Title : Enhanced analysis of the mouse plasma proteome using
cysteine-containing tryptic
glycopeptides
Abstract :
- A comprehensive understanding of the mouse plasma proteome is important for studies using mouse models to identify protein markers of human disease
- To enhance our analysis of the mouse plasma proteome, we have developed a method for isolating low-abundance proteins using a cysteine-containing glycopeptide strategy
- This method involves two orthogonal affinity capture steps
- First , glycoproteins are coupled to an azlactone copolymer gel using hydrazide chemistry and cysteine residues are then biotinylated
- After trypsinization and extensive washing, tethered N-glycosylated tryptic peptides are released from the gel using PNGase F . Biotinylated cysteinyl-containing glycopeptides are then affinity selected using a monomeric avidin gel and analyzed by LC-MS/MS
- We have applied the method to a proteome analysis of mouse plasma
- In two independent analyses using 200 muL each of C57BL mouse plasma, 51 proteins were detected
- Only 42 proteins were seen when the same plasma sample was analyzed by glycopeptides only
- A total of 104 N-glycosylation sites were identified
- Of these, 17 sites have hitherto not been annotated in the Swiss- Prot database whereas 48 were considered probable, potential, or by similarity - i.e., based on little or no experimental evidence
- We show that analysis by cysteine-containing glycopeptides allows detection of low-abundance proteins such as the epidermal growth factor receptor , the Vitamin K-dependent protein Z , the hepatocyte growth factor activator , and the lymphatic endothelium-specific hyaluronan receptor as these proteins were not detected in the glycopeptide control analysis
Output (sent_index, trigger,
protein,
sugar,
site):
- 0. glycopeptides, , -, -, glycopeptides
- 11. glycopeptide, , -, -, glycopeptide
- 11. glycopeptides, , -, -, glycopeptides
- 2. glycopeptide, , -, -, glycopeptide
- 4. glycoproteins, , glycoproteins, -, -
- 5. N-glycosylated, , -, -, peptides
- 5. glycopeptides, , -, -, glycopeptides
- 8. glycopeptides, , -, -, glycopeptides
- 9. N-glycosylation, , -, -, sites
Output(Part-Of) (sent_index,
protein,
site):
- 5. PNGase F, glycopeptides
*Output_Site_Fusion* (sent_index,
protein,
sugar,
site):