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Toukach P. Carbohydrate Structure Database: current state and recent developments. Anal Bioanal Chem 2024:10.1007/s00216-024-05383-w. [PMID: 38914734 DOI: 10.1007/s00216-024-05383-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2024] [Revised: 05/18/2024] [Accepted: 05/28/2024] [Indexed: 06/26/2024]
Abstract
Carbohydrate Structure Database (CSDB) is a curated glycan data collection and a glycoinformatic platform. In this report, its database, analytical, and other components that have appeared for the recent years are reviewed. The major improvements were achieving close-to-full coverage on glycans from microorganisms, launching modules for glycosyltransferases and saccharide conformations, online glycan builder and 3D modeler, NMR simulator, NMR-based structure predictor, and other tools.
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Affiliation(s)
- Philip Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow, Russia.
- Faculty of Chemistry, National Research University Higher School of Economics, Moscow, Russia.
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2
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Liu Y, Huang Y, Zhu R, Farag MA, Capanoglu E, Zhao C. Structural elucidation approaches in carbohydrates: A comprehensive review on techniques and future trends. Food Chem 2023; 400:134118. [DOI: 10.1016/j.foodchem.2022.134118] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 09/01/2022] [Indexed: 10/14/2022]
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3
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2019-2020. MASS SPECTROMETRY REVIEWS 2022:e21806. [PMID: 36468275 DOI: 10.1002/mas.21806] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/17/2023]
Abstract
This review is the tenth update of the original article published in 1999 on the application of matrix-assisted laser desorption/ionization (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2020. Also included are papers that describe methods appropriate to analysis by MALDI, such as sample preparation techniques, even though the ionization method is not MALDI. The review is basically divided into three sections: (1) general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, quantification and the use of arrays. (2) Applications to various structural types such as oligo- and polysaccharides, glycoproteins, glycolipids, glycosides and biopharmaceuticals, and (3) other areas such as medicine, industrial processes and glycan synthesis where MALDI is extensively used. Much of the material relating to applications is presented in tabular form. The reported work shows increasing use of incorporation of new techniques such as ion mobility and the enormous impact that MALDI imaging is having. MALDI, although invented nearly 40 years ago is still an ideal technique for carbohydrate analysis and advancements in the technique and range of applications show little sign of diminishing.
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Affiliation(s)
- David J Harvey
- Nuffield Department of Medicine, Target Discovery Institute, University of Oxford, Oxford, UK
- Department of Chemistry, University of Oxford, Oxford, Oxfordshire, United Kingdom
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4
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Toukach PV, Shirkovskaya AI. Carbohydrate Structure Database and Other Glycan Databases as an Important Element of Glycoinformatics. RUSSIAN JOURNAL OF BIOORGANIC CHEMISTRY 2022. [DOI: 10.1134/s1068162022030190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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5
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Toukach PV, Egorova KS. Source files of the Carbohydrate Structure Database: the way to sophisticated analysis of natural glycans. Sci Data 2022; 9:131. [PMID: 35354826 PMCID: PMC8968703 DOI: 10.1038/s41597-022-01186-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Accepted: 02/03/2022] [Indexed: 11/18/2022] Open
Abstract
The Carbohydrate Structure Database (CSDB, http://csdb.glycoscience.ru/ ) is a free curated repository storing various data on glycans of bacterial, fungal and plant origins. Currently, it maintains a close-to-full coverage on bacterial and fungal carbohydrates up to the year 2020. The CSDB web-interface provides free access to the database content and dedicated tools. Still, the number of these tools and the types of the corresponding analyses is limited, whereas the database itself contains data that can be used in a broader scope of analytical studies. In this paper, we present CSDB source data files and a self-contained SQL dump, and exemplify their possible application in glycan-related studies. By using CSDB in an SQL format, the user can gain access to the chain length distribution or charge distribution (as an example) in a given set of glycans defined according to specific structural, taxonomic, or other parameters, whereas the source text dump files can be imported to any dedicated database with a specific internal architecture differing from that of CSDB.
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Affiliation(s)
- Philip V Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia.
| | - Ksenia S Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prospect 47, Moscow, 119991, Russia.
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6
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Scherbinina SI, Toukach PV. Three-Dimensional Structures of Carbohydrates and Where to Find Them. Int J Mol Sci 2020; 21:E7702. [PMID: 33081008 PMCID: PMC7593929 DOI: 10.3390/ijms21207702] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 02/06/2023] Open
Abstract
Analysis and systematization of accumulated data on carbohydrate structural diversity is a subject of great interest for structural glycobiology. Despite being a challenging task, development of computational methods for efficient treatment and management of spatial (3D) structural features of carbohydrates breaks new ground in modern glycoscience. This review is dedicated to approaches of chemo- and glyco-informatics towards 3D structural data generation, deposition and processing in regard to carbohydrates and their derivatives. Databases, molecular modeling and experimental data validation services, and structure visualization facilities developed for last five years are reviewed.
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Affiliation(s)
- Sofya I. Scherbinina
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
- Higher Chemical College, D. Mendeleev University of Chemical Technology of Russia, Miusskaya Square 9, 125047 Moscow, Russia
| | - Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Science, Leninsky prospect 47, 119991 Moscow, Russia
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Böhm M, Bohne-Lang A, Frank M, Loss A, Rojas-Macias MA, Lütteke T. Glycosciences.DB: an annotated data collection linking glycomics and proteomics data (2018 update). Nucleic Acids Res 2020; 47:D1195-D1201. [PMID: 30357361 PMCID: PMC6323918 DOI: 10.1093/nar/gky994] [Citation(s) in RCA: 58] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2018] [Accepted: 10/12/2018] [Indexed: 12/21/2022] Open
Abstract
Glycosciences.DB, the glycan structure database of the Glycosciences.de portal, collects various kinds of data on glycan structures, including carbohydrate moieties from worldwide Protein Data Bank (wwPDB) structures. This way it forms a bridge between glycomics and proteomics resources. A major update of this database combines a redesigned web interface with a series of new functions. These include separate entry pages not only for glycan structures but also for literature references and wwPDB entries, improved substructure search options, a newly available keyword search covering all types of entries in one query, and new types of information that is added to glycan structures. These new features are described in detail in this article, and options how users can provide information to the database are discussed as well. Glycosciences.DB is available at http://www.glycosciences.de/database/ and can be freely accessed.
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Affiliation(s)
- Michael Böhm
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
| | - Andreas Bohne-Lang
- Medical Faculty Mannheim, University Heidelberg, Ludolf-Krehl-Str. 13-17, 68167 Mannheim, Germany
| | - Martin Frank
- Biognos AB, Generatorsgatan 1, Box 8963, 40274 Göteborg, Sweden
| | - Alexander Loss
- Gebrüder Gerstenberg GmbH & Co. KG, EDV, Rathausstraße 18-20, 31134 Hildesheim, Germany
| | - Miguel A Rojas-Macias
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
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8
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Aoki-Kinoshita KF, Lisacek F, Mazumder R, York WS, Packer NH. The GlySpace Alliance: toward a collaborative global glycoinformatics community. Glycobiology 2020; 30:70-71. [PMID: 31573039 PMCID: PMC6992953 DOI: 10.1093/glycob/cwz078] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Revised: 09/19/2019] [Accepted: 09/20/2019] [Indexed: 11/13/2022] Open
Affiliation(s)
- Kiyoko F Aoki-Kinoshita
- Glycan & Life Science Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo, Japan, 192-8577
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Computer Science Department, University of Geneva, route de Drize 7, CH - 1227 Geneva Switzerland, and also Section of Biology, University of Geneva, Geneva, Switzerland
| | - Raja Mazumder
- The Department of Biochemistry & Molecular Medicine, School of Medicine and Health Sciences, George Washington University Medical Center, Washington, DC, USA and the Department of Medicine, School of Medicine and Health Sciences, George Washington University, Ross Hall, 2300 Eye St., NW, Washington, DC 20037, USA
| | - William S York
- Complex Carbohydrate Research Center, University of Georgia, 315 Riverbend Road, Athens, GA 30602, USA
| | - Nicolle H Packer
- Department of Molecular Sciences, Faculty of Science & Engineering, Rm 307, Building E8C, Macquarie University, Sydney, NSW 2109, Australia
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9
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Toukach PV, Egorova KS. New Features of Carbohydrate Structure Database Notation (CSDB Linear), As Compared to Other Carbohydrate Notations. J Chem Inf Model 2019; 60:1276-1289. [DOI: 10.1021/acs.jcim.9b00744] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Affiliation(s)
- Philip V. Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
- National Research University Higher School of Economics, Myasnitskaya 20, Moscow, Russia 101000
| | - Ksenia S. Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Leninsky prosect 47, Moscow, Russia 119991
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10
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Nagai-Okatani C, Aoki-Kinoshita KF, Kakuda S, Nagai M, Hagiwara K, Kiyohara K, Fujita N, Suzuki Y, Sato T, Angata K, Kuno A. LM-GlycomeAtlas Ver. 1.0: A Novel Visualization Tool for Lectin Microarray-Based Glycomic Profiles of Mouse Tissue Sections. Molecules 2019; 24:molecules24162962. [PMID: 31443278 PMCID: PMC6719194 DOI: 10.3390/molecules24162962] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/05/2019] [Accepted: 08/12/2019] [Indexed: 12/24/2022] Open
Abstract
For the effective discovery of the biological roles and disease-specific alterations concerning protein glycosylation in tissue samples, it is important to know beforehand the quantitative and qualitative variations of glycan structures expressed in various types of cells, sites, and tissues. To this end, we used laser microdissection-assisted lectin microarray (LMA) to establish a simple and reproducible method for high-throughput and in-depth glycomic profiling of formalin-fixed paraffin-embedded tissue sections. Using this “tissue glycome mapping” approach, we present 234 glycomic profiling data obtained from nine tissue sections (pancreas, heart, lung, thymus, gallbladder, stomach, small intestine, colon, and skin) of two 8-week-old male C57BL/6J mice. We provided this LMA-based dataset in the similar interface as that of GlycomeAtlas, a previously developed tool for mass spectrometry-based tissue glycomic profiling, allowing easy comparison of the two types of data. This online tool, called “LM-GlycomeAtlas”, allows users to visualize the LMA-based tissue glycomic profiling data associated with the sample information as an atlas. Since the present dataset allows the comparison of glycomic profiles, it will facilitate the evaluation of site- and tissue-specific glycosylation patterns. Taking advantage of its extensibility, this tool will continue to be updated with the expansion of deposited data.
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Affiliation(s)
- Chiaki Nagai-Okatani
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan.
| | - Kiyoko F Aoki-Kinoshita
- Glycan & Life Science Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Shuichi Kakuda
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Misugi Nagai
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Kozue Hagiwara
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Katsue Kiyohara
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Noriaki Fujita
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Yoshinori Suzuki
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Takashi Sato
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Kiyohiko Angata
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan
| | - Atsushi Kuno
- Glycoscience and Glycotechnology Research Group, Biotechnology Research Institute for Drug Discovery, National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba, Ibaraki 305-8568, Japan.
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11
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Egorova KS, Toukach PV. Glykoinformatik: Brücken zwischen isolierten Inseln im Datenmeer. Angew Chem Int Ed Engl 2018. [DOI: 10.1002/ange.201803576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russland
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12
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Egorova KS, Toukach PV. Glycoinformatics: Bridging Isolated Islands in the Sea of Data. Angew Chem Int Ed Engl 2018; 57:14986-14990. [DOI: 10.1002/anie.201803576] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2018] [Indexed: 11/07/2022]
Affiliation(s)
- Ksenia S. Egorova
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
| | - Philip V. Toukach
- Zelinsky Institute of Organic ChemistryRussian Academy of Sciences Leninsky Prospect 47 Moscow 119991 Russia
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13
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Smith J, Mittermayr S, Váradi C, Bones J. Quantitative glycomics using liquid phase separations coupled to mass spectrometry. Analyst 2018; 142:700-720. [PMID: 28170017 DOI: 10.1039/c6an02715f] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Post-translational modification of proteins by the attachment of glycans is governed by a variety of highly specific enzymes and is associated with fundamental impacts on the parent protein's physical, chemical and biological properties. The inherent connection between cellular physiology and specific glycosylation patterns has been shown to offer potential for diagnostic and prognostic monitoring of altered glycosylation in the disease state. Conversely, glycoprotein based biopharmaceuticals have emerged as dominant therapeutic strategies in the treatment of intricate diseases. Glycosylation present on these biopharmaceuticals represents a major critical quality attribute with impacts on both pharmacokinetics and pharmacodynamics. The structural variety of glycans, based upon their non-template driven assembly, poses a significant analytical challenge for both qualitative and quantitative analysis. Labile monosaccharide constituents, isomeric species and often low sample availability from biological sources necessitates meticulous sample handling, ultra-high-resolution analytical separation and sensitive detection techniques, respectively. In this article a critical review of analytical quantitation approaches using liquid phase separations coupled to mass spectrometry for released glycans of biopharmaceutical and biomedical significance is presented. Considerations associated with sample derivatisation strategies, ionisation, relative quantitation through isotopic as well as isobaric labelling, metabolic/enzymatic incorporation and targeted analysis are all thoroughly discussed.
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Affiliation(s)
- Josh Smith
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland. and School of Biochemistry and Immunology, Trinity Biomedical Sciences Institute, Trinity College Dublin, 152-160 Pearse Street, Dublin 2, D02 R590, Ireland
| | - Stefan Mittermayr
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland.
| | - Csaba Váradi
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland.
| | - Jonathan Bones
- National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Dublin, A94 X099, Ireland. and School of Chemical and Bioprocess Engineering, University College Dublin, Belfield, Dublin 4, D04 V1 W8, Ireland
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14
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Corfield AP. The Interaction of the Gut Microbiota with the Mucus Barrier in Health and Disease in Human. Microorganisms 2018; 6:microorganisms6030078. [PMID: 30072673 PMCID: PMC6163557 DOI: 10.3390/microorganisms6030078] [Citation(s) in RCA: 81] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2018] [Revised: 07/25/2018] [Accepted: 07/30/2018] [Indexed: 02/07/2023] Open
Abstract
Glycoproteins are major players in the mucus protective barrier in the gastrointestinal and other mucosal surfaces. In particular the mucus glycoproteins, or mucins, are responsible for the protective gel barrier. They are characterized by their high carbohydrate content, present in their variable number, tandem repeat domains. Throughout evolution the mucins have been maintained as integral components of the mucosal barrier, emphasizing their essential biological status. The glycosylation of the mucins is achieved through a series of biosynthetic pathways processes, which generate the wide range of glycans found in these molecules. Thus mucins are decorated with molecules having information in the form of a glycocode. The enteric microbiota interacts with the mucosal mucus barrier in a variety of ways in order to fulfill its many normal processes. How bacteria read the glycocode and link to normal and pathological processes is outlined in the review.
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Affiliation(s)
- Anthony P Corfield
- Mucin Research Group, School of Clinical Sciences, Bristol Royal Infirmary, Level 7, Marlborough Street, Bristol BS2 8HW, UK.
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2013-2014. MASS SPECTROMETRY REVIEWS 2018; 37:353-491. [PMID: 29687922 DOI: 10.1002/mas.21530] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2016] [Accepted: 11/29/2016] [Indexed: 06/08/2023]
Abstract
This review is the eighth update of the original article published in 1999 on the application of Matrix-assisted laser desorption/ionization mass spectrometry (MALDI) mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2014. Topics covered in the first part of the review include general aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, fragmentation, and arrays. The second part of the review is devoted to applications to various structural types such as oligo- and poly- saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. The third part of the review covers medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2018 Wiley Periodicals, Inc. Mass Spec Rev 37:353-491, 2018.
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Affiliation(s)
- David J Harvey
- Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom
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16
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Adua E, Russell A, Roberts P, Wang Y, Song M, Wang W. Innovation Analysis on Postgenomic Biomarkers: Glycomics for Chronic Diseases. ACTA ACUST UNITED AC 2017; 21:183-196. [DOI: 10.1089/omi.2017.0035] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Affiliation(s)
- Eric Adua
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Alyce Russell
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Peter Roberts
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
| | - Youxin Wang
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Manshu Song
- Beijing Key Laboratory of Clinical Epidemiology, School of Public Health, Capital Medical University, Beijing, China
| | - Wei Wang
- School of Medical and Health Sciences, Edith Cowan University, Perth, Australia
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17
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A Golden Age for Working with Public Proteomics Data. Trends Biochem Sci 2017; 42:333-341. [PMID: 28118949 PMCID: PMC5414595 DOI: 10.1016/j.tibs.2017.01.001] [Citation(s) in RCA: 66] [Impact Index Per Article: 9.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2016] [Revised: 12/13/2016] [Accepted: 01/02/2017] [Indexed: 11/23/2022]
Abstract
Data sharing in mass spectrometry (MS)-based proteomics is becoming a common scientific practice, as is now common in the case of other, more mature ‘omics’ disciplines like genomics and transcriptomics. We want to highlight that this situation, unprecedented in the field, opens a plethora of opportunities for data scientists. First, we explain in some detail some of the work already achieved, such as systematic reanalysis efforts. We also explain existing applications of public proteomics data, such as proteogenomics and the creation of spectral libraries and spectral archives. Finally, we discuss the main existing challenges and mention the first attempts to combine public proteomics data with other types of omics data sets. The field of proteomics has matured and diversified substantially over the past 10 years. Proteomics data are increasingly shared through centralized, public repositories. Standardization efforts have ensured that a large proportion of these public data can be read and processed by any interested researcher. Because any proteomics data set is only partially understood, there is great opportunity for (orthogonal) reuse of public data. While public proteomics data has so far remained outside ethics and privacy discussions, recent work indicates that there is an inherent risk.
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Campbell MP. A Review of Software Applications and Databases for the Interpretation of Glycopeptide Data. TRENDS GLYCOSCI GLYC 2017. [DOI: 10.4052/tigg.1601.1e] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022]
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Abstract
Simple and complex carbohydrates (glycans) have long been known to play major metabolic, structural and physical roles in biological systems. Targeted microbial binding to host glycans has also been studied for decades. But such biological roles can only explain some of the remarkable complexity and organismal diversity of glycans in nature. Reviewing the subject about two decades ago, one could find very few clear-cut instances of glycan-recognition-specific biological roles of glycans that were of intrinsic value to the organism expressing them. In striking contrast there is now a profusion of examples, such that this updated review cannot be comprehensive. Instead, a historical overview is presented, broad principles outlined and a few examples cited, representing diverse types of roles, mediated by various glycan classes, in different evolutionary lineages. What remains unchanged is the fact that while all theories regarding biological roles of glycans are supported by compelling evidence, exceptions to each can be found. In retrospect, this is not surprising. Complex and diverse glycans appear to be ubiquitous to all cells in nature, and essential to all life forms. Thus, >3 billion years of evolution consistently generated organisms that use these molecules for many key biological roles, even while sometimes coopting them for minor functions. In this respect, glycans are no different from other major macromolecular building blocks of life (nucleic acids, proteins and lipids), simply more rapidly evolving and complex. It is time for the diverse functional roles of glycans to be fully incorporated into the mainstream of biological sciences.
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Affiliation(s)
- Ajit Varki
- Departments of Medicine and Cellular & Molecular Medicine, Glycobiology Research and Training Center, University of California at San Diego, La Jolla, CA 92093-0687, USA
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20
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Khan AH, Bayat H, Rajabibazl M, Sabri S, Rahimpour A. Humanizing glycosylation pathways in eukaryotic expression systems. World J Microbiol Biotechnol 2016; 33:4. [DOI: 10.1007/s11274-016-2172-7] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2016] [Accepted: 11/04/2016] [Indexed: 01/27/2023]
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21
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Walsh I, Zhao S, Campbell M, Taron CH, Rudd PM. Quantitative profiling of glycans and glycopeptides: an informatics' perspective. Curr Opin Struct Biol 2016; 40:70-80. [PMID: 27522273 DOI: 10.1016/j.sbi.2016.07.022] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Revised: 07/25/2016] [Accepted: 07/30/2016] [Indexed: 12/16/2022]
Abstract
Experimental techniques to identify and quantify glycan structures in a given sample are continuously improving. However, as they advance data analysis and annotation seems to become more complex. To address this issue, much progress has been made in developing software for interpretation of quantitative glycan profiles. Here, we focus on these informatics tools for high/ultra performance liquid chromatography (H/UPLC), mass spectrometry (MS), tandem mass spectrometry (MSn) and combinations thereof. Software for biomarker discovery, pathway, genomic and disease analysis and a final note on some future prospects for glycoinformatics are also mentioned.
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Affiliation(s)
- Ian Walsh
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore; New England Biolabs, Ipswich, MA, United States
| | - Sophie Zhao
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore
| | - Matthew Campbell
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | | | - Pauline M Rudd
- Bioprocessing Technology Institute, Agency for Science, Technology and Research (A*STAR), 20 Biopolis Way, #06-01 Centros, Singapore 138668, Singapore; National Institute for Bioprocessing Research & Training, Dublin, Ireland.
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Toukach PV, Egorova KS. Carbohydrate structure database merged from bacterial, archaeal, plant and fungal parts. Nucleic Acids Res 2015; 44:D1229-36. [PMID: 26286194 PMCID: PMC4702937 DOI: 10.1093/nar/gkv840] [Citation(s) in RCA: 134] [Impact Index Per Article: 14.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2015] [Accepted: 08/07/2015] [Indexed: 12/31/2022] Open
Abstract
The Carbohydrate Structure Databases (CSDBs, http://csdb.glycoscience.ru) store structural, bibliographic, taxonomic, NMR spectroscopic, and other data on natural carbohydrates and their derivatives published in the scientific literature. The CSDB project was launched in 2005 for bacterial saccharides (as BCSDB). Currently, it includes two parts, the Bacterial CSDB and the Plant&Fungal CSDB. In March 2015, these databases were merged to the single CSDB. The combined CSDB includes information on bacterial and archaeal glycans and derivatives (the coverage is close to complete), as well as on plant and fungal glycans and glycoconjugates (almost all structures published up to 1998). CSDB is regularly updated via manual expert annotation of original publications. Both newly annotated data and data imported from other databases are manually curated. The CSDB data are exportable in a number of modern formats, such as GlycoRDF. CSDB provides additional services for simulation of (1)H, (13)C and 2D NMR spectra of saccharides, NMR-based structure prediction, glycan-based taxon clustering and other.
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Affiliation(s)
- Philip V Toukach
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
| | - Ksenia S Egorova
- N.D. Zelinsky Institute of Organic Chemistry, Russian Academy of Sciences, Moscow 119991, Russia
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23
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Yuriev E, Ramsland PA. Carbohydrates in Cyberspace. Front Immunol 2015; 6:300. [PMID: 26113848 PMCID: PMC4462093 DOI: 10.3389/fimmu.2015.00300] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Accepted: 05/26/2015] [Indexed: 11/13/2022] Open
Affiliation(s)
- Elizabeth Yuriev
- Monash Institute of Pharmaceutical Sciences, Monash University , Parkville, VIC , Australia
| | - Paul A Ramsland
- Centre for Biomedical Research, Burnet Institute , Melbourne, VIC , Australia ; Department of Immunology, Alfred Medical Research and Education Precinct, Monash University , Melbourne, VIC , Australia ; Department of Surgery Austin Health, University of Melbourne , Heidelberg, VIC , Australia ; CHIRI Biosciences, School of Biomedical Sciences, Curtin University , Perth, WA , Australia
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24
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Liu G, Neelamegham S. Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data. WILEY INTERDISCIPLINARY REVIEWS-SYSTEMS BIOLOGY AND MEDICINE 2015; 7:163-81. [PMID: 25871730 DOI: 10.1002/wsbm.1296] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2014] [Revised: 02/08/2015] [Accepted: 03/04/2015] [Indexed: 12/22/2022]
Abstract
The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.
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Affiliation(s)
- Gang Liu
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
| | - Sriram Neelamegham
- Department of Chemical and Biological Engineering, State University of New York, Buffalo, NY, USA
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25
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Aoki-Kinoshita KF, Bolleman J, Campbell MP, Kawano S, Kim JD, Lütteke T, Matsubara M, Okuda S, Ranzinger R, Sawaki H, Shikanai T, Shinmachi D, Suzuki Y, Toukach P, Yamada I, Packer NH, Narimatsu H. Introducing glycomics data into the Semantic Web. J Biomed Semantics 2013; 4:39. [PMID: 24280648 PMCID: PMC4177142 DOI: 10.1186/2041-1480-4-39] [Citation(s) in RCA: 45] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2013] [Accepted: 10/17/2013] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND Glycoscience is a research field focusing on complex carbohydrates (otherwise known as glycans)a, which can, for example, serve as "switches" that toggle between different functions of a glycoprotein or glycolipid. Due to the advancement of glycomics technologies that are used to characterize glycan structures, many glycomics databases are now publicly available and provide useful information for glycoscience research. However, these databases have almost no link to other life science databases. RESULTS In order to implement support for the Semantic Web most efficiently for glycomics research, the developers of major glycomics databases agreed on a minimal standard for representing glycan structure and annotation information using RDF (Resource Description Framework). Moreover, all of the participants implemented this standard prototype and generated preliminary RDF versions of their data. To test the utility of the converted data, all of the data sets were uploaded into a Virtuoso triple store, and several SPARQL queries were tested as "proofs-of-concept" to illustrate the utility of the Semantic Web in querying across databases which were originally difficult to implement. CONCLUSIONS We were able to successfully retrieve information by linking UniCarbKB, GlycomeDB and JCGGDB in a single SPARQL query to obtain our target information. We also tested queries linking UniProt with GlycoEpitope as well as lectin data with GlycomeDB through PDB. As a result, we have been able to link proteomics data with glycomics data through the implementation of Semantic Web technologies, allowing for more flexible queries across these domains.
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Affiliation(s)
- Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Faculty of Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Jerven Bolleman
- Swiss Institute of Bioinformatics, CMU 1, rue Michel Servet 1211, Geneva 4, Switzerland
| | - Matthew P Campbell
- Biomolecular Frontiers Research Centre, Macquarie University, Sydney, New South Wales, Australia
| | - Shin Kawano
- Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Jin-Dong Kim
- Database Center for Life Science, Research Organization of Information and Systems, 2-11-16 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan
| | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Frankfurter Str. 100, 35392 Giessen, Germany
| | - Masaaki Matsubara
- Laboratory of Glyco-organic Chemistry, The Noguchi Institute, 1-8-1 Kaga, Itabashi-ku, Tokyo 173-0003, Japan
| | - Shujiro Okuda
- Department of Bioinformatics, College of Life Sciences, Ritsumeikan University, 1-1-1 Nojihigashi, Kusatsu, Shiga 525-8577, Japan
- Niigata University Graduate School of Medical and Dental Sciences, 1-757 Asahimachi-dori, Chuo-ku, Niigata 951-8510, Japan
| | - Rene Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602, USA
| | - Hiromichi Sawaki
- Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba Central-2, Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Toshihide Shikanai
- Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba Central-2, Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Daisuke Shinmachi
- Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba Central-2, Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Yoshinori Suzuki
- Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba Central-2, Umezono 1-1-1, Tsukuba 305-8568, Japan
| | - Philip Toukach
- NMR Laboratory, N.D. Zelinsky Institute of Organic Chemistry, Leninsky prospekt 47, 119991 Moscow, Russia
| | - Issaku Yamada
- Laboratory of Glyco-organic Chemistry, The Noguchi Institute, 1-8-1 Kaga, Itabashi-ku, Tokyo 173-0003, Japan
| | - Nicolle H Packer
- Biomolecular Frontiers Research Centre, Macquarie University, Sydney, New South Wales, Australia
| | - Hisashi Narimatsu
- Research Center for Medical Glycoscience, National Institute of Advanced Industrial Science and Technology, Tsukuba Central-2, Umezono 1-1-1, Tsukuba 305-8568, Japan
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