Computational kinetic model of VEGF trapping by soluble VEGF receptor-1: Effects of transendothelial and lymphatic macromolecular transport

Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Physiological Genomics (Impact Factor: 2.37). 05/2009; 38(1):29-41. DOI: 10.1152/physiolgenomics.00031.2009
Source: PubMed


Vascular endothelial growth factor (VEGF) signal transduction through the cell surface receptors VEGFR1 and VEGFR2 regulates angiogenesis-the growth of new capillaries from preexistent microvasculature. Soluble VEGF receptor-1 (sVEGFR1), a nonsignaling truncated variant of VEGFR1, has been postulated to inhibit angiogenic signaling via direct sequestration of VEGF ligands or dominant-negative heterodimerization with surface VEGFRs. The relative contributions of these two mechanisms to sVEGFR1's purported antiangiogenic effects in vivo are currently unknown. We previously developed a computational model for predicting the compartmental distributions of VEGF and sVEGFR1 throughout the healthy human body by simulating the molecular interaction networks of the VEGF ligand-receptor system as well as intercompartmental macromolecular biotransport processes. In this study, we decipher the dynamic processes that led to our prior prediction that sVEGFR1, through its ligand trapping mechanism alone, does not demonstrate significant steady-state antiangiogenic effects. We show that sVEGFR1-facilitated tissue-to-blood shuttling of VEGF accounts for a counterintuitive and drastic elevation in plasma free VEGF concentrations after both intramuscular and intravascular sVEGFR1 infusion. While increasing intramuscular VEGF production reduces free sVEGFR1 levels through increased VEGF-sVEGFR1 complex formation, we demonstrate a competing and opposite effect in which increased VEGF occupancy of neuropilin-1 (NRP1) and the corresponding reduction in NRP1 availability for internalization of sVEGFR1 unexpectedly increases free sVEGFR1 levels. In conclusion, dynamic intercompartmental transport processes give rise to our surprising prediction that VEGF trapping alone does not account for sVEGFR1's antiangiogenic potential. sVEGFR1's interactions with cell surface receptors such as NRP1 are also expected to affect its molecular interplay with VEGF.

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Available from: Aleksander S Popel,
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    • "However, the predictive power of these models has previously been limited by insufficient knowledge of cell surface receptor densities. Therefore, the data we report provide critical parameters needed to advance angiogenesis models [97], [98], [99], [100], [101], [102], [103], [104]. "
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    ABSTRACT: VEGFR surface localization plays a critical role in converting extracellular VEGF signaling towards angiogenic outcomes, and the quantitative characterization of these parameters is critical for advancing computational models; however the levels of these receptors on blood vessels is currently unknown. Therefore our aim is to quantitatively determine the VEGFR localization on endothelial cells from mouse hindlimb skeletal muscles. We contextualize this VEGFR quantification through comparison to VEGFR-levels on cells in vitro. Using quantitative fluorescence we measure and compare the levels of VEGFR1 and VEGFR2 on endothelial cells isolated from C57BL/6 and BALB/c gastrocnemius and tibialis anterior hindlimb muscles. Fluorescence measurements are calibrated using beads with known numbers of phycoerythrin molecules. The data show a 2-fold higher VEGFR1 surface localization relative to VEGFR2 with 2,000-3,700 VEGFR1/endothelial cell and 1,300-2,000 VEGFR2/endothelial cell. We determine that endothelial cells from the highly glycolytic muscle, tibialis anterior, contain 30% higher number of VEGFR1 surface receptors than gastrocnemius; BALB/c mice display ∼17% higher number of VEGFR1 than C57BL/6. When we compare these results to mouse fibroblasts in vitro, we observe high levels of VEGFR1 (35,800/cell) and very low levels of VEGFR2 (700/cell), while in human endothelial cells in vitro, we observe that the balance of VEGFRs is inverted, with higher levels VEGFR2 (5,800/cell) and lower levels of VEGFR1 (1,800/cell). Our studies also reveal significant cell-to-cell heterogeneity in receptor expression, and the quantification of these dissimilarities ex vivo for the first time provides insight into the balance of anti-angiogenic or modulatory (VEGFR1) and pro-angiogenic (VEGFR2) signaling.
    PLoS ONE 09/2012; 7(9):e44791. DOI:10.1371/journal.pone.0044791 · 3.23 Impact Factor
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    • "We have previously shown that the thin basement membrane layer does not significantly affect VEGF diffusion near the sprout [44], and thus do not consider the basement membrane here. As in previous models [41,42,50], receptors are considered pre-dimerized and binding of the ligand is a reversible single-step reaction; in addition, internalization and receptor insertion are balanced to keep plasma membrane receptor levels constant. Finally, though matrix-bound VEGF may be relevant to VEGFR2 activation [35], our model only considers receptors binding to soluble VEGF. "
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    ABSTRACT: The spatial distribution of vascular endothelial growth factor A (VEGF) is an important mediator of vascular patterning. Previous experimental studies in the mouse hindbrain and retina have suggested that VEGF alternative splicing, which controls the ability of VEGF to bind to heparan sulfate proteoglycans (HSPGs) in the extracellular matrix (ECM), plays a key role in controlling VEGF diffusion and gradients in tissues. Conversely, proteolysis notably by matrix metalloproteinases (MMPs), plays a critical role in pathological situations by releasing matrix-sequestered VEGF and modulating angiogenesis. However, computational models have predicted that HSPG binding alone does not affect VEGF localization or gradients at steady state. Using a 3D molecular-detailed reaction-diffusion model of VEGF ligand-receptor kinetics and transport, we test alternate models of VEGF transport in the extracellular environment surrounding an endothelial sprout. We show that differences in localization between VEGF isoforms, as observed experimentally in the mouse hindbrain, as well as the ability of proteases to redistribute VEGF in pathological situations, are consistent with a model where VEGF is endogenously cleared or degraded in an isoform-specific manner. We use our predictions of the VEGF distribution to quantify a tip cell's receptor binding and gradient sensing capacity. A novel prediction is that neuropilin-1, despite functioning as a coreceptor to VEGF₁₆₅-VEGFR2 binding, reduces the ability of a cell to gauge the relative steepness of the VEGF distribution. Comparing our model to available in vivo vascular patterning data suggests that vascular phenotypes are most consistently predicted at short range by the soluble fraction of the VEGF distributions, or at longer range by matrix-bound VEGF detected in a filopodia-dependent manner. Isoform-specific VEGF degradation provides a possible explanation for numerous examples of isoform specificity in VEGF patterning and examples of proteases relocation of VEGF upon release.
    BMC Systems Biology 05/2011; 5(1):59. DOI:10.1186/1752-0509-5-59 · 2.44 Impact Factor
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    ABSTRACT: Tumor growth is dependent on angiogenesis, a process whereby new capillaries are formed from pre-existing microvasculature. Inhibition of tumor-induced angiogenesis has proven to be an effective therapeutic strategy for several drugs currently in the clinic (1). One of the key molecules involved in tumor angiogenesis is vascular endothelial growth factor A (VEGF-A, commonly referred to as VEGF). Thus, an understanding of how VEGF is expressed, diffuses, and interacts with other important regulators such as VEGF receptors at molecular, cellular and tissue levels is critical to the evaluation of therapeutic effects (4). Computational modeling of these angiogenic events at multiple scales of biological organization provides a means to explore the regulatory mechanisms underlying the complex process. We have developed a number of computational models to investigate how VEGF transport events are linked with receptor signaling at the molecular scale, with cellular behavior and cell phenotypes at the cellular scale, and how these events combine to regulate angiogenesis at tissue and whole body levels (5, 6, 9). First, we apply an in vivo hemorheological model to simulate the distribution of blood flow and hematocrit in all the microvessels. We then use this distribution in a 3D convection-diffusion model to describe how oxygen is delivered by microvascular blood flow and its diffusion and utilization through the tissue. The oxygen transport model is important, as VEGF secretion by muscle and tumor cells is mediated by insufficient oxygen supply to the cell, in turn resulting in hypoxia or low partial pressure of oxygen (PO2); low PO2 is sensed by a transcription factor hypoxia-inducible factor HIF1alpha that activates hundreds of genes including VEGF (8). Next, we developed a reaction-diffusion model to predict molecular distribution of VEGF in the interstitial space and on the surface of endothelial and parenchymal cells (e.g. myocytes and tumor cells). The model describes the secretion of two major VEGF isoforms, VEGF121 and VEGF165, molecular transport of each isoform in the interstitial space, VEGF binding to heparan sulfate proteoglycans in the extracellular matrix (ECM), VEGF binding to receptors VEGFR1, VEGFR2 and co-receptors neuropilin-1 (NRP1) and neuropilin-2 (NRP2) on the surface of endothelial cells, myocytes, and tumor cells, and internalization of the ligand-receptor complexes. Next, a cellular agent-based model was developed to describe how capillary 6-3 Interdisciplinary Transport Phenomena VII, Dresden, Germany, 2011 Paper No.: ITP-2011-49 endothelial cells respond to stimuli, specifically VEGF concentration and gradients, during the time course of capillary sprout formation (7). The model applies logical rules, based on extensive experimental data, to define cell activation, elongation, migration and proliferation events, which in turn dynamically affect VEGF transport and gradients. In addition, a module-based istrategy was developed to integrate the computer models of the major steps of angiogenesis from the molecular to tissue levels, starting from blood flow, to oxygen transport, to HIF1alpha expression, to VEGF secretion and transport, to capillary growth (3). In order to understand the effect of VEGF concentration in the multiple tissues in the body and be able to simulate systemic administration of anti-VEGF therapeutic agents, we developed a whole-body compartmental model of VEGF transport (10, 12-14). The model is comprised of three compartments: normal tissue represented by skeletal muscle, the vascular or blood compartment, and tumor compartment; tissue interstitial space compartments are further subdivided into ECM, and endothelial and parenchymal basement membranes. The model includes the molecular interactions of VEGF121 and VEGF165, receptors VEGFR1, VEGFR2, and co-receptors NRP1 and NRP2. The density of VEGF receptors and co-receptors is determined experimentally using quantitative flow cytometry (2). We then add an anti-VEGF compound to the model, specifically bevacizumab, an anti-VEGF antibody used clinically for different types of cancer (11). Intercompartment transport of VEGF, anti-VEGF and their products includes vascular permeability and lymph flow. These molecules intravasate and extravasate via transendothelial macromolecular permeability, and can be convected from the normal tissue into the blood via lymphatic drainage, and they are removed from the blood via clearance. The compartmental model predicts the spatially-averaged interstitial concentration of VEGF and anti-VEGF, as well as ligand-receptor binding in different compartments. The model is used to investigate the effect of various therapies that target VEGF-mediated angiogenesis.
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