Metabolic control of glycosylation forms for establishing glycan-dependent protein interaction networks

• 成果类型: Article
• 署名作者: Liu, Xingyu; Yi, Li; Lin, Zongtao; Chen, Siyu; Wang, Shunyang; Sheng, Ying; Lebrilla, Carlito B.; Garcia, Benjamin A.; Xie, Yixuan
• 署名单位: Fudan University; Fudan University; Washington University (WUSTL); University of California System; University of California Davis; University of California System; University of California Davis
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-14766
• DOI: 10.1073/pnas.2422936122
• 发表日期: 2025-06-24
• 关键词: n-glycosylation; cell-membranes; cross-linking; identification

摘要

Protein-protein interactions (PPIs) are crucial for comprehending the molecular mechanisms and signaling pathways underlying diverse biological processes and disease progression. However, investigating PPIs involving membrane proteins is challenging due to the complexity and heterogeneity of glycosylation. To tackle this challenge, we developed an approach termed glycan-dependent affinity purification coupled with mass spectrometry (GAP-MS), specifically designed to characterize changes in glycoprotein PPIs under varying glycosylation conditions. GAP-MS integrates metabolic control of glycan profiles in cultured cells using small molecules referred to as glycan modifiers with affinity purification followed by mass spectrometry analysis (AP-MS). Here, GAP-MS was applied to characterize and compare the interaction networks under five different glycosylation states for four bait glycoproteins: BSG, CD44, EGFR, and SLC3A2. This analysis identified a network comprising 156 interactions, of which 131 were determined to be glycan dependent. Notably, the GAP-MS analysis of BSG provided distinct information regarding glycosylation-influenced interactions compared to the commonly used glycosylation site mutagenesis approach combined with AP-MS, emphasizing the unique advantages of GAP-MS. Collectively, GAP-MS presents distinct insights over existing methods in elucidating how specific glycosylation forms impact glycoprotein interactions. Additionally, the glycan-dependent interaction networks generated for these four glycoproteins serve as a valuable resource for guiding future functional investigations and therapeutic developments targeting the glycoproteins discussed in this study.