PMID: PMC6428843-2

 

    Legend: Gene, Sites

Title : In all , the AI-ETD method presented here is a straightforward approach to improve glycopeptide fragmentation by combining the strengths of electron-driven dissociation and vibrational activation to access information about both peptide and glycan moieties simultaneously

Abstract :

1. AI-ETD enabled the most in-depth glycoproteome profiling of a single tissue to date and this strategy is amenable to practically any biological system
2. Ultimately, this study demonstrates that >1500 N-glycosites can be characterized via intact glycopeptide analysis from a single tissue, adding to a growing body of much-needed large-scale studies to investigate the role of glycosylation in various biological systems
3. Further studies will be needed to explore the utility of AI-ETD for glycopeptides with more than one glycosite , such as those encountered in middle-down and top-down glycoproteomic experiments
4. Assigning correct glycan modifications for multiple glycosylated peptides poses significant challenges, so we excluded all glycopeptide identifications that harbored more than one glycan in this dataset to ensure higher quality identifications
5. The middle-down approach can add considerable information to glycoforms and co-occurring glycans, but middle-down analyses typically use specifically developed proteolytic and chromatographic methods
6. Electron-driven dissociation methods have been valuable in middle-down glycoproteomic experiments, so it is reasonable to suggest that AI-ETD may prove useful in characterizing multiple glycosylated peptides and proteins as well
7. Another caveat of any glycoproteomic experiment is that there is not a universal or ideal glycopeptide enrichment method
8. This is markedly different from other PTM-centric proteomic methodologies
9. Lectin-based methods tend to have high enrichment yields (high percentage of glycopeptides compared to remaining non-modified peptide background), but lectins have glycan specificities that make them better suited for certain glycopeptides/glycan classes than others
10. Hydrophilic interaction liquid chromatography (HILIC) and electrostatic repulsion hydrophilic interaction chromatography (ERLIC) have also been successfully explored as glycopeptide enrichment methods
11. ERLIC-based methods show the most promise for applicability to a broad range of glycan classes, but they can have a high background of non-modified peptides present post-enrichment (likely because of charged moieties on peptides that cause their retention on ERLIC material)
12. We relied on Concanavalin A ( ConA ) lectin for enrichment in this study, meaning there are some limitations in the range of glycan classes observed
13. ConA binds oligomannose-type N-glycans with high affinity (which includes hybrid-type N-glycans), but is also known to bind complex-type N-glycans, albeit it with lower affinity
14. Thus, there is a bias toward oligomannose-type glycans to consider in this dataset
15. Even with this, however, we do characterize a diverse pool of N-glycans and provide evidence of varying degrees of heterogeneity at the glycosite , glycoprotein , and subcellular location levels across the glycoproteome as discussed above
16. Furthermore, we also see many similar trends to other studies that used different enrichment methods
17. A prevalence of high-mannose structures was seen in early glycomics studies of rodent brain and has been noted in glycoproteomic studies of rodent brain tissue by Trinidad et al. and Medzihradszky et al. with a lectin-based approaches and Liu et al. with zwitterionic-HILIC methods
18. Woo et al. also noticed a significant degree of oligomannose glycopeptides even with their chemical-tag-based enrichment (although in human cell lines instead of rodent brain tissue), which enriches glycopeptides based on clickable metabolically-incorporated sugars
19. This makes our observations of a high degree of oligomannose glycopeptides , which is likely due in part to the use of ConA for enrichment, still in congruence with observations using several other enrichment strategies
20. Current and future experiments in our group are exploring combinations of lectin-based approaches with HILIC and ERLIC methods to observe an even broader scope of the glycoproteome
21. Profiling the glycoproteome at this depth also requires new ways to interpret complex data that comes from intact glycopeptide analysis
22. While others have commented on similar trends in smaller-scale datasets, e.g., glycosylation differences in cellular compartments or the observation of varying degrees of heterogeneity on the same protein , we can now comment on trends across more than a thousand glycosites with the data presented here
23. We present several ways to analyze and visualize large-scale glycoproteomic data, providing a new perspective into the site-specific microheterogeneity of protein N-glycosylation at a systems level
24. We also show that glycosylation profiles differ based on subcellular localization and protein domain types and that heterogeneity can present itself in many different forms that can even differ between glycosites on the same protein
25. This work underscores the value of intact glycopeptide analysis to capture this complexity and provides an avenue forward to continue advancing our understanding of protein glycosylation

Output (sent_index, trigger, protein, sugar, site):

• 0. glycopeptide, , -, -, glycopeptide
• 10. glycopeptide, , -, -, glycopeptide
• 15. glycoprotein, , glycoprotein, -, -
• 15. glycosite, , -, -, glycosite
• 18. glycopeptides, , -, -, glycopeptides
• 19. glycopeptides, , -, -, glycopeptides
• 2. N-glycosites, , -, -, N-glycosites
• 2. glycopeptide, , -, -, glycopeptide
• 21. glycopeptide, , -, -, glycopeptide
• 22. glycosites, , -, -, glycosites
• 23. microheterogeneity, , N-glycosylation, -, -
• 24. glycosites, , -, -, glycosites
• 25. glycopeptide, , -, -, glycopeptide
• 3. glycopeptides, , -, -, glycopeptides
• 3. glycosite, , -, -, glycosite
• 4. glycopeptide, , -, -, glycopeptide
• 4. glycosylated, , -, -, peptides
• 6. glycosylated, , -, -, peptides
• 7. glycopeptide, , -, -, glycopeptide
• 9. glycopeptides, , -, -, glycopeptides

Output(Part-Of) (sent_index, protein, site):

• 24. protein, glycosites

*Output_Site_Fusion* (sent_index, protein, sugar, site):

 

Protein	NCBI ID	SENTENCE INDEX