Computational Screening of Tip and Stalk Cell Behavior Proposes a Role for Apelin Signaling in Sprout Progression

Angiogenesis involves the formation of new blood vessels by sprouting or splitting of existing blood vessels. During sprouting, a highly motile type of endothelial cell, called the tip cell, migrates from the blood vessels followed by stalk cells, an endothelial cell type that forms the body of the sprout. To get more insight into how tip cells contribute to angiogenesis, we extended an existing computational model of vascular network formation based on the cellular Potts model with tip and stalk differentiation, without making a priori assumptions about the differences between tip cells and stalk cells. To predict potential differences, we looked for parameter values that make tip cells (a) move to the sprout tip, and (b) change the morphology of the angiogenic networks. The screening predicted that if tip cells respond less effectively to an endothelial chemoattractant than stalk cells, they move to the tips of the sprouts, which impacts the morphology of the networks. A comparison of this model prediction with genes expressed differentially in tip and stalk cells revealed that the endothelial chemoattractant Apelin and its receptor APJ may match the model prediction. To test the model prediction we inhibited Apelin signaling in our model and in an \emph{in vitro} model of angiogenic sprouting, and found that in both cases inhibition of Apelin or of its receptor APJ reduces sprouting. Based on the prediction of the computational model, we propose that the differential expression of Apelin and APJ yields a "self-generated" gradient mechanisms that accelerates the extension of the sprout.Comment: 48 pages, 10 figures, 8 supplementary figures. Accepted for publication in PLoS ON

The Role of Heparan Sulfate and Neuropilin 2 in VEGFA Signaling in Human Endothelial Tip Cells and Non-Tip Cells during Angiogenesis In Vitro

During angiogenesis, vascular endothelial growth factor A (VEGFA) regulates endothelial cell (EC) survival, tip cell formation, and stalk cell proliferation via VEGF receptor 2 (VEGFR2). VEGFR2 can interact with VEGFR2 co-receptors such as heparan sulfate proteoglycans (HSPGs) and neuropilin 2 (NRP2), but the exact roles of these co-receptors, or of sulfatase 2 (SULF2), an enzyme that removes sulfate groups from HSPGs and inhibits HSPG-mediated uptake of very low density lipoprotein (VLDL), in angiogenesis and tip cell biology are unknown. In the present study, we investigated whether the modulation of binding of VEGFA to VEGFR2 by knockdown of SULF2 or NRP2 affects sprouting angiogenesis, tip cell formation, proliferation of non-tip cells, and EC survival, or uptake of VLDL. To this end, we employed VEGFA splice variant 121, which lacks an HSPG binding domain, and VEGFA splice variant 165, which does have this domain, in in vitro models of angiogenic tip cells and vascular sprouting. We conclude that VEGFA165 and VEGFA121 have similar inducing effects on tip cells and sprouting in vitro, and that the binding of VEGFA165 to HSPGs in the extracellular matrix does not seem to play a role, as knockdown of SULF2 did not alter these effects. Co-binding of NRP2 appears to regulate VEGFA–VEGFR2-induced sprout initiation, but not tip cell formation. Finally, as the addition of VLDL increased sprout formation but not tip cell formation, and as VLDL uptake was limited to non-tip cells, our findings suggest that VLDL plays a role in sprout formation by providing biomass for stalk cell proliferation

The Role of Heparan Sulfate and Neuropilin 2 in VEGFA Signaling in Human Endothelial Tip Cells and Non-Tip Cells during Angiogenesis In Vitro

During angiogenesis, vascular endothelial growth factor A (VEGFA) regulates endothelial cell (EC) survival, tip cell formation, and stalk cell proliferation via VEGF receptor 2 (VEGFR2). VEGFR2 can interact with VEGFR2 co-receptors such as heparan sulfate proteoglycans (HSPGs) and neuropilin 2 (NRP2), but the exact roles of these co-receptors, or of sulfatase 2 (SULF2), an enzyme that removes sulfate groups from HSPGs and inhibits HSPG-mediated uptake of very low density lipoprotein (VLDL), in angiogenesis and tip cell biology are unknown. In the present study, we investigated whether the modulation of binding of VEGFA to VEGFR2 by knockdown of SULF2 or NRP2 affects sprouting angiogenesis, tip cell formation, proliferation of non-tip cells, and EC survival, or uptake of VLDL. To this end, we employed VEGFA splice variant 121, which lacks an HSPG binding domain, and VEGFA splice variant 165, which does have this domain, in in vitro models of angiogenic tip cells and vascular sprouting. We conclude that VEGFA165 and VEGFA121 have similar inducing effects on tip cells and sprouting in vitro, and that the binding of VEGFA165 to HSPGs in the extracellular matrix does not seem to play a role, as knockdown of SULF2 did not alter these effects. Co-binding of NRP2 appears to regulate VEGFA-VEGFR2-induced sprout initiation, but not tip cell formation. Finally, as the addition of VLDL increased sprout formation but not tip cell formation, and as VLDL uptake was limited to non-tip cells, our findings suggest that VLDL plays a role in sprout formation by providing biomass for stalk cell proliferation

IGF-binding proteins 3 and 4 are regulators of sprouting angiogenesis

Purpose: We have previously identified insulin-like growth factor 2 (IGF2) and insulin-like growth factor 1 receptor (IGF1R) as essential proteins for tip cell maintenance and sprouting angiogenesis. In this study, we aim to identify other IGF family members involved in endothelial sprouting angiogenesis. Methods: Effects on sprouting were analyzed in human umbilical vein endothelial cells (HUVECs) using the spheroid-based sprouting model, and were quantified as mean number of sprouts per spheroid and average sprout length. RNA silencing technology was used to knockdown gene expression. Recombinant forms of the ligands (IGF1 and IGF2, insulin) and the IGF-binding proteins (IGFBP) 3 and 4 were used to induce excess effects. Effects on the tip cell phenotype were analyzed by measuring the fraction of CD34+ tip cells using flow cytometry and immunohistochemistry in a 3D angiogenesis model. Experiments were performed in the presence and absence of serum. Results: Knockdown of IGF2 inhibited sprouting in HUVECs, in particular when cultured in the absence of serum, suggesting that components in serum influence the signaling of IGF2 in angiogenesis in vitro. We then determined the effects of IGFBP3 and IGFBP4, which are both present in serum, on IGF2-IGF1R signaling in sprouting angiogenesis in the absence of serum: knockdown of IGFBP3 significantly reduced sprouting angiogenesis, whereas knockdown of IGFBP4 resulted in increased sprouting angiogenesis in both flow cytometry analysis and immunohistochemical analysis of the 3D angiogenesis model. Other IGF family members except INSR did not affect IGF2-IGF1R signaling. Conclusions: Serum components and IGF binding proteins regulate IGF2 effects on sprouting angiogenesis. Whereas IGFBP3 acts as co-factor for IGF2-IGF1R binding, IGFBP4 inhibits IGF2 signaling

Comparison of networks formed with mixed cells and cells with average properties.

<p><b>A</b>, <b>F</b>, and <b>K</b> morphologies for mixed tip (red) and stalk (gray) cells (<i>F</i>_tip = 0.5). <b>B</b>, <b>G</b>, and <b>L</b> morphologies for averaged cells (<i>F</i>_tip = 0.5). <b>C</b>-<b>E</b>, <b>H</b>-<b>J</b>, and <b>M</b>-<b>O</b> morphometrics for a range of tip cell fractions for both the control and mixed model. The morphometrics were calculated for 50 simulations at 10 000 MCS (error bars represent the standard deviation). p-values were obtained with a Welch’s t-test for the null hypothesis that the mean of mixed model and the control model are identical.</p

Effects of Apelin or APJ silencing in spheroid sprouting assays.

<p><b>A</b>-<b>F</b> Microscopy images of the WT and CD34- spheroids in VEGF-enriched collage after 24 hours. <b>G</b>-<b>H</b> Number of sprouts, relative to siNT treatment, after 24 hours for spheroids with mixed cells and CD34- spheroids. These metrics are the mean of the normalized, average number of sprouts of each replicate with the error bars depicting standard deviation. The * denotes <i>p</i> < 0.05, see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0159478#sec011" target="_blank">Materials and Methods</a> for details of the normalization and statistical analysis. <b>I</b>-<b>L</b> Example morphologies formed in the computational angiogenesis model (750 MCS); <b>(I-J)</b> model including tip cells (<i>θ</i>_<i>NICD</i> = 0.2, in absence (<b>I</b>) and in presence (<b>J</b>) of chemoattractant inhibition; <b>(K-L)</b> model with reduced tip cell number (<i>θ</i>_<i>NICD</i> = 0) in presence (<b>K</b>) and in absence (<b>M</b>) of chemoattractant inhibition. <b>M</b> Number of sprouts after 750 MCS for <i>n</i> = 20 simulations; error bars show the standard deviation; asterisks denote <i>p</i> < 0.05 for p-values obtained with Welch’s t-test in comparison with controls (no inhibition).</p

Effects of reducing tip cell chemoattractant sensitivity for varying NICD thresholds.

<p>Morphospace of the final morphologies (10 000 MCS) with varying tip cell chemoattractant sensitivities (<i>χ</i>(tip)) and NICD thresholds (Θ_NICD).</p

Effects of tip cell selection on network formation.

<p><b>A</b>-<b>F</b> Networks formed with varying fractions of predefined tip cells (<i>F</i>_tip) with <i>χ</i>(tip) = 400 at 10 000 MCS. <b>G</b>-<b>L</b> Networks formed with the tip cell selection model for varying NICD thresholds (Θ_NICD) at 10 000 MCS. <b>M</b> Standard deviation of lacuna area in a network after 10 000 MCS. <b>N</b>-<b>Q</b> Close up of the evolution of a network with 20% predefined tip cells (marked area in <b>B</b>). <b>R</b>-<b>T</b> Comparison of the morphometrics for networks formed with predefined and selected tip cells with reduced chemoattractant sensitivity (<i>χ</i>(tip) = 400) and network at 10 000 MCS. For the simulations with tip cell selection, the average tip cell fraction was calculated for each NICD threshold. For all plots (<b>M</b> and <b>R</b>-<b>T</b>) the values were averaged over 50 simulations and error bars depict the standard deviation.</p

Overview of the angiogenesis model and the parameter search.

<p><b>A</b> Time-lapse of angiogenesis model behavior <b>B</b> For each parameter P that is tested in the parameter search a morphospace is created to compare the different parameter values for different tip cell fractions. <b>C</b> Each morphology is studied in detail to see if the sprout tips are occupied by tip cells (red). <b>D</b> Each row of morphologies is studied to find rows in which the morphologies differ, indicating that network formation depends on the tip cell fraction.</p