Login / Signup

Sequence Requirements for Neuropilin-2 Recognition by ST8SiaIV and Polysialylation of Its O-Glycans.

Gaurang P BhideNinoshka R J FernandesKaren J Colley
Published in: The Journal of biological chemistry (2016)
Polysialic acid is an oncofetal glycopolymer, added to the glycans of a small group of substrates, that controls cell adhesion and signaling. One of these substrates, neuropilin-2, is a VEGF and semaphorin co-receptor that is polysialylated on its O-glycans in mature dendritic cells and macrophages by the polysialyltransferase ST8SiaIV. To understand the biochemical basis of neuropilin-2 polysialylation, we created a series of domain swap chimeras with sequences from neuropilin-1, a protein for which polysialylation had not been previously reported. To our surprise, we found that membrane-associated neuropilin-1 is polysialylated at ∼50% of the level of neuropilin-2 but not polysialylated when it lacks its cytoplasmic tail and transmembrane region and is secreted from the cell. This was not the case for neuropilin-2, which is polysialylated when either membrane-associated or soluble. Evaluation of the soluble chimeric proteins demonstrated that the meprin A5 antigen-μ tyrosine phosphatase (MAM) domain and the O-glycan-containing linker region of neuropilin-2 are necessary and sufficient for its polysialylation and serve as better recognition and acceptor sites in the polysialylation process than those regions of neuropilin-1. In addition, specific acidic residues on the surface of the MAM domain are critical for neuropilin-2 polysialylation. Based on these data and pull-down experiments, we propose a model where ST8SiaIV recognizes and docks on an acidic surface of the neuropilin-2 MAM domain to polysialylate O-glycans on the adjacent linker region. These results together with those related to neural cell adhesion molecule polysialylation establish a paradigm for the process of protein-specific polysialylation.
Keyphrases
  • cell adhesion
  • dendritic cells
  • cell surface
  • stem cells
  • immune response
  • cell therapy
  • machine learning
  • electronic health record
  • protein protein
  • artificial intelligence
  • deep learning
  • amino acid
  • big data