Investigating pancreatic beta cell membrane epitopes using unbiased cell-based Fab-phage display

Abstract

The phenotypic and functional changes of cells in response to physiological and pathological conditions are strongly influenced by the roles of plasma membrane proteins. Recombinant Fab antibody-based phage display for an unbiased antigen-driven affinity selection is a suitable approach for identifying novel membrane proteins. Alterations in the function and distribution of cell membrane proteins in pancreatic {beta} cells have been observed in pathological conditions like diabetes. In this study, we integrated an unbiased cell-based Fab-phage display screening method with bioinformatics tools to identify and characterize Fabs that selectively bind to pancreatic {beta} cells in conditions simulated by a hyperglycemic environment. We isolated three Fab-phages, namely Fab_53, 538, and 54.68, that have binding properties matching specific epitopes on the MIN6 membrane. These Fabs are part of the immunoglobulin G groups that contain Kappa light chains. Bioinformatics analysis of the variable domains of their light and heavy chains (VL and VH) revealed that the potential epitope binding sites on the {beta} cell membrane are associated with pathways involved in insulin activity. Through FACS and IF analysis, we found that, of the three Fabs, Fab_538 exhibited the strongest binding to MIN6 cells. The use of InterProScan software resulted in the generation of 344 potential Fab_538 binding epitopes, and from these, AF2Complex predicted 10 interacting antigens. Our goal in combining Fab-phage display with bioinformatic tools is to develop a more effective, specific, and streamlined method for identifying disease-modifying membrane epitopes for monoclonal antibodies (mAbs).