101
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Sun Z, Ji G, Wang G, Wei L, Zhang Y, Lu H. One step carboxyl group isotopic labeling for quantitative analysis of intact N-glycopeptides by mass spectrometry. Chem Commun (Camb) 2021; 57:4154-4157. [PMID: 33908447 DOI: 10.1039/d1cc00197c] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
Here, we have developed an approach termed methylamine stable isotope labeling (MeSIL) to relatively quantify N-glycopeptides through one step labeling. It is the first time that this approach is applied to measure N-glycopeptide changes in huh7 cells after Zika virus infection and it has been revealed that differentially expressed N-glycopeptides played important roles in virus infection at the glycosylation site-specific level.
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Affiliation(s)
- Zhenyu Sun
- Institutes of Biomedical Sciences and Shanghai Cancer Center, Fudan University, Shanghai 200032, China.
| | - Guanghui Ji
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Guoli Wang
- Institutes of Biomedical Sciences and Shanghai Cancer Center, Fudan University, Shanghai 200032, China.
| | - Lei Wei
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Ying Zhang
- Institutes of Biomedical Sciences and Shanghai Cancer Center, Fudan University, Shanghai 200032, China. and Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Haojie Lu
- Institutes of Biomedical Sciences and Shanghai Cancer Center, Fudan University, Shanghai 200032, China. and Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
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102
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Kellman BP, Lewis NE. Big-Data Glycomics: Tools to Connect Glycan Biosynthesis to Extracellular Communication. Trends Biochem Sci 2021; 46:284-300. [PMID: 33349503 PMCID: PMC7954846 DOI: 10.1016/j.tibs.2020.10.004] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2020] [Revised: 10/05/2020] [Accepted: 10/22/2020] [Indexed: 12/12/2022]
Abstract
Characteristically, cells must sense and respond to environmental cues. Despite the importance of cell-cell communication, our understanding remains limited and often lacks glycans. Glycans decorate proteins and cell membranes at the cell-environment interface, and modulate intercellular communication, from development to pathogenesis. Providing further challenges, glycan biosynthesis and cellular behavior are co-regulating systems. Here, we discuss how glycosylation contributes to extracellular responses and signaling. We further organize approaches for disentangling the roles of glycans in multicellular interactions using newly available datasets and tools, including glycan biosynthesis models, omics datasets, and systems-level analyses. Thus, emerging tools in big data analytics and systems biology are facilitating novel insights on glycans and their relationship with multicellular behavior.
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Affiliation(s)
- Benjamin P Kellman
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA
| | - Nathan E Lewis
- Department of Pediatrics, University of California San Diego School of Medicine, La Jolla, CA, USA; Department of Bioengineering, University of California San Diego School of Medicine, La Jolla, CA, USA; Bioinformatics and Systems Biology Program, University of California San Diego School of Medicine, La Jolla, CA, USA; Novo Nordisk Foundation Center for Biosustainability at the University of California San Diego School of Medicine, La Jolla, CA, USA.
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103
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Zhao J, Mo T, Li X, Ding W, Zhang Q. Dissection of the Glycosylation in the Biosynthesis of the Heptadecaglycoside Antibiotic Saccharomicin A. J Org Chem 2021; 86:11117-11124. [PMID: 33724024 DOI: 10.1021/acs.joc.0c03056] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
Oligosaccharide natural products have diverse biological activities and represent a potentially important source for drug development. In this study, we focus on the glycosylation pathway in the biosynthesis of saccharomicin A (SA-A), an oligosaccharide antibiotic containing 17 sugar moieties. By extensive gene-knockout studies with comparative metabolic profile analysis, we established a complete pathway in assembling the heptadecasaccharide chain of SA-A, the longest saccharide chain found in natural products.
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Affiliation(s)
- Junfeng Zhao
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Tianlu Mo
- Department of Chemistry, Fudan University, Shanghai 200433, China.,State Key Laboratory of Microbial Metabolism, School of Life Sciences & Biotechnology Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xinhui Li
- Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Wei Ding
- State Key Laboratory of Microbial Metabolism, School of Life Sciences & Biotechnology Shanghai Jiao Tong University, Shanghai 200240, China
| | - Qi Zhang
- Department of Chemistry, Fudan University, Shanghai 200433, China
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104
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Wang M, Shajahan A, Pepi LE, Azadi P, Zaia J. Glycoproteomic Sample Processing, LC-MS, and Data Analysis Using GlycReSoft. Curr Protoc 2021. [PMID: 33761173 DOI: 10.1002/cpz1001.1084] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/02/2023]
Abstract
Identification of N- and O-glycosylation on specific sites of proteins, along with glycan structural information, is necessary to determine the roles glycoproteins play in normal and pathologic cellular functions. Because such glycosylation is macro- and micro-heterogeneous and alters the dissociation behavior of glycopeptides, specific sample preparation, mass spectrometry, and data analysis techniques are required. Advanced tandem mass spectrometry-based glycoproteomics coupled with powerful data mining algorithms are key elements for characterization of protein glycosylation. This article includes the detailed, streamlined sample preparation method for liquid chromatography-mass spectrometry data acquisition and subsequent bioinformatics-based data annotation using the publicly available GlycReSoft program for highly efficient identification and quantification of glycoprotein glycosylation. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Characterization of glycans and site occupancy on purified glycoprotein Support Protocol 1: In-gel digestion of glycoproteins Support Protocol 2: Detection of glycoproteins from cells/tissue through glycopeptide enrichment Basic Protocol 2: Acquisition of glycopeptides through high-resolution nano-LC-MS/MS Basic Protocol 3: Identification and quantification of glycopeptides using GlycReSoft.
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Affiliation(s)
- Meizhe Wang
- Department of Biochemistry, Boston University, Boston, Massachusetts
| | - Asif Shajahan
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Lauren E Pepi
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Parastoo Azadi
- Complex Carbohydrate Research Center, University of Georgia, Athens, Georgia
| | - Joseph Zaia
- Department of Biochemistry, Boston University, Boston, Massachusetts
- Bioinformatics Program, Center for Biomedical Mass Spectrometry, Boston University, Boston, Massachusetts
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105
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Chen R, Fulton KM, Twine SM, Li J. IDENTIFICATION OF MHC PEPTIDES USING MASS SPECTROMETRY FOR NEOANTIGEN DISCOVERY AND CANCER VACCINE DEVELOPMENT. MASS SPECTROMETRY REVIEWS 2021; 40:110-125. [PMID: 31875992 DOI: 10.1002/mas.21616] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Immunotherapy with neoantigens presented by major histocompatibility complex (MHC) is one of the most promising approaches in cancer treatment. Using this approach, cancer vaccines can be designed to target tumor-specific mutations that are not found in normal tissues. Clinical trials have demonstrated an increased immune response and eradication of tumors after injecting synthetic peptides selected from the immunopeptidome. Although the sequence of MHC binding peptides can be predicted from genome sequencing and prediction algorithms, this approach results in large numbers of predicted peptides, requiring the confirmation by mass spectrometry (MS) analysis. Identification of MHC peptides by direct MS analysis of immunopeptidome is accurate and sensitive, with tens of thousands of unique peptides potentially identified from either cancer cell line or tumor tissue. Peptides with mutations can also be identified with patient-specific protein databases constructed from genome or transcriptome sequencing data. MS analysis also enables the characterization of the post-translational modifications (PTMs) of those antigens that cannot be predicted. Moreover, PTMs were found to be more efficient in triggering an immune response. In addition to reviewing recent advances in the identification of neoantigens using MS, the techniques for cancer vaccine candidate selection and formulation, vaccine delivery systems, and the potential for use in combination with other therapeutics are also discussed. It is anticipated that MS-based techniques will play an important role in future cancer vaccine development. © 2019 John Wiley & Sons Ltd. Mass Spec Rev 40:110-125, 2021.
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Affiliation(s)
- Rui Chen
- Human Health Therapeutics Research Centre, National Research Council Canada, 100 Sussex Drive, Ottawa, Ontario, K1A 0R6, Canada
| | - Kelly M Fulton
- Human Health Therapeutics Research Centre, National Research Council Canada, 100 Sussex Drive, Ottawa, Ontario, K1A 0R6, Canada
| | - Susan M Twine
- Human Health Therapeutics Research Centre, National Research Council Canada, 100 Sussex Drive, Ottawa, Ontario, K1A 0R6, Canada
| | - Jianjun Li
- Human Health Therapeutics Research Centre, National Research Council Canada, 100 Sussex Drive, Ottawa, Ontario, K1A 0R6, Canada
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106
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Delafield DG, Li L. Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation. Mol Cell Proteomics 2021; 20:100054. [PMID: 32576592 PMCID: PMC8724918 DOI: 10.1074/mcp.r120.002095] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Indexed: 12/13/2022] Open
Abstract
Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.
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Affiliation(s)
- Daniel G Delafield
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin, USA; School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin, USA.
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107
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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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108
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Schulze S, Igiraneza AB, Kösters M, Leufken J, Leidel SA, Garcia BA, Fufezan C, Pohlschroder M. Enhancing Open Modification Searches via a Combined Approach Facilitated by Ursgal. J Proteome Res 2021; 20:1986-1996. [PMID: 33514075 DOI: 10.1021/acs.jproteome.0c00799] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
The identification of peptide sequences and their post-translational modifications (PTMs) is a crucial step in the analysis of bottom-up proteomics data. The recent development of open modification search (OMS) engines allows virtually all PTMs to be searched for. This not only increases the number of spectra that can be matched to peptides but also greatly advances the understanding of the biological roles of PTMs through the identification, and the thereby facilitated quantification, of peptidoforms (peptide sequences and their potential PTMs). Whereas the benefits of combining results from multiple protein database search engines have been previously established, similar approaches for OMS results have been missing so far. Here we compare and combine results from three different OMS engines, demonstrating an increase in peptide spectrum matches of 8-18%. The unification of search results furthermore allows for the combined downstream processing of search results, including the mapping to potential PTMs. Finally, we test for the ability of OMS engines to identify glycosylated peptides. The implementation of these engines in the Python framework Ursgal facilitates the straightforward application of the OMS with unified parameters and results files, thereby enabling yet unmatched high-throughput, large-scale data analysis.
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Affiliation(s)
- Stefan Schulze
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Aime Bienfait Igiraneza
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Manuel Kösters
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Johannes Leufken
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Sebastian A Leidel
- Department of Chemistry and Biochemistry, University of Bern, 3012 Bern, Switzerland
| | - Benjamin A Garcia
- Department of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
| | - Christian Fufezan
- Institute of Pharmacy and Molecular Biotechnology, Heidelberg University, 69120 Heidelberg, Germany
| | - Mechthild Pohlschroder
- Department of Biology, University of Pennsylvania, Philadelphia, Pennsylvania 19104, United States
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109
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The Protein-Binding Behavior of Platinum Anticancer Drugs in Blood Revealed by Mass Spectrometry. Pharmaceuticals (Basel) 2021; 14:ph14020104. [PMID: 33572935 PMCID: PMC7911130 DOI: 10.3390/ph14020104] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 01/22/2021] [Accepted: 01/26/2021] [Indexed: 02/06/2023] Open
Abstract
Cisplatin and its analogues are widely used as chemotherapeutic agents in clinical practice. After being intravenously administrated, a substantial amount of platinum will bind with proteins in the blood. This binding is vital for the transport, distribution, and metabolism of drugs; however, toxicity can also occur from the irreversible binding between biologically active proteins and platinum drugs. Therefore, it is very important to study the protein-binding behavior of platinum drugs in blood. This review summarizes mass spectrometry-based strategies to identify and quantitate the proteins binding with platinum anticancer drugs in blood, such as offline high-performance liquid chromatography/inductively coupled plasma mass spectrometry (HPLC–ICP-MS) combined with electrospray ionization mass spectrometry (ESI-MS/MS) and multidimensional LC–ESI-MS/MS. The identification of in vivo targets in blood cannot be accomplished without first studying the protein-binding behavior of platinum drugs in vitro; therefore, relevant studies are also summarized. This knowledge will further our understanding of the pharmacokinetics and toxicity of platinum anticancer drugs, and it will be beneficial for the rational design of metal-based anticancer drugs.
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110
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Towards structure-focused glycoproteomics. Biochem Soc Trans 2021; 49:161-186. [PMID: 33439247 PMCID: PMC7925015 DOI: 10.1042/bst20200222] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Revised: 12/07/2020] [Accepted: 12/11/2020] [Indexed: 02/06/2023]
Abstract
Facilitated by advances in the separation sciences, mass spectrometry and informatics, glycoproteomics, the analysis of intact glycopeptides at scale, has recently matured enabling new insights into the complex glycoproteome. While diverse quantitative glycoproteomics strategies capable of mapping monosaccharide compositions of N- and O-linked glycans to discrete sites of proteins within complex biological mixtures with considerable sensitivity, quantitative accuracy and coverage have become available, developments supporting the advancement of structure-focused glycoproteomics, a recognised frontier in the field, have emerged. Technologies capable of providing site-specific information of the glycan fine structures in a glycoproteome-wide context are indeed necessary to address many pending questions in glycobiology. In this review, we firstly survey the latest glycoproteomics studies published in 2018–2020, their approaches and their findings, and then summarise important technological innovations in structure-focused glycoproteomics. Our review illustrates that while the O-glycoproteome remains comparably under-explored despite the emergence of new O-glycan-selective mucinases and other innovative tools aiding O-glycoproteome profiling, quantitative glycoproteomics is increasingly used to profile the N-glycoproteome to tackle diverse biological questions. Excitingly, new strategies compatible with structure-focused glycoproteomics including novel chemoenzymatic labelling, enrichment, separation, and mass spectrometry-based detection methods are rapidly emerging revealing glycan fine structural details including bisecting GlcNAcylation, core and antenna fucosylation, and sialyl-linkage information with protein site resolution. Glycoproteomics has clearly become a mainstay within the glycosciences that continues to reach a broader community. It transpires that structure-focused glycoproteomics holds a considerable potential to aid our understanding of systems glycobiology and unlock secrets of the glycoproteome in the immediate future.
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111
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Affiliation(s)
- Tobias
P. Wörner
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Tatiana M. Shamorkina
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Joost Snijder
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
| | - Albert J. R. Heck
- Biomolecular
Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular
Research and Utrecht Institute of Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584
CH Utrecht, The Netherlands
- Netherlands
Proteomics Center, Padualaan
8, 3584 CH Utrecht, The Netherlands
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112
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Hackett WE, Zaia J. Calculating Glycoprotein Similarities From Mass Spectrometric Data. Mol Cell Proteomics 2021; 20:100028. [PMID: 32883803 PMCID: PMC8724611 DOI: 10.1074/mcp.r120.002223] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 08/24/2020] [Accepted: 09/03/2020] [Indexed: 12/23/2022] Open
Abstract
Complex protein glycosylation occurs through biosynthetic steps in the secretory pathway that create macro- and microheterogeneity of structure and function. Required for all life forms, glycosylation diversifies and adapts protein interactions with binding partners that underpin interactions at cell surfaces and pericellular and extracellular environments. Because these biological effects arise from heterogeneity of structure and function, it is necessary to measure their changes as part of the quest to understand nature. Quite often, however, the assumption behind proteomics that posttranslational modifications are discrete additions that can be modeled using the genome as a template does not apply to protein glycosylation. Rather, it is necessary to quantify the glycosylation distribution at each glycosite and to aggregate this information into a population of mature glycoproteins that exist in a given biological system. To date, mass spectrometric methods for assigning singly glycosylated peptides are well-established. But it is necessary to quantify glycosylation heterogeneity accurately in order to gauge the alterations that occur during biological processes. The task is to quantify the glycosylated peptide forms as accurately as possible and then apply appropriate bioinformatics algorithms to the calculation of micro- and macro-similarities. In this review, we summarize current approaches for protein quantification as they apply to this glycoprotein similarity problem.
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Affiliation(s)
- William E Hackett
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, Massachusetts, USA; Department of Biochemistry, Boston University, Boston, Massachusetts, USA.
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113
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Abstract
Glycoproteomics is unquestionably on the rise and its current development benefits from past experience in proteomics, in particular when attending to bioinformatics needs. An extensive range of software solutions is available, but the reproducibility of mass spectrometry data processing remains challenging. One of the key issues in running automated glycopeptide identification software is the selection of a reference glycan composition file. The default choices are often too broad, and a fastidious literature search to properly target this selection can be avoided. This chapter suggests the use of GlyConnect Compozitor to collect relevant information on glycosylation in a given tissue or cell line and shape an appropriate glycan composition set that can be input in the majority of search engines accommodating user-defined compositions.
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114
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Silbern I, Fang P, Ji Y, Christof L, Urlaub H, Pan KT. Relative Quantification of Phosphorylated and Glycosylated Peptides from the Same Sample Using Isobaric Chemical Labelling with a Two-Step Enrichment Strategy. Methods Mol Biol 2021; 2228:185-203. [PMID: 33950492 DOI: 10.1007/978-1-0716-1024-4_14] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Post-translational modifications (PTMs) are essential for the regulation of all cellular processes. The interplay of various PTMs on a single protein or different proteins comprises a complexity that we are far from understanding in its entirety. Reliable strategies for the enrichment and accurate quantification of PTMs are needed to study as many PTMs on proteins as possible. In this protocol we present a liquid chromatography-tandem mass spectrometry (LC/MS/MS)-based workflow that enables the enrichment and quantification of phosphorylated and N-glycosylated peptides from the same sample. After extraction and digestion of proteins, we label the peptides with stable isotope-coded tandem mass tags (TMTs) and enrich N-glycopeptides and phosphopeptides by using zwitterionic hydrophilic interaction chromatography (ZIC-HILIC) and titanium dioxide (TiO2) beads, respectively. Labelled and enriched N-glycopeptides and phosphopeptides are further separated by high pH (basic) reversed-phase chromatography and analyzed by LC/MS/MS. The enrichment strategies, together with quantification of two different PTM types from the same sample, allow investigation of the interplay of those two PTMs, which are important for signal transduction inside the cell (phosphorylation), as well as for messaging between cells through decoration of the cellular surface (glycosylation).
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Affiliation(s)
- Ivan Silbern
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
- Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany
| | - Pan Fang
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
| | - Yanlong Ji
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany
| | - Lenz Christof
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany
- Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany.
- Bioanalytics Group, Institute of Clinical Chemistry, University Medical Center Goettingen, Goettingen, Germany.
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany.
| | - Kuan-Ting Pan
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Biophysical Chemistry, Goettingen, Germany.
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, Frankfurt, Germany.
- Frankfurt Cancer Institute, Goethe University, Frankfurt am Main, Germany.
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115
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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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116
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Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis. Nat Methods 2021; 18:1304-1316. [PMID: 34725484 PMCID: PMC8566223 DOI: 10.1038/s41592-021-01309-x] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 09/22/2021] [Indexed: 12/17/2022]
Abstract
Glycoproteomics is a powerful yet analytically challenging research tool. Software packages aiding the interpretation of complex glycopeptide tandem mass spectra have appeared, but their relative performance remains untested. Conducted through the HUPO Human Glycoproteomics Initiative, this community study, comprising both developers and users of glycoproteomics software, evaluates solutions for system-wide glycopeptide analysis. The same mass spectrometrybased glycoproteomics datasets from human serum were shared with participants and the relative team performance for N- and O-glycopeptide data analysis was comprehensively established by orthogonal performance tests. Although the results were variable, several high-performance glycoproteomics informatics strategies were identified. Deep analysis of the data revealed key performance-associated search parameters and led to recommendations for improved 'high-coverage' and 'high-accuracy' glycoproteomics search solutions. This study concludes that diverse software packages for comprehensive glycopeptide data analysis exist, points to several high-performance search strategies and specifies key variables that will guide future software developments and assist informatics decision-making in glycoproteomics.
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117
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Blazev R, Ashwood C, Abrahams JL, Chung LH, Francis D, Yang P, Watt KI, Qian H, Quaife-Ryan GA, Hudson JE, Gregorevic P, Thaysen-Andersen M, Parker BL. Integrated Glycoproteomics Identifies a Role of N-Glycosylation and Galectin-1 on Myogenesis and Muscle Development. Mol Cell Proteomics 2020; 20:100030. [PMID: 33583770 PMCID: PMC8724610 DOI: 10.1074/mcp.ra120.002166] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 08/20/2020] [Accepted: 09/16/2020] [Indexed: 12/23/2022] Open
Abstract
Many cell surface and secreted proteins are modified by the covalent addition of glycans that play an important role in the development of multicellular organisms. These glycan modifications enable communication between cells and the extracellular matrix via interactions with specific glycan-binding lectins and the regulation of receptor-mediated signaling. Aberrant protein glycosylation has been associated with the development of several muscular diseases, suggesting essential glycan- and lectin-mediated functions in myogenesis and muscle development, but our molecular understanding of the precise glycans, catalytic enzymes, and lectins involved remains only partially understood. Here, we quantified dynamic remodeling of the membrane-associated proteome during a time-course of myogenesis in cell culture. We observed wide-spread changes in the abundance of several important lectins and enzymes facilitating glycan biosynthesis. Glycomics-based quantification of released N-linked glycans confirmed remodeling of the glycome consistent with the regulation of glycosyltransferases and glycosidases responsible for their formation including a previously unknown digalactose-to-sialic acid switch supporting a functional role of these glycoepitopes in myogenesis. Furthermore, dynamic quantitative glycoproteomic analysis with multiplexed stable isotope labeling and analysis of enriched glycopeptides with multiple fragmentation approaches identified glycoproteins modified by these regulated glycans including several integrins and growth factor receptors. Myogenesis was also associated with the regulation of several lectins, most notably the upregulation of galectin-1 (LGALS1). CRISPR/Cas9-mediated deletion of Lgals1 inhibited differentiation and myotube formation, suggesting an early functional role of galectin-1 in the myogenic program. Importantly, similar changes in N-glycosylation and the upregulation of galectin-1 during postnatal skeletal muscle development were observed in mice. Treatment of new-born mice with recombinant adeno-associated viruses to overexpress galectin-1 in the musculature resulted in enhanced muscle mass. Our data form a valuable resource to further understand the glycobiology of myogenesis and will aid the development of intervention strategies to promote healthy muscle development or regeneration.
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Affiliation(s)
- Ronnie Blazev
- Department of Physiology, Centre for Muscle Research, The University of Melbourne, Melbourne, Victoria, Australia
| | - Christopher Ashwood
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia; CardiOmics Program, Department of Cellular and Integrative Physiology, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Jodie L Abrahams
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia
| | - Long H Chung
- School of Life and Environmental Science, Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Deanne Francis
- School of Life and Environmental Science, Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
| | - Pengyi Yang
- School of Mathematics and Statistics, Charles Perkins Centre, The University of Sydney, Sydney, New South Wales, Australia; Computational Systems Biology Group, Children's Medical Research Institute, University of Sydney, Westmead, New South Wales, Australia
| | - Kevin I Watt
- Department of Physiology, Centre for Muscle Research, The University of Melbourne, Melbourne, Victoria, Australia; Department of Diabetes, Monash University, Melbourne, Victoria, Australia
| | - Hongwei Qian
- Department of Physiology, Centre for Muscle Research, The University of Melbourne, Melbourne, Victoria, Australia
| | - Gregory A Quaife-Ryan
- Cardiac Bioengineering Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - James E Hudson
- Cardiac Bioengineering Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia; School of Biomedical Sciences, The University of Queensland, Brisbane, Queensland, Australia
| | - Paul Gregorevic
- Department of Physiology, Centre for Muscle Research, The University of Melbourne, Melbourne, Victoria, Australia; Department of Biochemistry and Molecular Biology, Monash University, Melbourne, Victoria, Australia; Department of Neurology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Morten Thaysen-Andersen
- Department of Molecular Sciences, Faculty of Science and Engineering, Macquarie University, Sydney, New South Wales, Australia
| | - Benjamin L Parker
- Department of Physiology, Centre for Muscle Research, The University of Melbourne, Melbourne, Victoria, Australia; School of Life and Environmental Science, Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia.
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118
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Rosenbalm KE, Tiemeyer M, Wells L, Aoki K, Zhao P. Glycomics-informed glycoproteomic analysis of site-specific glycosylation for SARS-CoV-2 spike protein. STAR Protoc 2020; 1:100214. [PMID: 33377107 PMCID: PMC7757665 DOI: 10.1016/j.xpro.2020.100214] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
This protocol describes an integrated approach for analyzing site-specific N- and O-linked glycosylation of SARS-CoV-2 spike protein by mass spectrometry. Glycoproteomics analyzes intact glycopeptides to examine site-specific microheterogeneity of glycoproteins. Glycomics provides structural characterization on any glycan assignments by glycoproteomics. This procedure can be modified and applied to a variety of N- and/or O-linked glycoproteins. Combined with bioinformatics, the glycomics-informed glycoproteomics may be useful in generating 3D molecular dynamics simulations of certain glycoproteins alone or interacting with one another. For complete details on the use and execution of this protocol, please refer to Zhao et al. (2020).
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Affiliation(s)
- Katelyn E. Rosenbalm
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
| | - Lance Wells
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
- Department of Biochemistry and Molecular Biology, University of Georgia, Athens, GA 30602, USA
- Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
| | - Peng Zhao
- Complex Carbohydrate Research Center, University of Georgia, Athens, GA 30602, USA
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119
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Schulze S, Oltmanns A, Fufezan C, Krägenbring J, Mormann M, Pohlschröder M, Hippler M. SugarPy facilitates the universal, discovery-driven analysis of intact glycopeptides. Bioinformatics 2020; 36:5330-5336. [PMID: 33325487 DOI: 10.1093/bioinformatics/btaa1042] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/26/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023] Open
Abstract
MOTIVATION Protein glycosylation is a complex post-translational modification with crucial cellular functions in all domains of life. Currently, large-scale glycoproteomics approaches rely on glycan database dependent algorithms and are thus unsuitable for discovery-driven analyses of glycoproteomes. RESULTS Therefore, we devised SugarPy, a glycan database independent Python module, and validated it on the glycoproteome of human breast milk. We further demonstrated its applicability by analyzing glycoproteomes with uncommon glycans stemming from the green alga Chlamydomonas reinhardtii and the archaeon Haloferax volcanii. SugarPy also facilitated the novel characterization of glycoproteins from the red alga Cyanidioschyzon merolae. AVAILABILITY The source code is freely available on GitHub (https://github.com/SugarPy/SugarPy), and its implementation in Python ensures support for all operating systems. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Stefan Schulze
- University of Pennsylvania, Department of Biology, Leidy Laboratories, Philadelphia, USA.,University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany
| | - Anne Oltmanns
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany
| | - Christian Fufezan
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,Heidelberg University, Institute of Pharmacy and Molecular Biotechnology, Heidelberg, Germany
| | - Julia Krägenbring
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,University of Muenster, Institute for Hygiene, Muenster, Germany
| | - Michael Mormann
- University of Muenster, Institute for Hygiene, Muenster, Germany
| | - Mechthild Pohlschröder
- University of Pennsylvania, Department of Biology, Leidy Laboratories, Philadelphia, USA
| | - Michael Hippler
- University of Muenster, Institute of Plant Biology and Biotechnology, Muenster, Germany.,Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
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120
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Gutierrez Reyes CD, Jiang P, Donohoo K, Atashi M, Mechref YS. Glycomics and glycoproteomics: Approaches to address isomeric separation of glycans and glycopeptides. J Sep Sci 2020; 44:403-425. [PMID: 33090644 DOI: 10.1002/jssc.202000878] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2020] [Revised: 09/29/2020] [Accepted: 10/06/2020] [Indexed: 11/11/2022]
Abstract
Changes in the glycome of human proteins and cells are associated with the progression of multiple diseases such as Alzheimer's, diabetes mellitus, many types of cancer, and those caused by viruses. Consequently, several studies have shown essential modifications to the isomeric glycan moieties for diseases in different stages. However, the elucidation of extensive isomeric glycan profiles remains challenging because of the lack of analytical techniques with sufficient resolution power to separate all glycan and glycopeptide iso-forms. Therefore, the development of sensitive and accurate approaches for the characterization of all the isomeric forms of glycans and glycopeptides is essential to tracking the progression of pathology in glycoprotein-related diseases. This review describes the isomeric separation achievements reported in glycomics and glycoproteomics in the last decade. It focuses on the mass spectrometry-based analytical strategies, stationary phases, and derivatization techniques that have been developed to enhance the separation mechanisms in liquid chromatography systems and the detection capabilities of mass spectrometry systems.
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Affiliation(s)
| | - Peilin Jiang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Kaitlyn Donohoo
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Mojgan Atashi
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Yehia S Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
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121
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Roushan A, Wilson GM, Kletter D, Sen KI, Tang W, Kil YJ, Carlson E, Bern M. Peak Filtering, Peak Annotation, and Wildcard Search for Glycoproteomics. Mol Cell Proteomics 2020; 20:100011. [PMID: 33578083 PMCID: PMC8724605 DOI: 10.1074/mcp.ra120.002260] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2020] [Revised: 09/02/2020] [Accepted: 09/03/2020] [Indexed: 12/11/2022] Open
Abstract
Glycopeptides in peptide or digested protein samples pose a number of analytical and bioinformatics challenges beyond those posed by unmodified peptides or peptides with smaller posttranslational modifications. Exact structural elucidation of glycans is generally beyond the capability of a single mass spectrometry experiment, so a reasonable level of identification for tandem mass spectrometry, taken by several glycopeptide software tools, is that of peptide sequence and glycan composition, meaning the number of monosaccharides of each distinct mass, e.g., HexNAc(2)Hex(5) rather than man5. Even at this level, however, glycopeptide analysis poses challenges: finding glycopeptide spectra when they are a tiny fraction of the total spectra; assigning spectra with unanticipated glycans, not in the initial glycan database; and finding, scoring, and labeling diagnostic peaks in tandem mass spectra. Here, we discuss recent improvements to Byonic, a glycoproteomics search program, that address these three issues. Byonic now supports filtering spectra by m/z peaks, so that the user can limit attention to spectra with diagnostic peaks, e.g., at least two out of three of 204.087 for HexNAc, 274.092 for NeuAc (with water loss), and 366.139 for HexNAc-Hex, all within a set mass tolerance, e.g., ± 0.01 Da. Also, new is glycan "wildcard" search, which allows an unspecified mass within a user-set mass range to be applied to N- or O-linked glycans and enables assignment of spectra with unanticipated glycans. Finally, the next release of Byonic supports user-specified peak annotations from user-defined posttranslational modifications. We demonstrate the utility of these new software features by finding previously unrecognized glycopeptides in publicly available data, including glycosylated neuropeptides from rat brain.
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Affiliation(s)
- Abhishek Roushan
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Gary M Wilson
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Doron Kletter
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - K Ilker Sen
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Wilfred Tang
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Yong J Kil
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Eric Carlson
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA
| | - Marshall Bern
- Research and Development Group, Protein Metrics Inc, Cupertino, California, USA.
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122
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Xu Y, Sheng X, Zhao T, Zhang L, Ruan Y, Lu H. O-GlcNAcylation of MEK2 promotes the proliferation and migration of breast cancer cells. Glycobiology 2020; 31:571-581. [PMID: 33226073 DOI: 10.1093/glycob/cwaa103] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2020] [Revised: 11/06/2020] [Accepted: 11/14/2020] [Indexed: 12/13/2022] Open
Abstract
Mitogen-activated protein kinase kinases are an important part of evolutionary conserved signaling modules that are involved in a variety of cellular processes in response to environmental stimuli. Among them, mitogen-activated protein kinase kinase 2 (MEK2) is the most crucial upstream signaling pathway of ERK1/2 cascade as a therapeutic target for overcoming Ras-driven cancers. However, the mechanisms of MEK2 regulation during tumor progression remain not fully elucidated. Herein, we identified that MEK2 was post-translationally regulated by O-GlcNAcylation. We found that MEK2 associated with OGT and was modified by O-GlcNAc. Mass spectrometry analysis further verified that O-GlcNAcylation of MEK2 occurred at Thr13, which was in the docking domain for specifically identifying its target proteins. While total O-GlcNAcylation stimulated the protein stability and phosphorylation of MEK2, Thr13 O-GlcNAcylation of MEK2 specifically enhanced its Thr394 phosphorylation as well as downstream ERK1/2 activation. Genetic ablation of MEK2 O-GlcNAcylation at Thr13 abrogated its ability to promote the proliferation and migration of breast cancer cells. Together, our data demonstrate that O-GlcNAcylation of MEK2 might be a key regulatory mechanism during tumorigenesis and is a potential therapeutic target for tumor treatment.
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Affiliation(s)
- Yaoyao Xu
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Xiangying Sheng
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.,Department of Chemistry, Fudan University, Shanghai 200433, China
| | - Ting Zhao
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Lei Zhang
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Yuanyuan Ruan
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China.,Department of Biochemistry and Molecular Biology, School of Basic Medical Sciences, Fudan University, Shanghai 200032, China
| | - Haojie Lu
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China.,Department of Chemistry, Fudan University, Shanghai 200433, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
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123
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Dobie C, Skropeta D. Insights into the role of sialylation in cancer progression and metastasis. Br J Cancer 2020; 124:76-90. [PMID: 33144696 PMCID: PMC7782833 DOI: 10.1038/s41416-020-01126-7] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2020] [Revised: 09/11/2020] [Accepted: 10/08/2020] [Indexed: 02/07/2023] Open
Abstract
Upregulation of sialyltransferases—the enzymes responsible for the addition of sialic acid to growing glycoconjugate chains—and the resultant hypersialylation of up to 40–60% of tumour cell surfaces are established hallmarks of several cancers, including lung, breast, ovarian, pancreatic and prostate cancer. Hypersialylation promotes tumour metastasis by several routes, including enhancing immune evasion and tumour cell survival, and stimulating tumour invasion and migration. The critical role of enzymes that regulate sialic acid in tumour cell growth and metastasis points towards targeting sialylation as a potential new anti-metastatic cancer treatment strategy. Herein, we explore insights into the mechanisms by which hypersialylation plays a role in promoting metastasis, and explore the current state of sialyltransferase inhibitor development.
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Affiliation(s)
- Christopher Dobie
- School of Chemistry & Molecular Bioscience, Faculty of Science, Medicine & Health; and Molecular Horizons, University of Wollongong, NSW, 2522, Wollongong, Australia
| | - Danielle Skropeta
- School of Chemistry & Molecular Bioscience, Faculty of Science, Medicine & Health; and Molecular Horizons, University of Wollongong, NSW, 2522, Wollongong, Australia. .,Illawarra Health & Medical Research Institute, Wollongong, NSW, 2522, Australia.
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124
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Brown CJ, Gaunitz S, Wang Z, Strindelius L, Jacobson SC, Clemmer DE, Trinidad JC, Novotny MV. Glycoproteomic Analysis of Human Urinary Exosomes. Anal Chem 2020; 92:14357-14365. [PMID: 32985870 PMCID: PMC7875506 DOI: 10.1021/acs.analchem.0c01952] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Exosomes represent a class of secreted biological vesicles, which have recently gained attention due to their function as intertissue and interorganism transporters of genetic materials, small molecules, lipids, and proteins. Although the protein constituents of these exosomes are often glycosylated, a large-scale characterization of the glycoproteome has not yet been completed. This study identified 3144 unique glycosylation events belonging to 378 glycoproteins and 604 unique protein sites of glycosylation. With these data, we investigated the level of glycan microheterogeneity within the urinary exosomes, finding on average 5.9 glycans per site. The glycan family abundance on individual proteins showed subtle differences, providing an additional level of molecular characterization compared to the unmodified proteome. Finally, we show protein site-specific changes in regard to the common urinary glycoprotein, uromodulin. While uromodulin is an individual case, these same site-specific analyses provide a way forward for developing diagnostic glycoprotein biomarkers with urine as a noninvasive biological fluid. This study represents an important first step in understanding the functional urinary glycoproteome.
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Affiliation(s)
- Christopher J Brown
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Stefan Gaunitz
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Ziyu Wang
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Lena Strindelius
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Stephen C Jacobson
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - David E Clemmer
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Jonathan C Trinidad
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
| | - Milos V Novotny
- Department of Chemistry, Indiana University, 800 Kirkwood Avenue, Bloomington, Indiana 47401, United States
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125
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Lu L, Riley NM, Shortreed MR, Bertozzi CR, Smith LM. O-Pair Search with MetaMorpheus for O-glycopeptide characterization. Nat Methods 2020. [PMID: 33106676 DOI: 10.1101/2020.05.18.102327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
We report O-Pair Search, an approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides via an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus .
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Affiliation(s)
- Lei Lu
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Nicholas M Riley
- Department of Chemistry, University of Stanford, Stanford, CA, USA
| | | | - Carolyn R Bertozzi
- Department of Chemistry, University of Stanford, Stanford, CA, USA
- Howard Hughes Medical Institute, Stanford, CA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA.
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126
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Polasky DA, Yu F, Teo GC, Nesvizhskii AI. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat Methods 2020; 17:1125-1132. [PMID: 33020657 PMCID: PMC7606558 DOI: 10.1038/s41592-020-0967-9] [Citation(s) in RCA: 121] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2020] [Accepted: 08/31/2020] [Indexed: 12/15/2022]
Abstract
Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.
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Affiliation(s)
- Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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127
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Polasky DA, Yu F, Teo GC, Nesvizhskii AI. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat Methods 2020. [PMID: 33020657 DOI: 10.1101/2020.05.18.102665] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/30/2023]
Abstract
Recent advances in methods for enrichment and mass spectrometric analysis of intact glycopeptides have produced large-scale glycoproteomics datasets, but interpreting these data remains challenging. We present MSFragger-Glyco, a glycoproteomics mode of the MSFragger search engine, for fast and sensitive identification of N- and O-linked glycopeptides and open glycan searches. Reanalysis of recent N-glycoproteomics data resulted in annotation of 80% more glycopeptide spectrum matches (glycoPSMs) than previously reported. In published O-glycoproteomics data, our method more than doubled the number of glycoPSMs annotated when searching the same glycans as the original search, and yielded 4- to 6-fold increases when expanding searches to include additional glycan compositions and other modifications. Expanded searches also revealed many sulfated and complex glycans that remained hidden to the original search. With greatly improved spectral annotation, coupled with the speed of index-based scoring, MSFragger-Glyco makes it possible to comprehensively interrogate glycoproteomics data and illuminate the many roles of glycosylation.
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Affiliation(s)
- Daniel A Polasky
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Fengchao Yu
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Guo Ci Teo
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA
| | - Alexey I Nesvizhskii
- Department of Pathology, University of Michigan, Ann Arbor, MI, USA.
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
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128
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Lu L, Riley NM, Shortreed MR, Bertozzi CR, Smith LM. O-Pair Search with MetaMorpheus for O-glycopeptide characterization. Nat Methods 2020; 17:1133-1138. [PMID: 33106676 PMCID: PMC7606753 DOI: 10.1038/s41592-020-00985-5] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2020] [Accepted: 09/21/2020] [Indexed: 11/23/2022]
Abstract
We report O-Pair Search, a new approach to identify O-glycopeptides and localize O-glycosites. Using paired collision- and electron-based dissociation spectra, O-Pair Search identifies O-glycopeptides using an ion-indexed open modification search and localizes O-glycosites using graph theory and probability-based localization. O-Pair Search reduces search times more than 2,000-fold compared to current O-glycopeptide processing software, while defining O-glycosite localization confidence levels and generating more O-glycopeptide identifications. Beyond the mucin-type O-glycopeptides discussed here, O-Pair Search also accepts user-defined glycan databases, making it compatible with many types of O-glycosylation. O-Pair Search is freely available within the open-source MetaMorpheus platform at https://github.com/smith-chem-wisc/MetaMorpheus.
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Affiliation(s)
- Lei Lu
- Department of Chemistry, University of Wisconsin, Madison, WI, USA
| | - Nicholas M Riley
- Department of Chemistry, University of Stanford, Stanford, CA, USA
| | | | - Carolyn R Bertozzi
- Department of Chemistry, University of Stanford, Stanford, CA, USA.,Howard Hughes Medical Institute, Stanford, CA, USA
| | - Lloyd M Smith
- Department of Chemistry, University of Wisconsin, Madison, WI, USA.
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129
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Mun DG, Renuse S, Saraswat M, Madugundu A, Udainiya S, Kim H, Park SKR, Zhao H, Nirujogi RS, Na CH, Kannan N, Yates JR, Lee SW, Pandey A. PASS-DIA: A Data-Independent Acquisition Approach for Discovery Studies. Anal Chem 2020; 92:14466-14475. [PMID: 33079518 DOI: 10.1021/acs.analchem.0c02513] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
A data-independent acquisition (DIA) approach is being increasingly adopted as a promising strategy for identification and quantitation of proteomes. As most DIA data sets are acquired with wide isolation windows, highly complex MS/MS spectra are generated, which negatively impacts obtaining peptide information through classical protein database searches. Therefore, the analysis of DIA data mainly relies on the evidence of the existence of peptides from prebuilt spectral libraries. Consequently, one major weakness of this method is that it does not account for peptides that are not included in the spectral library, precluding the use of DIA for discovery studies. Here, we present a strategy termed Precursor ion And Small Slice-DIA (PASS-DIA) in which MS/MS spectra are acquired with small isolation windows (slices) and MS/MS spectra are interpreted with accurately determined precursor ion masses. This method enables the direct application of conventional spectrum-centric analysis pipelines for peptide identification and precursor ion-based quantitation. The performance of PASS-DIA was observed to be superior to both data-dependent acquisition (DDA) and conventional DIA experiments with 69 and 48% additional protein identifications, respectively. Application of PASS-DIA for the analysis of post-translationally modified peptides again highlighted its superior performance in characterizing phosphopeptides (77% more), N-terminal acetylated peptides (56% more), and N-glycopeptides (83% more) as compared to DDA alone. Finally, the use of PASS-DIA to characterize a rare proteome of human fallopian tube organoids enabled 34% additional protein identifications than DDA alone and revealed biologically relevant pathways including low abundance proteins. Overall, PASS-DIA is a novel DIA approach for use as a discovery tool that outperforms both conventional DDA and DIA experiments to provide additional protein information. We believe that the PASS-DIA method is an important strategy for discovery-type studies when deeper proteome characterization is required.
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Affiliation(s)
- Dong-Gi Mun
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Santosh Renuse
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Mayank Saraswat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Anil Madugundu
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
| | - Savita Udainiya
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India
| | - Hokeun Kim
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Sung-Kyu Robin Park
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Hui Zhao
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - Raja Sekhar Nirujogi
- Medical Research Council Protein Phosphorylation and Ubiquitylation Unit, University of Dundee, Dundee DD1 5EH, United Kingdom
| | - Chan Hyun Na
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States.,Neurology, Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, United States
| | - Nagarajan Kannan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States
| | - John R Yates
- Department of Molecular Medicine and Neurobiology, The Scripps Research Institute, 10550 North Torrey Pines Road, La Jolla, California 92037, United States
| | - Sang-Won Lee
- Department of Chemistry, Center for Proteogenome Research, Korea University, Seoul 136-701, Republic of Korea
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota 55905, United States.,Center for Molecular Medicine, National Institute of Mental Health and Neurosciences (NIMHANS), Hosur Road, Bangalore 560029, India.,Institute of Bioinformatics, International Technology Park, Bangalore 560066, Karnataka, India.,Manipal Academy of Higher Education (MAHE), Manipal 576104 Karnataka, India
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130
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A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics. Nat Commun 2020; 11:5268. [PMID: 33077710 PMCID: PMC7572468 DOI: 10.1038/s41467-020-19052-w] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/23/2020] [Indexed: 02/07/2023] Open
Abstract
Regulation of protein N-glycosylation is essential in human cells. However, large-scale, accurate, and site-specific quantification of glycosylation is still technically challenging. We here introduce SugarQuant, an integrated mass spectrometry-based pipeline comprising protein aggregation capture (PAC)-based sample preparation, multi-notch MS3 acquisition (Glyco-SPS-MS3) and a data-processing tool (GlycoBinder) that enables confident identification and quantification of intact glycopeptides in complex biological samples. PAC significantly reduces sample-handling time without compromising sensitivity. Glyco-SPS-MS3 combines high-resolution MS2 and MS3 scans, resulting in enhanced reporter signals of isobaric mass tags, improved detection of N-glycopeptide fragments, and lowered interference in multiplexed quantification. GlycoBinder enables streamlined processing of Glyco-SPS-MS3 data, followed by a two-step database search, which increases the identification rates of glycopeptides by 22% compared with conventional strategies. We apply SugarQuant to identify and quantify more than 5,000 unique glycoforms in Burkitt’s lymphoma cells, and determine site-specific glycosylation changes that occurred upon inhibition of fucosylation at high confidence. Comprehensive quantitative profiling of intact glycopeptides remains technically challenging. To address this, the authors here develop an integrated quantitative glycoproteomic workflow, including optimized sample preparation, multiplexed quantification and a dedicated data processing tool.
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131
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Tüshaus J, Müller SA, Kataka ES, Zaucha J, Sebastian Monasor L, Su M, Güner G, Jocher G, Tahirovic S, Frishman D, Simons M, Lichtenthaler SF. An optimized quantitative proteomics method establishes the cell type-resolved mouse brain secretome. EMBO J 2020; 39:e105693. [PMID: 32954517 PMCID: PMC7560198 DOI: 10.15252/embj.2020105693] [Citation(s) in RCA: 47] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 08/12/2020] [Accepted: 08/14/2020] [Indexed: 12/21/2022] Open
Abstract
To understand how cells communicate in the nervous system, it is essential to define their secretome, which is challenging for primary cells because of large cell numbers being required. Here, we miniaturized secretome analysis by developing the "high-performance secretome protein enrichment with click sugars" (hiSPECS) method. To demonstrate its broad utility, hiSPECS was used to identify the secretory response of brain slices upon LPS-induced neuroinflammation and to establish the cell type-resolved mouse brain secretome resource using primary astrocytes, microglia, neurons, and oligodendrocytes. This resource allowed mapping the cellular origin of CSF proteins and revealed that an unexpectedly high number of secreted proteins in vitro and in vivo are proteolytically cleaved membrane protein ectodomains. Two examples are neuronally secreted ADAM22 and CD200, which we identified as substrates of the Alzheimer-linked protease BACE1. hiSPECS and the brain secretome resource can be widely exploited to systematically study protein secretion and brain function and to identify cell type-specific biomarkers for CNS diseases.
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Affiliation(s)
- Johanna Tüshaus
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Stephan A Müller
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Evans Sioma Kataka
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | - Jan Zaucha
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | | | - Minhui Su
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Institute of Neuronal Cell BiologyTechnical University MunichMunichGermany
| | - Gökhan Güner
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Georg Jocher
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
| | - Sabina Tahirovic
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
| | - Dmitrij Frishman
- Department of BioinformaticsWissenschaftszentrum WeihenstephanTechnical University of MunichFreisingGermany
| | - Mikael Simons
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- Institute of Neuronal Cell BiologyTechnical University MunichMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
| | - Stefan F Lichtenthaler
- German Center for Neurodegenerative Diseases (DZNE)MunichGermany
- NeuroproteomicsSchool of MedicineKlinikum rechts der IsarTechnical University of MunichMunichGermany
- Munich Cluster for Systems Neurology (SyNergy)MunichGermany
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132
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Zhao P, Praissman JL, Grant OC, Cai Y, Xiao T, Rosenbalm KE, Aoki K, Kellman BP, Bridger R, Barouch DH, Brindley MA, Lewis NE, Tiemeyer M, Chen B, Woods RJ, Wells L. Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor. Cell Host Microbe 2020; 28:586-601.e6. [PMID: 32841605 PMCID: PMC7443692 DOI: 10.1016/j.chom.2020.08.004] [Citation(s) in RCA: 291] [Impact Index Per Article: 72.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Revised: 07/22/2020] [Accepted: 08/10/2020] [Indexed: 12/13/2022]
Abstract
The SARS-CoV-2 betacoronavirus uses its highly glycosylated trimeric Spike protein to bind to the cell surface receptor angiotensin converting enzyme 2 (ACE2) glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatics analyses of natural variants and with existing 3D structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein both alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation. Taken together, these data can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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Affiliation(s)
- Peng Zhao
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Jeremy L Praissman
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Oliver C Grant
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Yongfei Cai
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Tianshu Xiao
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Katelyn E Rosenbalm
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Benjamin P Kellman
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Robert Bridger
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Dan H Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Melinda A Brindley
- Department of Infectious Diseases, Department of Population Health, Center for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
| | - Nathan E Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA; Novo Nordisk Foundation Center for Biosustainability at UC San Diego, La Jolla, CA 92093, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA
| | - Bing Chen
- Division of Molecular Medicine, Children's Hospital and Department of Pediatrics, Harvard Medical School, Boston, MA 02115, USA
| | - Robert J Woods
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA.
| | - Lance Wells
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, GA 30602, USA.
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133
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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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134
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Ahmad Izaham AR, Scott NE. Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching. Mol Cell Proteomics 2020. [PMID: 32576591 DOI: 10.1101/2020.04.21.052845] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/15/2023] Open
Abstract
Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.
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Affiliation(s)
- Ameera Raudah Ahmad Izaham
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Nichollas E Scott
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.
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135
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Ahmad Izaham AR, Scott NE. Open Database Searching Enables the Identification and Comparison of Bacterial Glycoproteomes without Defining Glycan Compositions Prior to Searching. Mol Cell Proteomics 2020; 19:1561-1574. [PMID: 32576591 PMCID: PMC8143609 DOI: 10.1074/mcp.tir120.002100] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 05/27/2020] [Indexed: 12/23/2022] Open
Abstract
Mass spectrometry has become an indispensable tool for the characterization of glycosylation across biological systems. Our ability to generate rich fragmentation of glycopeptides has dramatically improved over the last decade yet our informatic approaches still lag behind. Although glycoproteomic informatics approaches using glycan databases have attracted considerable attention, database independent approaches have not. This has significantly limited high throughput studies of unusual or atypical glycosylation events such as those observed in bacteria. As such, computational approaches to examine bacterial glycosylation and identify chemically diverse glycans are desperately needed. Here we describe the use of wide-tolerance (up to 2000 Da) open searching as a means to rapidly examine bacterial glycoproteomes. We benchmarked this approach using N-linked glycopeptides of Campylobacter fetus subsp. fetus as well as O-linked glycopeptides of Acinetobacter baumannii and Burkholderia cenocepacia revealing glycopeptides modified with a range of glycans can be readily identified without defining the glycan masses before database searching. Using this approach, we demonstrate how wide tolerance searching can be used to compare glycan use across bacterial species by examining the glycoproteomes of eight Burkholderia species (B. pseudomallei; B. multivorans; B. dolosa; B. humptydooensis; B. ubonensis, B. anthina; B. diffusa; B. pseudomultivorans). Finally, we demonstrate how open searching enables the identification of low frequency glycoforms based on shared modified peptides sequences. Combined, these results show that open searching is a robust computational approach for the determination of glycan diversity within bacterial proteomes.
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Affiliation(s)
- Ameera Raudah Ahmad Izaham
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia
| | - Nichollas E Scott
- Department of Microbiology and Immunology, University of Melbourne at the Peter Doherty Institute for Infection and Immunity, Melbourne, Australia.
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136
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Zenker HE, Wichers HJ, Tomassen MMM, Boeren S, De Jong NW, Hettinga KA. Peptide Release after Simulated Infant In Vitro Digestion of Dry Heated Cow's Milk Protein and Transport of Potentially Immunoreactive Peptides across the Caco-2 Cell Monolayer. Nutrients 2020; 12:nu12082483. [PMID: 32824739 PMCID: PMC7468992 DOI: 10.3390/nu12082483] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2020] [Revised: 07/30/2020] [Accepted: 08/13/2020] [Indexed: 02/07/2023] Open
Abstract
Dry heating of cow’s milk protein, as applied in the production of “baked milk”, facilitates the resolution of cow’s milk allergy symptoms upon digestion. The heating and glycation-induced changes of the protein structure can affect both digestibility and immunoreactivity. The immunological consequences may be due to changes in the peptide profile of the digested dry heated milk protein. Therefore, cow’s milk protein powder was heated at low temperature (60 °C) and high temperature (130 °C) and applied to simulated infant in vitro digestion. Digestion-derived peptides after 10 min and 60 min in the intestinal phase were measured using LC-MS/MS. Moreover, digests after 10 min intestinal digestion were applied to a Caco-2 cell monolayer. T-cell epitopes were analysed using prediction software, while specific immunoglobin E (sIgE) binding epitopes were identified based on the existing literature. The largest number of sIgE binding epitopes was found in unheated samples, while T-cell epitopes were equally represented in all samples. Transport of glycated peptide indicated a preference for glucosyl lysine and lactosyl-lysine-modified peptides, while transport of peptides containing epitope structures was limited. This showed that the release of immunoreactive peptides can be affected by the applied heating conditions; however, availability of peptides containing epitopes might be limited.
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Affiliation(s)
- Hannah E. Zenker
- Food Quality & Design Group, Wageningen University & Research Centre, 6708 WG Wageningen, The Netherlands;
| | - Harry J. Wichers
- Wageningen Food & Biobased Research, Wageningen University & Research Centre, 6708 WG Wageningen, The Netherlands; (H.J.W.); (M.M.M.T.)
- Laboratory of Food chemistry, Wageningen University & Research Centre, 6708 WG Wageningen, The Netherlands
| | - Monic M. M. Tomassen
- Wageningen Food & Biobased Research, Wageningen University & Research Centre, 6708 WG Wageningen, The Netherlands; (H.J.W.); (M.M.M.T.)
| | - Sjef Boeren
- Laboratory of Biochemistry, Wageningen University & Research Centre, 6708 WE Wageningen, The Netherlands;
| | - Nicolette W. De Jong
- Internal Medicine, Department of Allergology & Clinical Immunology, Erasmus Medical Centre, 3000 CA Rotterdam, The Netherlands;
| | - Kasper A. Hettinga
- Food Quality & Design Group, Wageningen University & Research Centre, 6708 WG Wageningen, The Netherlands;
- Correspondence:
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137
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Matyushin DD, Sholokhova AY, Buryak AK. Deep Learning Driven GC-MS Library Search and Its Application for Metabolomics. Anal Chem 2020; 92:11818-11825. [PMID: 32867500 DOI: 10.1021/acs.analchem.0c02082] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Preliminary compound identification and peak annotation in gas chromatography-mass spectrometry is usually made using mass spectral databases. There are a few algorithms that enable performing a search of a spectrum in a large mass spectral library. In many cases, a library search procedure returns a wrong answer even if a correct compound is contained in a library. In this work, we present a deep learning driven approach to a library search in order to reduce the probability of such cases. Machine learning ranking (learning to rank) is a class of machine learning and deep learning algorithms that perform a comparison (ranking) of objects. This work introduces the usage of deep learning ranking for small molecules identification using low-resolution electron ionization mass spectrometry. Instead of simple similarity measures for two spectra, such as the dot product or the Euclidean distance between vectors that represent spectra, a deep convolutional neural network is used. The deep learning ranking model outperforms other approaches and enables reducing a fraction of wrong answers (at rank-1) by 9-23% depending on the used data set. Spectra from the Golm Metabolome Database, Human Metabolome Database, and FiehnLib were used for testing the model.
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Affiliation(s)
- Dmitriy D Matyushin
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, Moscow, GSP-1, 119071, Russia
| | - Anastasia Yu Sholokhova
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, Moscow, GSP-1, 119071, Russia
| | - Aleksey K Buryak
- A.N. Frumkin Institute of Physical Chemistry and Electrochemistry, Russian Academy of Sciences, 31 Leninsky Prospect, Moscow, GSP-1, 119071, Russia
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138
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Riley N, Malaker SA, Driessen MD, Bertozzi CR. Optimal Dissociation Methods Differ for N- and O-Glycopeptides. J Proteome Res 2020; 19:3286-3301. [PMID: 32500713 PMCID: PMC7425838 DOI: 10.1021/acs.jproteome.0c00218] [Citation(s) in RCA: 141] [Impact Index Per Article: 35.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Indexed: 01/29/2023]
Abstract
Site-specific characterization of glycosylation requires intact glycopeptide analysis, and recent efforts have focused on how to best interrogate glycopeptides using tandem mass spectrometry (MS/MS). Beam-type collisional activation, i.e., higher-energy collisional dissociation (HCD), has been a valuable approach, but stepped collision energy HCD (sceHCD) and electron transfer dissociation with HCD supplemental activation (EThcD) have emerged as potentially more suitable alternatives. Both sceHCD and EThcD have been used with success in large-scale glycoproteomic experiments, but they each incur some degree of compromise. Most progress has occurred in the area of N-glycoproteomics. There is growing interest in extending this progress to O-glycoproteomics, which necessitates comparisons of method performance for the two classes of glycopeptides. Here, we systematically explore the advantages and disadvantages of conventional HCD, sceHCD, ETD, and EThcD for intact glycopeptide analysis and determine their suitability for both N- and O-glycoproteomic applications. For N-glycopeptides, HCD and sceHCD generate similar numbers of identifications, although sceHCD generally provides higher quality spectra. Both significantly outperform EThcD methods in terms of identifications, indicating that ETD-based methods are not required for routine N-glycoproteomics even if they can generate higher quality spectra. Conversely, ETD-based methods, especially EThcD, are indispensable for site-specific analyses of O-glycopeptides. Our data show that O-glycopeptides cannot be robustly characterized with HCD-centric methods that are sufficient for N-glycopeptides, and glycoproteomic methods aiming to characterize O-glycopeptides must be constructed accordingly.
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Affiliation(s)
- Nicholas
M. Riley
- Department
of Chemistry, Stanford University, Stanford, California 94305-6104, United States
| | - Stacy A. Malaker
- Department
of Chemistry, Stanford University, Stanford, California 94305-6104, United States
| | - Marc D. Driessen
- Department
of Chemistry, Stanford University, Stanford, California 94305-6104, United States
| | - Carolyn R. Bertozzi
- Department
of Chemistry, Stanford University, Stanford, California 94305-6104, United States
- Howard
Hughes Medical Institute, Stanford, California 94305-6104, United States
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139
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Liu S, Jiang X, Shang Z, Ji Y, Wang H, Wang Z, Wang P, Zhang Y, Xiao H. N-glycan structures of target cancer biomarker characterized by two-dimensional gel electrophoresis and mass spectrometry. Anal Chim Acta 2020; 1123:18-27. [DOI: 10.1016/j.aca.2020.04.063] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2019] [Revised: 03/30/2020] [Accepted: 04/25/2020] [Indexed: 12/11/2022]
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140
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Yang J, Wang W, Chen Z, Lu S, Yang F, Bi Z, Bao L, Mo F, Li X, Huang Y, Hong W, Yang Y, Zhao Y, Ye F, Lin S, Deng W, Chen H, Lei H, Zhang Z, Luo M, Gao H, Zheng Y, Gong Y, Jiang X, Xu Y, Lv Q, Li D, Wang M, Li F, Wang S, Wang G, Yu P, Qu Y, Yang L, Deng H, Tong A, Li J, Wang Z, Yang J, Shen G, Zhao Z, Li Y, Luo J, Liu H, Yu W, Yang M, Xu J, Wang J, Li H, Wang H, Kuang D, Lin P, Hu Z, Guo W, Cheng W, He Y, Song X, Chen C, Xue Z, Yao S, Chen L, Ma X, Chen S, Gou M, Huang W, Wang Y, Fan C, Tian Z, Shi M, Wang FS, Dai L, Wu M, Li G, Wang G, Peng Y, Qian Z, Huang C, Lau JYN, Yang Z, Wei Y, Cen X, Peng X, Qin C, Zhang K, Lu G, Wei X. A vaccine targeting the RBD of the S protein of SARS-CoV-2 induces protective immunity. Nature 2020; 586:572-577. [PMID: 32726802 DOI: 10.1038/s41586-020-2599-8] [Citation(s) in RCA: 517] [Impact Index Per Article: 129.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Accepted: 07/23/2020] [Indexed: 02/05/2023]
Abstract
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes a respiratory disease called coronavirus disease 2019 (COVID-19), the spread of which has led to a pandemic. An effective preventive vaccine against this virus is urgently needed. As an essential step during infection, SARS-CoV-2 uses the receptor-binding domain (RBD) of the spike protein to engage with the receptor angiotensin-converting enzyme 2 (ACE2) on host cells1,2. Here we show that a recombinant vaccine that comprises residues 319-545 of the RBD of the spike protein induces a potent functional antibody response in immunized mice, rabbits and non-human primates (Macaca mulatta) as early as 7 or 14 days after the injection of a single vaccine dose. The sera from the immunized animals blocked the binding of the RBD to ACE2, which is expressed on the cell surface, and neutralized infection with a SARS-CoV-2 pseudovirus and live SARS-CoV-2 in vitro. Notably, vaccination also provided protection in non-human primates to an in vivo challenge with SARS-CoV-2. We found increased levels of RBD-specific antibodies in the sera of patients with COVID-19. We show that several immune pathways and CD4 T lymphocytes are involved in the induction of the vaccine antibody response. Our findings highlight the importance of the RBD domain in the design of SARS-CoV-2 vaccines and provide a rationale for the development of a protective vaccine through the induction of antibodies against the RBD domain.
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Affiliation(s)
- Jingyun Yang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Wang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zimin Chen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Shuaiyao Lu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Fanli Yang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenfei Bi
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Linlin Bao
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Fei Mo
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xue Li
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yong Huang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Weiqi Hong
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yun Yang
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Yuan Zhao
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Fei Ye
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Sheng Lin
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Deng
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Hua Chen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Lei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Ziqi Zhang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Min Luo
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Hong Gao
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Yue Zheng
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yanqiu Gong
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaohua Jiang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yanfeng Xu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Qi Lv
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Dan Li
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Manni Wang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Fengdi Li
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Shunyi Wang
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Guanpeng Wang
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Pin Yu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Yajin Qu
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Li Yang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Hongxin Deng
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Aiping Tong
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Jiong Li
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhenling Wang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Jinliang Yang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Guobo Shen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiwei Zhao
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yuhua Li
- National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Jingwen Luo
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Hongqi Liu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Wenhai Yu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Mengli Yang
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Jingwen Xu
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Junbin Wang
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Haiyan Li
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Haixuan Wang
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Dexuan Kuang
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China
| | - Panpan Lin
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhengtao Hu
- National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Wei Guo
- National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Wei Cheng
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Yanlin He
- National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Xiangrong Song
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Chong Chen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhihong Xue
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Shaohua Yao
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Lu Chen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Siyuan Chen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Maling Gou
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Weijin Huang
- National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Youchun Wang
- National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Changfa Fan
- National Institutes for Food and Drug Control (NIFDC), Beijing, China
| | - Zhixin Tian
- Shanghai Key Laboratory of Chemical Assessment and Sustainability, School of Chemical Science & Engineering, Tongji University, Shanghai, China
| | - Ming Shi
- Treatment and Research Center for Infectious Diseases, The Fifth Medical Center of PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing, China
| | - Fu-Sheng Wang
- Treatment and Research Center for Infectious Diseases, The Fifth Medical Center of PLA General Hospital, National Clinical Research Center for Infectious Diseases, Beijing, China
| | - Lunzhi Dai
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Min Wu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Gen Li
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macau, China
| | - Guangyu Wang
- Department of Computer Science and Technology, Tsinghua University, Beijing, China
| | - Yong Peng
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Zhiyong Qian
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Canhua Huang
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Johnson Yiu-Nam Lau
- Department of Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China
| | - Zhenglin Yang
- Sichuan Provincial Key Laboratory for Human Disease Gene Study and Institute of Laboratory Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuquan Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China
| | - Xiaobo Cen
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.,National Chengdu Center for Safety Evaluation of Drugs, State Key Laboratory of Biotherapy and Cancer Center, West China Hospital, Sichuan University, Collaborative Innovation Center for Biotherapy, Chengdu, China
| | - Xiaozhong Peng
- National Kunming High-level Biosafety Primate Research Center, Institute of Medical Biology, Chinese Academy of Medical Sciences and Peking Union Medical College, Yunnan, China.,State Key Laboratory of Medical Molecular Biology, Chinese Academy of Medical Sciences, School of Basic Medicine, Peking Union Medical College, Beijing, China
| | - Chuan Qin
- Key Laboratory of Human Disease Comparative Medicine, Chinese Ministry of Health, Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases, Institute of Laboratory Animal Science, Chinese Academy of Medical Sciences and Comparative Medicine Center, Peking Union Medical College, Beijing, China
| | - Kang Zhang
- Center for Biomedicine and Innovations, Faculty of Medicine, Macau University of Science and Technology, Macau, China.
| | - Guangwen Lu
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China. .,Emergency Department, State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
| | - Xiawei Wei
- Laboratory of Aging Research and Cancer Drug Target, State Key Laboratory of Biotherapy and Cancer Center, National Clinical Research Center for Geriatrics, West China Hospital, Sichuan University, Chengdu, China.
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141
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One-step synthesis of hydrophilic microspheres for highly selective enrichment of N-linked glycopeptides. Anal Chim Acta 2020; 1130:91-99. [PMID: 32892942 DOI: 10.1016/j.aca.2020.07.049] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2020] [Revised: 07/17/2020] [Accepted: 07/19/2020] [Indexed: 11/22/2022]
Abstract
A polyacrylamide-based hydrophilic microsphere with a lot of hydroxyl groups on surface (PAM-OH HMS) was prepared in one step. The synthetic process was simple reverse suspension polymerization without any chemical derivation or grafting steps. The properties of obtained HMS were characterized by scanning electron microscopy (SEM), static water contact angle measurement, and FT-IR. The abundant hydroxyl groups on the surface make the material highly good hydrophilic and thus it was utilized for N-glycopeptides enrichment. The enrichment efficiency of PAM-OH HMSs was demonstrated by capturing N-linked glycopeptides from tryptic digest of human immunoglobulin G (IgG). The detection sensitivity for N-glycopeptides identification by MALDI-TOF MS was as low as 10 fmol for tryptic digest of standard human IgG. The selectivity of the HMS towards N-glycopeptides had almost no decrease when the molar ratio of BSA tryptic digest to IgG tryptic digest was increased from 10:1 to 100:1. Moreover, in the LC-MS/MS analysis of real biological sample, a total of 344 unique N-glycosites in 598 unique N-glycopeptides from 172 N-glycoproteins were identified from 2 μL human serum after deglycosylated by PNGase F, and 825 intact N-glycopeptides with different types of glycoform were detected when directly analyzed the N-glycopeptides enriched by PAM-OH HMS. To show the potential of our material in solving real biological issues, N-glycopeptides in the serum from hepatocelluar carcinoma (HCC) patient and health control were enriched and quantified. All the experiments demonstrated that this polyacrylamide-based hydrophilic microsphere shows a great potential to enrich the low-abundance N-glycopeptides for glycoproteome analysis of real complicated biological samples.
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142
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Zhao P, Praissman JL, Grant OC, Cai Y, Xiao T, Rosenbalm KE, Aoki K, Kellman BP, Bridger R, Barouch DH, Brindley MA, Lewis NE, Tiemeyer M, Chen B, Woods RJ, Wells L. Virus-Receptor Interactions of Glycosylated SARS-CoV-2 Spike and Human ACE2 Receptor. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2020:2020.06.25.172403. [PMID: 32743578 PMCID: PMC7386495 DOI: 10.1101/2020.06.25.172403] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
Abstract
The current COVID-19 pandemic is caused by the SARS-CoV-2 betacoronavirus, which utilizes its highly glycosylated trimeric Spike protein to bind to the cell surface receptor ACE2 glycoprotein and facilitate host cell entry. We utilized glycomics-informed glycoproteomics to characterize site-specific microheterogeneity of glycosylation for a recombinant trimer Spike mimetic immunogen and for a soluble version of human ACE2. We combined this information with bioinformatic analyses of natural variants and with existing 3D-structures of both glycoproteins to generate molecular dynamics simulations of each glycoprotein alone and interacting with one another. Our results highlight roles for glycans in sterically masking polypeptide epitopes and directly modulating Spike-ACE2 interactions. Furthermore, our results illustrate the impact of viral evolution and divergence on Spike glycosylation, as well as the influence of natural variants on ACE2 receptor glycosylation that, taken together, can facilitate immunogen design to achieve antibody neutralization and inform therapeutic strategies to inhibit viral infection.
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Affiliation(s)
- Peng Zhao
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Jeremy L. Praissman
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Oliver C. Grant
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Yongfei Cai
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Tianshu Xiao
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Katelyn E. Rosenbalm
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Kazuhiro Aoki
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Benjamin P. Kellman
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, California, 92093, USA
| | - Robert Bridger
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Dan H. Barouch
- Center for Virology and Vaccine Research, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, 02215, USA
| | - Melinda A. Brindley
- Department of Infectious Diseases, Department of Population Health, Center for Vaccines and Immunology, College of Veterinary Medicine, University of Georgia, Athens, GA 30602, USA
| | - Nathan E. Lewis
- Departments of Pediatrics and Bioengineering, University of California, San Diego, La Jolla, California, 92093, USA
- Novo Nordisk Foundation Center for Biosustainability at UC San Diego, La Jolla, California, 92093, USA
| | - Michael Tiemeyer
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Bing Chen
- Division of Molecular Medicine, Children’s Hospital and Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, 02115, USA
| | - Robert J. Woods
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
| | - Lance Wells
- Complex Carbohydrate Research Center, Department of Biochemistry and Molecular Biology, and Department of Chemistry, University of Georgia, Athens, Georgia, 30602, USA
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143
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Park GW, Lee JW, Lee HK, Shin JH, Kim JY, Yoo JS. Classification of Mucin-Type O-Glycopeptides Using Higher-Energy Collisional Dissociation in Mass Spectrometry. Anal Chem 2020; 92:9772-9781. [PMID: 32584546 DOI: 10.1021/acs.analchem.0c01218] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Changes in mucin-type O-glycosylation of human proteins affect protein function, immune response, and cancer progression. Since O-glycoproteins are characterized by the microheterogeneity of diverse O-glycans with no conserved sequence and the macroheterogeneity of multiple glycosylation sites on serine and/or threonine in human proteins, the assessment of different mucin types, such as Tn-antigen, core 1, and core 2, and their extended core types in O-glycopeptides, is extremely challenging. Here, we present an O-GlycoProteome Analyzer (O-GPA) that automatically classifies mucin-type O-glycosylation using higher-energy collisional dissociation (HCD) in mass spectrometry. First, we estimated the number of GlcNAc residues using the intensity ratio of GlcNAc-specific fragment ions (HexNAc-CH6O3 and HexNAc-2H2O) over GalNAc-specific fragment ions (HexNAc-C2H6O3 and HexNAc-C2H4O2) in the HCD spectrum. Furthermore, we classified the different mucin types of O-glycopeptides from characteristic B2 (HexNAc2) or Y2α (PEP + HexNAc2), and Y2β (PEP + HexNAcHex) fragment ions, along with the number of GlcNAc. Furthermore, O-GPA automatically determined single or multiple O-glycosylation, regardless of the mucin types. The mucin type of O-glycopeptides from human urine and plasma was confirmed with an overall accuracy of 96%. We found 97 core 1, 56 core 2, 13 extended core 1, and 12 extended core 2 glycopeptides from urine; and 22 core 1, 13 core 2, 7 extended core 1, 1 extended core 2, and 1 Tn-antigen from plasma. Our strategy can be used to successfully characterize specific mucin types of O-glycoproteins in human biological samples.
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Affiliation(s)
- Gun Wook Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea
| | - Ji Won Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Hyun Kyoung Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Jong Hwan Shin
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea
| | - Jin Young Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, Ochang-eup 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
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144
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Zhang Y, Lin T, Zhao Y, Mao Y, Tao Y, Huang Y, Wang S, Hu L, Cheng J, Yang H. Characterization of N-linked intact glycopeptide signatures of plasma IgGs from patients with prostate carcinoma and benign prostatic hyperplasia for diagnosis pre-stratification. Analyst 2020; 145:5353-5362. [PMID: 32568312 DOI: 10.1039/d0an00225a] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The discovery of novel non-invasive biomarkers for discriminating between prostate carcinoma (PCa) patients and benign prostatic hyperplasia (BPH) patients is necessary to reduce the burden of biopsies, avoid overdiagnosis and improve quality of life. Previous studies suggest that abnormal glycosylation of immunoglobulin gamma molecules (IgGs) is strongly associated with immunological diseases and prostate diseases. Hence, characterizing N-linked intact glycopeptides of IgGs that correspond to the N-glycan structure with specific site information might enable a better understanding of the molecular pathogenesis and discovery of novel signatures in preoperative discrimination of BPH from PCa. In this study, we profiled N-linked intact glycopeptides of purified IgGs from 51 PCa patients and 45 BPH patients by our developed N-glycoproteomic method using hydrophilic interaction liquid chromatography enrichment coupled with high resolution LC-MS/MS. The quantitative analysis of the N-linked intact glycopeptides using pGlyco 2.0 and MaxQuant software provided quantitative information on plasma IgG subclass-specific and site-specific N-glycosylation. As a result, we found four aberrantly expressed N-linked intact glycopeptides across different IgG subclasses. In particular, the N-glycopeptide IgG2-GP09 (EEQFNSTFR (H5N5S1)) was dramatically elevated in plasma from PCa patients, compared with that in BPH patients (PCa/BPH ratio = 5.74, p = 0.001). Additionally, the variations in these N-linked intact glycopeptide abundances were not caused by the changes in the IgG concentrations. Furthermore, IgG2-GP09 displayed a more powerful prediction capability (auROC = 0.702) for distinguishing PCa from BPH than the clinical index t-PSA (auROC = 0.681) when used alone or in combination with other indicators (auROC = 0.853). In conclusion, these abnormally expressed N-linked intact glycopeptides have potential for non-invasive monitoring and pre-stratification of prostate diseases.
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Affiliation(s)
- Yong Zhang
- Key Lab of Transplant Engineering and Immunology, MOH; West China-Washington Mitochondria and Metabolism Research Center; Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China.
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145
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Yin H, Zhu J, Wang M, Yao ZP, Lubman DM. Quantitative Analysis of α-1-Antitrypsin Glycosylation Isoforms in HCC Patients Using LC-HCD-PRM-MS. Anal Chem 2020; 92:8201-8208. [PMID: 32426967 PMCID: PMC7373126 DOI: 10.1021/acs.analchem.0c00420] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Abstract
The change in glycosylation of serum proteins is often associated with the development of various diseases and thus can be used for diagnosis. In this study, a liquid chromatography-tandem mass spectrometry-based method is used for accurate structural analysis and quantification of site-specific glycoforms of serum α-1-antitrypsin (A1AT) in early-stage HCC and cirrhosis patients. Serum protein A1AT was purified from patient sera by immunoprecipitation with anti-A1AT antibody conjugated agarose beads, and the isolated A1AT protein was digested and analyzed by LC-MS/MS. Two tandem mass spectrometry strategies are integrated in this study: a nontargeted stepped HCD strategy for structural analysis of A1AT glycopeptides and a targeted parallel reaction monitoring (PRM) strategy for quantification of site-specific glycoforms of A1AT in HCC and cirrhosis patient sera. Accordingly, pGlyco2.0 software was used for glycopeptide identification, and Skyline software was used for glycoform quantification using the Y1 ion (peptide+GlcNAc) in MS/MS spectra. Ten site-specific glycopeptides of A1AT were identified with stepped HCD-MS/MS in patient samples, 7 of which were further quantified using HCD-PRM-MS among patient samples. We found that our strategy was able to distinguish isomers of glycopeptides where several isomers showed distinctly different patterns between cirrhosis and HCC patients. We also found that the ratio of different charge states (2+/3+) of one glycopeptide of A1AT can significantly discriminate early-stage HCC from cirrhosis with the area under the receiver operating characteristic curve AUC of 0.9. Further analysis showed that the difference may be related to the sialic acid/galactose linkage of the glycan motif.
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Affiliation(s)
- Haidi Yin
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, People’s Republic of China; Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - Jianhui Zhu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Mengmeng Wang
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Zhong-Ping Yao
- The Hong Kong Polytechnic University Shenzhen Research Institute, Shenzhen, People’s Republic of China; Department of Applied Biology and Chemical Technology, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
| | - David M. Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
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146
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Li J, Jia L, Hao Z, Xu Y, Shen J, Ma C, Wu J, Zhao T, Zhi Y, Li P, Li J, Zhu B, Sun S. Site-Specific N-Glycoproteomic Analysis Reveals Upregulated Sialylation and Core Fucosylation during Transient Regeneration Loss in Neonatal Mouse Hearts. J Proteome Res 2020; 19:3191-3200. [PMID: 32425043 DOI: 10.1021/acs.jproteome.0c00172] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Myocardial infarction (MI) is one of the leading causes of deaths worldwide. Because of the incapability of regeneration, the cardiomyocyte loss with MI is replaced by fibrotic scar tissue, which eventually leads to heart failure. Reconstructing regeneration of an adult human heart has been recognized as a promising strategy for cardiac therapeutics. A neonatal mouse heart, which possesses transient regenerative capacity at the first week after birth, represents an ideal model to investigate processes associated with cardiac regeneration. In this work, an integrated glycoproteomic and proteomic analysis was performed to investigate the differences in glycoprotein abundances and site-specific glycosylation between postneonatal day 1 (P1) and day 7 (P7) of mouse hearts. By large-scale profiling and quantifying more than 2900 intact N-glycopeptides in neonatal mouse hearts, we identified 227 altered N-glycopeptides between P1 and P7 hearts. By extracting protein changes from the global proteome data, the normalized glycosylation changes for site-specific glycans were obtained, which showed heterogeneity on glycosites and glycoproteins. Systematic analysis of the glycosylation changes demonstrated an overall upregulation of sialylation and core fucosylation in P7 mice. Notably, the upregulated sialylation was a comprehensive result of increased sialylated glycans with Neu5Gc, with both Neu5Gc and core fucose, and decreased sialylated glycans with Neu5Ac. The upregulated core fucosylation resulted from the increase of glycans containing both core fucose and Neu5Gc but not glycans containing sole core fucose. These data provide a valuable resource for future functional and mechanism studies on heart regeneration and discovery of novel therapeutic targets. All mass spectrometry proteomic data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the data set identifier PXD017139.
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Affiliation(s)
- Jun Li
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Li Jia
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Zhifang Hao
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Yintai Xu
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Jiechen Shen
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Chen Ma
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Jingyu Wu
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Ting Zhao
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Yuan Zhi
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Pengfei Li
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Jing Li
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Bojing Zhu
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
| | - Shisheng Sun
- College of Life Science, Northwest University, Xi'an, Shaanxi province 710069, China
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147
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Zhu J, Huang J, Zhang J, Chen Z, Lin Y, Grigorean G, Li L, Liu S, Singal AG, Parikh ND, Lubman DM. Glycopeptide Biomarkers in Serum Haptoglobin for Hepatocellular Carcinoma Detection in Patients with Nonalcoholic Steatohepatitis. J Proteome Res 2020; 19:3452-3466. [PMID: 32412768 DOI: 10.1021/acs.jproteome.0c00270] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Nonalcoholic steatohepatitis (NASH) is rising in prevalence in the United States and is a growing cause of hepatocellular carcinomas (HCCs). Site-specific glycan heterogeneity on glycoproteins has been shown as a potential diagnostic biomarker for HCC. Herein, we have performed a comprehensive screening of site-specific N-glycopeptides in serum haptoglobin (Hp), a reporter molecule for aberrant glycosylation in HCC, to characterize glycopeptide markers for NASH-related HCCs. In total, 70 NASH patients (22 early HCC, 15 advanced HCC, and 33 cirrhosis cases) were analyzed, with Hp purified from 20 μL of serum in each patient, and 140 sets of mass spectrometry (MS) data were collected using liquid chromatography coupled with electron-transfer high-energy collisional dissociation tandem MS (LC-EThcD-MS/MS) for quantitative analysis on a novel software platform, Byos. Differential quantitation analysis revealed that five N-glycopeptides at sites N184 and N241 were significantly elevated during the progression from NASH cirrhosis to HCC (p < 0.05). Receiver operating characteristic (ROC) curve analysis demonstrated that the N-glycopeptides at sites N184 and N241 bearing a monofucosylated triantennary glycan A3G3F1S3 had the best diagnostic performance in detection of early NASH HCC, area under the curve (AUC) = 0.733 and 0.775, respectively, whereas α-fetoprotein (AFP) had an AUC of 0.692. When combined with AFP, the two panels improved the sensitivity for early NASH HCC from 59% (AFP alone) to 73% while maintaining a specificity of 70%, based on the optimal cutoff. Two-dimensional (2-D) scatter plots of the AFP value and N-glycopeptides showed that these N-glycopeptide markers detected 58% of AFP-negative HCC patients as distinct from cirrhosis. These site-specific N-glycopeptides could serve as potential markers for early detection of HCC in patients with NASH-related cirrhosis.
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Affiliation(s)
- Jianhui Zhu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Junfeng Huang
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Jie Zhang
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Zhengwei Chen
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States
| | - Yu Lin
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Gabriela Grigorean
- Department of Chemistry, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, Wisconsin 53706, United States.,School of Pharmacy, University of Wisconsin-Madison, Madison, Wisconsin 53705, United States
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, Texas 77030, United States
| | - Amit G Singal
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, Texas 75390, United States
| | - Neehar D Parikh
- Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
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148
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Potential Plasticity of the Mannoprotein Repertoire Associated to Mycobacterium tuberculosis Virulence Unveiled by Mass Spectrometry-Based Glycoproteomics. Molecules 2020; 25:molecules25102348. [PMID: 32443484 PMCID: PMC7287972 DOI: 10.3390/molecules25102348] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 05/04/2020] [Accepted: 05/13/2020] [Indexed: 12/17/2022] Open
Abstract
To date, Mycobacterium tuberculosis (Mtb) remains the world’s greatest infectious killer. The rise of multidrug-resistant strains stresses the need to identify new therapeutic targets to fight the epidemic. We previously demonstrated that bacterial protein-O-mannosylation is crucial for Mtb infectiousness, renewing the interest of the bacterial-secreted mannoproteins as potential drug-targetable virulence factors. The difficulty of inventorying the mannoprotein repertoire expressed by Mtb led us to design a stringent multi-step workflow for the reliable identification of glycosylated peptides by large-scale mass spectrometry-based proteomics. Applied to the differential analyses of glycoproteins secreted by the wild-type Mtb strain—and by its derived mutant invalidated for the protein-O-mannosylating enzyme PMTub—this approach led to the identification of not only most already known mannoproteins, but also of yet-unknown mannosylated proteins. In addition, analysis of the glycoproteome expressed by the isogenic recombinant Mtb strain overexpressing the PMTub gene revealed an unexpected mannosylation of proteins, with predicted or demonstrated functions in Mtb growth and interaction with the host cell. Since in parallel, a transient increased expression of the PMTub gene has been observed in the wild-type bacilli when infecting macrophages, our results strongly suggest that the Mtb mannoproteome may undergo adaptive regulation during infection of the host cells. Overall, our results provide deeper insights into the complexity of the repertoire of mannosylated proteins expressed by Mtb, and open the way to novel opportunities to search for still-unexploited potential therapeutic targets.
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149
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Zhu H, Wang S, Liu D, Ding L, Chen C, Liu Y, Wu Z, Bollag R, Liu K, Alexander WM, Yin J, Ma C, Li L, Wang PG. Identifying Sialylation Linkages at the Glycopeptide Level by Glycosyltransferase Labeling Assisted Mass Spectrometry (GLAMS). Anal Chem 2020; 92:6297-6303. [PMID: 32271005 PMCID: PMC7750919 DOI: 10.1021/acs.analchem.9b05068] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Precise assignment of sialylation linkages at the glycopeptide level is of importance in bottom-up glycoproteomics and an indispensable step to understand the function of glycoproteins in pathogen-host interactions and cancer progression. Even though some efforts have been dedicated to the discrimination of α2,3/α2,6-sialylated isomers, unambiguous identification of sialoglycopeptide isomers is still needed. Herein, we developed an innovative glycosyltransferase labeling assisted mass spectrometry (GLAMS) strategy. After specific enzymatic labeling, oxonium ions from higher-energy C-trap dissociation (HCD) fragmentation of α2,3-sailoglycopeptides then generate unique reporters to distinctly differentiate those of α2,6-sailoglycopeptide isomers. With this strategy, a total of 1236 linkage-specific sialoglycopeptides were successfully identified from 161 glycoproteins in human serum.
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Affiliation(s)
- He Zhu
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Shuaishuai Wang
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Ding Liu
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Lang Ding
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Congcong Chen
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Yunpeng Liu
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Zhigang Wu
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Roni Bollag
- Georgia Cancer Center, Augusta University, Augusta, Georgia 30912, United States
| | - Kebin Liu
- Department of Biochemistry and Molecular Biology, Augusta University, Augusta, Georgia 30912, United States
| | - William Max Alexander
- Department of Cancer Biology and Blais Proteomics Center, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, United States
| | - Jun Yin
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Cheng Ma
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Lei Li
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Peng George Wang
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
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150
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Huang J, Jiang B, Zhao H, Wu M, Kong S, Liu M, Yang P, Cao W. Development of a Computational Tool for Automated Interpretation of Intact O-Glycopeptide Tandem Mass Spectra from Single Proteins. Anal Chem 2020; 92:6777-6784. [PMID: 32275135 DOI: 10.1021/acs.analchem.0c01091] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Precise and automated analysis of site-specific O-glycosylation on single proteins is crucial for comprehensive characterization of some important glycoproteins, such as tumor biomarkers and recombinant drug proteins. Mass spectrometry has been proven to be a powerful technique for protein sequencing and N-glycosylation analysis. However, challenges remain in developing computational tools for intact O-glycopeptide analysis, which has greatly hindered the development of mass-spectrometry-based O-glycosylation analysis. Herein, an integrated strategy together with a dedicated automated computational tool termed AOGP was developed for intact O-glycopeptide analysis on single proteins. AOGP utilized de novo sequencing for O-glycans and a database search strategy for peptide backbones. The false discovery rate (FDR) of the identification results was controlled and validated by a mixed Gaussian distribution estimation method. AOGP exhibited superior performance in identifying intact O-glycopeptides of the human erythropoietin with a total of 188 O-glycopeptide spectra reported under 1% FDR. AOGP is developed in Python, is fully open-sourced, and is equipped with a user-friendly interface. Such an easy-operating and robust tool would greatly facilitate O-glycosylation analysis on single proteins in tumor biomarker and recombinant drug protein development.
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