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Liang Y, Fu B, Zhang Y, Lu H. Progress of proteomics-driven precision medicine: From a glycosylation view. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2022; 36:e9288. [PMID: 35261114 DOI: 10.1002/rcm.9288] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 02/23/2022] [Accepted: 02/26/2022] [Indexed: 05/08/2023]
Abstract
Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.
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Affiliation(s)
- Yuying Liang
- Shanghai Cancer Center and Department of Chemistry, Fudan University, Shanghai, People's Republic of China
| | - Bin Fu
- Shanghai Cancer Center and Department of Chemistry, Fudan University, Shanghai, People's Republic of China
| | - Ying Zhang
- Shanghai Cancer Center and Department of Chemistry, Fudan University, Shanghai, People's Republic of China
- Institutes of Biomedical Sciences and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, People's Republic of China
| | - Haojie Lu
- Shanghai Cancer Center and Department of Chemistry, Fudan University, Shanghai, People's Republic of China
- Institutes of Biomedical Sciences and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, People's Republic of China
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2
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Pujić I, Perreault H. Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins. MASS SPECTROMETRY REVIEWS 2022; 41:488-507. [PMID: 33393161 DOI: 10.1002/mas.21679] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.
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Affiliation(s)
- Ivona Pujić
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Hélène Perreault
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
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3
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Meng X, Li L, Wang X. An integrated strategy for the construction of a species-specific glycan library for mass spectrometry-based intact glycopeptide analyses. Talanta 2021; 234:122626. [PMID: 34364435 DOI: 10.1016/j.talanta.2021.122626] [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: 04/08/2021] [Revised: 06/14/2021] [Accepted: 06/15/2021] [Indexed: 11/15/2022]
Abstract
Mass spectrometry (MS)-based strategies and related software tools using glycan mass lists have greatly facilitated the analysis of intact glycopeptides. Most glycan mass lists are derived from normal glycans of mammals and contain limited monosaccharides, which has significantly hindered high throughput studies of unusual glycosylation events observed in other species. In this work, an integrated strategy was developed for the construction of a species-specific glycan mass list from glycan structure databases and published papers. We developed a computational tool called LibGlycan, which could process the different formats of glycans. Then, the software tool generated a glycan library that contained the monoisotope mass, average mass, isotope distribution, and glycan mass list for input into Byonic software. This strategy was applied to analyze the N-glycosylation of rice roots and O-glycosylation of Acinetobacter baumannii ATCC17978, leading to the identification of 296 and 145 intact glycopeptides respectively. Combined, these results show that this strategy is a robust computational approach for the determination of glycan diversity within different complex biological systems.
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Affiliation(s)
- Xianbin Meng
- Center of Excellence for Environmental Safety and Biological Effects, Beijing Key Laboratory for Green Catalysis and Separation, Department of Chemistry and Biology, Beijing University of Technology, Beijing, 100124, China
| | - Lijie Li
- Center of Excellence for Environmental Safety and Biological Effects, Beijing Key Laboratory for Green Catalysis and Separation, Department of Chemistry and Biology, Beijing University of Technology, Beijing, 100124, China
| | - Xiayan Wang
- Center of Excellence for Environmental Safety and Biological Effects, Beijing Key Laboratory for Green Catalysis and Separation, Department of Chemistry and Biology, Beijing University of Technology, Beijing, 100124, China.
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4
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Cao W, Liu M, Kong S, Wu M, Zhang Y, Yang P. Recent Advances in Software Tools for More Generic and Precise Intact Glycopeptide Analysis. Mol Cell Proteomics 2021; 20:100060. [PMID: 33556625 PMCID: PMC8724820 DOI: 10.1074/mcp.r120.002090] [Citation(s) in RCA: 59] [Impact Index Per Article: 19.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Abstract
Intact glycopeptide identification has long been known as a key and challenging barrier to the comprehensive and accurate understanding the role of glycosylation in an organism. Intact glycopeptide analysis is a blossoming field that has received increasing attention in recent years. MS-based strategies and relative software tools are major drivers that have greatly facilitated the analysis of intact glycopeptides, particularly intact N-glycopeptides. This article provides a systematic review of the intact glycopeptide-identification process using MS data generated in shotgun proteomic experiments, which typically focus on N-glycopeptide analysis. Particular attention is paid to the software tools that have been recently developed in the last decade for the interpretation and quality control of glycopeptide spectra acquired using different MS strategies. The review also provides information about the characteristics and applications of these software tools, discusses their advantages and disadvantages, and concludes with a discussion of outstanding tools.
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Affiliation(s)
- Weiqian Cao
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China; The 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.
| | - Mingqi Liu
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Siyuan Kong
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mengxi Wu
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; Department of Chemistry, Fudan University, Shanghai, China
| | - Yang Zhang
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; The 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
| | - Pengyuan Yang
- The Fifth People's Hospital of Fudan University and Institutes of Biomedical Sciences, Fudan University, Shanghai, China; NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China; The 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; Department of Chemistry, Fudan University, Shanghai, China.
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5
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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: 81] [Impact Index Per Article: 27.0] [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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Riley NM, Bertozzi CR, Pitteri SJ. A Pragmatic Guide to Enrichment Strategies for Mass Spectrometry-Based Glycoproteomics. Mol Cell Proteomics 2020; 20:100029. [PMID: 33583771 PMCID: PMC8724846 DOI: 10.1074/mcp.r120.002277] [Citation(s) in RCA: 103] [Impact Index Per Article: 25.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 09/14/2020] [Accepted: 09/16/2020] [Indexed: 12/26/2022] Open
Abstract
Glycosylation is a prevalent, yet heterogeneous modification with a broad range of implications in molecular biology. This heterogeneity precludes enrichment strategies that can be universally beneficial for all glycan classes. Thus, choice of enrichment strategy has profound implications on experimental outcomes. Here we review common enrichment strategies used in modern mass spectrometry-based glycoproteomic experiments, including lectins and other affinity chromatographies, hydrophilic interaction chromatography and its derivatives, porous graphitic carbon, reversible and irreversible chemical coupling strategies, and chemical biology tools that often leverage bioorthogonal handles. Interest in glycoproteomics continues to surge as mass spectrometry instrumentation and software improve, so this review aims to help equip researchers with the necessary information to choose appropriate enrichment strategies that best complement these efforts.
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Affiliation(s)
- Nicholas M Riley
- Department of Chemistry, Stanford University, Stanford, California, USA.
| | - Carolyn R Bertozzi
- Department of Chemistry, Stanford University, Stanford, California, USA; Howard Hughes Medical Institute, Stanford, California, USA
| | - Sharon J Pitteri
- Department of Radiology, Canary Center at Stanford for Cancer Early Detection, Stanford University School of Medicine, Palo Alto, California, USA.
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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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Cao WQ, Liu MQ, Kong SY, Wu MX, Huang ZZ, Yang PY. Novel methods in glycomics: a 2019 update. Expert Rev Proteomics 2020; 17:11-25. [PMID: 31914820 DOI: 10.1080/14789450.2020.1708199] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Introduction: Glycomics, which aims to define the glycome of a biological system to better assess the biological attributes of the glycans, has attracted increasing interest. However, the complexity and diversity of glycans present challenging barriers to glycome definition. Technological advances are major drivers in glycomics.Areas covered: This review summarizes the main methods and emphasizes the most recent advances in mass spectrometry-based methods regarding glycomics following the general workflow in glycomic analysis.Expert opinion: Recent mass spectrometry-based technological advances have significantly lowered the barriers in glycomics. The field of glycomics is moving toward both generic and precise analysis.
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Affiliation(s)
- Wei-Qian Cao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Ming-Qi Liu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Si-Yuan Kong
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Meng-Xi Wu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
| | - Zheng-Ze Huang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Peng-Yuan Yang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
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Development of Immobilized Enzyme Reactors for the characterization of the glycosylation heterogeneity of a protein. Talanta 2020; 206:120171. [DOI: 10.1016/j.talanta.2019.120171] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Revised: 07/17/2019] [Accepted: 07/22/2019] [Indexed: 11/19/2022]
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