DEVELOPMENT OF INFORMATICS PROCEDURES FOR GLYCOMICS LC/MS DATASETS

Boston University Medical Campus, Boston MA

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Abstract

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Heparin and heparan sulfate (HS) are involved in important physiological and pathological processes (regulation of enzymes, bacterial infection, etc.) via direct interaction with intra- and inter-cellular proteins. The structural elucidation of patterns of sulfation, N-acetylation and uronosyl epimerization is extremely important to better understand their function but remains a very difficult analytical challenge. On-line LC/MS methods enable highly sensitive and rapid mass profiling of HS oligosaccharides. This work examines issues related to the exploitation of automated on-line CAD tandem MS for HS: 1. What are the most valid approaches for interpretation of CAD tandem MS data for highly sulfated glycans? 2. How can the tandem MS data be used to differentiate isomeric HS glycoforms from different mammalian organ tissues? Preparation of heparan sulfate HS from bovine aorta, lung and porcine intestinal mucosa were exhaustively digested with heparin Lyase III. Oligosaccharides were then fractionated using a size exclusion column. Fractions corresponding to a degree of polymerization (dp) of 4, 6 were analyzed by tandem mass spectrometry. Mass spectrometry conditions A ThermoFisher Scientific LTQ Orbitrap Discovery mass spectrometer coupled with a Triversa Nanomate system was operated in negative ion mode. The HS samples were sprayed in 50/50 methanol/water. Tandem mass spectrometry data were automatically acquired in the LTQ with a resolution of 15000. The window of isolation was 5u and the collision energy was maintained at 15%. Data interpretation Data clustering was applied to assist the data interpretation