Role of Membrane Dynamics In Cell Surface Glycan Recognition

PROJECT SUMMARY Lectin-glycan interactions play an essential role in a wide range of physiological and pathological processes. Most lectins bind to glycans via multivalent interactions, in which multiple binding domains in a single lectin simultaneously interact with multiple glycan molecules. Lectins are pattern recognition molecules that recognize a spectrum of glycan ligands. The interplay between spatial organization of glycans and lectins adds a delicate approach to control cellular processes. Because glycans attached to lipids or membrane proteins can diffuse on the two-dimensional cell membrane, changes in membrane dynamics can modulate glycan patterns, leading to alternation in lectin activities. Understanding the molecular basis of the lectin-glycan recognition principle will help to predict cellular processes. In the first research area, the PI plans to investigate how spatial organization of glycan ligands influences glycobiology processes on the cell membrane. Probing lectin-glycan interactions on the cell surface is challenging, due to the inherent complexity of the cell membrane. The techniques in microscopy and spectroscopy will be combined with multiscale computational modeling to unravel microscopic binding mechanisms. This integrated approach offers a feedback loop allowing not only the refinement of the simulation model using experimental data, but also theoretical predictions to guide the experimental designs. In addition, the PI will explore innovative approaches to control cellular processes by manipulating local glycan patterns via membrane perturbation. The influence of drugs, nutrition, and cell polarity on lectin activities will be investigated. The knowledge will catalyze therapeutic advancements to combat glycan-associated diseases. In the second area, analytical approaches that can directly monitor glycan patterns on cells will be developed. Alternation in glycan patterns is highly dynamic and can be affected by cell environments and growth conditions. Frequent monitoring glycan patterns is critical to elucidating lectin functions and predicting biological consequences. However, the lack of effective glycan analysis tools delays the development of glycobiology research. The PI will integrate vibrational microscopy and machine learning algorithms to profile glycans on glycoproteins and cells. The success will provide a rapid and accessible sensing platform for the scientific community to conduct glycan analysis at single-cell level.