(229ax) Integrative Meta-Modeling Ranks RTK Signaling and Identifies Connection Between Nuclear Translocation and Extracellular Ligand Concentrations

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

Introduction:

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] = k_off/k_on) - 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.

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 (k_on) or [L] three orders of magnitude above the
physiological concentrations (Table 1) exponentially increases membrane
signaling, while increasing the ligand-receptor off-rate (k_off) 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.