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Bailey LS, Huang F, Gao T, Zhao J, Basso KB, Guo Z. Characterization of Glycosphingolipids and Their Diverse Lipid Forms through Two-Stage Matching of LC-MS/MS Spectra. Anal Chem 2021; 93:3154-3162. [PMID: 33534538 DOI: 10.1021/acs.analchem.0c04542] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Glycosphingolipids (GSLs) play a key role in various biological and pathological events. Thus, determination of the complete GSL compositions in human tissues is essential for comparative and functional studies of GSLs. In this work, a new strategy was developed for GSL characterization and glycolipidomics analysis based on two-stage matching of experimental and reference MS/MS spectra. In the first stage, carbohydrate fragments, which contain only glycans and thus are conserved within a GSL species, are directly matched to yield a species identification. In the second stage, glycolipid fragments from the matched GSL species, which contain both the lipid and glycans and thus shift due to lipid structural changes, are treated according to lipid rule-based matching to characterize the lipid compositions. This new strategy uses the whole spectrum for GSL characterization. Furthermore, simple databases containing only a single lipid form per GSL species can be utilized to identify multiple GSL lipid forms. It is expected that this method will help accelerate glycolipidomics analysis and disclose new and diverse lipid forms of GSLs.
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Hwang H, Jeong HK, Lee HK, Park GW, Lee JY, Lee SY, Kang YM, An HJ, Kang JG, Ko JH, Kim JY, Yoo JS. Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry. Sci Rep 2020; 10:318. [PMID: 31941975 PMCID: PMC6962204 DOI: 10.1038/s41598-019-57274-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 12/27/2019] [Indexed: 12/14/2022] Open
Abstract
Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.
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Affiliation(s)
- Heeyoun Hwang
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Hoi Keun Jeong
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Hyun Kyoung Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Gun Wook Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Ju Yeon Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Soo Youn Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Young-Mook Kang
- Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Korea
| | - Hyun Joo An
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea.,Asia Glycomics Reference Site, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Jeong Gu Kang
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Jeong-Heon Ko
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.,Department of Biomolecular Science, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Jin Young Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea. .,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea.
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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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Lee LY, Moh ESX, Parker BL, Bern M, Packer NH, Thaysen-Andersen M. Toward Automated N-Glycopeptide Identification in Glycoproteomics. J Proteome Res 2016; 15:3904-3915. [DOI: 10.1021/acs.jproteome.6b00438] [Citation(s) in RCA: 89] [Impact Index Per Article: 11.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ling Y. Lee
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Edward S. X. Moh
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Benjamin L. Parker
- Charles
Perkins Centre, School of Molecular Bioscience, The University of Sydney, Sydney, Australia
| | - Marshall Bern
- Protein Metrics
Inc., San Carlos, California 94070, United States
| | - Nicolle H. Packer
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Morten Thaysen-Andersen
- Department
of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW 2109, Australia
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Thaysen-Andersen M, Packer NH, Schulz BL. Maturing Glycoproteomics Technologies Provide Unique Structural Insights into the N-glycoproteome and Its Regulation in Health and Disease. Mol Cell Proteomics 2016; 15:1773-90. [PMID: 26929216 PMCID: PMC5083109 DOI: 10.1074/mcp.o115.057638] [Citation(s) in RCA: 137] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Revised: 02/09/2016] [Indexed: 12/21/2022] Open
Abstract
The glycoproteome remains severely understudied because of significant analytical challenges associated with glycoproteomics, the system-wide analysis of intact glycopeptides. This review introduces important structural aspects of protein N-glycosylation and summarizes the latest technological developments and applications in LC-MS/MS-based qualitative and quantitative N-glycoproteomics. These maturing technologies provide unique structural insights into the N-glycoproteome and its synthesis and regulation by complementing existing methods in glycoscience. Modern glycoproteomics is now sufficiently mature to initiate efforts to capture the molecular complexity displayed by the N-glycoproteome, opening exciting opportunities to increase our understanding of the functional roles of protein N-glycosylation in human health and disease.
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Affiliation(s)
- Morten Thaysen-Andersen
- From the ‡Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia;
| | - Nicolle H Packer
- From the ‡Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Benjamin L Schulz
- §School of Chemistry & Molecular Biosciences, St Lucia, The University of Queensland, Brisbane, QLD, Australia
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A review of methods for interpretation of glycopeptide tandem mass spectral data. Glycoconj J 2015; 33:285-96. [PMID: 26612686 DOI: 10.1007/s10719-015-9633-3] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2015] [Revised: 10/13/2015] [Accepted: 10/21/2015] [Indexed: 12/25/2022]
Abstract
Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.
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Spahn PN, Lewis NE. Systems glycobiology for glycoengineering. Curr Opin Biotechnol 2014; 30:218-24. [PMID: 25202878 DOI: 10.1016/j.copbio.2014.08.004] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2014] [Revised: 08/14/2014] [Accepted: 08/15/2014] [Indexed: 12/21/2022]
Abstract
Glycosylation serves essential functions on many proteins produced in biopharmaceutical manufacturing, making it mandatory to thoroughly consider its biogenesis during the production process. Glycoengineering efforts involve the rational design of glycosylation through adjustments in culturing conditions or genetic modifications. Computational models have been developed to aid this process, aiming to offer cheaper and faster alternatives to costly screening strategies. Recently, these models have been successfully utilized to predict glycosylation of products of industrial relevance. Furthermore, systems-level analyses of glycan diversity are elucidating deeper insights into the mechanisms underlying glycosylation. As computational models of glycosylation continue to be expanded, refined, and leveraged for detailed analysis of glycomics data, they will become invaluable resources for cell line development and glycoengineering.
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Affiliation(s)
- Philipp N Spahn
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, United States
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, United States.
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