pH‐induced conformation changes of adsorbed vitronectin maximize its bovine aortic endothelial cell binding ability
Source abstract
Abstract
Abstract Vitronectin (VN) is an important matricellular protein that plays a role in cell adhesion, cell migration, wound healing, and inflammation. VN is present in both serum and plasma and studies have shown that it has significant surface enrichment on implanted biomaterials as compared to other proteins present in plasma. Previously, charged self‐assembled monolayers (SAMs) have been shown to influence the orientation and conformation of adsorbed proteins and their subsequent bioactivity. In addition, numerous studies have shown that the conformation of VN plays a critical role in its bioactive properties. In this study, both the orientation and conformation of adsorbed VN are systematically studied to determine which of these has the greater influence on its cell binding ability. Atomic force microscopy was used to qualitatively asses the adsorbed amount of VN on positively and negatively charged SAMs both before and after conformation changes induced by pH. Cell adhesion assays were then performed to compare the bovine aortic endothelial cell (BAEC) binding ability of VN in three different conformations on positively and negatively charged surfaces. The results indicate that conformation, not orientation, plays the more important role in the BAEC adhesion properties of VN. In addition, the cell binding properties of VN were maximized when the protein is in the conformation imparted to it by acidic conditions. This indicates that the bioactivity of VN is maximized when in its conformation typical of an acidic wound healing environment, emphasizing the biological relevance of this protein for cell binding under these natural biological conditions. © 2008 Wiley Periodicals, Inc. J Biomed Mater Res, 2008
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Research context
OpenAlex topics: Polymer Surface Interaction Studies · Cell Adhesion Molecules Research · Protease and Inhibitor Mechanisms
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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