A Glycoproteome Data Mining Strategy for Characterizing Structural Features of Altered Glycans with Thymic Involution

Glycosylation plays an important role in regulating innate and adaptive immunity. With promising advances in structural and site-specific glycoproteomics, how to thoroughly extract important information from these multi-dimensional data has become another unresolved issue. The present study reports a comprehensive data mining strategy to systematically extract overall and altered glycan features from quantitative glycoproteome data. By applying the strategy to investigation of thymic involution, the study not only presents a high-resolution glycoproteome map of the mouse thymus, displaying distinct glycan structure patterns among immune-relevant cellular components, but also uncovers four major altered glycan features associated with thymic involution, including elevated LacdiNAc mainly on the MHC class I complex, increased sialoglycans that perform multiple immune functions, down-regulated bisecting glycans mostly linked to a sole GlcNAc branch, as well as possible shifts of glycan structures at the same glycosites. Regulatory network analyses further reveal the coordinated interactions of altered glycans with upstream regulators, including glycosyltransferases, glycosidases, and glycan-binding proteins, as well as downstream signaling pathways. These data offer valuable resources for future functional studies on glycosylation and the mechanistic investigation of thymic involution, supporting the strategy as a powerful tool for in-depth mining of structural and site-specific glycoproteome data from various biomedical samples.