Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: EC:2.4.99.6 (sialyltransferase)
 1,546 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

A mathematical model is developed of the compartmentalized sialylation of N-linked oligosaccharides in order to understand and predict the outcome of sialylation reactions. A set of assumptions are presented, including Michaelis-Menten-type dependency of reaction rate on the concentration of the glycoprotein substrate. The resulting model predicts the heterogeneous outcome of a posttranslational oligosaccharide biosynthesis step, a critical aspect that is not accounted for in the modeling of the cotranslational attachment of oligosaccharides to glycosylation sites (Shelikoff et al., Biotech. Bioeng., 50, 73-90, 1996) or general models of the secretion process (Noe and Delenick, J. Cell Sci., 92, 449-459, 1989). In the steady-state for the likely case where the concentration of substrate is much less than the Km of the sialyltransferase, the model predicts that the extent of sialylation, x, will depend upon the enzyme concentration, enzyme kinetic parameters and substrate residence time in the reaction compartment. The value of x predicted by the model using available literature data is consistent with the values of x that have been recently determined for the glycoproteins CD4 (Spellman et al., Biochemistry, 30, 2395-2406, 1991) and t-PA (Spellman et al., J. Biol. Chem., 264, 14100-14111, 1989) secreted by Chinese hamster ovary cells. For the unsaturated case, the model also predicts that x is independent of the concentration of secreted glycoprotein in the Golgi. The general modeling approach outlined in this article may be applicable to other glycosylation reactions and posttranslational modifications.
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PMID:A mathematical model of sialylation of N-linked oligosaccharides in the trans-Golgi network. 918 32

The mammalian Galbeta1,3GalNAc-specific alpha2,3-sialyltransferase (ST3Gal I) was expressed as a secreted glycoprotein in High Five (Trichoplusia ni) cells. Using this recombinant ST3Gal I, we screened the synthetic hexapeptide combinatorial library to explore a sialyltransferase inhibitor. We found that the hexapeptide, NH(2)-GNWWWW, exhibited the most strong inhibition of ST3Gal I among five different hexapeptides that were finally selected. The kinetic analysis of ST3Gal I inhibition demonstrated that this hexapeptide could act as a competitive inhibitor (K(i) = 1.1 microm) on CMP-NeuAc binding to the enzyme. Moreover, the hexapeptide was shown to strongly inhibit both N-glycan-specific alpha2,3- and alpha2,6-sialyltranferase in vitro, suggesting that this peptide may inhibit the broad range of sialyltransferases regardless of their linkage specificity. The inhibitory activity in vivo was investigated by RCA-I lectin blot analyses and by metabolic d-[6-(3)H]GlcNH(2) radiolabeling analyses of N- and O-linked oligosaccharides in Chines hamster ovary cells. Our results demonstrate that the hexapeptide can act as a generic inhibitor of the N- and O-glycan-specific sialyltransferases in mammalian cells, which results in the significantly reduced NeuAc expression on cellular glycoproteins in vivo.
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PMID:The Hexapeptide inhibitor of Galbeta 1,3GalNAc-specific alpha 2,3-sialyltransferase as a generic inhibitor of sialyltransferases. 1237 42

Recent studies have demonstrated the utility of DNA microarray technology in engineering cellular properties. For instance, cellular adhesion, the necessity of cells to attach to a surface in order to to proliferate, was examined by comparing two distinct HeLa cell lines. Two genes, one encoding a type II membrane glycosylating sialyltransferase (siat7e) and the other encoding a secreted glycoprotein (lama4), were found to influence adhesion. The expression of siat7e correlated with reduced adhesion, whereas expression of lama4 correlated with increased adhesion, as shown by various assays. In a separate example, a gene encoding a mitochondrial assembly protein (cox15) and a gene encoding a kinase (cdkl3), were found to influence cellular growth. Enhanced expression of either gene resulted in slightly higher specific growth rates and higher maximum cell densities for HeLa, HEK-293, and CHO cell lines. Another investigated property was the adaptation of HEK-293 cells to serum-free media. The genes egr1 and gas6, both with anti-apoptotic properties, were identified as potentially improving adaptability by impacting viability at low serum levels. In trying to control apoptosis, researchers found that by altering the expression levels of four genes faim, fadd, alg-2, and requiem, apoptotic response could be altered. In the present work, these and related studies in microorganisms (prokaryote and eukaryote) are examined in greater detail focusing on the approach of using DNA microarrays to direct cellular behavior by targeting select genes.
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PMID:Cells by design: a mini-review of targeting cell engineering using DNA microarrays. 1832 55