The aim of the current study was to investigate the impact of long-acting fibroblast growth factor 21 (FGF21) on retinal vascular leakage utilizing machine learning and to clarify the mechanism underlying the protection. To assess the effect on retinal vascular leakage, C57BL/6J mice were pre-treated with long-acting FGF21 analog or vehicle (Phosphate Buffered Saline; PBS) intraperitoneally (i.p.) before induction of retinal vascular leakage with intravitreal injection of mouse (m) vascular endothelial growth factor 164 (VEGF164) or PBS control. Five hours after mVEGF164 injection, we retro-orbitally injected Fluorescein isothiocyanate (FITC) -dextran and quantified fluorescence intensity as a readout of vascular leakage, using the Image Analysis Module with a machine learning algorithm. In FGF21- or vehicle-treated primary human retinal microvascular endothelial cells (HRMECs), cell permeability was induced with human (h) VEGF165 and evaluated using FITC-dextran and trans-endothelial electrical resistance (TEER). Western blots for tight junction markers were performed. Retinal vascular leakage in vivo was reduced in the FGF21 versus vehicle- treated mice. In HRMECs in vitro, FGF21 versus vehicle prevented hVEGF-induced increase in cell permeability, identified with FITC-dextran. FGF21 significantly preserved TEER compared to hVEGF. Taken together, FGF21 regulates permeability through tight junctions; in particular, FGF21 increases Claudin-1 protein levels in hVEGF-induced HRMECs. Long-acting FGF21 may help reduce retinal vascular leakage in retinal disorders and machine learning assessment can help to standardize vascular leakage quantification.
Keywords: Claudin-1; fibroblast growth factor 21 (FGF21); machine learning analysis; tight junction; vascular endothelial growth factor (VEGF); vascular leakage.