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Exploring functional pairing between surface glycoconjugates and human galectins using programmable glycodendrimersomes
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DOI：10.1073/pnas.1720055115.png)
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Precise translation of glycan-encoded information into cellular activity depends critically on highly specific functional pairing between glycans and their human lectin counter receptors. Sulfoglycolipids, such as sulfatides, are important glycolipid components of the biological membranes found in the nervous and immune systems. The optimal molecular and spatial design aspects of sulfated and nonsulfated glycans with high specificity for lectin-mediated bridging are unknown. To elucidate how different molecular and spatial aspects combine to ensure the high specificity of lectin-mediated bridging, a bottom-up toolbox is devised. To this end, negatively surface-charged glycodendrimersomes (GDSs), of different nanoscale dimensions, containing sulfo-lactose groups are self-assembled in buffer from a synthetic sulfatide mimic: Janus glycodendrimer (JGD) containing a 3'-O-sulfo-lactose headgroup. Also prepared for comparative analysis are GDSs with nonsulfated lactose, a common epitope of human membranes. These self-assembled GDSs are employed in aggregation assays with 15 galectins, comprising disease-related human galectins, and other natural and engineered variants from four families, having homodimeric, heterodimeric, and chimera architectures. There are pronounced differences in aggregation capacity between human homodimeric and heterodimeric galectins, and also with respect to their responsiveness to the charge of carbohydrate-derived ligand. Assays reveal strong differential impact of ligand surface charge and density, as well as lectin concentration and structure, on the extent of surface cross-linking. These findings demonstrate how synthetic JGD-headgroup tailoring teamed with protein engineering and network assays can help explain how molecular matchmaking operates in the cellular context of glycan and lectin complexity.
Keyword:
glycodendrimers
glycolipids
aggregation
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9.1
论文数:
10.8W
被引数:
73.5W
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