472684 VEGFR1 Mediates Cell Migration through Activation of PI3K and PLCɣ

Tuesday, November 15, 2016: 1:06 PM
Carmel I (Hotel Nikko San Francisco)
Jared Weddell and Princess Imoukhuede, University of Illinois at Urbana-Champaign, Urbana, IL

VEGFR1 mediates cell migration through activation of PI3K and PLCɣ



VEGF-VEGFR  regulate neovascularization; despite this, efforts to control this signaling axis towards directing neovascularization have yet to be achieved [1].  This challenge underlies a continuing need to increase our understanding of signal-to-response. Indeed, there is significantly less knowledge of the VEGFR1 role, which is often described as a decoy receptor due to its high VEGF affinity but low kinase activity [2], relative to the strongly pro-angiogenic VEGFR2. However, our recent computational models have identified that high-VEGFR1 levels may negate the effect of a VEGF inhibitor [3]. Thus, to understand the VEGFR1 signaling role, we develop computational models of the initial intracellular signaling mechanisms connected to VEGFR1: tyrosine site-specific VEGFR1-adapter interactions. Furthermore, we examine how cooperativity in adapter binding to specific VEGFR1 tyrosine sites, and adapter-adapter interactions, direct VEGFR1 signaling. We introduce a novel weighing scheme that correlates adapter-VEGFR1 binding to a cell response (e.g., proliferation, migration, or ubiquitination). We validate our computational predictions using wound healing assays, in vitro.

Materials and Methods:

Computational methods: We develop and compare four endothelial VEGFR1-adapter interaction models in MATLAB Simbiology: each increases in complexity to gauge how each mechanistic component affects the physiologic response (Fig. 1A). The four models are as follows: Model 1, simplest: adapters bind to one C-terminal Tyr site; Model 2: Model 1 + adapter-adapter binding: adapter-adapter interactions can occur, with binding to one Tyr site; Model 3: Model 1 + Tyr site specificity: adapters bind to physiologically known Tyr sites; Model 4, most physiologically accurate: adapter-adapter interactions occur with binding to known Tyr sites. Therefore, the following comparisons provide insight into VEGFR1 signaling: Model 1 vs. Model 3 = site-specific binding, Model 1 vs. Model 2 = adapter cooperativity, and Model 1 vs. 4 = site-specific binding + cooperativity. We take a data-driven approach towards model development: our experimental qFlow (quantitative) cytometry provides membrane-receptor concentrations while literature mining provides adapter concentrations via western blots, kinetics via surface plasmon resonance, and protein-protein interactions via co-immunoprecipitation. We mine literature to determine how the cell response (migration, proliferation, or ubiquitination) changes when each adapter is inhibited. We quantify these changes in migration, proliferation, and ubiquitination, and assign a corresponding weight to each adapter and linearly correlate adapter contributions to cell response.

Experimental methods: Wound healing assays are performed using mouse RAW 264.7 macrophages: their high VEGFR1 levels (4,820 ± 112 VEGFR1/cell) relative to VEGFR2 (1,770 ± 132 VEGFR2/cell) allows us to focus on VEGF-VEGFR1 response. RAW macrophages are seeded into a 12-well plate and cultured as monolayer to ~90% confluence. The cells are serum starved overnight with DMEM supplemented with 0.5% FBS and 1% PS. The monolayer is scratched with a 100 µL pipette tip and washed once with PBS to remove floating cells. Wells are treated with 750 µL of the serum starved growth factor media, VEGF-A164 (50 ng/mL), 10 µM Wortmannin (Anti-PI3K, IC50 = 3 nM), 10 µM U73122 (Anti-PLCɣ, IC50 = 1 µM), 10 µM Imatinib Mesylate (Anti-Abl, IC50 = 600 nM), or a combination of VEGF-A164 and an inhibitor. Images of the wounded cell monolayer are taken using a brightfield microsocope at 0 h and 24 h after scratching. All experiments are independently carried out in triplicate. Cell migration is quantified as the number of cells contained within the total gap area relative to the number of cells immediately after the scratch, using Image J.

Results and Discussion:

Adapter negative cooperativity decreases VEGFR1-mediated migration. We find that VEGFR1 regulates HUVEC migration: in Model 1, the integrated migratory response is 2.4-fold greater than the proliferation, and 4.6-fold greater than the ubiquitination, integrated responses. Examining how adapter cooperativity (Model 2) affects VEGFR1-mediated cell responses reveals two key findings. First, VEGFR1 still primarily promotes migration when accounting for adapter cooperativity, evidenced by the migration pathway having the greatest integrated response, 2.1-fold and 3.1-fold greater than proliferation and ubiquitination, respectively. Second, accounting for adapter cooperativity significantly alters the phosphorylation of multiple adapters. Specifically, the integrated response and phosphorylation amplitude of the adapter Nck decrease 88% and 86%, respectively, when adapter-adapter interactions occur (Model 2) compared to no adapter cooperativity (Model 1). Thus, cooperativity in adapter binding mechanistically preventing VEGFR1-mediated Nck activation, implying that Nck negatively cooperates with the other adapters.


VEGFR1 Tyr sites preferentially activate the PI3K and PLCɣ adapters. Accounting for VEGFR1 tyrosine site specificity directs the preferentiality of adapter-VEGFR1 binding and subsequent adapter phosphorylation. Specifically, we find that accounting for VEGFR1 tyrosine sites (Model 3) increases PI3K and PLCɣ signaling; we observe a 1.7-fold and 1.5-fold increase in PI3K and PLCɣ signaling, respectfully, when accounting for VEGFR1 tyrosine sites (Model 3) compared to no tyrosine site specificity (Model 1). Thus, when accounting for specific VEGFR1 tyrosine sites (Model 3), we identify that VEGFR1 is structured to preferentially activate PI3K and PLCɣ.

Tyr site specificity with adapter cooperativity amplifies VEGFR1 through increased PLCɣ activation. By comparing VEGFR1 signaling in the simplest (Model 1) and most physiologically relevant (Model 4) cases, we find that VEGFR1. First, the cell response is unchanged: VEGFR1 signaling primarily promotes cell migration, followed by proliferation, with ubiquitination having the lowest activation. Second, both adapter cooperativity and binding specificity are necessary to enable signal amplification. We arrive at this conclusion because all integrated responses increase in Model 4 compared to Model 1: 6.5-fold for proliferation and 3.5-fold for both migration and degradation. These increased cell responses do not occur with only Tyr site specificity (Model 3) or adapter-adapter interactions (Model 2). Furthermore, we find that signal amplification primarily results from the preferential activation of PLCɣ; the PLCɣ integrated response is 2.5-fold greater than the next most activated adapter. We believe this high PLCɣ activation is due to coordination between VEGFR1 Tyr sites and adapter cooperativity. Indeed, PLCɣ has the highest number of available Tyr binding sites on VEGFR1, in addition to undergoing cooperative binding with

Wound healing assays validate VEGFR1-induced migration predictions. Our results indicate that PI3K and PLCɣ are the two adapters primarily regulating cell migration within the VEGF-VEGFR1 signaling axis. To test the robustness of this prediction, we extend our model to macrophages, changing model parameters to match literature-minded adapter and receptor concentrations (e.g., [VEGFR1] >> [VEGFR2]).  Again, we predict that PI3K and PLCɣ regulate macrophage migration. Moreover, we rank-predict that PLCɣ inhibition (max=58%/min=24%) > PI3K inhibition (max=51%/min=15%) >> Abl inhibition (< 1% decrease) (Fig. 1B). We validate these predictions to wound healing assays (Fig. 1C).



Our findings further define VEGFR1 as an active controller in angiogenesis-rather than a mere decoy receptor. Moreover, we define the key proteins mediating this migration role, as PI3K and PLCɣ. Since these pathways are linked to Ca2+signaling [4], these models offer a path towards further understanding VEGFR1-migration via calcium signaling. Future studies can also apply this modeling methodology to understand and control VEGFR1-signaling in diseases where VEGFR1 concentrations are high, such as in breast cancer [3] and ischemia [5].




[1] Eichmann A. Curr Opin Cell Biol (2012) 24:188. [2] Zhang Z. Cell Death Differ (2010) 17:499-512. [3] Weddell JC. PLOS ONE (2014) 9:e97271. [4] Rameh LE. J Biol Chem (1998) 273:23750. [5] Imoukhuede PI. Am J Physiol Heart Circ Physiol (2013) 304:H1085.

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