Publication Date

10-1-2024

Journal

Biochemical and Biophysical Research Communications

DOI

10.1016/j.bbrc.2024.150321

PMID

38954982

PMCID

PMC11298814

PubMedCentral® Posted Date

10-1-2025

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes

Keywords

Humans, Vascular Endothelial Growth Factor A, Human Umbilical Vein Endothelial Cells, HEK293 Cells, Protein Binding, Protein Isoforms, Receptors, Vascular Endothelial Growth Factor, Bacteriophage T7, Cell Surface Display Techniques, Heparitin Sulfate, Recombinant Fusion Proteins, Heparin sulfate, Phage display, VEGF, VEGF isoforms, VEGF-Cell binding assay, Vascular endothelial growth factor

Abstract

Vascular endothelial growth factor (VEGF) is a pleiotropic growth factor that binds a broad spectrum of cell types and regulates diverse cellular processes, including angiogenesis, growth and survival. However, it is technically difficult to quantify VEGF-cell binding activity because of reversible nature of ligand-receptor interactions. Here we used T7 bacteriophage display to quantify and compare binding activity of three human VEGF-A (hVEGF) isoforms, including hVEGF111, 165 and 206. All three isoforms bound equally well to immobilized aflibercept, a decoy VEGF receptor. hVEGF111-Phage exhibited minimal binding to immobilized heparan sulfate, whereas hVEGF206-Phage and hVEGF165-Phage had the highest and intermediate binding to heparan, respectively. In vitro studies revealed that all three isoforms bound to human umbilical vein endothelial cells (HUVECs), HEK293 epithelial and SK-N-AS neuronal cells. hVEGF111-Phage has the lowest binding activity, while hVEGF206-Phage has the highest binding. hVEGF206-Phage was the most sensitive to detect VEGF-cell binding, albeit with the highest background binding to SK-N-AS cells. These results suggest that hVEGF206-Phage is the best-suited isoform to quantify VEGF-cell binding even though VEGF165 is the most biologically active. Furthermore, this study demonstrates the utility of T7 phage display as a platform for rapid and convenient ligand-cell binding quantification with pros and cons discussed.

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