1s-1)









koff (s-1)









Ligand in Serum (pg/mL)









Membrane-receptor integrated response










Nuclear-receptor integrated response










Complex concentration determines the extent of nuclear translocation. Our meta-analysis also reveals that the extent of nuclear translocation varies significantly across the eight RTKs (Table 1). This is evidenced by nuclear signaling, which we find ranges between 3.3% - 27%, given by FGFR1 and EGFR, respectively, of the total receptor signaling within the cell. By analyzing the three RTK-specific parameters, we predict that, like membrane-based RTK signaling, nuclear-based RTK signaling is determined by the complex concentration. Indeed, EGFR has the highest complex concentration, while FGFR1 has the lowest, among the eight RTKs. Therefore, the extent of nuclear-based RTK signaling is also dependent on the RTK and can be predicted by the complex concentration.

The signaling given by a RTK can be tuned with the extracellular ligand concentration. Our results show that absolute receptor signaling is variable among these eight RTKs. To understand which individual RTK parameter best regulates signaling within a RTK, we alter three RTK parameters and observe membrane (Fig. 1A) and nuclear (Fig. 1B) signaling. We find that membrane RTK signaling is well regulated by [Kd] and [L] but not by [R]; increasing the ligand-receptor on-rate (kon) or [L] three orders of magnitude above the physiological concentrations (Table 1) exponentially increases membrane signaling, while increasing the ligand-receptor off-rate (koff) abrogates membrane RTK signaling (Fig. 1A). Conversely, nuclear RTK signaling is regulated by [L] and [R], but not [Kd]; increasing [R] eight orders of magnitude above physiological concentrations (Table 1) increases nuclear signaling from 3% to 22% of the total cell signaling – increasing [L] the same amount increases nuclear signaling to 24% of the total cell signaling (Fig. 1B). Like signaling within a single RTK, we find that [L] strongly predicts nuclear signaling across the eight RTKs (Fig. 1C), whereas [R] (Fig. 1D) and [Kd] (Fig. 1E) are weak predictors. Indeed, we find that increasing [L] one order of magnitude increases nuclear RTK signaling 3.2-fold (Fig. 1C). We also find that [R] and [Kd] have low weight, while [L] has high weight, in mediating membrane-based RTK signaling. Overall, these results suggest that [L] is the only RTK parameter capable of regulating both membrane- and nuclear-based RTK signaling, in addition to [L] being the RTK parameter that best mediates RTK signaling at these two compartments.

Conclusions: We introduce a new integrative, data-driven, computational approach that provides a “signaling template” for understanding RTK receptor signaling. This is an important step, because our template can be applied to develop new therapeutic approaches targeting specific RTK signaling, optimizing treatment for many pathologies, including cancers [9] and vascular diseases [10]. We also arrive at important findings with this computational approach: We also arrive at important findings with this computational approach: receptor signaling can best be regulated by controlling the extracellular ligand concentration, whereas altering the membrane receptor concentrations will have negligible effect on receptor signaling. This finding has significant implications for drug delivery, suggesting that therapies inhibiting membrane receptor signaling alone will be ineffective, and should, instead, target intracellular receptors or extracellular ligands.

References: [1] Corcoran C. Methods Mol Biol (2015) 1233:169. [2] Niepel M. Sci Signal (2013) 294:ra84. [3] Murphy E. Circ Res (2011) 109:687. [4] Maruyama IN. Cells (2014) 3:304. [5] Seshacharyulu P. Expert Opin Ther Targets (2012) 16:15. [6] Kabbani N. Proteomics (2008) 8:4146. [7] Arish M. Biochimie (2015) 113:111. [8] Mukherjee S. Circ Res (2006) 98:743. [9] Pálfy M. Trends Cell Biol (2015) 22:447.

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