A-Transferase, 338 ABO Blood Group System, 330 and Cloning, 338
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
Promiscuity and Specificity of Eukaryotic Glycosyltransferases
Biochemical Society Transactions (2020) 48 891–900 https://doi.org/10.1042/BST20190651 Review Article Promiscuity and specificity of eukaryotic glycosyltransferases Ansuman Biswas and Mukund Thattai Simons Centre for the Study of Living Machines, National Centre for Biological Sciences, TIFR, Bangalore, India Correspondence: Mukund Thattai ([email protected]) Glycosyltransferases are a large family of enzymes responsible for covalently linking sugar monosaccharides to a variety of organic substrates. These enzymes drive the synthesis of complex oligosaccharides known as glycans, which play key roles in inter-cellular interac- tions across all the kingdoms of life; they also catalyze sugar attachment during the syn- thesis of small-molecule metabolites such as plant flavonoids. A given glycosyltransferase enzyme is typically responsible for attaching a specific donor monosaccharide, via a spe- cific glycosidic linkage, to a specific moiety on the acceptor substrate. However these enzymes are often promiscuous, able catalyze linkages between a variety of donors and acceptors. In this review we discuss distinct classes of glycosyltransferase promiscuity, each illustrated by enzymatic examples from small-molecule or glycan synthesis. We high- light the physical causes of promiscuity, and its biochemical consequences. Structural studies of glycosyltransferases involved in glycan synthesis show that they make specific contacts with ‘recognition motifs’ that are much smaller than the full oligosaccharide sub- strate. There is a wide range in the sizes of glycosyltransferase recognition motifs: highly promiscuous enzymes recognize monosaccharide or disaccharide motifs across multiple oligosaccharides, while highly specific enzymes recognize large, complex motifs found on few oligosaccharides. In eukaryotes, the localization of glycosyltransferases within compartments of the Golgi apparatus may play a role in mitigating the glycan variability caused by enzyme promiscuity. -
Fucosyltransferase Genes on Porcine Chromosome 6Q11 Are Closely Linked to the Blood Group Inhibitor (S) and Escherichia Coli F18 Receptor (ECF18R) Loci
Mammalian Genome 8, 736–741 (1997). © Springer-Verlag New York Inc. 1997 Two a(1,2) fucosyltransferase genes on porcine Chromosome 6q11 are closely linked to the blood group inhibitor (S) and Escherichia coli F18 receptor (ECF18R) loci E. Meijerink,1 R. Fries,1,*P.Vo¨geli,1 J. Masabanda,1 G. Wigger,1 C. Stricker,1 S. Neuenschwander,1 H.U. Bertschinger,2 G. Stranzinger1 1Institute of Animal Science, Swiss Federal Institute of Technology, ETH-Zentrum, CH-8092 Zurich, Switzerland 2Institute of Veterinary Bacteriology, University of Zurich, CH 8057 Zurich, Switzerland Received: 17 February 1997 / Accepted: 30 May 1997 Abstract. The Escherichia coli F18 receptor locus (ECF18R) has fimbriae F107, has been shown to be genetically controlled by the been genetically mapped to the halothane linkage group on porcine host and is inherited as a dominant trait (Bertschinger et al. 1993) Chromosome (Chr) 6. In an attempt to obtain candidate genes for with B being the susceptibility allele and b the resistance allele. this locus, we isolated 5 cosmids containing the a(1,2)fucosyl- The genetic locus for this E. coli F18 receptor (ECF18R) has been transferase genes FUT1, FUT2, and the pseudogene FUT2P from mapped to porcine Chr 6 (SSC6), based on its close linkage to the a porcine genomic library. Mapping by fluorescence in situ hy- S locus and other loci of the halothane (HAL) linkage group (Vo¨- bridization placed all these clones in band q11 of porcine Chr 6 geli et al. 1996). The epistatic S locus suppresses the phenotypic (SSC6q11). Sequence analysis of the cosmids resulted in the char- expression of the A-0 blood group system when being SsSs (Vo¨geli acterization of an open reading frame (ORF), 1098 bp in length, et al. -
Detecting Substrate Glycans of Fucosyltransferases on Glycoproteins with Fluorescent Fucose
bioRxiv preprint doi: https://doi.org/10.1101/2020.01.28.919860; this version posted January 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Detecting substrate glycans of fucosyltransferases on glycoproteins with fluorescent fucose Key words: Fucose/Fucosylation/fucosyltransferase/core-fucose/glycosylation Supplementary Data Included: Supplemental Fig.1 to Fig. 2 Zhengliang L Wu1*, Mark Whitaker, Anthony D Person1, Vassili Kalabokis1 1Bio-techne, R&D Systems, Inc. 614 McKinley Place N.E. Minneapolis, MN, 55413, USA *Correspondence: Phone: 612-656-4544. Email: [email protected], bioRxiv preprint doi: https://doi.org/10.1101/2020.01.28.919860; this version posted January 29, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. Abstract Like sialylation, fucose usually locates at the non-reducing ends of various glycans on glycoproteins and constitutes important glycan epitopes. Detecting the substrate glycans of fucosyltransferases is important for understanding how these glycan epitopes are regulated in response to different growth conditions and external stimuli. Here we report the detection of these glycans via enzymatic incorporation of fluorescent tagged fucose using fucosyltransferases including FUT2, FUT6, FUT7, and FUT8 and FUT9. More specifically, we describe the detection of substrate glycans of FUT8 and FUT9 on therapeutic antibodies and the detection of high mannose glycans on glycoproteins by enzymatic conversion of high mannose glycans to the substrate glycans of FUT8. -
(12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown Et Al
US 20030082511A1 (19) United States (12) Patent Application Publication (10) Pub. No.: US 2003/0082511 A1 Brown et al. (43) Pub. Date: May 1, 2003 (54) IDENTIFICATION OF MODULATORY Publication Classification MOLECULES USING INDUCIBLE PROMOTERS (51) Int. Cl." ............................... C12O 1/00; C12O 1/68 (52) U.S. Cl. ..................................................... 435/4; 435/6 (76) Inventors: Steven J. Brown, San Diego, CA (US); Damien J. Dunnington, San Diego, CA (US); Imran Clark, San Diego, CA (57) ABSTRACT (US) Correspondence Address: Methods for identifying an ion channel modulator, a target David B. Waller & Associates membrane receptor modulator molecule, and other modula 5677 Oberlin Drive tory molecules are disclosed, as well as cells and vectors for Suit 214 use in those methods. A polynucleotide encoding target is San Diego, CA 92121 (US) provided in a cell under control of an inducible promoter, and candidate modulatory molecules are contacted with the (21) Appl. No.: 09/965,201 cell after induction of the promoter to ascertain whether a change in a measurable physiological parameter occurs as a (22) Filed: Sep. 25, 2001 result of the candidate modulatory molecule. Patent Application Publication May 1, 2003 Sheet 1 of 8 US 2003/0082511 A1 KCNC1 cDNA F.G. 1 Patent Application Publication May 1, 2003 Sheet 2 of 8 US 2003/0082511 A1 49 - -9 G C EH H EH N t R M h so as se W M M MP N FIG.2 Patent Application Publication May 1, 2003 Sheet 3 of 8 US 2003/0082511 A1 FG. 3 Patent Application Publication May 1, 2003 Sheet 4 of 8 US 2003/0082511 A1 KCNC1 ITREXCHO KC 150 mM KC 2000000 so 100 mM induced Uninduced Steady state O 100 200 300 400 500 600 700 Time (seconds) FIG. -
Mitochondrial ABC Transporters Vxpx Cargo-Targeting to Cilium Alpha
PA2GF PA2G5PA2GD PA21BPA2GEPA2GXPA2G3 Acyl chainAcyl chainremodelling remodelling of PI of PG Acyl chain remodelling of PS PA24C AcylAcyl chain chain remodelling remodelling of PCof PE GABA A receptor activation Neurotransmitter receptors and postsynaptic signal transmission GBRA2 GBRB3GBRA6GBRA5 PA2GA GBRA1GBRG2GBRB2GBRB1 GBRT GBRG3GBRA4GBRA3 PA24B Ligand-gated ion channel transport Acyl-CoA:dihydroxyacetonephosphateacyltransferase PPBI Hydrolysis of LPC Hydrolysis ofSynthesis LPE of PA LA Post-translational modification: synthesis of GPI-anchored proteins Catecholamine biosynthesis Plasmalogen biosynthesis GABAGBRR3 AGBRR2 (rho) receptor activation RNA Polymerase III Transcription Termination GBRR1 Creatine metabolism RNA Polymerase III AbortivePNMT AndDDC Retractive Initiation ReuptakeS6A11 of GABA PPBT DigestionPPBN S6A12SC6A7 Interaction between L1 and Ankyrins GLRA1 SC6A1 S6A13 SerotoninSodium/myo-inositol andInositol melatonin transporters cotransporter biosynthesis 2 GLRA2 KCRM FOLR2 KCNQ2 5HT3E5HT3D SC6A3 Astrocytic Glutamate-Glutamine Uptake And Metabolism 5HT3B EAA1 5HT3A5HT3C Na+/Cl- dependent neurotransmitter transportersDopamine clearance from the synaptic cleft SCN8AEAA2 Methylation of TransportINMTMeSeH forof inorganicexcretionSCNAASCN3ASCN2A cations/anions and amino acids/oligopeptidesGlycosyltransferase-likeO-linked glycosylation protein LARGE1 SC6A5SC6A9S6A15 SCN9ASCN4ASCN1ASCNBA SC6A6 SCN7ASCN5A Insulin effects increased synthesis of Xylulose-5-Phosphate SC6A2 TPH1 Potassium transport channels MOT2 Glutamine synthetaseCAC1I -
Development and Validation of a Protein-Based Risk Score for Cardiovascular Outcomes Among Patients with Stable Coronary Heart Disease
Supplementary Online Content Ganz P, Heidecker B, Hveem K, et al. Development and validation of a protein-based risk score for cardiovascular outcomes among patients with stable coronary heart disease. JAMA. doi: 10.1001/jama.2016.5951 eTable 1. List of 1130 Proteins Measured by Somalogic’s Modified Aptamer-Based Proteomic Assay eTable 2. Coefficients for Weibull Recalibration Model Applied to 9-Protein Model eFigure 1. Median Protein Levels in Derivation and Validation Cohort eTable 3. Coefficients for the Recalibration Model Applied to Refit Framingham eFigure 2. Calibration Plots for the Refit Framingham Model eTable 4. List of 200 Proteins Associated With the Risk of MI, Stroke, Heart Failure, and Death eFigure 3. Hazard Ratios of Lasso Selected Proteins for Primary End Point of MI, Stroke, Heart Failure, and Death eFigure 4. 9-Protein Prognostic Model Hazard Ratios Adjusted for Framingham Variables eFigure 5. 9-Protein Risk Scores by Event Type This supplementary material has been provided by the authors to give readers additional information about their work. Downloaded From: https://jamanetwork.com/ on 10/02/2021 Supplemental Material Table of Contents 1 Study Design and Data Processing ......................................................................................................... 3 2 Table of 1130 Proteins Measured .......................................................................................................... 4 3 Variable Selection and Statistical Modeling ........................................................................................ -
CDG and Immune Response: from Bedside to Bench and Back Authors
CDG and immune response: From bedside to bench and back 1,2,3 1,2,3,* 2,3 1,2 Authors: Carlota Pascoal , Rita Francisco , Tiago Ferro , Vanessa dos Reis Ferreira , Jaak Jaeken2,4, Paula A. Videira1,2,3 *The authors equally contributed to this work. 1 Portuguese Association for CDG, Lisboa, Portugal 2 CDG & Allies – Professionals and Patient Associations International Network (CDG & Allies – PPAIN), Caparica, Portugal 3 UCIBIO, Departamento Ciências da Vida, Faculdade de Ciências e Tecnologia, Universidade NOVA de Lisboa, 2829-516 Caparica, Portugal 4 Center for Metabolic Diseases, UZ and KU Leuven, Leuven, Belgium Word count: 7478 Number of figures: 2 Number of tables: 3 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as doi: 10.1002/jimd.12126 This article is protected by copyright. All rights reserved. Abstract Glycosylation is an essential biological process that adds structural and functional diversity to cells and molecules, participating in physiological processes such as immunity. The immune response is driven and modulated by protein-attached glycans that mediate cell-cell interactions, pathogen recognition and cell activation. Therefore, abnormal glycosylation can be associated with deranged immune responses. Within human diseases presenting immunological defects are Congenital Disorders of Glycosylation (CDG), a family of around 130 rare and complex genetic diseases. In this review, we have identified 23 CDG with immunological involvement, characterised by an increased propensity to – often life-threatening – infection. -
Supplementary Materials and Tables a and B
SUPPLEMENTARY MATERIAL 1 Table A. Main characteristics of the subset of 23 AML patients studied by high-density arrays (subset A) WBC BM blasts MYST3- MLL Age/Gender WHO / FAB subtype Karyotype FLT3-ITD NPM status (x109/L) (%) CREBBP status 1 51 / F M4 NA 21 78 + - G A 2 28 / M M4 t(8;16)(p11;p13) 8 92 + - G G 3 53 / F M4 t(8;16)(p11;p13) 27 96 + NA G NA 4 24 / M PML-RARα / M3 t(15;17) 5 90 - - G G 5 52 / M PML-RARα / M3 t(15;17) 1.5 75 - - G G 6 31 / F PML-RARα / M3 t(15;17) 3.2 89 - - G G 7 23 / M RUNX1-RUNX1T1 / M2 t(8;21) 38 34 - + ND G 8 52 / M RUNX1-RUNX1T1 / M2 t(8;21) 8 68 - - ND G 9 40 / M RUNX1-RUNX1T1 / M2 t(8;21) 5.1 54 - - ND G 10 63 / M CBFβ-MYH11 / M4 inv(16) 297 80 - - ND G 11 63 / M CBFβ-MYH11 / M4 inv(16) 7 74 - - ND G 12 59 / M CBFβ-MYH11 / M0 t(16;16) 108 94 - - ND G 13 41 / F MLLT3-MLL / M5 t(9;11) 51 90 - + G R 14 38 / F M5 46, XX 36 79 - + G G 15 76 / M M4 46 XY, der(10) 21 90 - - G NA 16 59 / M M4 NA 29 59 - - M G 17 26 / M M5 46, XY 295 92 - + G G 18 62 / F M5 NA 67 88 - + M A 19 47 / F M5 del(11q23) 17 78 - + M G 20 50 / F M5 46, XX 61 59 - + M G 21 28 / F M5 46, XX 132 90 - + G G 22 30 / F AML-MD / M5 46, XX 6 79 - + M G 23 64 / M AML-MD / M1 46, XY 17 83 - + M G WBC: white blood cell. -
Influenza-Specific Effector Memory B Cells Predict Long-Lived Antibody Responses to Vaccination in Humans
bioRxiv preprint doi: https://doi.org/10.1101/643973; this version posted February 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Influenza-specific effector memory B cells predict long-lived antibody responses to vaccination in humans Anoma Nellore1, Esther Zumaquero2, Christopher D. Scharer3, Rodney G. King2, Christopher M. Tipton4, Christopher F. Fucile5, Tian Mi3, Betty Mousseau2, John E. Bradley6, Fen Zhou2, Paul A. Goepfert1, Jeremy M. Boss3, Troy D. Randall6, Ignacio Sanz4, Alexander F. Rosenberg2,5, Frances E. Lund2 1Dept. of Medicine, Division of Infectious Disease, 2Dept of Microbiology, 5Informatics Institute, 6Dept. of Medicine, Division of Clinical Immunology and Rheumatology and at The University of Alabama at Birmingham, Birmingham, AL 35294 USA 3Dept. of Microbiology and Immunology and 4Department of Medicine, Division of Rheumatology Emory University, Atlanta, GA 30322, USA Correspondence should be addressed to: Frances E. Lund, PhD Charles H. McCauley Professor and Chair Dept of Microbiology University of Alabama at Birmingham 276 BBRB Box 11 1720 2nd Avenue South Birmingham AL 35294-2170 [email protected] SHORT RUNNING TITLE: Effector memory B cell development after influenza vaccination 1 bioRxiv preprint doi: https://doi.org/10.1101/643973; this version posted February 18, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract Seasonal influenza vaccination elicits hemagglutinin (HA)-specific CD27+ memory B cells (Bmem) that differ in expression of T-bet, BACH2 and TCF7. -
Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum- Induced Differentiation
See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/41720955 Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum- Induced Differentiation Article in Journal of Proteome Research · March 2010 Impact Factor: 4.25 · DOI: 10.1021/pr900793a · Source: PubMed CITATIONS READS 21 107 11 authors, including: Yongjie Ji Waldemar Priebe University of Texas MD Anderson Cancer C… University of Texas MD Anderson Cancer C… 14 PUBLICATIONS 550 CITATIONS 275 PUBLICATIONS 6,260 CITATIONS SEE PROFILE SEE PROFILE Frederick F Lang Charles A Conrad University of Texas MD Anderson Cancer C… University of Texas MD Anderson Cancer C… 254 PUBLICATIONS 10,474 CITATIONS 84 PUBLICATIONS 2,169 CITATIONS SEE PROFILE SEE PROFILE Available from: Frederick F Lang Retrieved on: 26 May 2016 Glycomic and Transcriptomic Response of GSC11 Glioblastoma Stem Cells to STAT3 Phosphorylation Inhibition and Serum-Induced Differentiation Huan He,†,‡ Carol L. Nilsson,*,†,# Mark R. Emmett,†,‡ Alan G. Marshall,†,‡ Roger A. Kroes,§ Joseph R. Moskal,§ Yongjie Ji,| Howard Colman,| Waldemar Priebe,⊥ Frederick F. Lang,| and Charles A. Conrad| Ion Cyclotron Resonance Program, National High Magnetic Field Laboratory, Florida State University, Tallahassee, Florida 32310, Department of Chemistry and Biochemistry, Florida State University, Tallahassee, Florida 32306-43903, Falk Center for Molecular Therapeutics, Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60201, Department of Neuro-oncology, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030, and Department of Experimental Therapeutics, The University of Texas M.D. Anderson Cancer Center, Houston, Texas 77030 Received September 05, 2009 A glioblastoma stem cell (GSC) line, GSC11, grows as neurospheres in serum-free media supplemented with EGF (epidermal growth factor) and bFGF (basic fibroblast growth factor), and, if implanted in nude mice brains, will recapitulate high-grade glial tumors. -
Gene % HDV Positive Cells % Infection Compared to Sictrl P-Value FDR
% HDV % infection Gene positive compared to p-value FDR Toxicity cells siCtrl A2M 9,45 63,81 0,0001 0,0002 0,11 A4GALT 17,61 118,94 0,0887 0,1607 -0,13 A4GNT 16,11 108,83 0,0939 0,1683 0,20 AACS 15,76 106,48 0,5123 0,5979 0,00 AADAC 14,41 97,34 0,5533 0,6323 -0,03 AADACL1 14,44 97,53 0,2998 0,3678 0,14 AADAT 13,36 90,26 0,1557 0,2308 0,17 AAK1 18,00 121,58 0,0007 0,0028 0,04 AANAT 12,21 82,46 0,0004 0,0012 0,09 AARS 17,17 115,94 0,0671 0,1111 -0,13 AARSD1 14,78 99,83 0,8307 0,8622 -0,09 AASDH 18,69 126,24 0,0054 0,0129 -0,04 AASDHPPT 16,84 113,73 0,2685 0,3738 -0,05 AASS 16,33 110,31 0,0531 0,0836 -0,05 AATF 18,57 125,42 0,0111 0,0254 0,07 AATK 14,80 99,98 0,6699 0,7166 0,18 ABAT 16,31 110,16 0,0725 0,1215 0,34 ABCA1 14,86 100,36 0,9669 0,9774 0,18 ABCA10 13,09 88,44 0,3205 0,4130 0,09 ABCA12 17,44 117,81 < 0.0001 < 0.0001 -0,03 ABCA13 14,77 99,76 0,9409 0,9609 -0,04 ABCA2 10,93 73,82 0,0001 0,0003 0,19 ABCA3 10,03 67,74 0,0913 0,1645 0,23 ABCA4 12,89 87,10 0,2968 0,3890 -0,08 ABCA5 17,00 114,80 0,0598 0,1252 0,04 ABCA6 15,58 105,20 0,2367 0,3178 0,13 ABCA7 11,40 77,03 < 0.0001 < 0.0001 0,35 ABCA8 15,88 107,30 0,0001 0,0005 0,06 ABCA9 11,86 80,11 < 0.0001 < 0.0001 0,29 ABCB1 12,79 86,41 0,2516 0,3539 0,09 ABCB10 13,81 93,28 0,4109 0,4855 0,12 ABCB11 19,08 128,88 0,0079 0,0194 0,00 ABCB4 14,67 99,12 0,9630 0,9736 0,10 ABCB5 15,34 103,59 0,4524 0,5677 -0,02 ABCB6 11,31 76,40 < 0.0001 < 0.0001 0,19 ABCB7 10,89 73,56 0,0285 0,0575 0,04 ABCB8 13,69 92,50 0,0355 0,0652 0,16 ABCB9 14,27 96,37 0,8926 0,9239 0,05 ABCC1 15,92 107,56 0,2517 0,3138 -
Glycan Metabolism a Validated Grna Library for CRISPR/Cas9
Glycobiology, 2018, vol. 28, no. 5, 295–305 doi: 10.1093/glycob/cwx101 Advance Access Publication Date: 5 January 2018 Original Article Glycan Metabolism A validated gRNA library for CRISPR/Cas9 targeting of the human glycosyltransferase Downloaded from https://academic.oup.com/glycob/article-abstract/28/5/295/4791732 by guest on 08 October 2018 genome Yoshiki Narimatsu2,3,1, Hiren J Joshi2, Zhang Yang2,3, Catarina Gomes2,4, Yen-Hsi Chen2, Flaminia C Lorenzetti 2, Sanae Furukawa2, Katrine T Schjoldager2, Lars Hansen2, Henrik Clausen2, Eric P Bennett2,1, and Hans H Wandall2 2Copenhagen Center for Glycomics, Departments of Cellular and Molecular Medicine and Odontology, Faculty of Health Sciences, University of Copenhagen, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark, 3GlycoDisplay Aps, Blegdamsvej 3, DK-2200 Copenhagen N, Denmark, and 4Instituto de Investigação e Inovação em Saúde,i3S; Institute of Molecular Pathology and Immunology of University of Porto, Ipatimup, Rua Júlio Amaral de Carvalho, 45, Porto 4200-135, Portugal 1To whom correspondence should be addressed: Tel: +45-35335528; Fax: +45-35367980; e-mail: [email protected] (Y.N.); Tel: +4535326630; Fax: +45-35367980; e-mail: [email protected] (E.P.B.) Received 25 September 2017; Revised 20 November 2017; Editorial decision 5 December 2017; Accepted 7 December 2017 Abstract Over 200 glycosyltransferases are involved in the orchestration of the biosynthesis of the human glycome, which is comprised of all glycan structures found on different glycoconjugates in cells. The glycome is vast, and despite advancements in analytic strategies it continues to be difficult to decipher biological roles of glycans with respect to specific glycan structures, type of glycoconju- gate, particular glycoproteins, and distinct glycosites on proteins.