Tyrosine-protein kinase Yes controls endothelial junctional plasticity and barrier integrity by regulating VE-cadherin phosphorylation and endocytosis

Abstract

Vascular endothelial (VE)-cadherin in endothelial adherens junctions is an essential component of the vascular barrier, critical for tissue homeostasis and implicated in diseases such as cancer and retinopathies. Inhibitors of Src cytoplasmic tyrosine kinase have been applied to suppress VE-cadherin tyrosine phosphorylation and prevent excessive leakage, edema and high interstitial pressure. Here we show that the Src-related Yes tyrosine kinase, rather than Src, is localized at endothelial cell (EC) junctions where it becomes activated in a flow-dependent manner. EC-specific Yes1 deletion suppresses VE-cadherin phosphorylation and arrests VE-cadherin at EC junctions. This is accompanied by loss of EC collective migration and exaggerated agonist-induced macromolecular leakage. Overexpression of Yes1 causes ectopic VE-cadherin phosphorylation, while vascular leakage is unaffected. In contrast, in EC-specific Src-deficiency, VE-cadherin internalization is maintained, and leakage is suppressed. In conclusion, Yes-mediated phosphorylation regulates constitutive VE-cadherin turnover, thereby maintaining endothelial junction plasticity and vascular integrity.

Method for retinal EC distribution analysis

Chimeric recombination was induced in iSuRe-Cre+ mice at P3 by i.p. injection of tamoxifen (100 µg/mouse, Sigma). Retinas were taken at P7 and P15, immunostained for CD31 and flat-mounted. Images were taken by z-stack tile scanning using a 10X objective on a confocal microscope (Leica SP8). Maximum intensity projection images of whole retinas were used for image segmentation, which was performed with ImageJ resources. The maximum projection of the MbTomato channel threshold was established to distinguish MbTomato+ cells from the background. Outliers with a radius between 0.2-1.0 µm were removed. The CD31 channel (after maximum projection) was used to define the outlines of veins and arteries; the optic nerve was used as a mask to define a referential system. For computational analysis, a bespoken Python-based workflow was employed, accessible on GitHub (https://github.com/wgiese/retina-vein-artery-cs). For every pixel in the image, three numbers were computed (using the mask as referential): (1) distance to the nearest vein (dv), (2) distance to the nearest artery (da) and (3) radial distance to the optic nerve (r). From these measures, the relative distances by ϕv-a = dv/(dv + da) were obtained. The EC distribution was computed by performing the operation for all YFP-positive pixels, which were used as a proxy for EC distribution. A kernel density estimation was used to approximate the underlying EC distribution in the 2D coordinate system spanned by ϕv-a and r.