The Use of Multiple Reaction Monitoring on QQQ-MS for the Analysis of Protein- and Site-Specific Glycosylation Patterns in Serum

Author(s):  
L. Renee Ruhaak
2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Alexander A. Merleev ◽  
Dayoung Park ◽  
Yixuan Xie ◽  
Muchena J. Kailemia ◽  
Gege Xu ◽  
...  

Abstract Alterations in the human glycome have been associated with cancer and autoimmunity. Thus, constructing a site-specific map of the human glycome for biomarker research and discovery has been a highly sought-after objective. However, due to analytical barriers, comprehensive site-specific glycoprofiling is difficult to perform. To develop a platform to detect easily quantifiable, site-specific, disease-associated glycan alterations for clinical applications, we have adapted the multiple reaction monitoring mass spectrometry method for use in glycan biomarker research. The adaptations allow for highly precise site-specific glycan monitoring with minimum sample prep. Using this technique, we successfully mapped out the relative abundances of the most common 159 glycopeptides in the plasma of 97 healthy volunteers. This plasma glycome map revealed 796 significant (FDR < 0.05) site-specific inter-protein and intra-protein glycan associations, of which the vast majority were previously unknown. Since age and gender are relevant covariants in biomarker research, these variables were also characterized. 13 glycopeptides were found to be associated with gender and 41 to be associated with age. Using just five age-associated glycopeptides, a highly accurate age prediction model was constructed and validated (r2 = 0.62 ± 0.12). The human plasma site-specific glycan map described herein has utility in applications ranging from glycan biomarker research and discovery to the development of novel glycan-altering interventions.


2011 ◽  
Vol 83 (22) ◽  
pp. 8802-8809 ◽  
Author(s):  
Yan Zhao ◽  
Wei Jia ◽  
Jifeng Wang ◽  
Wantao Ying ◽  
Yangjun Zhang ◽  
...  

Circulation ◽  
2012 ◽  
Vol 126 (15) ◽  
pp. 1828-1837 ◽  
Author(s):  
Pingbo Zhang ◽  
Jonathan A. Kirk ◽  
Weihua Ji ◽  
Cristobal G. dos Remedios ◽  
David A. Kass ◽  
...  

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 362-363
Author(s):  
Daniil Khvostov ◽  
Natalya Vostrikova ◽  
Irina M Chernukha

Abstract Functional, particularly personalized meat-based foods are of more in demand by a consumer today. Functional additives, such as plant components and animal proteins from bovine or porcine tissues have been successfully used. With many ingredients added to foods, it is important to provide quality and composition monitoring to confirm the products’ authenticity, to identify undeclared or rarely used types of raw meat in product formulations. For example, if animal heart tissue is a component of a product formulation or if aorta tissue presents in a product due to improper trimming. Different methods are used to identify raw materials, including new approaches in proteomics and peptidomics that are considered the most effective modern methods nowadays. The purpose of the study is meat product composition analysis and special biomarker peptide identification to confirm the presence of heart and aorta tissue in a finished meat product. Over 20 amino acid sequences were checked based on earlier obtained data. Those amino acid sequences were analyzed with a high-performance liquid chromatography with mass spectrometric detection as described. The MS settings were selected using the Skyline. Signal-to-Noise ratio (S/N) over 10 units were used to choose the best peptide candidates. Seven peptides were found in porcine hearts. The best candidate was peptide VNVDEVGGEALGR (S/N - 73.10±5.3) from β-Hemoglobin. Two marker peptides from serum albumin were selected for pork aorta: TVLGNFAAFVQK (S/N 53.51±2.4) and EVTEFAK (S/N 31.69±4.1). These biomarkers showed the best detection and specificity. The multiply reaction monitoring method made it possible to identify the most/best specific peptides—biomarkers that could confirm the heart and/or aorta in meat products. The method can be used for comparative research or identification of best peptides that are specific to any type of animal tissue. The work was supported by the Russian Science Foundation, project no. 16-16- 10073.


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