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      Ser and Thr acceptor preferences of the GalNAc-Ts vary among isoenzymes to modulate mucin-type O-glycosylation

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          Abstract

          A family of polypeptide GalNAc-transferases (GalNAc-Ts) initiates mucin-type O-glycosylation, transferring GalNAc onto hydroxyl groups of Ser and Thr residues of target substrates. The 20 GalNAc-T isoenzymes in humans are classified into nine subfamilies according to sequence similarity. GalNAc-Ts select their sites of glycosylation based on weak and overlapping peptide sequence motifs, as well prior substrate O-GalNAc glycosylation at sites both remote (long-range) and neighboring (short-range) the acceptor. Together, these preferences vary among GalNAc-Ts imparting each isoenzyme with its own unique specificity. Studies on the first identified GalNAc-Ts showed Thr acceptors were preferred over Ser acceptors; however studies comparing Thr vs. Ser glycosylation across the GalNAc-T family are lacking. Using a series of identical random peptide substrates, with single Thr or Ser acceptor sites, we determined the rate differences (Thr/Ser rate ratio) between Thr and Ser substrate glycosylation for 12 isoenzymes (representing 7 GalNAc-T subfamilies). These Thr/Ser rate ratios varied across subfamilies, ranging from ~2 to ~18 (for GalNAc-T4/GalNAc-T12 and GalNAc-T3/GalNAc-T6, respectively), while nearly identical Thr/Ser rate ratios were observed for isoenzymes within subfamilies. Furthermore, the Thr/Ser rate ratios did not appreciably vary over a series of fixed sequence substrates of different relative activities, suggesting the ratio is a constant for each isoenzyme against single acceptor substrates. Finally, based on GalNAc-T structures, the different Thr/Ser rate ratios likely reflect differences in the strengths of the Thr acceptor methyl group binding to the active site pocket. With this work, another activity that further differentiates substrate specificity among the GalNAc-Ts has been identified.

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          Most cited references76

          • Record: found
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          The genomic landscapes of human breast and colorectal cancers.

          Human cancer is caused by the accumulation of mutations in oncogenes and tumor suppressor genes. To catalog the genetic changes that occur during tumorigenesis, we isolated DNA from 11 breast and 11 colorectal tumors and determined the sequences of the genes in the Reference Sequence database in these samples. Based on analysis of exons representing 20,857 transcripts from 18,191 genes, we conclude that the genomic landscapes of breast and colorectal cancers are composed of a handful of commonly mutated gene "mountains" and a much larger number of gene "hills" that are mutated at low frequency. We describe statistical and bioinformatic tools that may help identify mutations with a role in tumorigenesis. These results have implications for understanding the nature and heterogeneity of human cancers and for using personal genomics for tumor diagnosis and therapy.
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            • Record: found
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            • Article: not found

            Glycosylation in health and disease

            The glycome describes the complete repertoire of glycoconjugates composed of carbohydrate chains, or glycans, that are covalently linked to lipid or protein molecules. Glycoconjugates are formed through a process called glycosylation and can differ in their glycan sequences, the connections between them and their length. Glycoconjugate synthesis is a dynamic process that depends on the local milieu of enzymes, sugar precursors and organelle structures as well as the cell types involved and cellular signals. Studies of rare genetic disorders that affect glycosylation first highlighted the biological importance of the glycome, and technological advances have improved our understanding of its heterogeneity and complexity. Researchers can now routinely assess how the secreted and cell-surface glycomes reflect overall cellular status in health and disease. In fact, changes in glycosylation can modulate inflammatory responses, enable viral immune escape, promote cancer cell metastasis or regulate apoptosis; the composition of the glycome also affects kidney function in health and disease. New insights into the structure and function of the glycome can now be applied to therapy development and could improve our ability to fine-tune immunological responses and inflammation, optimize the performance of therapeutic antibodies and boost immune responses to cancer. These examples illustrate the potential of the emerging field of 'glycomedicine'.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Newly identified loci that influence lipid concentrations and risk of coronary artery disease.

              To identify genetic variants influencing plasma lipid concentrations, we first used genotype imputation and meta-analysis to combine three genome-wide scans totaling 8,816 individuals and comprising 6,068 individuals specific to our study (1,874 individuals from the FUSION study of type 2 diabetes and 4,184 individuals from the SardiNIA study of aging-associated variables) and 2,758 individuals from the Diabetes Genetics Initiative, reported in a companion study in this issue. We subsequently examined promising signals in 11,569 additional individuals. Overall, we identify strongly associated variants in eleven loci previously implicated in lipid metabolism (ABCA1, the APOA5-APOA4-APOC3-APOA1 and APOE-APOC clusters, APOB, CETP, GCKR, LDLR, LPL, LIPC, LIPG and PCSK9) and also in several newly identified loci (near MVK-MMAB and GALNT2, with variants primarily associated with high-density lipoprotein (HDL) cholesterol; near SORT1, with variants primarily associated with low-density lipoprotein (LDL) cholesterol; near TRIB1, MLXIPL and ANGPTL3, with variants primarily associated with triglycerides; and a locus encompassing several genes near NCAN, with variants strongly associated with both triglycerides and LDL cholesterol). Notably, the 11 independent variants associated with increased LDL cholesterol concentrations in our study also showed increased frequency in a sample of coronary artery disease cases versus controls.
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                Author and article information

                Journal
                Glycobiology
                Oxford University Press (OUP)
                1460-2423
                November 2020
                October 21 2020
                April 18 2020
                November 2020
                October 21 2020
                April 18 2020
                : 30
                : 11
                : 910-922
                Affiliations
                [1 ]Department of Biochemistry, Case Western Reserve University, Cleveland, OH 44106, USA
                [2 ]BIFI and Laboratorio de Microscopías Avanzada (LMA), University of Zaragoza, Mariano Esquillor s/n, Campus Rio Ebro, Edificio I+D, Zaragoza, 50018, Spain
                [3 ]Department of Cellular and Molecular Medicine, Faculty of Health Sciences, Copenhagen Center for Glycomics (CCG), University of Copenhagen, Copenhagen N DK-2200, Denmark
                [4 ]Department of Dentistry, Faculty of Health Sciences, Copenhagen Center for Glycomics (CCG), University of Copenhagen, Copenhagen N DK-2200, Denmark
                [5 ]Fundación ARAID, Zaragoza, 50018, Spain
                [6 ]Department of Chemistry, Case Western Reserve University, Cleveland, OH 44106, USA
                [7 ]Department of Pediatrics, Case Western Reserve University, Cleveland, OH 44106, USA
                Article
                10.1093/glycob/cwaa036
                32304323
                4ccd0600-d3b9-4e7a-86ba-d92687b885c3
                © 2020

                https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model

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