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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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Zhu Y. Plasma/Serum Proteomics based on Mass Spectrometry. Protein Pept Lett 2024; 31:192-208. [PMID: 38869039 PMCID: PMC11165715 DOI: 10.2174/0109298665286952240212053723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 06/14/2024]
Abstract
Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.
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Affiliation(s)
- Yiying Zhu
- Department of Chemistry, Tsinghua University, Beijing, China
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3
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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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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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Veličković D, Bečejac T, Mamedov S, Sharma K, Ambalavanan N, Alexandrov T, Anderton CR. Rapid Automated Annotation and Analysis of N-Glycan Mass Spectrometry Imaging Data Sets Using NGlycDB in METASPACE. Anal Chem 2021; 93:13421-13425. [PMID: 34581565 DOI: 10.1021/acs.analchem.1c02347] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
Imaging N-glycan spatial distribution in tissues using mass spectrometry imaging (MSI) is emerging as a promising tool in biological and clinical applications. However, there is currently no high-throughput tool for visualization and molecular annotation of N-glycans in MSI data, which significantly slows down data processing and hampers the applicability of this approach. Here, we present how METASPACE, an open-source cloud engine for molecular annotation of MSI data, can be used to automatically annotate, visualize, analyze, and interpret high-resolution mass spectrometry-based spatial N-glycomics data. METASPACE is an emerging tool in spatial metabolomics, but the lack of compatible glycan databases has limited its application for comprehensive N-glycan annotations from MSI data sets. We created NGlycDB, a public database of N-glycans, by adapting available glycan databases. We demonstrate the applicability of NGlycDB in METASPACE by analyzing MALDI-MSI data from formalin-fixed paraffin-embedded (FFPE) human kidney and mouse lung tissue sections. We added NGlycDB to METASPACE for public use, thus, facilitating applications of MSI in glycobiology.
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Affiliation(s)
- Dušan Veličković
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Tamara Bečejac
- Energy and Environment Directorate, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sergii Mamedov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany
| | - Kumar Sharma
- Center for Renal Precision Medicine, Division of Nephrology, Department of Medicine, The University of Texas Health, San Antonio, Texas 78229, United States
| | - Namasivayam Ambalavanan
- Department of Pediatrics, School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama 35294, United States
| | - Theodore Alexandrov
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany.,Molecular Medicine Partnership Unit, European Molecular Biology Laboratory, 69117, Heidelberg, Germany.,Skaggs School of Pharmacy and Pharmaceutical Sciences, University of California, San Diego, California 92093, United States
| | - Christopher R Anderton
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, Washington 99354, United States.,Center for Renal Precision Medicine, Division of Nephrology, Department of Medicine, The University of Texas Health, San Antonio, Texas 78229, United States
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6
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Glycoinformatics Tools for Comprehensive Characterization of Glycans Enzymatically Released from Proteins. Methods Mol Biol 2021. [PMID: 34611862 DOI: 10.1007/978-1-0716-1685-7_1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register]
Abstract
Glycosylation is important in biology, contributing to both protein conformation and function. Structurally, glycosylation is complex and diverse. This complexity is reflected in the topology, composition, monosaccharide linkages, and isomerism of each oligosaccharide. Glycoanalytics is a discipline that addresses the understanding and characterization of this complexity and its correlation with biology. It includes analytical steps such as sample preparation, instrument measurements, and data analyses. Of these, data analysis has emerged as a critical bottleneck because data collection has increasingly become high-throughput. This has resulted in data-rich workflows that lack rapid and automated data analytics. To address this issue, the field has been developing software for interpretation of quantitative glycomics studies. Here, we describe a protocol using available informatics tools for analysis of data from analysis of released glycans using high-/ultraperformance liquid chromatography (H/UPLC) coupled with mass spectrometry (MS).
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7
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Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJH, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 2021; 184:5031-5052.e26. [PMID: 34534465 PMCID: PMC8654574 DOI: 10.1016/j.cell.2021.08.023] [Citation(s) in RCA: 227] [Impact Index Per Article: 75.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/19/2021] [Accepted: 08/18/2021] [Indexed: 02/07/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.
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Affiliation(s)
- Liwei Cao
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - T Mamie Lih
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | | | | | - Weiming Yang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ruiyang Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Houxiang Zhu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Peter Ronning
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Hariharan Easwaran
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ludmila Danilova
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Seungyeul Yoo
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | | | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael H Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Pei Wang
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | | | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Oliver F Bathe
- Departments of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
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8
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Heffner KM, Wang Q, Hizal DB, Can Ö, Betenbaugh MJ. Glycoengineering of Mammalian Expression Systems on a Cellular Level. ADVANCES IN BIOCHEMICAL ENGINEERING/BIOTECHNOLOGY 2021. [PMID: 29532110 DOI: 10.1007/10_2017_57] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Mammalian expression systems such as Chinese hamster ovary (CHO), mouse myeloma (NS0), and human embryonic kidney (HEK) cells serve a critical role in the biotechnology industry as the production host of choice for recombinant protein therapeutics. Most of the recombinant biologics are glycoproteins that contain complex oligosaccharide or glycan attachments representing a principal component of product quality. Both N-glycans and O-glycans are present in these mammalian cells, but the engineering of N-linked glycosylation is of critical interest in industry and many efforts have been directed to improve this pathway. This is because altering the N-glycan composition can change the product quality of recombinant biotherapeutics in mammalian hosts. In addition, sialylation and fucosylation represent components of the glycosylation pathway that affect circulatory half-life and antibody-dependent cellular cytotoxicity, respectively. In this chapter, we first offer an overview of the glycosylation, sialylation, and fucosylation networks in mammalian cells, specifically CHO cells, which are extensively used in antibody production. Next, genetic engineering technologies used in CHO cells to modulate glycosylation pathways are described. We provide examples of their use in CHO cell engineering approaches to highlight these technologies further. Specifically, we describe efforts to overexpress glycosyltransferases and sialyltransfereases, and efforts to decrease sialidase cleavage and fucosylation. Finally, this chapter covers new strategies and future directions of CHO cell glycoengineering, such as the application of glycoproteomics, glycomics, and the integration of 'omics' approaches to identify, quantify, and characterize the glycosylated proteins in CHO cells. Graphical Abstract.
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Affiliation(s)
- Kelley M Heffner
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Qiong Wang
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Deniz Baycin Hizal
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA
| | - Özge Can
- Department of Medical Engineering, Acibadem Mehmet Ali Aydinlar University, Istanbul, Turkey
| | - Michael J Betenbaugh
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, USA.
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9
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Shakyawar SK, Pandey S, Harvey DJ, Bousfield G, Guda C. FGDB: Database of follicle stimulating hormone glycans. Comput Struct Biotechnol J 2021; 19:1635-1640. [PMID: 33897975 PMCID: PMC8050109 DOI: 10.1016/j.csbj.2021.03.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 03/04/2021] [Accepted: 03/06/2021] [Indexed: 11/12/2022] Open
Abstract
Glycomics, the study of the entire complement of sugars of an organism has received significant attention in the recent past due to the advances made in high throughput mass spectrometry technologies. These analytical advancements have facilitated the characterization of glycans associated with the follicle-stimulating hormones (FSH), which play a central role in the human reproductive system both in males and females utilizing regulating gonadal (testicular and ovarian) functions. The irregularities in FSH activity are also directly linked with osteoporosis. The glycoanalytical studies have been tremendously helpful in understanding the biological roles of FSH. Subsequently, the increasing number of characterized FSH glycan structures and related glycoform data has thrown a challenge to the glycoinformatics community in terms of data organization, storage and access. Also, a user-friendly platform is needed for providing easy access to the database and performing integrated analysis using a high volume of experimental data to accelerate FSH-focused research. FSH Glycans DataBase (FGDB) serves as a comprehensive and unique repository of structures, features, and related information of glycans associated with FSH. Apart from providing multiple search options, the database also facilitates an integrated user-friendly interface to perform the glycan abundance and comparative analyses using experimental data. The automated integrated pipelines present the possible structures of glycans and variants of FSH based on the input data, and allow the user to perform various analyses. The potential application of FGDB will significantly help both glycoinformaticians as well as wet-lab researchers to stimulate the research in this area. FGDB web access: https://fgdb.unmc.edu/.
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Affiliation(s)
- Sushil K Shakyawar
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - Sanjit Pandey
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, United States
| | - David J Harvey
- Target Discovery Institute, Nuffield Department of Medicine, Oxford University, Oxford OX3 7FZ, United Kingdom
| | - George Bousfield
- Fairmount College of Liberal Arts and Sciences, Wichita State University, KS 67260, United States
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE 68198, United States
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10
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Yamada I, Campbell MP, Edwards N, Castro LJ, Lisacek F, Mariethoz J, Ono T, Ranzinger R, Shinmachi D, Aoki-Kinoshita KF. The glycoconjugate ontology (GlycoCoO) for standardizing the annotation of glycoconjugate data and its application. Glycobiology 2021; 31:741-750. [PMID: 33677548 PMCID: PMC8351504 DOI: 10.1093/glycob/cwab013] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 12/31/2020] [Accepted: 01/01/2021] [Indexed: 01/19/2023] Open
Abstract
Recent years have seen great advances in the development of glycoproteomics protocols and methods resulting in a sustainable increase in the reporting proteins, their attached glycans and glycosylation sites. However, only very few of these reports find their way into databases or data repositories. One of the major reasons is the absence of digital standard to represent glycoproteins and the challenging annotations with glycans. Depending on the experimental method, such a standard must be able to represent glycans as complete structures or as compositions, store not just single glycans but also represent glycoforms on a specific glycosylation side, deal with partially missing site information if no site mapping was performed, and store abundances or ratios of glycans within a glycoform of a specific site. To support the above, we have developed the GlycoConjugate Ontology (GlycoCoO) as a standard semantic framework to describe and represent glycoproteomics data. GlycoCoO can be used to represent glycoproteomics data in triplestores and can serve as a basis for data exchange formats. The ontology, database providers and supporting documentation are available online (https://github.com/glycoinfo/GlycoCoO).
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Affiliation(s)
- Issaku Yamada
- Research Department, The Noguchi Institute, 1-9-7 Kaga, Itabashi, Tokyo 173-0003, Japan
| | - Matthew P Campbell
- Institute for Glycomics, Griffith University at Gold Coast, Southport, QLD 4215, Australia
| | - Nathan Edwards
- Department of Biochemistry, Molecular and Cellular Biology, Georgetown University Medical Center, Washington, D.C. 20007, USA
| | - Leyla Jael Castro
- ZB MED Information Centre for Life Sciences, Gleueler Str. 60, 50931 Cologne, Germany
| | - 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
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, 7 Route de Drize, 1227 Geneva, Switzerland
| | - Tamiko Ono
- Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
| | - Rene Ranzinger
- Complex Carbohydrate Research Center, The University of Georgia, 315 Riverbend Rd, Athens, Georgia 30602, USA
| | - Daisuke Shinmachi
- R&D Department, SparqLite LLC., 1615-22 Ishikawamachi, Hachioji, Tokyo 192-0032, Japan
| | - Kiyoko F Aoki-Kinoshita
- Glycan & Life Science Integration Center (GaLSIC), Faculty of Science and Engineering, Soka University, 1-236 Tangi-machi, Hachioji, Tokyo 192-8577, Japan
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11
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Zeng WF, Cao WQ, Liu MQ, He SM, Yang PY. Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3. Nat Methods 2021; 18:1515-1523. [PMID: 34824474 PMCID: PMC8648562 DOI: 10.1038/s41592-021-01306-0] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 09/21/2021] [Indexed: 11/09/2022]
Abstract
Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.
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Affiliation(s)
- Wen-Feng Zeng
- Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China. .,University of Chinese Academy of Sciences, Beijing, China. .,Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.
| | - Wei-Qian Cao
- grid.8547.e0000 0001 0125 2443Shanghai Fifth People’s Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China
| | - Ming-Qi Liu
- grid.8547.e0000 0001 0125 2443Shanghai Fifth People’s Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China
| | - Si-Min He
- grid.424936.e0000 0001 2221 3902Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China ,grid.410726.60000 0004 1797 8419University of Chinese Academy of Sciences, Beijing, China
| | - Peng-Yuan Yang
- grid.8547.e0000 0001 0125 2443Shanghai Fifth People’s Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443The Shanghai Key Laboratory of Medical Epigenetics and the International Co-laboratory of Medical Epigenetics and Metabolism, Ministry of Science and Technology, Fudan University, Shanghai, China ,grid.8547.e0000 0001 0125 2443Department of Chemistry, Fudan University, Shanghai, China
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12
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Insights into Bioinformatic Applications for Glycosylation: Instigating an Awakening towards Applying Glycoinformatic Resources for Cancer Diagnosis and Therapy. Int J Mol Sci 2020; 21:ijms21249336. [PMID: 33302373 PMCID: PMC7762546 DOI: 10.3390/ijms21249336] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2020] [Revised: 11/26/2020] [Accepted: 12/01/2020] [Indexed: 01/10/2023] Open
Abstract
Glycosylation plays a crucial role in various diseases and their etiology. This has led to a clear understanding on the functions of carbohydrates in cell communication, which eventually will result in novel therapeutic approaches for treatment of various disease. Glycomics has now become one among the top ten technologies that will change the future. The direct implication of glycosylation as a hallmark of cancer and for cancer therapy is well established. As in proteomics, where bioinformatics tools have led to revolutionary achievements, bioinformatics resources for glycosylation have improved its practical implication. Bioinformatics tools, algorithms and databases are a mandatory requirement to manage and successfully analyze large amount of glycobiological data generated from glycosylation studies. This review consolidates all the available tools and their applications in glycosylation research. The achievements made through the use of bioinformatics into glycosylation studies are also presented. The importance of glycosylation in cancer diagnosis and therapy is discussed and the gap in the application of widely available glyco-informatic tools for cancer research is highlighted. This review is expected to bring an awakening amongst glyco-informaticians as well as cancer biologists to bridge this gap, to exploit the available glyco-informatic tools for cancer.
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13
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Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer. Nat Commun 2020; 11:6139. [PMID: 33262351 PMCID: PMC7708455 DOI: 10.1038/s41467-020-19976-3] [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: 04/04/2020] [Accepted: 10/26/2020] [Indexed: 02/08/2023] Open
Abstract
Inter-tumor heterogeneity is a result of genomic, transcriptional, translational, and post-translational molecular features. To investigate the roles of protein glycosylation in the heterogeneity of high-grade serous ovarian carcinoma (HGSC), we perform mass spectrometry-based glycoproteomic characterization of 119 TCGA HGSC tissues. Cluster analysis of intact glycoproteomic profiles delineates 3 major tumor clusters and 5 groups of intact glycopeptides. It also shows a strong relationship between N-glycan structures and tumor molecular subtypes, one example of which being the association of fucosylation with mesenchymal subtype. Further survival analysis reveals that intact glycopeptide signatures of mesenchymal subtype are associated with a poor clinical outcome of HGSC. In addition, we study the expression of mRNAs, proteins, glycosites, and intact glycopeptides, as well as the expression levels of glycosylation enzymes involved in glycoprotein biosynthesis pathways in each tumor. The results show that glycoprotein levels are mainly controlled by the expression of their individual proteins, and, furthermore, that the glycoprotein-modifying glycans correspond to the protein levels of glycosylation enzymes. The variation in glycan types further shows coordination to the tumor heterogeneity. Deeper understanding of the glycosylation process and glycosylation production in different subtypes of HGSC may provide important clues for precision medicine and tumor-targeted therapy. Altered protein glycosylation is increasingly recognized as a hallmark of cancer. Here, the authors profile the glycoproteome of 119 high-grade serous ovarian carcinoma tissues, showing that glycosylation patterns correlate with tumor molecular subtypes and clinical outcomes.
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14
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Hu Y, Pan J, Shah P, Ao M, Thomas SN, Liu Y, Chen L, Schnaubelt M, Clark DJ, Rodriguez H, Boja ES, Hiltke T, Kinsinger CR, Rodland KD, Li QK, Qian J, Zhang Z, Chan DW, Zhang H. Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma. Cell Rep 2020; 33:108276. [PMID: 33086064 PMCID: PMC7970828 DOI: 10.1016/j.celrep.2020.108276] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2019] [Revised: 06/18/2020] [Accepted: 09/23/2020] [Indexed: 12/12/2022] Open
Abstract
Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potential roles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.
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Affiliation(s)
- Yingwei Hu
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Jianbo Pan
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Punit Shah
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Minghui Ao
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Stefani N Thomas
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Yang Liu
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Tara Hiltke
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Christopher R Kinsinger
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Karin D Rodland
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA 99352, USA
| | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Jiang Qian
- Department of Ophthalmology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Zhen Zhang
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA.
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, School of Medicine, Baltimore, MD 21287, USA.
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15
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Databases and Bioinformatic Tools for Glycobiology and Glycoproteomics. Int J Mol Sci 2020; 21:ijms21186727. [PMID: 32937895 PMCID: PMC7556027 DOI: 10.3390/ijms21186727] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/03/2020] [Accepted: 09/11/2020] [Indexed: 02/07/2023] Open
Abstract
Glycosylation plays critical roles in various biological processes and is closely related to diseases. Deciphering the glycocode in diverse cells and tissues offers opportunities to develop new disease biomarkers and more effective recombinant therapeutics. In the past few decades, with the development of glycobiology, glycomics, and glycoproteomics technologies, a large amount of glycoscience data has been generated. Subsequently, a number of glycobiology databases covering glycan structure, the glycosylation sites, the protein scaffolds, and related glycogenes have been developed to store, analyze, and integrate these data. However, these databases and tools are not well known or widely used by the public, including clinicians and other researchers who are not in the field of glycobiology, but are interested in glycoproteins. In this study, the representative databases of glycan structure, glycoprotein, glycan-protein interactions, glycogenes, and the newly developed bioinformatic tools and integrated portal for glycoproteomics are reviewed. We hope this overview could assist readers in searching for information on glycoproteins of interest, and promote further clinical application of glycobiology.
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16
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Wang Y, Bu D, Huang C, Wang H, Zhou J, Dong J, Pan W, Zhang J, Zhang Q, Li Y, Sun S. Best-first search guided multistage mass spectrometry-based glycan identification. Bioinformatics 2020; 35:2991-2997. [PMID: 30689704 DOI: 10.1093/bioinformatics/btz056] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2019] [Revised: 01/02/2019] [Accepted: 01/19/2019] [Indexed: 12/22/2022] Open
Abstract
MOTIVATION Glycan identification has long been hampered by complicated branching patterns and various isomeric structures of glycans. Multistage mass spectrometry (MSn) is a promising glycan identification technique as it generates multiple-level fragments of a glycan, which can be explored to deduce branching pattern of the glycan and further distinguish it from other candidates with identical mass. However, the automatic glycan identification still remains a challenge since it mainly relies on expertise to guide a MSn instrument to generate spectra. RESULTS Here, we proposed a novel method, named bestFSA, based on a best-first search algorithm to guide the process of spectrum producing in glycan identification using MSn. BestFSA is able to select the most appropriate peaks for next round of experiments and complete the identification using as few experimental rounds. Our analysis of seven representative glycans shows that bestFSA correctly distinguishes actual glycans efficiently and suggested bestFSA could be used in practical glycan identification. The combination of the MSn technology coupled with bestFSA should greatly facilitate the automatic identification of glycan branching patterns, with significantly improved identification sensitivity, and reduce time and cost of MSn experiments. AVAILABILITY AND IMPLEMENTATION http://glycan.ict.ac.cn. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Yaojun Wang
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,Guanghua School of Management, Peking University, Beijing, China
| | - Dongbo Bu
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China
| | - Chuncui Huang
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Hui Wang
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jinyu Zhou
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Junchuan Dong
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Weiyi Pan
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Jingwei Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Qi Zhang
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Yan Li
- Institute of Biophysics, Chinese Academy of Sciences, Beijing, China
| | - Shiwei Sun
- Key Lab of Intelligent Information Processing, Institute of Computing and Technology, Chinese Academy of Sciences, Beijing, China
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17
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Multistage mass spectrometry with intelligent precursor selection for N-glycan branching pattern analysis. Carbohydr Polym 2020; 237:116122. [DOI: 10.1016/j.carbpol.2020.116122] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2019] [Revised: 02/10/2020] [Accepted: 03/03/2020] [Indexed: 12/28/2022]
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18
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Shu Q, Li M, Shu L, An Z, Wang J, Lv H, Yang M, Cai T, Hu T, Fu Y, Yang F. Large-scale Identification of N-linked Intact Glycopeptides in Human Serum using HILIC Enrichment and Spectral Library Search. Mol Cell Proteomics 2020; 19:672-689. [PMID: 32102970 PMCID: PMC7124471 DOI: 10.1074/mcp.ra119.001791] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 02/10/2020] [Indexed: 11/12/2022] Open
Abstract
Large-scale identification of N-linked intact glycopeptides by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) in human serum is challenging because of the wide dynamic range of serum protein abundances, the lack of a complete serum N-glycan database and the existence of proteoforms. In this regard, a spectral library search method was presented for the identification of N-linked intact glycopeptides from N-linked glycoproteins in human serum with target-decoy and motif-specific false discovery rate (FDR) control. Serum proteins were firstly separated into low-abundance and high-abundance proteins by acetonitrile (ACN) precipitation. After digestion, the N-linked intact glycopeptides were enriched by hydrophilic interaction liquid chromatography (HILIC) and a portion of the enriched N-linked intact glycopeptides were processed by Peptide-N-Glycosidase F (PNGase F) to generate N-linked deglycopeptides. Both N-linked intact glycopeptides and deglycopeptides were analyzed by LC-MS/MS. From N-linked deglycopeptides data sets, 764 N-linked glycoproteins, 1699 N-linked glycosites and 3328 unique N-linked deglycopeptides were identified. Four types of N-linked glycosylation motifs (NXS/T/C/V, X≠P) were used to recognize the N-linked deglycopeptides. The spectra of these N-linked deglycopeptides were utilized for N-linked deglycopeptides library construction and identification of N-linked intact glycopeptides. A database containing 739 N-glycan masses was constructed and utilized during spectral library search for the identification of N-linked intact glycopeptides. In total, 526 N-linked glycoproteins, 1036 N-linked glycosites, 22,677 N-linked intact glycopeptides and 738 N-glycan masses were identified under 1% FDR, representing the most in-depth serum N-glycoproteome identified by LC-MS/MS at N-linked intact glycopeptide level.
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Affiliation(s)
- Qingbo Shu
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112
| | - Mengjie Li
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112
| | - Lian Shu
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhiwu An
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112
| | - Jifeng Wang
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China
| | - Hao Lv
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112; Research Center for Basic Sciences of Medicine, Basic Medical College, Guizhou Medical University, Guiyang 550025, China
| | - Ming Yang
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112
| | - Tanxi Cai
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112
| | - Tony Hu
- National Center for Mathematics and Interdisciplinary Sciences, Key Laboratory of Random Complex Structures and Data Science, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
| | - Yan Fu
- Computer Network Information Center, Chinese Academy of Sciences, Beijing 100101, China; Center for Cellular and Molecular Diagnostics, Department of Biochemistry and Molecular Biology, School of Medicine, Tulane University, New Orleans, Louisiana 70112.
| | - Fuquan Yang
- Laboratory of Protein and Peptide Pharmaceuticals & Proteomics Laboratory, Institute of Biophysics, Chinese Academy of Sciences, Beijing 100101, China; University of Chinese Academy of Sciences, Beijing 100049, China.
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19
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Weatherly DB, Arpinar FS, Porterfield M, Tiemeyer M, York WS, Ranzinger R. GRITS Toolbox-a freely available software for processing, annotating and archiving glycomics mass spectrometry data. Glycobiology 2020; 29:452-460. [PMID: 30913289 DOI: 10.1093/glycob/cwz023] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 03/13/2019] [Accepted: 03/20/2019] [Indexed: 11/14/2022] Open
Abstract
Mass spectrometry (MS) is one of the most effective techniques for high-throughput, high-resolution characterization of glycan structures. Although many software applications have been developed over the last decades for the interpretation of MS data of glycan structures, only a few are capable of dealing with the large data sets produced by glycomics analysis. Furthermore, these applications utilize databases that can lead to redundant glycan annotations and do not support post-processing of the data within the software or by third party applications. To address the needs, we present GRITS Toolbox, a freely-available, platform-independent software application capable of storing and processing glycomics MS data along with associated metadata. GRITS Toolbox automatically annotates MS data using an integrated glycan identification module that references manually curated databases of mammalian glycans (provided with the software) or any user-defined databases. Extensive display routines are provided to post-process the data and refine the automated annotation using expert knowledge of the user. The software also allows side by side comparison of annotations from different MS runs or samples and exporting of annotations into Excel format.
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Affiliation(s)
- D Brent Weatherly
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - F Sena Arpinar
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Melody Porterfield
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - William S York
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
| | - Rene Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, USA
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20
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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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21
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Nagarajan B, Sankaranarayanan NV, Desai UR. Perspective on computational simulations of glycosaminoglycans. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL MOLECULAR SCIENCE 2019; 9:e1388. [PMID: 31080520 PMCID: PMC6504973 DOI: 10.1002/wcms.1388] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Accepted: 07/07/2018] [Indexed: 01/06/2023]
Abstract
Glycosaminoglycans (GAGs) represent a formidable frontier for chemists, biochemists, biologists, medicinal chemists and drug delivery specialists because of massive structural complexity. GAGs are arguably the most complex, natural linear biopolymers with theoretical diversity orders of magnitude higher than proteins and nucleic acids. Yet, this diversity remains generally untapped. Computational approaches offer major routes to understand GAG structure and dynamics so as to enable novel applications of these biopolymers. In fact, computational algorithms, softwares, online tools and techniques have reached a level of sophistication that help understand atomistic details of conformational variation and protein recognition of individual GAG sequences. This review describes current approaches and challenges in computational study of GAGs. It presents a history of major findings since the earliest mention of GAGs (the 1960s), the development of parameters and force fields specific for GAGs, and the application of these tools in understanding GAG structure-function relationship. This review also presents a section on how to perform simulation of GAGs, which is directed toward researchers interested in entering this promising field with potential to impact therapy.
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Affiliation(s)
- Balaji Nagarajan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Nehru Viji Sankaranarayanan
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
| | - Umesh R. Desai
- Institute for Structural Biology, Drug Discovery and Development, Virginia Commonwealth University, Richmond,
VA 23298, USA
- Department of Medicinal Chemistry, Virginia Commonwealth University, Richmond, VA 23298, USA
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22
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Sun S, Huang C, Wang Y, Liu Y, Zhang J, Zhou J, Gao F, Yang F, Chen R, Mulloy B, Chai W, Li Y, Bu D. Toward Automated Identification of Glycan Branching Patterns Using Multistage Mass Spectrometry with Intelligent Precursor Selection. Anal Chem 2018; 90:14412-14422. [DOI: 10.1021/acs.analchem.8b03967] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
- Shiwei Sun
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Chuncui Huang
- Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China
| | - Yaojun Wang
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Yaming Liu
- Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Jingwei Zhang
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Jinyu Zhou
- Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Feng Gao
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Fei Yang
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Runsheng Chen
- Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Barbara Mulloy
- Glycosciences Laboratory, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Wengang Chai
- Glycosciences Laboratory, Faculty of Medicine, Imperial College London, London W12 0NN, United Kingdom
| | - Yan Li
- Institute of Biophysics, Chinese Academy of Sciences, 15 Datun Road, Beijing 100101, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
| | - Dongbo Bu
- Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, 6 Kexueyuan South Road, Beijing 100080, China
- University of Chinese Academy of Sciences, 19 Yuquan Road, Beijing 100049, China
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23
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Ma Q, Adua E, Boyce MC, Li X, Ji G, Wang W. IMass Time: The Future, in Future! ACTA ACUST UNITED AC 2018; 22:679-695. [DOI: 10.1089/omi.2018.0162] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Qingwei Ma
- Bioyong (Beijing) Technology Co., Ltd., Beijing, China
| | - Eric Adua
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Mary C. Boyce
- School of Science, Edith Cowan University, Joondalup, Australia
| | - Xingang Li
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
| | - Guang Ji
- China-Canada Centre of Research for Digestive Diseases, University of Ottawa, Ottawa, Canada
- Institute of Digestive Diseases, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, China
| | - Wei Wang
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Australia
- School of Public Health, Taishan Medical University, Taian, China
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24
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N-Linked Glycopeptide Identification Based on Open Mass Spectral Library Search. BIOMED RESEARCH INTERNATIONAL 2018; 2018:1564136. [PMID: 30186849 PMCID: PMC6112209 DOI: 10.1155/2018/1564136] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 07/16/2018] [Accepted: 07/29/2018] [Indexed: 02/05/2023]
Abstract
Confident characterization of intact glycopeptides is a challenging task in mass spectrometry-based glycoproteomics due to microheterogeneity of glycosylation, complexity of glycans, and insufficient fragmentation of peptide bones. Open mass spectral library search is a promising computational approach to peptide identification, but its potential in the identification of glycopeptides has not been fully explored. Here we present pMatchGlyco, a new spectral library search tool for intact N-linked glycopeptide identification using high-energy collisional dissociation (HCD) tandem mass spectrometry (MS/MS) data. In pMatchGlyco, (1) MS/MS spectra of deglycopeptides are used to create spectral library, (2) MS/MS spectra of glycopeptides are matched to the spectra in library in an open (precursor tolerant) manner and the glycans are inferred, and (3) a false discovery rate is estimated for top-scored matches above a threshold. The efficiency and reliability of pMatchGlyco were demonstrated on a data set of mixture sample of six standard glycoproteins and a complex glycoprotein data set generated from human cancer cell line OVCAR3.
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25
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Hu Y, Shah P, Clark DJ, Ao M, Zhang H. Reanalysis of Global Proteomic and Phosphoproteomic Data Identified a Large Number of Glycopeptides. Anal Chem 2018; 90:8065-8071. [PMID: 29741879 PMCID: PMC6440470 DOI: 10.1021/acs.analchem.8b01137] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Protein glycosylation plays fundamental roles in many cellular processes, and previous reports have shown dysregulation to be associated with several human diseases, including diabetes, cancer, and neurodegenerative disorders. Despite the vital role of glycosylation for proper protein function, the analysis of glycoproteins has been lagged behind to other protein modifications. In this study, we describe the reanalysis of global proteomic data from breast cancer xenograft tissues using recently developed software package GPQuest 2.0, revealing a large number of previously unidentified N-linked glycopeptides. More importantly, we found that using immobilized metal affinity chromatography (IMAC) technology for the enrichment of phosphopeptides had coenriched a substantial number of sialoglycopeptides, allowing for a large-scale analysis of sialoglycopeptides in conjunction with the analysis of phosphopeptides. Collectively, combined tandem mass spectrometry (MS/MS) analyses of global proteomic and phosphoproteomic data sets resulted in the identification of 6 724 N-linked glycopeptides from 617 glycoproteins derived from two breast cancer xenograft tissues. Next, we utilized GPQuest 2.0 for the reanalysis of global and phosphoproteomic data generated from 108 human breast cancer tissues that were previously analyzed by Clinical Proteomic Analysis Consortium (CPTAC). Reanalysis of the CPTAC data set resulted in the identification of 2 683 glycopeptides from the global proteomic data set and 4 554 glycopeptides from phosphoproteomic data set, respectively. Together, 11 292 N-linked glycopeptides corresponding to 1 731 N-linked glycosites from 883 human glycoproteins were identified from the two data sets. This analysis revealed an extensive number of glycopeptides hidden in the global and enriched in IMAC-based phosphopeptide-enriched proteomic data, information which would have remained unknown from the original study otherwise. The reanalysis described herein can be readily applied to identify glycopeptides from already existing data sets, providing insight into many important facets of protein glycosylation in different biological, physiological, and pathological processes.
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Affiliation(s)
- Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Punit Shah
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - David J. Clark
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Minghui Ao
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21287, United States
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, Maryland 21287, United States
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26
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Khatri K, Pu Y, Klein JA, Wei J, Costello CE, Lin C, Zaia J. Comparison of Collisional and Electron-Based Dissociation Modes for Middle-Down Analysis of Multiply Glycosylated Peptides. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2018; 29:1075-1085. [PMID: 29663256 PMCID: PMC6004259 DOI: 10.1007/s13361-018-1909-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2017] [Revised: 01/09/2018] [Accepted: 01/29/2018] [Indexed: 05/12/2023]
Abstract
Analysis of singly glycosylated peptides has evolved to a point where large-scale LC-MS analyses can be performed at almost the same scale as proteomics experiments. While collisionally activated dissociation (CAD) remains the mainstay of bottom-up analyses, it performs poorly for the middle-down analysis of multiply glycosylated peptides. With improvements in instrumentation, electron-activated dissociation (ExD) modes are becoming increasingly prevalent for proteomics experiments and for the analysis of fragile modifications such as glycosylation. While these methods have been applied for glycopeptide analysis in isolated studies, an organized effort to compare their efficiencies, particularly for analysis of multiply glycosylated peptides (termed here middle-down glycoproteomics), has not been made. We therefore compared the performance of different ExD modes for middle-down glycopeptide analyses. We identified key features among the different dissociation modes and show that increased electron energy and supplemental activation provide the most useful data for middle-down glycopeptide analysis. Graphical Abstract.
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Affiliation(s)
- Kshitij Khatri
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA
| | - Yi Pu
- Department of Chemistry, Boston University, Boston, MA, USA
| | - Joshua A Klein
- Program in Bioinformatics, Boston University, Boston, MA, USA
| | - Juan Wei
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA
| | - Catherine E Costello
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA
- Department of Chemistry, Boston University, Boston, MA, USA
| | - Cheng Lin
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA.
- Boston University Medical Campus, 670 Albany St., Suite 504, Boston, MA, 02118, USA.
| | - Joseph Zaia
- Department of Biochemistry, Center for Biomedical Mass Spectrometry, Boston University School of Medicine, Boston, MA, USA.
- Program in Bioinformatics, Boston University, Boston, MA, USA.
- Boston University Medical Campus, 670 Albany St., Suite 504, Boston, MA, 02118, USA.
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27
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Sun W, Liu Y, Zhang K. An approach for N-linked glycan identification from MS/MS spectra by target-decoy strategy. Comput Biol Chem 2018; 74:391-398. [PMID: 29580737 DOI: 10.1016/j.compbiolchem.2018.03.014] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2018] [Accepted: 03/13/2018] [Indexed: 12/28/2022]
Abstract
Glycan structure determination serves as an essential step for the thorough investigation of the structure and function of protein. Currently, appropriate sample preparation followed by tandem mass spectrometry has emerged as the dominant technique for the characterization of glycans and glycopeptides. Although extensive efforts have been made to the development of computational approaches for the automated interpretation of glycopeptide spectra, the previously appeared methods lack a reasonable quality control strategy for the statistical validation of reported results. In this manuscript, we introduced a novel method that constructed a decoy glycan database based on the glycan structures in the target database, and searched the experimental spectra against both the target and decoy databases to find the best matched glycans. Specifically, a two-layer scoring scheme for calculating a normalized matching score is applied in the search procedure which enables the unbiased ranking of the matched glycans. Experimental analysis showed that our proposed method can report more structures with high confidence compared with previous approaches.
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Affiliation(s)
- Weiping Sun
- Department of Computer Science, University of Western Ontario, London, ON N6A5B7, Canada.
| | - Yi Liu
- Department of Computer Science, University of Western Ontario, London, ON N6A5B7, Canada
| | - Kaizhong Zhang
- Department of Computer Science, University of Western Ontario, London, ON N6A5B7, Canada
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28
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Horlacher O, Jin C, Alocci D, Mariethoz J, Müller M, Karlsson NG, Lisacek F. Glycoforest 1.0. Anal Chem 2017; 89:10932-10940. [PMID: 28901741 DOI: 10.1021/acs.analchem.7b02754] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Tandem mass spectrometry, when combined with liquid chromatography and applied to complex mixtures, produces large amounts of raw data, which needs to be analyzed to identify molecular structures. This technique is widely used, particularly in glycomics. Due to a lack of high throughput glycan sequencing software, glycan spectra are predominantly sequenced manually. A challenge for writing glycan-sequencing software is that there is no direct template that can be used to infer structures detectable in an organism. To help alleviate this bottleneck, we present Glycoforest 1.0, a partial de novo algorithm for sequencing glycan structures based on MS/MS spectra. Glycoforest was tested on two data sets (human gastric and salmon mucosa O-linked glycomes) for which MS/MS spectra were annotated manually. Glycoforest generated the human validated structure for 92% of test cases. The correct structure was found as the best scoring match for 70% and among the top 3 matches for 83% of test cases. In addition, the Glycoforest algorithm detected glycan structures from MS/MS spectra missing a manual annotation. In total 1532 MS/MS previously unannotated spectra were annotated by Glycoforest. A portion containing 521 spectra was manually checked confirming that Glycoforest annotated an additional 50 MS/MS spectra overlooked during manual annotation.
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Affiliation(s)
- Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva, 1211, Switzerland.,University of Geneva , Geneva, 1211, Switzerland
| | - Chunsheng Jin
- Glyco Inflammatory Group, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg , Gothenburg, SE405 30, Sweden
| | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva, 1211, Switzerland.,University of Geneva , Geneva, 1211, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva, 1211, Switzerland.,University of Geneva , Geneva, 1211, Switzerland
| | - Markus Müller
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva, 1211, Switzerland.,University of Geneva , Geneva, 1211, Switzerland
| | - Niclas G Karlsson
- Glyco Inflammatory Group, Department of Medical Biochemistry and Cell Biology, Institute of Biomedicine, Sahlgrenska Academy, University of Gothenburg , Gothenburg, SE405 30, Sweden
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics , Geneva, 1211, Switzerland.,University of Geneva , Geneva, 1211, Switzerland
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29
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Yang S, Clark D, Liu Y, Li S, Zhang H. High-throughput analysis of N-glycans using AutoTip via glycoprotein immobilization. Sci Rep 2017; 7:10216. [PMID: 28860471 PMCID: PMC5578957 DOI: 10.1038/s41598-017-10487-8] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2017] [Accepted: 08/11/2017] [Indexed: 12/15/2022] Open
Abstract
Analysis of a large number of samples requires an efficient, rapid and reproducible method. Automation is an ideal approach for high-throughput sample preparation. Multi-plexing sample preparation via a 96-well plate format becomes popular in recent years; however, those methods lack specificity and require several cleanup steps via chromatography purification. To overcome these drawbacks, a chemoenzymatic method has been developed utilizing protein conjugation on solid-phase. Previously, sample preparation was successfully performed in a snap-cap spin-column (SCSC) format. However, sample preparation using SCSC is time-consuming and lacks reproducibility. In this work, we integrated the chemoenzymatic technique in a pipette tip (AutoTip) that was operated by an automated liquid handler. We established a multi-step protocol involving protein immobilization, sialic acid modification, and N-glycan release. We first optimized our automated protocol using bovine fetuin as a standard glycoprotein, and then assessed the reproducibility of the AutoTip using isobaric tags for relative N-linked glycan quantification. We then applied this methodology to profile N-glycans from 58 prostate cancer patient urine samples, revealing increased sialyation on urinary N-glycans derived from prostate cancer patients. Our results indicated AutoTip has applications for high-throughput sample preparation for studying the N-linked glycans.
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Affiliation(s)
- Shuang Yang
- Department of Pathology, Johns Hopkins Medicine, Baltimore, MD, USA.
| | - David Clark
- Department of Pathology, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Yang Liu
- Department of Pathology, Johns Hopkins Medicine, Baltimore, MD, USA
| | - Shuwei Li
- Institute for Bioscience and Biotechnology Research, University of Maryland College Park, Rockville, MD, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins Medicine, Baltimore, MD, USA
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30
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Aeschbacher T, Zierke M, Smieško M, Collot M, Mallet JM, Ernst B, Allain FHT, Schubert M. A Secondary Structural Element in a Wide Range of Fucosylated Glycoepitopes. Chemistry 2017; 23:11598-11610. [PMID: 28654715 DOI: 10.1002/chem.201701866] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Indexed: 01/12/2023]
Abstract
The increasing understanding of the essential role of carbohydrates in development, and in a wide range of diseases fuels a rapidly growing interest in the basic principles governing carbohydrate-protein interactions. A still heavily debated issue regarding the recognition process is the degree of flexibility or rigidity of oligosaccharides. Combining NMR structure determination based on extensive experimental data with DFT and database searches, we have identified a set of trisaccharide motifs with a similar conformation that is characterized by a non-conventional C-H⋅⋅⋅O hydrogen bond. These motifs are present in numerous classes of oligosaccharides, found in everything from bacteria to mammals, including Lewis blood group antigens but also unusual motifs from amphibians and marine invertebrates. The set of trisaccharide motifs can be summarized with the consensus motifs X-β1,4-[Fucα1,3]-Y and X-β1,3-[Fucα1,4]-Y-a secondary structure we name [3,4]F-branch. The wide spectrum of possible modifications of this scaffold points toward a large variety of glycoepitopes, which nature generated using the same underlying architecture.
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Affiliation(s)
- Thomas Aeschbacher
- Institute of Molecular Biology and Biophysics, ETH Zürich, 8093, Zurich, Switzerland
| | - Mirko Zierke
- Institute of Molecular Pharmacy, University of Basel, Klingelbergstr. 50, 4056, Basel, Switzerland.,Department of Chemistry, University of British Columbia, V6T 1Z1, Vancouver, British Columbia, Canada
| | - Martin Smieško
- Institute of Molecular Pharmacy, University of Basel, Klingelbergstr. 50, 4056, Basel, Switzerland
| | - Mayeul Collot
- Laboratoire des Biomolécules, Département de Chimie, École normale supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 24 rue Lhomond, 75005, Paris, France.,UMR 7213 CNRS, Laboratoire de Biophotonique et Pharmacologie, Faculté de Pharmacie, 74 route du Rhin, CS 60024, 67401, Illkirch, France
| | - Jean-Maurice Mallet
- Laboratoire des Biomolécules, Département de Chimie, École normale supérieure, PSL Research University, Sorbonne Universités, UPMC Univ. Paris 06, CNRS, 24 rue Lhomond, 75005, Paris, France
| | - Beat Ernst
- Institute of Molecular Pharmacy, University of Basel, Klingelbergstr. 50, 4056, Basel, Switzerland
| | - Frédéric H-T Allain
- Institute of Molecular Biology and Biophysics, ETH Zürich, 8093, Zurich, Switzerland
| | - Mario Schubert
- Institute of Molecular Biology and Biophysics, ETH Zürich, 8093, Zurich, Switzerland.,Institute of Molecular Biology, University of Salzburg, Billrothstr. 11, 5020, Salzburg, Austria
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31
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Harvey DJ. Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: An update for 2011-2012. MASS SPECTROMETRY REVIEWS 2017; 36:255-422. [PMID: 26270629 DOI: 10.1002/mas.21471] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2014] [Accepted: 01/15/2015] [Indexed: 06/04/2023]
Abstract
This review is the seventh update of the original article published in 1999 on the application of MALDI mass spectrometry to the analysis of carbohydrates and glycoconjugates and brings coverage of the literature to the end of 2012. General aspects such as theory of the MALDI process, matrices, derivatization, MALDI imaging, and fragmentation are covered in the first part of the review and applications to various structural types constitute the remainder. The main groups of compound are oligo- and poly-saccharides, glycoproteins, glycolipids, glycosides, and biopharmaceuticals. Much of this material is presented in tabular form. Also discussed are medical and industrial applications of the technique, studies of enzyme reactions, and applications to chemical synthesis. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 36:255-422, 2017.
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Affiliation(s)
- David J Harvey
- Department of Biochemistry, Oxford Glycobiology Institute, University of Oxford, Oxford, OX1 3QU, UK
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32
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Matsubara M, Aoki-Kinoshita KF, Aoki NP, Yamada I, Narimatsu H. WURCS 2.0 Update To Encapsulate Ambiguous Carbohydrate Structures. J Chem Inf Model 2017; 57:632-637. [DOI: 10.1021/acs.jcim.6b00650] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
| | - Kiyoko F. Aoki-Kinoshita
- Faculty
of Science and Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
- 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
| | - Nobuyuki P. Aoki
- Faculty
of Science and Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Issaku Yamada
- The Noguchi Institute, Itabashi, Tokyo 173-0003, Japan
| | - Hisashi Narimatsu
- 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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33
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Moriarty NW, Draizen EJ, Adams PD. An editor for the generation and customization of geometry restraints. Acta Crystallogr D Struct Biol 2017; 73:123-130. [PMID: 28177308 PMCID: PMC5297915 DOI: 10.1107/s2059798316016570] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 10/17/2016] [Indexed: 11/17/2022] Open
Abstract
Chemical restraints for use in macromolecular structure refinement are produced by a variety of methods, including a number of programs that use chemical information to generate the required bond, angle, dihedral, chiral and planar restraints. These programs help to automate the process and therefore minimize the errors that could otherwise occur if it were performed manually. Furthermore, restraint-dictionary generation programs can incorporate chemical and other prior knowledge to provide reasonable choices of types and values. However, the use of restraints to define the geometry of a molecule is an approximation introduced with efficiency in mind. The representation of a bond as a parabolic function is a convenience and does not reflect the true variability in even the simplest of molecules. Another complicating factor is the interplay of the molecule with other parts of the macromolecular model. Finally, difficult situations arise from molecules with rare or unusual moieties that may not have their conformational space fully explored. These factors give rise to the need for an interactive editor for WYSIWYG interactions with the restraints and molecule. Restraints Editor, Especially Ligands (REEL) is a graphical user interface for simple and error-free editing along with additional features to provide greater control of the restraint dictionaries in macromolecular refinement.
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Affiliation(s)
- Nigel W. Moriarty
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
| | - Eli J. Draizen
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
| | - Paul D. Adams
- Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, One Cyclotron Road, Berkeley, CA 94720, USA
- Department of Bioengineering, University of California Berkeley, Berkeley, CA 94720, USA
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34
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Toghi Eshghi S, Yang W, Hu Y, Shah P, Sun S, Li X, Zhang H. Classification of Tandem Mass Spectra for Identification of N- and O-linked Glycopeptides. Sci Rep 2016; 6:37189. [PMID: 27869200 PMCID: PMC5116676 DOI: 10.1038/srep37189] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2016] [Accepted: 10/26/2016] [Indexed: 11/29/2022] Open
Abstract
Analysis of intact glycopeptides by mass spectrometry is essential to determining the microheterogeneity of protein glycosylation. Higher-energy collisional dissociation (HCD) fragmentation of glycopeptides generates mono- or disaccharide ions called oxonium ions that carry information about the structure of the fragmented glycans. Here, we investigated the link between glycan structures and the intensity of oxonium ions in the spectra of glycopeptides and utilized this information to improve the identification of glycopeptides in biological samples. Tandem spectra of glycopeptides from fetuin, glycophorin A, ovalbumin and gp120 tryptic digests were used to build a spectral database of N- and O-linked glycopeptides. Logistic regression was applied to this database to develop model to distinguish between the spectra of N- and O-linked glycopeptides. Remarkably, the developed model was found to reliably distinguish between the N- and O-linked glycopeptides using the spectral features of the oxonium ions using verification spectral set. Finally, the performance of the developed predictive model was evaluated in HILIC enriched glycopeptides extracted from human serum. The results showed that pre-classification of tandem spectra based on their glycosylation type improved the identification of N-linked glycopeptides. The developed model facilitates interpretation of tandem mass spectrometry data for assignment of glycopeptides.
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Affiliation(s)
- Shadi Toghi Eshghi
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Weiming Yang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Punit Shah
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Shisheng Sun
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Xingde Li
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
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35
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Ranzinger R, Kochut KJ, Miller JA, Eavenson M, Lütteke T, York WS. GLYDE-II: The GLYcan data exchange format. ACTA ACUST UNITED AC 2016; 11:24-30. [PMID: 28955652 PMCID: PMC5611833 DOI: 10.1016/j.pisc.2016.05.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
The GLYcan Data Exchange (GLYDE) standard has been developed for the representation of the chemical structures of monosaccharides, glycans and glycoconjugates using a connection table formalism formatted in XML. This format allows structures, including those that do not exist in any database, to be unambiguously represented and shared by diverse computational tools. GLYDE implements a partonomy model based on human language along with rules that provide consistent structural representations, including a robust namespace for specifying monosaccharides. This approach facilitates the reuse of data processing software at the level of granularity that is most appropriate for extraction of the desired information. GLYDE-II has already been used as a key element of several glycoinformatics tools. The philosophical and technical underpinnings of GLYDE-II and recent implementation of its enhanced features are described.
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Affiliation(s)
- Rene Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, USA
| | - Krys J Kochut
- Computer Science Department, University of Georgia, USA
| | - John A Miller
- Computer Science Department, University of Georgia, USA
| | | | - Thomas Lütteke
- Institute of Veterinary Physiology and Biochemistry, Justus-Liebig-University Giessen, Germany
| | - William S York
- Complex Carbohydrate Research Center, University of Georgia, USA.,Computer Science Department, University of Georgia, USA
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36
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Chiang AW, Li S, Spahn PN, Richelle A, Kuo CC, Samoudi M, Lewis NE. Modulating carbohydrate-protein interactions through glycoengineering of monoclonal antibodies to impact cancer physiology. Curr Opin Struct Biol 2016; 40:104-111. [PMID: 27639240 DOI: 10.1016/j.sbi.2016.08.008] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2016] [Revised: 08/08/2016] [Accepted: 08/29/2016] [Indexed: 01/05/2023]
Abstract
Diverse glycans on proteins impact cell and organism physiology, along with drug activity. Since many protein-based biotherapeutics are glycosylated and these glycans have biological activity, there is a desire to engineer glycosylation for recombinant protein-based biotherapeutics. Engineered glycosylation can impact the recombinant protein efficacy and also influence many cell pathways by first changing glycan-protein interactions and consequently modulating disease physiologies. However, its complexity is enormous. Recent advances in glycoengineering now make it easier to modulate protein-glycan interactions. Here, we discuss how engineered glycans contribute to therapeutic monoclonal antibodies (mAbs) in the treatment of cancers, how these glycoengineered therapeutic mAbs affect the transformed phenotypes and downstream cell pathways. Furthermore, we suggest how systems biology can help in the next generation mAb glycoengineering process by aiding in data analysis and guiding engineering efforts to tailor mAb glycan and ultimately drug efficacy, safety and affordability.
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Affiliation(s)
- Austin Wt Chiang
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA
| | - Shangzhong Li
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA; Department of Bioengineering, University of California, San Diego, CA, USA
| | - Philipp N Spahn
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA
| | - Anne Richelle
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA
| | - Chih-Chung Kuo
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA; Department of Bioengineering, University of California, San Diego, CA, USA
| | - Mojtaba Samoudi
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California, San Diego, CA, USA; The Novo Nordisk Foundation Center for Biosustainability at the University of California, San Diego, CA, USA.
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37
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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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38
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Bennun SV, Hizal DB, Heffner K, Can O, Zhang H, Betenbaugh MJ. Systems Glycobiology: Integrating Glycogenomics, Glycoproteomics, Glycomics, and Other ‘Omics Data Sets to Characterize Cellular Glycosylation Processes. J Mol Biol 2016; 428:3337-3352. [DOI: 10.1016/j.jmb.2016.07.005] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2016] [Revised: 07/05/2016] [Accepted: 07/07/2016] [Indexed: 12/17/2022]
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39
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Pai PJ, Hu Y, Lam H. Direct glycan structure determination of intact N-linked glycopeptides by low-energy collision-induced dissociation tandem mass spectrometry and predicted spectral library searching. Anal Chim Acta 2016; 934:152-62. [DOI: 10.1016/j.aca.2016.05.049] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Revised: 05/24/2016] [Accepted: 05/30/2016] [Indexed: 11/24/2022]
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40
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Hou W, Qiu Y, Hashimoto N, Ching WK, Aoki-Kinoshita KF. A systematic framework to derive N-glycan biosynthesis process and the automated construction of glycosylation networks. BMC Bioinformatics 2016; 17 Suppl 7:240. [PMID: 27454116 PMCID: PMC4965717 DOI: 10.1186/s12859-016-1094-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
Background Abnormalities in glycan biosynthesis have been conclusively related to various diseases, whereas the complexity of the glycosylation process has impeded the quantitative analysis of biochemical experimental data for the identification of glycoforms contributing to disease. To overcome this limitation, the automatic construction of glycosylation reaction networks in silico is a critical step. Results In this paper, a framework K2014 is developed to automatically construct N-glycosylation networks in MATLAB with the involvement of the 27 most-known enzyme reaction rules of 22 enzymes, as an extension of previous model KB2005. A toolbox named Glycosylation Network Analysis Toolbox (GNAT) is applied to define network properties systematically, including linkages, stereochemical specificity and reaction conditions of enzymes. Our network shows a strong ability to predict a wider range of glycans produced by the enzymes encountered in the Golgi Apparatus in human cell expression systems. Conclusions Our results demonstrate a better understanding of the underlying glycosylation process and the potential of systems glycobiology tools for analyzing conventional biochemical or mass spectrometry-based experimental data quantitatively in a more realistic and practical way.
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Affiliation(s)
- Wenpin Hou
- Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China.
| | - Yushan Qiu
- Hematology Oncology Division, Northwestern University, Evanston, IL 60208, USA
| | - Nobuyuki Hashimoto
- Faculty of Science and Engineering, Soka University, Tokyo, 192-8577, Japan
| | - Wai-Ki Ching
- Department of Mathematics, The University of Hong Kong, Hong Kong, 999077, China
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Bolleman JT, Mungall CJ, Strozzi F, Baran J, Dumontier M, Bonnal RJP, Buels R, Hoehndorf R, Fujisawa T, Katayama T, Cock PJA. FALDO: a semantic standard for describing the location of nucleotide and protein feature annotation. J Biomed Semantics 2016; 7:39. [PMID: 27296299 PMCID: PMC4907002 DOI: 10.1186/s13326-016-0067-z] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2014] [Accepted: 03/17/2016] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Nucleotide and protein sequence feature annotations are essential to understand biology on the genomic, transcriptomic, and proteomic level. Using Semantic Web technologies to query biological annotations, there was no standard that described this potentially complex location information as subject-predicate-object triples. DESCRIPTION We have developed an ontology, the Feature Annotation Location Description Ontology (FALDO), to describe the positions of annotated features on linear and circular sequences. FALDO can be used to describe nucleotide features in sequence records, protein annotations, and glycan binding sites, among other features in coordinate systems of the aforementioned "omics" areas. Using the same data format to represent sequence positions that are independent of file formats allows us to integrate sequence data from multiple sources and data types. The genome browser JBrowse is used to demonstrate accessing multiple SPARQL endpoints to display genomic feature annotations, as well as protein annotations from UniProt mapped to genomic locations. CONCLUSIONS Our ontology allows users to uniformly describe - and potentially merge - sequence annotations from multiple sources. Data sources using FALDO can prospectively be retrieved using federalised SPARQL queries against public SPARQL endpoints and/or local private triple stores.
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Affiliation(s)
- Jerven T Bolleman
- Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, 1 rue Michel, Servet, Geneva 4, 1211, Switzerland.
| | | | | | - Joachim Baran
- CODAMONO, 5-121 Marion Street, Toronto, M6R 1E6, Ontario, Canada
| | - Michel Dumontier
- Stanford Center for Biomedical Informatics Research, 1265 Welch Road, Room X223, Stanford, 94305-5479, CA, US
| | - Raoul J P Bonnal
- Integrative Biology Program, Istituto Nazionale Genetica Molecolare, Milan, Italy
| | - Robert Buels
- University of California, Berkeley, Berkeley, CA, USA
| | | | - Takatomo Fujisawa
- Center for Information Biology, National Institute of Genetics, Research Organization of Information and Systems, 1111 Yata, Mishima, Shizuoka, 411-08540, Japan
| | - Toshiaki Katayama
- Database Center for Life Science, Research Organization of Information and Systems, 2-11-16, Yayoi, Bunkyo-ku, Tokyo, 113-0032, Japan
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Barnett CB, Aoki-Kinoshita KF, Naidoo KJ. The Glycome Analytics Platform: an integrative framework for glycobioinformatics. Bioinformatics 2016; 32:3005-11. [PMID: 27288496 DOI: 10.1093/bioinformatics/btw341] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2015] [Accepted: 05/26/2016] [Indexed: 11/13/2022] Open
Abstract
MOTIVATION Complex carbohydrates play a central role in cellular communication and in disease development. O- and N-glycans, which are post-translationally attached to proteins and lipids, are sugar chains that are rooted, tree structures. Independent efforts to develop computational tools for analyzing complex carbohydrate structures have been designed to exploit specific databases requiring unique formatting and limited transferability. Attempts have been made at integrating these resources, yet it remains difficult to communicate and share data across several online resources. A disadvantage of the lack of coordination between development efforts is the inability of the user community to create reproducible analyses (workflows). The latter results in the more serious unreliability of glycomics metadata. RESULTS In this paper, we realize the significance of connecting multiple online glycan resources that can be used to design reproducible experiments for obtaining, generating and analyzing cell glycomes. To address this, a suite of tools and utilities, have been integrated into the analytic functionality of the Galaxy bioinformatics platform to provide a Glycome Analytics Platform (GAP).Using this platform, users can design in silico workflows to manipulate various formats of glycan sequences and analyze glycomes through access to web data and services. We illustrate the central functionality and features of the GAP by way of example; we analyze and compare the features of the N-glycan glycome of monocytic cells sourced from two separate data depositions.This paper highlights the use of reproducible research methods for glycomics analysis and the GAP presents an opportunity for integrating tools in glycobioinformatics. AVAILABILITY AND IMPLEMENTATION This software is open-source and available online at https://bitbucket.org/scientificomputing/glycome-analytics-platform CONTACTS chris.barnett@uct.ac.za or kevin.naidoo@uct.ac.za SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Christopher B Barnett
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
| | - Kiyoko F Aoki-Kinoshita
- Department of Bioinformatics, Faculty of Engineering, Soka University, Hachioji, Tokyo 192-8577, Japan
| | - Kevin J Naidoo
- Scientific Computing Research Unit and Department of Chemistry, University of Cape Town, Rondebosch 7701, South Africa
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Khatri K, Klein JA, White MR, Grant OC, Leymarie N, Woods RJ, Hartshorn KL, Zaia J. Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions. Mol Cell Proteomics 2016; 15:1895-912. [PMID: 26984886 PMCID: PMC5083086 DOI: 10.1074/mcp.m116.058016] [Citation(s) in RCA: 79] [Impact Index Per Article: 9.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2016] [Revised: 03/04/2016] [Indexed: 02/04/2023] Open
Abstract
Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.
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Affiliation(s)
- Kshitij Khatri
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Joshua A Klein
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118; §Bioinformatics Program, Boston University, Boston, Massachusetts 02215
| | - Mitchell R White
- ¶Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Oliver C Grant
- ‖Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602
| | - Nancy Leymarie
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Robert J Woods
- ‖Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia 30602
| | - Kevan L Hartshorn
- ¶Department of Medicine, Boston University School of Medicine, Boston, Massachusetts 02118
| | - Joseph Zaia
- From the ‡Center for Biomedical Mass Spectrometry, Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts 02118; §Bioinformatics Program, Boston University, Boston, Massachusetts 02215;
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Zeng WF, Liu MQ, Zhang Y, Wu JQ, Fang P, Peng C, Nie A, Yan G, Cao W, Liu C, Chi H, Sun RX, Wong CCL, He SM, Yang P. pGlyco: a pipeline for the identification of intact N-glycopeptides by using HCD- and CID-MS/MS and MS3. Sci Rep 2016; 6:25102. [PMID: 27139140 PMCID: PMC4853738 DOI: 10.1038/srep25102] [Citation(s) in RCA: 68] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2016] [Accepted: 04/04/2016] [Indexed: 12/23/2022] Open
Abstract
Confident characterization of the microheterogeneity of protein glycosylation through identification of intact glycopeptides remains one of the toughest analytical challenges for glycoproteomics. Recently proposed mass spectrometry (MS)-based methods still have some defects such as lack of the false discovery rate (FDR) analysis for the glycan identification and lack of sufficient fragmentation information for the peptide identification. Here we proposed pGlyco, a novel pipeline for the identification of intact glycopeptides by using complementary MS techniques: 1) HCD-MS/MS followed by product-dependent CID-MS/MS was used to provide complementary fragments to identify the glycans, and a novel target-decoy method was developed to estimate the false discovery rate of the glycan identification; 2) data-dependent acquisition of MS3 for some most intense peaks of HCD-MS/MS was used to provide fragments to identify the peptide backbones. By integrating HCD-MS/MS, CID-MS/MS and MS3, intact glycopeptides could be confidently identified. With pGlyco, a standard glycoprotein mixture was analyzed in the Orbitrap Fusion, and 309 non-redundant intact glycopeptides were identified with detailed spectral information of both glycans and peptides.
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Affiliation(s)
- Wen-Feng Zeng
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Ming-Qi Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Yang Zhang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Jian-Qiang Wu
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Pan Fang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Chao Peng
- National Center for Protein Science (Shanghai), Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Aiying Nie
- Thermo Fisher Scientific Co., Ltd, Shanghai, China
| | - Guoquan Yan
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Weiqian Cao
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Chao Liu
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Hao Chi
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Rui-Xiang Sun
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
| | - Catherine C L Wong
- National Center for Protein Science (Shanghai), Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Si-Min He
- Key Lab of Intelligent information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China.,University of Chinese Academy of Sciences, Beijing, China
| | - Pengyuan Yang
- Institutes of Biomedical Sciences, Fudan University, Shanghai, China
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45
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Lih TM, Choong WK, Chen CC, Cheng CW, Lin HN, Chen CT, Chang HY, Hsu WL, Sung TY. MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification. Nucleic Acids Res 2016; 44:W575-80. [PMID: 27084943 PMCID: PMC4987873 DOI: 10.1093/nar/gkw254] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 04/02/2016] [Indexed: 01/25/2023] Open
Abstract
MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS2 spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.
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Affiliation(s)
- T Mamie Lih
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chen-Chun Chen
- Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Cheng-Wei Cheng
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Hsin-Nan Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Hui-Yin Chang
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
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46
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Aoki-Kinoshita K, Agravat S, Aoki NP, Arpinar S, Cummings RD, Fujita A, Fujita N, Hart GM, Haslam SM, Kawasaki T, Matsubara M, Moreman KW, Okuda S, Pierce M, Ranzinger R, Shikanai T, Shinmachi D, Solovieva E, Suzuki Y, Tsuchiya S, Yamada I, York WS, Zaia J, Narimatsu H. GlyTouCan 1.0--The international glycan structure repository. Nucleic Acids Res 2016; 44:D1237-42. [PMID: 26476458 PMCID: PMC4702779 DOI: 10.1093/nar/gkv1041] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 09/15/2015] [Accepted: 09/30/2015] [Indexed: 11/14/2022] Open
Abstract
Glycans are known as the third major class of biopolymers, next to DNA and proteins. They cover the surfaces of many cells, serving as the 'face' of cells, whereby other biomolecules and viruses interact. The structure of glycans, however, differs greatly from DNA and proteins in that they are branched, as opposed to linear sequences of amino acids or nucleotides. Therefore, the storage of glycan information in databases, let alone their curation, has been a difficult problem. This has caused many duplicated efforts when integration is attempted between different databases, making an international repository for glycan structures, where unique accession numbers are assigned to every identified glycan structure, necessary. As such, an international team of developers and glycobiologists have collaborated to develop this repository, called GlyTouCan and is available at http://glytoucan.org/, to provide a centralized resource for depositing glycan structures, compositions and topologies, and to retrieve accession numbers for each of these registered entries. This will thus enable researchers to reference glycan structures simply by accession number, as opposed to by chemical structure, which has been a burden to integrate glycomics databases in the past.
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Affiliation(s)
- Kiyoko Aoki-Kinoshita
- Faculty of Science and Engineering, Soka University, Tokyo 192-8577, Japan Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
| | - Sanjay Agravat
- Department of Mathematics and Computer Science, Emory University, Atlanta, GA, 30322, USA
| | - Nobuyuki P Aoki
- Faculty of Science and Engineering, Soka University, Tokyo 192-8577, Japan
| | - Sena Arpinar
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Richard D Cummings
- Department of Surgery, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02115,USA
| | - Akihiro Fujita
- Faculty of Science and Engineering, Soka University, Tokyo 192-8577, Japan
| | - Noriaki Fujita
- Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
| | - Gerald M Hart
- Department of Biological Chemistry, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
| | - Stuart M Haslam
- Department of Life Sciences, Faculty of National Sciences, Imperial College London, London, SW7 2AZ, UK
| | - Toshisuke Kawasaki
- Research Center for Glycobiotechnology, Ritsumeikan University, Kusatsu, Shiga 525-8577, Japan
| | | | - Kelley W Moreman
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Shujiro Okuda
- Niigata University Graduate School of Medical and Dental Sciences, Niigata 951-8510, Japan
| | - Michael Pierce
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, 30602, USA
| | - René Ranzinger
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Toshihide Shikanai
- Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
| | - Daisuke Shinmachi
- Faculty of Science and Engineering, Soka University, Tokyo 192-8577, Japan
| | - Elena Solovieva
- Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
| | - Yoshinori Suzuki
- Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
| | | | | | - William S York
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA, 30602, USA
| | - Joseph Zaia
- Center for Biomedical Mass Spectrometry, Dept. of Biochemistry, Boston University School of Medicine, Boston, MA, 02118, USA
| | - Hisashi Narimatsu
- Glycoscience and Glycotechnology Research Group, AIST, Ibaraki 305-8568, Japan
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47
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Mariethoz J, Khatib K, Alocci D, Campbell MP, Karlsson NG, Packer NH, Mullen EH, Lisacek F. SugarBindDB, a resource of glycan-mediated host-pathogen interactions. Nucleic Acids Res 2016; 44:D1243-50. [PMID: 26578555 PMCID: PMC4702881 DOI: 10.1093/nar/gkv1247] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2015] [Revised: 10/22/2015] [Accepted: 10/31/2015] [Indexed: 12/16/2022] Open
Abstract
The SugarBind Database (SugarBindDB) covers knowledge of glycan binding of human pathogen lectins and adhesins. It is a curated database; each glycan-protein binding pair is associated with at least one published reference. The core data element of SugarBindDB is a set of three inseparable components: the pathogenic agent, a lectin/adhesin and a glycan ligand. Each entity (agent, lectin or ligand) is described by a range of properties that are summarized in an entity-dedicated page. Several search, navigation and visualisation tools are implemented to investigate the functional role of glycans in pathogen binding. The database is cross-linked to protein and glycan-relaled resources such as UniProtKB and UniCarbKB. It is tightly bound to the latter via a substructure search tool that maps each ligand to full structures where it occurs. Thus, a glycan-lectin binding pair of SugarBindDB can lead to the identification of a glycan-mediated protein-protein interaction, that is, a lectin-glycoprotein interaction, via substructure search and the knowledge of site-specific glycosylation stored in UniCarbKB. SugarBindDB is accessible at: http://sugarbind.expasy.org.
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Affiliation(s)
- Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland
| | | | - Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland Department of Computer Science, University of Geneva, Geneva, Switzerland
| | - Matthew P Campbell
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | - Niclas G Karlsson
- University of Gothenburg, Sahlgrenska Academy, Institute of Biomedicine, Department of Medical Biochemistry and Cell Biology, Gothenburg, Sweden
| | - Nicolle H Packer
- Biomolecular Frontiers Research Centre, Macquarie University, North Ryde, NSW, Australia
| | | | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, Switzerland Department of Computer Science, University of Geneva, Geneva, Switzerland
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48
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Alocci D, Mariethoz J, Horlacher O, Bolleman JT, Campbell MP, Lisacek F. Property Graph vs RDF Triple Store: A Comparison on Glycan Substructure Search. PLoS One 2015; 10:e0144578. [PMID: 26656740 PMCID: PMC4684231 DOI: 10.1371/journal.pone.0144578] [Citation(s) in RCA: 32] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2015] [Accepted: 11/22/2015] [Indexed: 11/18/2022] Open
Abstract
Resource description framework (RDF) and Property Graph databases are emerging technologies that are used for storing graph-structured data. We compare these technologies through a molecular biology use case: glycan substructure search. Glycans are branched tree-like molecules composed of building blocks linked together by chemical bonds. The molecular structure of a glycan can be encoded into a direct acyclic graph where each node represents a building block and each edge serves as a chemical linkage between two building blocks. In this context, Graph databases are possible software solutions for storing glycan structures and Graph query languages, such as SPARQL and Cypher, can be used to perform a substructure search. Glycan substructure searching is an important feature for querying structure and experimental glycan databases and retrieving biologically meaningful data. This applies for example to identifying a region of the glycan recognised by a glycan binding protein (GBP). In this study, 19,404 glycan structures were selected from GlycomeDB (www.glycome-db.org) and modelled for being stored into a RDF triple store and a Property Graph. We then performed two different sets of searches and compared the query response times and the results from both technologies to assess performance and accuracy. The two implementations produced the same results, but interestingly we noted a difference in the query response times. Qualitative measures such as portability were also used to define further criteria for choosing the technology adapted to solving glycan substructure search and other comparable issues.
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Affiliation(s)
- Davide Alocci
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
| | - Julien Mariethoz
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Oliver Horlacher
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
| | - Jerven T. Bolleman
- Swiss-Prot Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
| | - Matthew P. Campbell
- Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia
| | - Frederique Lisacek
- Proteome Informatics Group, SIB Swiss Institute of Bioinformatics, Geneva, 1211, Switzerland
- Computer Science Department, University of Geneva, Geneva, 1227, Switzerland
- * E-mail:
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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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Kültz D, Li J, Sacchi R, Morin D, Buckpitt A, Van Winkle L. Alterations in the proteome of the respiratory tract in response to single and multiple exposures to naphthalene. Proteomics 2015; 15:2655-68. [PMID: 25825134 DOI: 10.1002/pmic.201400445] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2014] [Revised: 01/16/2015] [Accepted: 03/25/2015] [Indexed: 12/12/2022]
Abstract
Protein adduction is considered to be critical to the loss of cellular homeostasis associated with environmental chemicals undergoing metabolic activation. Despite considerable effort, our understanding of the key proteins mediating the pathologic consequences from protein modification by electrophiles is incomplete. This work focused on naphthalene (NA) induced acute injury of respiratory epithelial cells and tolerance which arises after multiple toxicant doses to define the initial cellular proteomic response and later protective actions related to tolerance. Airways and nasal olfactory epithelium from mice exposed to 15 ppm NA either for 4 h (acute) or for 4 h/day × 7 days (tolerant) were used for label-free protein quantitation by LC/MS/MS. Cytochrome P450 2F2 and secretoglobin 1A1 are decreased dramatically in airways of mice exposed for 4 h, a finding consistent with the fact that CYPs are localized primarily in Clara cells. A number of heat shock proteins and protein disulfide isomerases, which had previously been identified as adduct targets for reactive metabolites from several lung toxicants, were upregulated in airways but not olfactory epithelium of tolerant mice. Protein targets that are upregulated in tolerance may be key players in the pathophysiology associated with reactive metabolite protein adduction. All MS data have been deposited in the ProteomeXchange with identifier PXD000846 (http://proteomecentral.proteomexchange.org/dataset/PXD000846).
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Affiliation(s)
- Dietmar Kültz
- Department of Animal Science, College of Agricultural and Environmental Sciences, University of California, Davis, CA, USA
| | - Johnathon Li
- Department of Animal Science, College of Agricultural and Environmental Sciences, University of California, Davis, CA, USA
| | - Romina Sacchi
- Department of Animal Science, College of Agricultural and Environmental Sciences, University of California, Davis, CA, USA
| | - Dexter Morin
- Depatment of Molecular Biosciences, University of California, Davis, CA, USA
| | - Alan Buckpitt
- Depatment of Molecular Biosciences, University of California, Davis, CA, USA
| | - Laura Van Winkle
- Department of Anatomy, Physiology and Cell Biology, School of Veterinary Medicine, University of California, Davis, CA, USA
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