19 research outputs found
Exploring the Utility of Carbohydrate Associated Transferase Activities as Potential Tumor Markers for Human Gastric Cancer
Thioglycosides Are Efficient Metabolic Decoys of Glycosylation that Reduce Selectin Dependent Leukocyte Adhesion
© 2018 Elsevier Ltd Small-molecule inhibitors of glycosylation can be applied in basic science studies, and clinical investigations as anti-inflammatory, anti-metastatic, and anti-viral therapies. This article demonstrates that thioglycosides represent a class of potent metabolic decoys that resist hydrolysis, and block E-selectin-dependent leukocyte adhesion in models of inflammation
Enhancing MS(n) mass spectrometry strategy for carbohydrate analysis: A b2 ion spectral library
UNLABELLED: Searchable mass spectral libraries for glycans may be enhanced using a B2 ion library. Using a quadrupole ion-trap mass spectrometer, successive fragmentations of sodiated oligosaccharides were carried out in the positive ion mode. In B,Y-type fragmentation, disaccharide B2 ions are generated which correspond to specific glycosidic linkages using progressive MS stages. Fragmentation of B2 ions corresponding to glycosidic linkages such as Hex-Fuc, Hex-Hex, Hex-HexNAc, HexNAc-Hex and HexNAc-HexNAc, were systematically studied in low energy CID and collected to form a B2 library . Linkages produce characteristic fragmentation patterns in the absence of cross-ring fragmentation. Patterns of B2 ions rely on relative stability of glycosidic bonds and carbohydrate-metal complexes in the gas phase. MS(n) studies of linear, branched trisaccharides and tetrasaccharides show that isomers for which B2 ion information is not available are rarely a problem in practice by their absence in an isomeric sequence or by their scarcity in nature. This MS strategy for linkage determination of carbohydrates aided by a B2 library was developed with a scope for expansion, providing an improved tool for glycomics. We validated this method examining levels of expressed activities of two glycosyl transferases in cancer cell lines: β3(B3GALNT2) and β4GalNAcT(B4GALNT3&4) that generate GalNAcβ3GlcNAcβ and GalNAcβ4GlcNAcβ. BIOLOGICAL SIGNIFICANCE: Glycosylation is an important class of the postranslationome , which includes manifold aspects of post-translational protein modification, affecting protein conformation, providing ligands for protein receptors [1-5], and encoding unique haptenic [6,7] or antigenic markers for oncology [8-11] and other applications. Identification of individual monomeric units, linkages, ring size, branching and anomerity has posed significant challenges to mass spectrometrists. MS(n) is a growing key instrumental method to differentiate among isomers [12]. While the potential isomers in oligosaccharides are impossibly large [12], likely possibilities can be limited by the biological system, including the expressed glycosyl transferases [13-20]. Mass spectra from sequential stages of collision activation (MS(n)) can supply structural details for precise characterization of linkage, monomer ID, substitutions, anomerity and branching [21-25]. There is a fundamental need for high throughput tools in glycomics to complement proteome studies. In that regard, nothing could be more important than searchable spectral library files for structural confirmation. The National Academy of Science (NAS) report (http://glyco.nas.edu) recommends the need of more than 10,000 synthetic structures of carbohydrates to advance the field of glycomics. This study demonstrates that the general reproducibility of ion trap spectra, and energy independence from modes of ionization and collisional activation, make compiling an MS(n) library for carbohydrate identification an achievable research target [26]. We intend to use the new B2 library for carbohydrate differences found on cancers, where we profile the glycosyltransferases to predict classes of potential structures, and use the library for MS identification of the expected cohort of altered structures
Complex Oligosaccharide Investigations: Synthesis of an Octasaccharide Incorporating the Dimeric Le x
Altered eosinophil profile in mice with ST6Gal-1 deficiency: an additional role for ST6Gal-1 generated by the P1 promoter in regulating allergic inflammation
Cumulative evidence indicates that the sialyltransferase ST6Gal-1 and the sialyl-glycans, which it constructs, are functionally pleiotropic. Expression of the ST6Gal-1 gene is mediated by six distinct promoter/regulatory regions, and we hypothesized that these promoters may be used differentially to produce ST6Gal-1 for different biologic purposes. To examine this hypothesis, we compared a mouse with a complete deficiency in ST6Gal-1 (Siat1 null) with another mouse that we have created previously with a disruption only in the P1 promoter (Siat1ΔP1). We noted previously greater neutrophilic inflammation associated with ST6Gal-1 deficiency. Here, we report that ST6Gal-1-deficient mice also have significantly elevated eosinophilic responses. Upon i.p. thioglycollate elicitation, eosinophils accounted for over 20% of the total peritoneal inflammatory cell pool in ST6Gal-1-deficient animals, which was threefold greater than in corresponding wild-type animals. A principal feature of allergic respiratory inflammation is pulmonary eosinophilia, we evaluated the role of ST6Gal-1 in allergic lung inflammation. Using OVA and ABPA experimental models of allergic airways, we showed that ST6Gal-1 deficiency led to greater airway inflammation characterized by excessive airway eosinophilia. The severity of airway inflammation was similar between Siat1ΔP1 and Siat1 null mice, indicating a role for P1-generated ST6Gal-1 in regulating eosinophilic inflammation. Colony-forming assays suggested greater IL-5-dependent eosinophil progenitor numbers in the marrow of ST6Gal-1-deficient animals. Moreover, allergen provocation of wild-type mice led to a significant reduction in P1-mediated ST6Gal-1 mRNA and accompanied decline in circulatory ST6Gal-1 levels. Taken together, the data implicate ST6Gal-1 as a participant in regulating not only Th1 but also Th2 responses, and ST6Gal-1 deficiency can lead to the development of more severe allergic inflammation with excessive eosinophil production
Characterization of Cancer Associated Mucin Type O-Glycans Using the Exchange Sialylation Properties of Mammalian Sialyltransferase ST3Gal-II
Our previous studies suggest that the α2,3sialylated
T-antigen (NeuAcα2,3Galβ1,3GalNac-) and associated glycan
structures are likely to be elevated during cancer. An easy and reliable
strategy to label mucinous glycans that contain such carbohydrates
can enable the identification of novel glycoproteins that are cancer
associated. To this end, the present study demonstrates that the exchange
sialylation property of mammalian ST3Gal-II can facilitate the labeling
of mucin glycoproteins in cancer cells, tumor specimens, and glycoproteins
in cancer sera. Results show that (i) the radiolabeled mucin glycoproteins
of each of the cancer cell lines studied (T47D, MCF7, LS180, LNCaP,
SKOV<sub>3</sub>, HL60, DU4475, and HepG<sub>2</sub>) is distinct
either in terms of the specific glycans presented or their relative
distribution. While some cell lines like T47D had only one single
sialylated O-glycan, others like LS180 and DU4475 contained a complex
mixture of mucinous carbohydrates. (ii) [<sup>14</sup>C]sialyl labeling
of primary tumor cells identified a 25–35 kDa mucin glycoprotein
unique to pancreatic tumor. Labeled glycoproteins for other cancers
had higher molecular weight. (iii) Studies of [<sup>14</sup>C] sialylated
human sera showed larger mucin glycopeptides and >2-fold larger
mucin-type chains in human serum compared to [<sup>14</sup>C]sialyl
labeled glycans of fetuin. Overall, the exchange sialylation property
of ST3Gal-II provides an efficient avenue to identify mucinous proteins
for applications in glycoproteomics and cancer research
Systems-level modeling of cellular glycosylation reaction networks: O-linked glycan formation on natural selectin ligands
Motivation: The emerging field of Glycomics requires the development of systems-based modeling strategies to relate glycosyltransferase gene expression and enzyme activity with carbohydrate structure and function