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DeBono NJ, Moh ESX, Packer NH. Experimentally Determined Diagnostic Ions for Identification of Peptide Glycotopes. J Proteome Res 2024; 23:2661-2673. [PMID: 38888225 DOI: 10.1021/acs.jproteome.3c00858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/20/2024]
Abstract
The analysis of the structures of glycans present on glycoproteins is an essential component for determining glycoprotein function; however, detailed glycan structural assignment on glycopeptides from proteomics mass spectrometric data remains challenging. Glycoproteomic analysis by mass spectrometry currently can provide significant, yet incomplete, information about the glycans present, including the glycan monosaccharide composition and in some circumstances the site(s) of glycosylation. Advancements in mass spectrometric resolution, using high-mass accuracy instrumentation and tailored MS/MS fragmentation parameters, coupled with a dedicated definition of diagnostic fragmentation ions have enabled the determination of some glycan structural features, or glycotopes, expressed on glycopeptides. Here we present a collation of diagnostic glycan fragments produced by traditional positive-ion-mode reversed-phase LC-ESI MS/MS proteomic workflows and describe the specific fragmentation energy settings required to identify specific glycotopes presented on N- or O-linked glycopeptides in a typical proteomics MS/MS experiment.
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Affiliation(s)
- Nicholas J DeBono
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Edward S X Moh
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
| | - Nicolle H Packer
- ARC Centre of Excellence in Synthetic Biology, School of Natural Sciences, Macquarie University, Sydney, NSW 2109, Australia
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2
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Li Y, Guo W, Zhang Q, Yang B, Zhang Y, Yang Y, Liu G, Pan L, Zhang W, Kong D. Improved analysis ZIC-HILIC-HCD-Orbitrap method for mapping the glycopeptide by mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci 2023; 1228:123852. [PMID: 37633008 DOI: 10.1016/j.jchromb.2023.123852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 07/29/2023] [Accepted: 08/15/2023] [Indexed: 08/28/2023]
Abstract
Glycosylation is one of the most common post-translational modifications (PTMs). Protein glycosylation analysis is the bottleneck to deeply understand their functions. At present, the LC-MS analysis of glycosylated post-translational modification is mainly focused on the analysis of glycopeptides. However, the factors affecting the identification of glycopeptides were not fully elucidated. In the paper, we have carefully studied the factors, e.g., HILIC materials, search engines, protein amount, gradient duration, extraction solution, etc. According to the results, HILIC materials were the most important factors affecting the glycopeptides identification, and the amphoteric sulfoalkyl betaine stationary phase enriched glycopeptides 6-fold more compared to the amphiphilic ion-bonded fully porous spherical silica stationary phase. We explored the influence of the extraction solutions on glycan identification. Comparing sodium dodecyl sulfate (SDS) and urea (UA), the results showed that N-glycolylneuraminic acid (NeuGc) type of glycan content was found to be increased 1.4-fold in the SDS compared to UA. Besides, we explored the influence of the search engine on glycopeptide identification. Comparing pGlyco3.0 and MSFragger-Glyco, it was observed that pGlyco3.0 outperformed MSFragger-Glyco in identifying glycopeptides. Then, using our optimized method we found that there was a significant difference in the distribution of monosaccharide types in plasma and brain tissue, e.g., the content of NeuAc in brain was 5-fold higher than that in plasma. To importantly, two glycoproteins (Neurexin-2 and SUN domain-containing protein 2) were also found for the first time by our method. In summary, we have comprehensively studied the factors influencing glycopeptide identification than any previous research, and the optimized method could be widely used for identifying the glycoproteins or glycolpeptides biomarkers for disease detection and therapeutic targets.
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Affiliation(s)
- Yahui Li
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Wenyan Guo
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Qingning Zhang
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Bingkun Yang
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China; School of Pharmacy, Hebei Medical University, Shijiazhuang, China
| | - Yuyu Zhang
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Yi Yang
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China; The Fourth Hospital of Hebei Medical University, Shijiazhuang, China
| | - Guangyuan Liu
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Liangyu Pan
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China
| | - Wei Zhang
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China.
| | - Dezhi Kong
- Department of Pharmacology of Chinese Materia Medica, Institution of Chinese Integrative Medicine, School of Chinese Integrative Medicine, Hebei Medical University, Shijiazhuang, China.
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3
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Peng W, Reyes CDG, Gautam S, Yu A, Cho BG, Goli M, Donohoo K, Mondello S, Kobeissy F, Mechref Y. MS-based glycomics and glycoproteomics methods enabling isomeric characterization. MASS SPECTROMETRY REVIEWS 2023; 42:577-616. [PMID: 34159615 PMCID: PMC8692493 DOI: 10.1002/mas.21713] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Revised: 06/01/2021] [Accepted: 06/02/2021] [Indexed: 05/03/2023]
Abstract
Glycosylation is one of the most significant and abundant posttranslational modifications in mammalian cells. It mediates a wide range of biofunctions, including cell adhesion, cell communication, immune cell trafficking, and protein stability. Also, aberrant glycosylation has been associated with various diseases such as diabetes, Alzheimer's disease, inflammation, immune deficiencies, congenital disorders, and cancers. The alterations in the distributions of glycan and glycopeptide isomers are involved in the development and progression of several human diseases. However, the microheterogeneity of glycosylation brings a great challenge to glycomic and glycoproteomic analysis, including the characterization of isomers. Over several decades, different methods and approaches have been developed to facilitate the characterization of glycan and glycopeptide isomers. Mass spectrometry (MS) has been a powerful tool utilized for glycomic and glycoproteomic isomeric analysis due to its high sensitivity and rich structural information using different fragmentation techniques. However, a comprehensive characterization of glycan and glycopeptide isomers remains a challenge when utilizing MS alone. Therefore, various separation methods, including liquid chromatography, capillary electrophoresis, and ion mobility, were developed to resolve glycan and glycopeptide isomers before MS. These separation techniques were coupled to MS for a better identification and quantitation of glycan and glycopeptide isomers. Additionally, bioinformatic tools are essential for the automated processing of glycan and glycopeptide isomeric data to facilitate isomeric studies in biological cohorts. Here in this review, we discuss commonly employed MS-based techniques, separation hyphenated MS methods, and software, facilitating the separation, identification, and quantitation of glycan and glycopeptide isomers.
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Affiliation(s)
- Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | | | - Sakshi Gautam
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Aiying Yu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Byeong Gwan Cho
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | - Kaitlyn Donohoo
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
| | | | - Firas Kobeissy
- Program for Neurotrauma, Neuroproteomics & Biomarkers Research, Departments of Emergency Medicine, University of Florida, Gainesville, Florida, USA
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas, USA
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4
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Xu Y, Wang Y, Höti N, Clark DJ, Chen SY, Zhang H. The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection. MASS SPECTROMETRY REVIEWS 2023; 42:822-843. [PMID: 34766650 PMCID: PMC9095761 DOI: 10.1002/mas.21748] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 10/07/2021] [Accepted: 10/24/2021] [Indexed: 05/02/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.
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Affiliation(s)
- Yuanwei Xu
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuefan Wang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Naseruddin Höti
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David J Clark
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shao-Yung Chen
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hui Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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5
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Yin H, Zhu J. Methods for quantification of glycopeptides by liquid separation and mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:887-917. [PMID: 35099083 PMCID: PMC9339036 DOI: 10.1002/mas.21771] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 11/14/2021] [Accepted: 01/13/2022] [Indexed: 05/05/2023]
Abstract
Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.
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Affiliation(s)
- Haidi Yin
- Shenzhen Bay Laboratory, Shenzhen, Guangdong, 518132, China
- Correspondence to: Haidi Yin, Shenzhen Bay Laboratory, A1201, Shenzhen, Guangdong, 518132, China. Phone: 0755-26849276. , Jianhui Zhu, Department of Surgery, University of Michigan, 1150 West Medical Center Drive, Building MSRB1, Rm A500, Ann Arbor, MI 48109-0656, USA. Tel: 734-615-2567. Fax: 734-615-2088.
| | - Jianhui Zhu
- Department of Surgery, University of Michigan, Ann Arbor, MI 48109, USA
- Correspondence to: Haidi Yin, Shenzhen Bay Laboratory, A1201, Shenzhen, Guangdong, 518132, China. Phone: 0755-26849276. , Jianhui Zhu, Department of Surgery, University of Michigan, 1150 West Medical Center Drive, Building MSRB1, Rm A500, Ann Arbor, MI 48109-0656, USA. Tel: 734-615-2567. Fax: 734-615-2088.
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6
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Li S, Zhu J, Lubman DM, Zhou H, Tang H. GlycoSLASH: Concurrent Glycopeptide Identification from Multiple Related LC-MS/MS Data Sets by Using Spectral Clustering and Library Searching. J Proteome Res 2023; 22:1501-1509. [PMID: 36802412 PMCID: PMC10164058 DOI: 10.1021/acs.jproteome.3c00066] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Liquid chromatography coupled with tandem mass spectrometry is commonly adopted in large-scale glycoproteomic studies involving hundreds of disease and control samples. The software for glycopeptide identification in such data (e.g., the commercial software Byonic) analyzes the individual data set and does not exploit the redundant spectra of glycopeptides presented in the related data sets. Herein, we present a novel concurrent approach for glycopeptide identification in multiple related glycoproteomic data sets by using spectral clustering and spectral library searching. The evaluation on two large-scale glycoproteomic data sets showed that the concurrent approach can identify 105%-224% more spectra as glycopeptides compared to the glycopeptide identification on individual data sets using Byonic alone. The improvement of glycopeptide identification also enabled the discovery of several potential biomarkers of protein glycosylations in hepatocellular carcinoma patients.
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Affiliation(s)
- Sujun Li
- Department of Blood Transfusion, The First Affiliated Hospital of Nanchang University, Nanchang 330000, China.,JiangXi Key Laboratory of Transfusion Medicine, Nanchang 330000, China.,Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - David M Lubman
- Department of Surgery, University of Michigan, Medical Center, Ann Arbor, Michigan 48109, United States
| | - He Zhou
- Shenzhen Dengding Biopharma Co. Ltd., Shenzhen 518000, China
| | - Haixu Tang
- Luddy School of Informatics, Computing and Engineering, Indiana University, Bloomington, Indiana 47408, United States
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Kim H, Bang G, Park YE, Park M, Choi JH, Oh MJ, An HJ, Yoo JS, Park YH, Kim JY, Hwang H. Advanced assessment through intact glycopeptide analysis of Infliximab’s biologics and biosimilar. Front Mol Biosci 2022; 9:1006866. [DOI: 10.3389/fmolb.2022.1006866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Accepted: 10/26/2022] [Indexed: 11/30/2022] Open
Abstract
Characterization of therapeutic monoclonal antibodies (mAbs) represents a major challenge for analytical sciences due to their heterogeneity associated with post-translational modifications (PTMs). The protein glycosylation requires comprehensive identification, which could influence on the mAbs’ structure and their function. Here, we demonstrated high-resolution tandem mass spectrometry with an ultra-high-performance liquid chromatography for characterization and comparison between biologics and biosimilar of infliximab at an advanced level. Comparing the N- and O-glycopeptides profiles, a total of 49 and 54 glycopeptides was identified for each product of the biologics and biosimilar, respectively. We also discovered one novel N-glycosylation site at the light chain from both biopharmaceuticals and one novel O-glycopeptide at the heavy chain from only biosimilar. Site-specific glycopeptide analysis process will be a robust and useful technique for evaluating therapeutic mAbs and complex glycoprotein products.
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8
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Zhou Y, Cai X, Wu L, Lin N. Comparative glycoproteomics study on the surface of SKOV3 versus IOSE80 cell lines. Front Chem 2022; 10:1010642. [DOI: 10.3389/fchem.2022.1010642] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 11/02/2022] [Indexed: 11/23/2022] Open
Abstract
Objective: Site- and structure-specific quantitative N-glycoproteomics study of differential cell-surface N-glycosylation of ovarian cancer SKOV3 cells with the non-cancerous ovarian epithelial IOSE80 cells as the control.Methods: C18-RPLC-MS/MS (HCD with stepped normalized collision energies) was used to analyze the 1: 1 mixture of labeled intact N-glycopeptides from SKOV3 and IOSE80 cells, and the site- and structure-specific intact N-glycopeptide search engine GPSeeker was used to conduct qualitative and quantitative search on the obtained raw datasets.Results: With the control of the spectrum-level false discovery rate ≤1%, 13,822 glycopeptide spectral matches coming from 2,918 N-glycoproteins with comprehensive N-glycosite and N-glycan structure information were identified; 3,733 N-glycosites and 3,754 N-glycan sequence structures were confirmed by site-determining and structure-diagnostic fragment ions, respectively. With the control of no less than two observations among the three technical replicates, fold change ≥1.5, and p-value ≤ 0.05, 746 DEPGs in SKOV3 cells relative to IOSE80 cells were quantified, where 421 were upregulated and 325 downregulated.Conclusion: Differential cell-surface N-glycosylation of ovarian cancer SKOV3 cells were quantitatively analyzed by isotopic labeling and site- and structure-specific N-glycoproteomics. This discovery study provides putative N-glycoprotein biomarker candidates for future validation study using multiple reaction monitoring and biochemical methods.
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Mechref Y, Peng W, Gautam S, Ahmadi P, Lin Y, Zhu J, Zhang J, Liu S, Singal AG, Parikh ND, Lubman DM. Mass spectrometry based biomarkers for early detection of HCC using a glycoproteomic approach. Adv Cancer Res 2022; 157:23-56. [PMID: 36725111 PMCID: PMC10014290 DOI: 10.1016/bs.acr.2022.07.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Hepatocellular carcinoma (HCC) is the fourth most common cause of cancer-related mortality worldwide and 80%-90% of HCC develops in patients that have underlying cirrhosis. Better methods of surveillance are needed to increase early detection of HCC and the proportion of patients that can be offered curative therapies. Recent work in novel mass spec-based methods for glycomic and glycopeptide analysis for discovery and confirmation of markers for early detection of HCC versus cirrhosis is reviewed in this chapter. Results from recent work in these fields by several groups and the progress made in developing markers of early HCC which can outperform the current serum-based markers are described and discussed. Also, recent developments in isoform analysis of glycans and glycopeptides and in various mass spec fragmentation methods will be described and discussed.
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Affiliation(s)
- Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States.
| | - Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Sakshi Gautam
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Parisa Ahmadi
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Yu Lin
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Jianhui Zhu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Jie Zhang
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - Suyu Liu
- Department of Biostatistics, University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Amit G Singal
- Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, United States
| | - Neehar D Parikh
- Division of Gastroenterology and Hepatology, University of Michigan Medical Center, Ann Arbor, MI, United States
| | - David M Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, MI, United States.
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10
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Mackay S, Hitefield NL, Oduor IO, Roberts AB, Burch TC, Lance RS, Cunningham TD, Troyer DA, Semmes OJ, Nyalwidhe JO. Site-Specific Intact N-Linked Glycopeptide Characterization of Prostate-Specific Membrane Antigen from Metastatic Prostate Cancer Cells. ACS OMEGA 2022; 7:29714-29727. [PMID: 36061737 PMCID: PMC9435049 DOI: 10.1021/acsomega.2c02265] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Accepted: 08/04/2022] [Indexed: 06/15/2023]
Abstract
The composition of N-linked glycans that are conjugated to the prostate-specific membrane antigen (PSMA) and their functional significance in prostate cancer progression have not been fully characterized. PSMA was isolated from two metastatic prostate cancer cell lines, LNCaP and MDAPCa2b, which have different tissue tropism and localization. Isolated PSMA was trypsin-digested, and intact glycopeptides were subjected to LC-HCD-EThcD-MS/MS analysis on a Tribrid Orbitrap Fusion Lumos mass spectrometer. Differential qualitative and quantitative analysis of site-specific N-glycopeptides was performed using Byonic and Byologic software. Comparative quantitative analysis demonstrates that multiple glycopeptides at asparagine residues 51, 76, 121, 195, 336, 459, 476, and 638 were in significantly different abundance in the two cell lines (p < 0.05). Biochemical analysis using endoglycosidase treatment and lectin capture confirm the MS and site occupancy data. The data demonstrate the effectiveness of the strategy for comprehensive analysis of PSMA glycopeptides. This approach will form the basis of ongoing experiments to identify site-specific glycan changes in PSMA isolated from disease-stratified clinical samples to uncover targets that may be associated with disease progression and metastatic phenotypes.
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Affiliation(s)
- Stephen Mackay
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
- University
of North Carolina, Chapel Hill, North Carolina 27516, United States
| | - Naomi L. Hitefield
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
- University
of Georgia, Athens, Georgia 30602, United
States
| | - Ian O. Oduor
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Autumn B. Roberts
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Tanya C. Burch
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Raymond S. Lance
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Spokane
Urology, Spokane, Washington 99202, United States
| | - Tina D. Cunningham
- School of
Health Professions, Eastern Virginia Medical
School, Norfolk, Virginia 23507, United States
| | - Dean A. Troyer
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Oliver J. Semmes
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
| | - Julius O. Nyalwidhe
- Leroy
T. Canoles Jr. Cancer Research Center, Eastern
Virginia Medical School, Norfolk, Virginia 23507, United States
- Department
of Microbiology and Molecular Cell Biology, Eastern Virginia Medical School, Norfolk, Virginia 23507, United States
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11
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Liu S, Wang H, Jiang X, Ji Y, Wang Z, Zhang Y, Wang P, Xiao H. Integrated N-glycoproteomics Analysis of Human Saliva for Lung Cancer. J Proteome Res 2022; 21:1589-1602. [PMID: 35715216 DOI: 10.1021/acs.jproteome.1c00701] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
Aberrant protein N-glycosylation is a cancer hallmark, which has great potential for cancer detection. However, large-scale and in-depth analysis of N-glycosylation remains challenging because of its high heterogeneity, complexity, and low abundance. Human saliva is an attractive diagnostic body fluid, while few efforts explored its N-glycoproteome for lung cancer. Here, we utilized a zwitterionic-hydrophilic interaction chromatography-based strategy to specifically enrich salivary glycopeptides. Through quantitative proteomics analysis, 1492 and 1234 intact N-glycopeptides were confidently identified from pooled saliva samples of 10 subjects in the nonsmall-cell lung cancer group and 10 subjects in the normal control group. Accordingly, 575 and 404 N-glycosites were revealed for the lung cancer group and normal control group. In particular, 154 N-glycosites and 259 site-specific glycoforms were significantly dysregulated in the lung cancer group. Several N-glycosites located at the same glycoprotein and glycans attached to the same N-glycosites were observed with differential expressions, including haptoglobin, Mucin-5B, lactotransferrin, and α-1-acid glycoprotein 1. These N-glycoproteins were mainly related to inflammatory responses, infectious diseases, and cancers. Our study achieved comprehensive characterization of salivary N-glycoproteome, and dysregulated site-specific glycoforms hold promise for noninvasive detection of lung cancer.
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Affiliation(s)
- Sha Liu
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Huiyu Wang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiaoteng Jiang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yin Ji
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Pharmaceutical Co., Ltd., Nanjing 210042, China
| | - Zeyuan Wang
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yan Zhang
- School of Pharmacy, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Peng Wang
- State Key Laboratory of Translational Medicine and Innovative Drug Development, Jiangsu Simcere Pharmaceutical Co., Ltd., Nanjing 210042, China
| | - Hua Xiao
- State Key Laboratory of Microbial Metabolism, Joint International Research Laboratory of Metabolic & Developmental Sciences, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai 200240, China
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12
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Majek P, Sovova Z, Pecankova K, Cermak J, Gasova Z, Pecherkova P, Ignjatovic V, Dyr JE. Mass spectrometry, data re-analysis, and homology modelling predict posttranslational modifications of leucine-rich alpha-2-glycoprotein as a marker of myelodysplastic syndrome. Cancer Biomark 2022; 34:485-492. [PMID: 35275518 DOI: 10.3233/cbm-210033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
BACKGROUND Leucine-rich alpha-2-glycoprotein (LRG) has been repeatedly proposed as a potential plasma biomarker for myelodysplastic syndrome (MDS). OBJECTIVE The goal of our work was to establish the total LRG plasma level and LRG posttranslational modifications (PTMs) as a suitable MDS biomarker. METHODS The total plasma LRG concentration was determined with ELISA, whilst the LRG-specific PTMs and their locations, were established using mass spectrometry and public mass spectrometry data re-analysis. Homology modelling and sequence analysis were used to establish the potential impact of PTMs on LRG functions via their impact on the LRG structure. RESULTS While the results showed that the total LRG plasma concentration is not a suitable MDS marker, alterations within two LRG sites correlated with MDS diagnosis (p= 0.0011). Sequence analysis and the homology model suggest the influence of PTMs within the two LRG sites on the function of this protein. CONCLUSIONS We report the presence of LRG proteoforms that correlate with diagnosis in the plasma of MDS patients. The combination of mass spectrometry, re-analysis of publicly available data, and homology modelling, represents an approach that can be used for any protein to predict clinically relevant protein sites for biomarker research despite the character of the PTMs being unknown.
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Affiliation(s)
- Pavel Majek
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Zofie Sovova
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Klara Pecankova
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Jaroslav Cermak
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Zdenka Gasova
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Pavla Pecherkova
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
| | - Vera Ignjatovic
- Murdoch Children's Research Institute, Parkville Victoria, Australia.,Department of Paediatrics, The University of Melbourne, Parkville Victoria, Australia
| | - Jan E Dyr
- Institute of Hematology and Blood Transfusion, Prague, Czech Republic
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13
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Glycomic and Glycoproteomic Techniques in Neurodegenerative Disorders and Neurotrauma: Towards Personalized Markers. Cells 2022; 11:cells11030581. [PMID: 35159390 PMCID: PMC8834236 DOI: 10.3390/cells11030581] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/22/2022] [Accepted: 02/03/2022] [Indexed: 12/16/2022] Open
Abstract
The proteome represents all the proteins expressed by a genome, a cell, a tissue, or an organism at any given time under defined physiological or pathological circumstances. Proteomic analysis has provided unparalleled opportunities for the discovery of expression patterns of proteins in a biological system, yielding precise and inclusive data about the system. Advances in the proteomics field opened the door to wider knowledge of the mechanisms underlying various post-translational modifications (PTMs) of proteins, including glycosylation. As of yet, the role of most of these PTMs remains unidentified. In this state-of-the-art review, we present a synopsis of glycosylation processes and the pathophysiological conditions that might ensue secondary to glycosylation shortcomings. The dynamics of protein glycosylation, a crucial mechanism that allows gene and pathway regulation, is described. We also explain how-at a biomolecular level-mutations in glycosylation-related genes may lead to neuropsychiatric manifestations and neurodegenerative disorders. We then analyze the shortcomings of glycoproteomic studies, putting into perspective their downfalls and the different advanced enrichment techniques that emanated to overcome some of these challenges. Furthermore, we summarize studies tackling the association between glycosylation and neuropsychiatric disorders and explore glycoproteomic changes in neurodegenerative diseases, including Alzheimer's disease, Parkinson's disease, Huntington disease, multiple sclerosis, and amyotrophic lateral sclerosis. We finally conclude with the role of glycomics in the area of traumatic brain injury (TBI) and provide perspectives on the clinical application of glycoproteomics as potential diagnostic tools and their application in personalized medicine.
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14
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Fang P, Ji Y, Oellerich T, Urlaub H, Pan KT. Strategies for Proteome-Wide Quantification of Glycosylation Macro- and Micro-Heterogeneity. Int J Mol Sci 2022; 23:ijms23031609. [PMID: 35163546 PMCID: PMC8835892 DOI: 10.3390/ijms23031609] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/26/2022] [Accepted: 01/27/2022] [Indexed: 12/03/2022] Open
Abstract
Protein glycosylation governs key physiological and pathological processes in human cells. Aberrant glycosylation is thus closely associated with disease progression. Mass spectrometry (MS)-based glycoproteomics has emerged as an indispensable tool for investigating glycosylation changes in biological samples with high sensitivity. Following rapid improvements in methodologies for reliable intact glycopeptide identification, site-specific quantification of glycopeptide macro- and micro-heterogeneity at the proteome scale has become an urgent need for exploring glycosylation regulations. Here, we summarize recent advances in N- and O-linked glycoproteomic quantification strategies and discuss their limitations. We further describe a strategy to propagate MS data for multilayered glycopeptide quantification, enabling a more comprehensive examination of global and site-specific glycosylation changes. Altogether, we show how quantitative glycoproteomics methods explore glycosylation regulation in human diseases and promote the discovery of biomarkers and therapeutic targets.
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Affiliation(s)
- Pan Fang
- Department of Biochemistry and Molecular Biology, School of Biology & Basic Medical Sciences, Suzhou Medical College of Soochow University, Suzhou 215123, China;
| | - Yanlong Ji
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany;
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, 60590 Frankfurt am Main, Germany;
- Frankfurt Cancer Institute, Johann Wolfgang Goethe University, 60596 Frankfurt am Main, Germany
| | - Thomas Oellerich
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, 60590 Frankfurt am Main, Germany;
- Frankfurt Cancer Institute, Johann Wolfgang Goethe University, 60596 Frankfurt am Main, Germany
- German Cancer Consortium (DKTK), Partner Site Frankfurt/Mainz, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Henning Urlaub
- Bioanalytical Mass Spectrometry Group, Max Planck Institute for Multidisciplinary Sciences, 37077 Göttingen, Germany;
- Institute of Clinical Chemistry, University Medical Center Göttingen, 37075 Göttingen, Germany
- Correspondence: (H.U.); (K.-T.P.)
| | - Kuan-Ting Pan
- Hematology/Oncology, Department of Medicine II, Johann Wolfgang Goethe University, 60590 Frankfurt am Main, Germany;
- Frankfurt Cancer Institute, Johann Wolfgang Goethe University, 60596 Frankfurt am Main, Germany
- Correspondence: (H.U.); (K.-T.P.)
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15
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Kawahara R, Chernykh A, Alagesan K, Bern M, Cao W, Chalkley RJ, Cheng K, Choo MS, Edwards N, Goldman R, Hoffmann M, Hu Y, Huang Y, Kim JY, Kletter D, Liquet B, Liu M, Mechref Y, Meng B, Neelamegham S, Nguyen-Khuong T, Nilsson J, Pap A, Park GW, Parker BL, Pegg CL, Penninger JM, Phung TK, Pioch M, Rapp E, Sakalli E, Sanda M, Schulz BL, Scott NE, Sofronov G, Stadlmann J, Vakhrushev SY, Woo CM, Wu HY, Yang P, Ying W, Zhang H, Zhang Y, Zhao J, Zaia J, Haslam SM, Palmisano G, Yoo JS, Larson G, Khoo KH, Medzihradszky KF, Kolarich D, Packer NH, Thaysen-Andersen M. Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis. Nat Methods 2021; 18:1304-1316. [PMID: 34725484 DOI: 10.1101/2021.03.14.435332] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Accepted: 09/22/2021] [Indexed: 05/18/2023]
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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Affiliation(s)
- Rebeca Kawahara
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Anastasia Chernykh
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
| | - Kathirvel Alagesan
- Institute for Glycomics, Griffith University Gold Coast Campus, Southport, QLD, Australia
| | | | - Weiqian Cao
- Institutes of Biomedical Sciences, and the NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Robert J Chalkley
- UCSF, School of Pharmacy, Department of Pharmaceutical Chemistry, San Francisco, CA, USA
| | - Kai Cheng
- State University of New York, Buffalo, NY, USA
| | - Matthew S Choo
- Analytics Group, Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore, Singapore
| | - Nathan Edwards
- Clinical and Translational Glycoscience Research Center (CTGRC), Georgetown University, Washington, DC, USA
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, DC, USA
| | - Radoslav Goldman
- Clinical and Translational Glycoscience Research Center (CTGRC), Georgetown University, Washington, DC, USA
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, DC, USA
- Department of Oncology, Georgetown University, Washington, DC, USA
| | - Marcus Hoffmann
- Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, Magdeburg, Germany
| | - Yingwei Hu
- Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA
| | - Yifan Huang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Jin Young Kim
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Daejeon, Republic of Korea
| | | | - Benoit Liquet
- Department of Mathematics and Statistics, Macquarie University, Sydney, NSW, Australia
- CNRS, Laboratoire de Mathématiques et de leurs Applications de PAU, E2S-UPPA, Pau, France
| | - Mingqi Liu
- Institutes of Biomedical Sciences, and the NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Bo Meng
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, China
| | | | - Terry Nguyen-Khuong
- Analytics Group, Bioprocessing Technology Institute, Agency for Science, Technology and Research, Singapore, Singapore
| | - Jonas Nilsson
- Proteomics Core Facility, Sahlgrenska academy, University of Gothenburg, Gothenburg, Sweden
| | - Adam Pap
- BRC, Laboratory of Proteomics Research, Szeged, Hungary
- Doctoral School in Biology, Faculty of Science and Informatics, University of Szeged, Szeged, Hungary
| | - Gun Wook Park
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Daejeon, Republic of Korea
| | - Benjamin L Parker
- Department of Anatomy and Physiology, University of Melbourne, Melbourne, VIC, Australia
| | - Cassandra L Pegg
- School of Chemistry and Molecular Biosciences, University of Queensland, Queensland, QLD, Australia
| | - Josef M Penninger
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria
- Department of Medical Genetics, Life Sciences Institute, University of British Columbia, Vancouver, BC, Canada
| | - Toan K Phung
- School of Chemistry and Molecular Biosciences, University of Queensland, Queensland, QLD, Australia
| | - Markus Pioch
- Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, Magdeburg, Germany
| | - Erdmann Rapp
- Max Planck Institute for Dynamics of Complex Technical Systems, Bioprocess Engineering, Magdeburg, Germany
- glyXera GmbH, Magdeburg, Germany
| | - Enes Sakalli
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria
| | - Miloslav Sanda
- Clinical and Translational Glycoscience Research Center (CTGRC), Georgetown University, Washington, DC, USA
- Department of Oncology, Georgetown University, Washington, DC, USA
| | - Benjamin L Schulz
- School of Chemistry and Molecular Biosciences, University of Queensland, Queensland, QLD, Australia
| | - Nichollas E Scott
- Deparment of Microbiology and Immunology, University of Melbourne, Melbourne, VIC, Australia
| | - Georgy Sofronov
- Department of Mathematics and Statistics, Macquarie University, Sydney, NSW, Australia
| | - Johannes Stadlmann
- IMBA, Institute of Molecular Biotechnology of the Austrian Academy of Sciences, Vienna, Austria
| | - Sergey Y Vakhrushev
- Copenhagen Center for Glycomics, Department of Cellular and Molecular Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Christina M Woo
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Hung-Yi Wu
- Department of Chemistry and Chemical Biology, Harvard University, Cambridge, MA, USA
| | - Pengyuan Yang
- Institutes of Biomedical Sciences, and the NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Wantao Ying
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, China
| | - Hui Zhang
- Department of Pathology, The Johns Hopkins University, Baltimore, MD, USA
| | - Yong Zhang
- State Key Laboratory of Proteomics, Beijing Institute of Lifeomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, China
| | - Jingfu Zhao
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, USA
| | - Joseph Zaia
- Department of Biochemistry, Boston University Medical Campus, Boston, MA, USA
| | - Stuart M Haslam
- Department of Life Sciences, Imperial College London, London, UK
| | - Giuseppe Palmisano
- Instituto de Ciências Biomédicas, Departamento de Parasitologia, Universidade de São Paulo, São Paulo, SP, Brazil
| | - Jong Shin Yoo
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Daejeon, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, Republic of Korea
| | - Göran Larson
- Department of Laboratory Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Kai-Hooi Khoo
- Institute of Biological Chemistry, Academia Sinica, Taipei, Taiwan
| | - Katalin F Medzihradszky
- UCSF, School of Pharmacy, Department of Pharmaceutical Chemistry, San Francisco, CA, USA
- BRC, Laboratory of Proteomics Research, Szeged, Hungary
| | - Daniel Kolarich
- Institute for Glycomics, Griffith University Gold Coast Campus, Southport, QLD, Australia
| | - Nicolle H Packer
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia
- Institute for Glycomics, Griffith University Gold Coast Campus, Southport, QLD, Australia
- Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia
| | - Morten Thaysen-Andersen
- Department of Molecular Sciences, Macquarie University, Sydney, NSW, Australia.
- Biomolecular Discovery Research Centre, Macquarie University, Sydney, NSW, Australia.
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16
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Evaluating the Performance of 193 nm Ultraviolet Photodissociation for Tandem Mass Tag Labeled Peptides. ANALYTICA 2021. [DOI: 10.3390/analytica2040014] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
Despite the successful application of tandem mass tags (TMT) for peptide quantitation, missing reporter ions in higher energy collisional dissociation (HCD) spectra remains a challenge for consistent quantitation, especially for peptides with labile post-translational modifications. Ultraviolet photodissociation (UVPD) is an alternative ion activation method shown to provide superior coverage for sequencing of peptides and intact proteins. Here, we optimized and evaluated 193 nm UVPD for the characterization of TMT-labeled model peptides, HeLa proteome, and N-glycopeptides from model proteins. UVPD yielded the same TMT reporter ions as HCD, at m/z 126–131. Additionally, UVPD produced a wide range of fragments that yielded more complete characterization of glycopeptides and less frequent missing TMT reporter ion channels, whereas HCD yielded a strong tradeoff between characterization and quantitation of TMT-labeled glycopeptides. However, the lower fragmentation efficiency of UVPD yielded fewer peptide identifications than HCD. Overall, 193 nm UVPD is a valuable tool that provides an alternative to HCD for the quantitation of large and highly modified peptides with labile PTMs. Continued development of instrumentation specific to UVPD will yield greater fragmentation efficiency and fulfil the potential of UVPD to be an all-in-one spectrum ion activation method for broad use in the field of proteomics.
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17
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Zhong L, Zhu L, Cai ZW. Mass Spectrometry-based Proteomics and Glycoproteomics in COVID-19 Biomarkers Identification: A Mini-review. JOURNAL OF ANALYSIS AND TESTING 2021; 5:298-313. [PMID: 34513131 PMCID: PMC8423835 DOI: 10.1007/s41664-021-00197-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 07/27/2021] [Indexed: 12/11/2022]
Abstract
The first corona-pandemic, coronavirus disease 2019 (COVID-19) caused a huge health crisis and incalculable damage worldwide. Knowledge of how to cure the disease is urgently needed. Emerging immune escaping mutants of the virus suggested that it may be potentially persistent in human society as a regular health threat as the flu virus. Therefore, it is imperative to identify appropriate biomarkers to indicate pathological and physiological states, and more importantly, clinic outcomes. Proteins are the performers of life functions, and their abundance and modification status can directly reflect the immune status. Protein glycosylation serves a great impact in modulating protein function. The use of both unmodified and glycosylated proteins as biomarkers has also been proved feasible in the studies of SARS, Zika virus, influenza, etc. In recent years, mass spectrometry-based glycoproteomics, as well as proteomics approaches, advanced significantly due to the evolution of mass spectrometry. We focus on the current development of the mass spectrometry-based strategy for COVID-19 biomarkers' investigation. Potential application of glycoproteomics approaches and challenges in biomarkers identification are also discussed.
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Affiliation(s)
- Li Zhong
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Hong Kong SAR, China
| | - Lin Zhu
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Hong Kong SAR, China
| | - Zong-Wei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, 224 Waterloo Road, Kowloon Tong, Hong Kong SAR, China
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18
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Remoroza CA, Burke MC, Liu Y, Mirokhin YA, Tchekhovskoi DV, Yang X, Stein SE. Representing and Comparing Site-Specific Glycan Abundance Distributions of Glycoproteins. J Proteome Res 2021; 20:4475-4486. [PMID: 34327998 PMCID: PMC9830564 DOI: 10.1021/acs.jproteome.1c00442] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
A method for representing and comparing distributions of N-linked glycans located at specific sites on proteins is presented. The representation takes the form of a simple mass spectrum for a given peptide sequence, with each peak corresponding to a different glycopeptide. The mass (in place of m/z) of each peak is that of the glycan mass, and its abundance corresponds to its relative abundance in the electrospray MS1 spectrum. This provides a facile means of representing all identifiable glycopeptides arising from a single protein "sequon" on a specific sequence, thereby enabling the comparison and searching of these distributions as routinely done for mass spectra. Likewise, these reference glycopeptide abundance distribution spectra (GADS) can be stored in searchable libraries. A set of such libraries created from available data is provided along with an adapted version of the widely used NIST-MS library-search software. Since GADS contain only MS1 abundances and identifications, they are equally suitable for expressing collision-induced fragmentation and electron-transfer dissociation determinations of glycopeptide identity. Comparisons of GADS for N-glycosylated sites on several proteins, especially the SARS-CoV-2 spike protein, demonstrate the potential reproducibility of GADS and their utility for comparing site-specific distributions.
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19
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Oliveira T, Thaysen-Andersen M, Packer NH, Kolarich D. The Hitchhiker's guide to glycoproteomics. Biochem Soc Trans 2021; 49:1643-1662. [PMID: 34282822 PMCID: PMC8421054 DOI: 10.1042/bst20200879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/03/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Protein glycosylation is one of the most common post-translational modifications that are essential for cell function across all domains of life. Changes in glycosylation are considered a hallmark of many diseases, thus making glycoproteins important diagnostic and prognostic biomarker candidates and therapeutic targets. Glycoproteomics, the study of glycans and their carrier proteins in a system-wide context, is becoming a powerful tool in glycobiology that enables the functional analysis of protein glycosylation. This 'Hitchhiker's guide to glycoproteomics' is intended as a starting point for anyone who wants to explore the emerging world of glycoproteomics. The review moves from the techniques that have been developed for the characterisation of single glycoproteins to technologies that may be used for a successful complex glycoproteome characterisation. Examples of the variety of approaches, methodologies, and technologies currently used in the field are given. This review introduces the common strategies to capture glycoprotein-specific and system-wide glycoproteome data from tissues, body fluids, or cells, and a perspective on how integration into a multi-omics workflow enables a deep identification and characterisation of glycoproteins - a class of biomolecules essential in regulating cell function.
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Affiliation(s)
- Tiago Oliveira
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
| | | | - Nicolle H. Packer
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Nanoscale BioPhotonics, Griffith University, QLD and Macquarie University, NSW, Australia
| | - Daniel Kolarich
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
- ARC Centre of Excellence for Nanoscale BioPhotonics, Griffith University, QLD and Macquarie University, NSW, Australia
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20
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Zhang R, Peng W, Gautam S, Huang Y, Mechref Y, Tang H. GlycanGUI: Automated Glycan Annotation and Quantification Using Glucose Unit Index. Front Chem 2021; 9:707382. [PMID: 34211962 PMCID: PMC8239159 DOI: 10.3389/fchem.2021.707382] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2021] [Accepted: 06/03/2021] [Indexed: 11/13/2022] Open
Abstract
The retention time provides critical information for glycan annotation and quantification from the Liquid Chromatography Mass Spectrometry (LC-MS) data. However, the variation of the precise retention time of glycans is highly dependent on the experimental conditions such as the specific separating columns, MS instruments and/or the buffer used. This variation hampers the exploitation of retention time for the glycan annotation from LC-MS data, especially when inter-laboratory data are compared. To incorporate the retention time of glycan across experiments, Glucose Unit Index (GUI) can be computed using the dextrin ladder as internal standard. The retention time of glycans are then calibrated with respect to glucose units derived from dextrin ladders. Despite the successful application of the GUI approach, the manual calibration process is quite tedious and often error prone. In this work, we present a standalone software tool GlycanGUI, with a graphic user interface to automatically carry out the GUI-based glycan annotation/quantification and subsequent data analysis. When tested on experimental data, GlycanGUI reported accurate GUI values compared with manual calibration, and thus is ready to be used for automated glycan annotation and quantification using GUI.
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Affiliation(s)
- Rui Zhang
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
| | - Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Sakshi Gautam
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Yifan Huang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, TX, United States
| | - Haixu Tang
- Department of Computer Science, Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington, Bloomington, IN, United States
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21
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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: 57] [Impact Index Per Article: 19.0] [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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22
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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: 66] [Impact Index Per Article: 22.0] [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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23
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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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24
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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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25
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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: 8] [Impact Index Per Article: 2.0] [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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26
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Jeong HK, Hwang H, Kang YM, Lee HK, Park GW, Lee JY, Kim DG, Lee JW, Lee SY, An HJ, Kim JY, Yoo JS. Computational classification of core and outer fucosylation of N-glycoproteins in human plasma using collision-induced dissociation in mass spectrometry. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2020; 34:e8917. [PMID: 32754952 DOI: 10.1002/rcm.8917] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 07/23/2020] [Accepted: 08/02/2020] [Indexed: 06/11/2023]
Abstract
RATIONALE Glycoprotein fucosylation, one of the major posttranslational modifications, is known to be highly involved in proteins related to various cancers. Fucosylation occurs in the core and/or outer sites of N-glycopeptides. Elucidation of the fucosylation type of N-glycoproteins is therefore important. However, it has remained a challenge to classify the fucosylation types of N-glycopeptides using collision-induced dissociation (CID) tandem mass (MS/MS) spectra. METHODS The relative intensities of the Y1 F, Y2 F, Y3 F, and Y4 F product ions in the CID-MS/MS spectra of the IgG N-glycopeptides were measured for core fucosylation. The Core Fucose Index (CFI) was then calculated by multiplication of the relative intensities with a weight factor from logistic regression to differentiate between the core and none fucosylation. From the relative intensities of the B2 F and B3 SF ions of the MS/MS spectra of the AGP N-glycopeptides for outer fucosylation, the Outer Fucose Index (OFI) was calculated to differentiate between the outer and none fucosylation. RESULTS In order to classify core and/or outer fucosylation of N-glycoproteins, we defined the fucosylation score (F-score) by a sigmoidal equation using a combination of the CFI and the OFI. For application, we classified the fucosylation types of N-glycoproteins in human plasma with 99.7% accuracy from the F-score. Human plasma samples showed 54.4%, 33.3%, 10.3%, and 1.6% for none, core, outer, and dual fucosylated N-glycopeptides, respectively. Core fucosylation was abundant at mono- and bi-antennary N-glycopeptides. Outer fucosylation was abundant at tri- and tetra-antennary N-glycopeptides. In total, 113 N-glycopeptides of 29 glycoproteins from 3365 glycopeptide spectral matches (GPSMs) were classified for different types of fucosylation. CONCLUSIONS We established an F-score to classify three different fucosylation types: core, outer, and dual types of N-glycopeptides. The fucosylation types of 20 new N-glycopeptides from 11 glycoproteins in human plasma were classified using the F-score. Therefore, the F-score can be useful for the automatic classification of different types of fucosylation in N-glycoproteins of biological fluids including plasma, serum, and urine.
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Affiliation(s)
- Hoi Keun Jeong
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Heeyoun Hwang
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Young-Mook Kang
- Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Republic of Korea
| | - Hyun Kyoung Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Gun Wook Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Ju Yeon Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Dong Geun Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Ji Won Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Sang Yoon Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Hyun Joo An
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
- Asia-Pacific Glycomics Reference Site, Daejeon, 34134, Republic of Korea
| | - Jin Young Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
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27
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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: 25] [Impact Index Per Article: 6.3] [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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28
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Lee HK, Ha DI, Kang JG, Park GW, Lee JY, Cho K, Bin Moon S, Shin JH, Kim YS, An HJ, Kim JY, Yoo JS, Ko JH. Selective Identification of α-Galactosyl Epitopes in N-Glycoproteins Using Characteristic Fragment Ions from Higher-Energy Collisional Dissociation. Anal Chem 2020; 92:13144-13154. [PMID: 32902264 DOI: 10.1021/acs.analchem.0c02276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
The α-galactosyl epitope is a terminal N-glycan moiety of glycoproteins found in mammals except in humans, and thus, it is recognized as an antigen that provokes an immunogenic response in humans. Accordingly, it is necessary to analyze the α-galactosyl structure in biopharmaceuticals or organ transplants. Due to an identical glycan composition and molecular mass between α-galactosyl N-glycans and hybrid/high-mannose-type N-glycans, it is challenging to characterize α-galactosyl epitopes in N-glycoproteins using mass spectrometry. Here, we describe a method to identify α-galactosyl N-glycopeptides in mice glycoproteins using liquid chromatography with tandem mass spectrometry with higher-energy collisional dissociation (HCD). The first measure was an absence of [YHM] ion peaks in the HCD spectra, which was exclusively observed in hybrid and/or high-mannose-type N-glycopeptides. The second complementary criterion was the ratio of an m/z 528.19 (Hex2HexNAc1) ion to m/z 366.14 (Hex1HexNAc1) ion (Im/z528/Im/z366). The measure of [Im/z528/Im/z366 > 0.3] enabled a clear-cut determination of α-galactosyl N-glycopeptides with high accuracy. In Ggta1 knockout mice, we could not find any α-galactosyl N-glycoproteins identified in WT mice plasma. Using this method, we could screen for α-galactosyl N-glycoproteins from mice spleen, lungs, and plasma samples in a highly sensitive and specific manner. Conclusively, we suggest that this method will provide a robust analytical tool for determination of α-galactosyl epitopes in pharmaceuticals and complex biological samples.
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Affiliation(s)
- Hyun Kyoung Lee
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Dae-In Ha
- Genome Editing Research Center, KRIBB, 125 Gwahak-ro, Daejeon 34141, Republic of Korea.,Department of Biomolecular Science, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Daejeon 34113, Republic of Korea
| | - Jeong Gu Kang
- Genome Editing Research Center, KRIBB, 125 Gwahak-ro, Daejeon 34141, Republic of Korea
| | - Gun Wook Park
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea
| | - Ju Yeon Lee
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea
| | - Kun Cho
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea
| | - Su Bin Moon
- Genome Editing Research Center, KRIBB, 125 Gwahak-ro, Daejeon 34141, Republic of Korea.,Department of Biomolecular Science, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Daejeon 34113, Republic of Korea
| | - Jong Hwan Shin
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Yong-Sam Kim
- Genome Editing Research Center, KRIBB, 125 Gwahak-ro, Daejeon 34141, Republic of Korea.,Department of Biomolecular Science, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Daejeon 34113, Republic of Korea
| | - Hyun Joo An
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jin Young Kim
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea
| | - Jong Shin Yoo
- Research Center of Bioconvergence Analysis, Korea Basic Science Institute, Ochang-eup, Cheongju 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon 34134, Republic of Korea
| | - Jeong-Heon Ko
- Genome Editing Research Center, KRIBB, 125 Gwahak-ro, Daejeon 34141, Republic of Korea.,Department of Biomolecular Science, KRIBB School of Bioscience, Korea University of Science and Technology (UST), 217 Gajeong-ro, Daejeon 34113, Republic of Korea
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29
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Phung TK, Pegg CL, Schulz BL. GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis. Beilstein J Org Chem 2020; 16:2127-2135. [PMID: 32952729 PMCID: PMC7476601 DOI: 10.3762/bjoc.16.180] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2020] [Accepted: 08/20/2020] [Indexed: 12/22/2022] Open
Abstract
Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.
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Affiliation(s)
- Toan K Phung
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Cassandra L Pegg
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, 4072, Australia
| | - Benjamin L Schulz
- School of Chemistry and Molecular Biosciences, The University of Queensland, St Lucia, Queensland, 4072, Australia.,ARC Training Centre for Biopharmaceutical Innovation, Australian Institute for Bioengineering and Nanotechnology, The University of Queensland, St. Lucia, QLD 4072, Australia
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30
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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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31
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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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32
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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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33
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Autophagic Inhibition via Lysosomal Integrity Dysfunction Leads to Antitumor Activity in Glioma Treatment. Cancers (Basel) 2020; 12:cancers12030543. [PMID: 32120820 PMCID: PMC7139627 DOI: 10.3390/cancers12030543] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2020] [Revised: 02/22/2020] [Accepted: 02/25/2020] [Indexed: 12/19/2022] Open
Abstract
Manipulating autophagy is a promising strategy for treating cancer as several autophagy inhibitors are shown to induce autophagic cell death. One of these, autophagonizer (APZ), induces apoptosis-independent cell death by binding an unknown target via an unknown mechanism. To identify APZ targets, we used a label-free drug affinity responsive target stability (DARTS) approach with a liquid chromatography/tandem mass spectrometry (LC–MS/MS) readout. Of 35 protein interactors, we identified Hsp70 as a key target protein of unmodified APZ in autophagy. Either APZ treatment or Hsp70 inhibition attenuates integrity of lysosomes, which leads to autophagic cell death exhibiting an excellent synergism with a clinical drug, temozolomide, in vitro, in vivo, and orthotropic glioma xenograft model. These findings demonstrate the potential of APZ to induce autophagic cell death and its development to combinational chemotherapeutic agent for glioma treatment. Collectively, our study demonstrated that APZ, a new autophagy inhibitor, can be used as a potent antitumor drug candidate to get over unassailable glioma and revealed a novel function of Hsp70 in lysosomal integrity regulation of autophagy.
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34
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Profiling the Protein Targets of Unmodified Bio‐Active Molecules with Drug Affinity Responsive Target Stability and Liquid Chromatography/Tandem Mass Spectrometry. Proteomics 2020; 20:e1900325. [DOI: 10.1002/pmic.201900325] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2019] [Revised: 11/28/2019] [Indexed: 12/17/2022]
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35
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Hwang H, Jeong HK, Lee HK, Park GW, Lee JY, Lee SY, Kang YM, An HJ, Kang JG, Ko JH, Kim JY, Yoo JS. Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry. Sci Rep 2020; 10:318. [PMID: 31941975 PMCID: PMC6962204 DOI: 10.1038/s41598-019-57274-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2018] [Accepted: 12/27/2019] [Indexed: 12/14/2022] Open
Abstract
Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core- and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The best-performing model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri- and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides.
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Affiliation(s)
- Heeyoun Hwang
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Hoi Keun Jeong
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Hyun Kyoung Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Gun Wook Park
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Ju Yeon Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Soo Youn Lee
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea
| | - Young-Mook Kang
- Drug Information Platform Center, Korea Research Institute of Chemical Technology, Daejeon, 34114, Korea
| | - Hyun Joo An
- Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea.,Asia Glycomics Reference Site, Chungnam National University, Daejeon, 34134, Republic of Korea
| | - Jeong Gu Kang
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea
| | - Jeong-Heon Ko
- Genome Editing Research Center, Korea Research Institute of Bioscience and Biotechnology, Daejeon, 34141, Republic of Korea.,Department of Biomolecular Science, Korea University of Science and Technology (UST), Daejeon, 34113, Republic of Korea
| | - Jin Young Kim
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea.
| | - Jong Shin Yoo
- Research Center for Bioconvergence Analysis, Korea Basic Science Institute, Cheongju, 28119, Republic of Korea. .,Graduate School of Analytical Science and Technology, Chungnam National University, Daejeon, 34134, Republic of Korea.
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36
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Cao WQ, Liu MQ, Kong SY, Wu MX, Huang ZZ, Yang PY. Novel methods in glycomics: a 2019 update. Expert Rev Proteomics 2020; 17:11-25. [PMID: 31914820 DOI: 10.1080/14789450.2020.1708199] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023]
Abstract
Introduction: Glycomics, which aims to define the glycome of a biological system to better assess the biological attributes of the glycans, has attracted increasing interest. However, the complexity and diversity of glycans present challenging barriers to glycome definition. Technological advances are major drivers in glycomics.Areas covered: This review summarizes the main methods and emphasizes the most recent advances in mass spectrometry-based methods regarding glycomics following the general workflow in glycomic analysis.Expert opinion: Recent mass spectrometry-based technological advances have significantly lowered the barriers in glycomics. The field of glycomics is moving toward both generic and precise analysis.
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Affiliation(s)
- Wei-Qian Cao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Ming-Qi Liu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Si-Yuan Kong
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Meng-Xi Wu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
| | - Zheng-Ze Huang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Peng-Yuan Yang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China.,NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China.,Department of Chemistry, Fudan University, Shanghai, China
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37
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Zhu H, Aloor A, Ma C, Kondengaden SM, Wang PG. Mass Spectrometric Analysis of Protein Glycosylation. ACS SYMPOSIUM SERIES 2020. [DOI: 10.1021/bk-2020-1346.ch010] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Affiliation(s)
- He Zhu
- These authors contributed equally
| | | | | | | | - Peng George Wang
- Current Address: Department of Chemistry, Southern University of Science and Technology, Shenzhen, Guangdong 518055, P. R. China
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38
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Zhang S, Cao X, Liu C, Li W, Zeng W, Li B, Chi H, Liu M, Qin X, Tang L, Yan G, Ge Z, Liu Y, Gao Q, Lu H. N-glycopeptide Signatures of IgA 2 in Serum from Patients with Hepatitis B Virus-related Liver Diseases. Mol Cell Proteomics 2019; 18:2262-2272. [PMID: 31501225 PMCID: PMC6823847 DOI: 10.1074/mcp.ra119.001722] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2019] [Indexed: 12/11/2022] Open
Abstract
N-glycosylation alteration has been reported in liver diseases. Characterizing N-glycopeptides that correspond to N-glycan structure with specific site information enables better understanding of the molecular pathogenesis of liver damage and cancer. Here, unbiased quantification of N-glycopeptides of a cluster of serum glycoproteins with 40-55 kDa molecular weight (40-kDa band) was investigated in hepatitis B virus (HBV)-related liver diseases. We used an N-glycopeptide method based on 18O/16O C-terminal labeling to obtain 82 comparisons of serum from patients with HBV-related hepatocellular carcinoma (HCC) and liver cirrhosis (LC). Then, multiple reaction monitoring (MRM) was performed to quantify N-glycopeptide relative to the protein content, especially in the healthy donor-HBV-LC-HCC cascade. TPLTAN205ITK (H5N5S1F1) and (H5N4S2F1) corresponding to the glycopeptides of IgA2 were significantly elevated in serum from patients with HBV infection and even higher in HBV-related LC patients, as compared with healthy donor. In contrast, the two glycopeptides of IgA2 fell back down in HBV-related HCC patients. In addition, the variation in the abundance of two glycopeptides was not caused by its protein concentration. The altered N-glycopeptides might be part of a unique glycan signature indicating an IgA-mediated mechanism and providing potential diagnostic clues in HBV-related liver diseases.
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Affiliation(s)
- Shu Zhang
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China
| | - Xinyi Cao
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Chao Liu
- Beijing Advanced Innovation Center for Precision Medicine, Beihang University, Beijing 100083, China
| | - Wei Li
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Wenfeng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China
| | - Baiwen Li
- Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiaotong University, Shanghai 201620, China
| | - Hao Chi
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing 100190, China
| | - Mingqi Liu
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Xue Qin
- Department of Clinical Laboratory, First Affiliated Hospital of Guangxi Medical University, Nanning 530021, Guangxi, China
| | - Lingyi Tang
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030
| | - Guoquan Yan
- Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Zefan Ge
- State Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210046, China
| | - Yinkun Liu
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China; Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Qiang Gao
- Liver Cancer Institute, Zhongshan Hospital, and Key Laboratory of Carcinogenesis and Cancer Invasion (Ministry of Education), Fudan University, Shanghai 200032, China.
| | - Haojie Lu
- 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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39
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Hahm YH, Lee JY, Ahn YH. Investigation of Site-Specific Differences in Glycan Microheterogeneity by N-Glycopeptide Mapping of VEGFR-IgG Fusion Protein. Molecules 2019; 24:molecules24213924. [PMID: 31671706 PMCID: PMC6864772 DOI: 10.3390/molecules24213924] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2019] [Revised: 10/24/2019] [Accepted: 10/29/2019] [Indexed: 12/13/2022] Open
Abstract
A biosimilar fusion protein VEGFR-IgG consisting of vascular endothelial growth factor receptors 1 and 2 (VEGFR-1, VEGFR-2) and the Fc portion of human IgG1 was prepared for this study. The prepared fusion protein was expected to possess a total of five N-linked glycosylation sites: two sites in the VEGFR-1 region, two sites in the VEGFR-2 region, and one site in the human IgG Fc region. For site-specific glycan analysis, the fusion protein was hydrolyzed with trypsin, and the resulting tryptic digests were analyzed by liquid chromatography-electrospray ionization tandem mass spectrometry (LC-ESI MS/MS). The expected N-linked glycosylation sites were successfully identified and site-specific glycopeptide mapping was completed by Integrated GlycoProteome Analyzer (I-GPA) for the resulting raw tandem mass data. Finally, it was clearly confirmed that N-linked glycans for each glycosylation site showed significantly different patterns in microheterogeneity, which may indicate certain functions for each glycosylation site in the protein. Based on the mapping results, the unique features in glycan microheterogeneity for the five glycosylation sites of VEGFR-IgG fusion protein were compared site-specifically and further discussed to understand the functional meaning of each glycosylation pattern.
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Affiliation(s)
| | - Ju Yeon Lee
- Korea Basic Science Institute, Biomedical Omics Research Group, Cheongju 28119, Korea.
| | - Yeong Hee Ahn
- Department of Biomedical Science, Cheongju University, Cheongju 28160, Korea.
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40
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Chen Z, Huang J, Li L. Recent advances in mass spectrometry (MS)-based glycoproteomics in complex biological samples. Trends Analyt Chem 2019; 118:880-892. [PMID: 31579312 PMCID: PMC6774629 DOI: 10.1016/j.trac.2018.10.009] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/15/2023]
Abstract
Protein glycosylation plays a key role in various biological processes and disease-related pathological progression. Mass spectrometry (MS)-based glycoproteomics is a powerful approach that provides a system-wide profiling of the glycoproteome in a high-throughput manner. There have been numerous significant technological advances in this field, including improved glycopeptide enrichment, hybrid fragmentation techniques, emerging specialized software packages, and effective quantitation strategies, as well as more dedicated workflows. With increasingly sophisticated glycoproteomics tools on hand, researchers have extensively adapted this approach to explore different biological systems both in terms of in-depth glycoproteome profiling and comparative glycoproteome analysis. Quantitative glycoproteomics enables researchers to discover novel glycosylation-based biomarkers in various diseases with potential to offer better sensitivity and specificity for disease diagnosis. In this review, we present recent methodological developments in MS-based glycoproteomics and highlight its utility and applications in answering various questions in complex biological systems.
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Affiliation(s)
- Zhengwei Chen
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Junfeng Huang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53705, USA
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
- School of Life Sciences, Tianjin University, Tianjin, 300072, China
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41
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Chen Z, Huang J, Li L. Recent advances in mass spectrometry (MS)-based glycoproteomics in complex biological samples. Trends Analyt Chem 2019. [PMID: 31579312 DOI: 10.1016/jtrac.2018.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/19/2023]
Abstract
Protein glycosylation plays a key role in various biological processes and disease-related pathological progression. Mass spectrometry (MS)-based glycoproteomics is a powerful approach that provides a system-wide profiling of the glycoproteome in a high-throughput manner. There have been numerous significant technological advances in this field, including improved glycopeptide enrichment, hybrid fragmentation techniques, emerging specialized software packages, and effective quantitation strategies, as well as more dedicated workflows. With increasingly sophisticated glycoproteomics tools on hand, researchers have extensively adapted this approach to explore different biological systems both in terms of in-depth glycoproteome profiling and comparative glycoproteome analysis. Quantitative glycoproteomics enables researchers to discover novel glycosylation-based biomarkers in various diseases with potential to offer better sensitivity and specificity for disease diagnosis. In this review, we present recent methodological developments in MS-based glycoproteomics and highlight its utility and applications in answering various questions in complex biological systems.
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Affiliation(s)
- Zhengwei Chen
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Junfeng Huang
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, 53705, USA
- School of Pharmacy, University of Wisconsin-Madison, Madison, WI, 53705, USA
- School of Life Sciences, Tianjin University, Tianjin, 300072, China
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42
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Xiao K, Tian Z. Site‐ and Structure‐Specific Quantitative N‐Glycoproteomics Using RPLC‐pentaHILIC Separation and the Intact N‐Glycopeptide Search Engine GPSeeker. ACTA ACUST UNITED AC 2019; 97:e94. [DOI: 10.1002/cpps.94] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Kaijie Xiao
- School of Chemical Science & Engineering and Shanghai Key Laboratory of Chemical Assessment and SustainabilityTongji University Shanghai China
| | - Zhixin Tian
- School of Chemical Science & Engineering and Shanghai Key Laboratory of Chemical Assessment and SustainabilityTongji University Shanghai China
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43
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Li Q, Xie Y, Wong M, Lebrilla CB. Characterization of Cell Glycocalyx with Mass Spectrometry Methods. Cells 2019; 8:E882. [PMID: 31412618 PMCID: PMC6721671 DOI: 10.3390/cells8080882] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 08/05/2019] [Accepted: 08/12/2019] [Indexed: 02/06/2023] Open
Abstract
The cell membrane plays an important role in protecting the cell from its extracellular environment. As such, extensive work has been devoted to studying its structure and function. Crucial intercellular processes, such as signal transduction and immune protection, are mediated by cell surface glycosylation, which is comprised of large biomolecules, including glycoproteins and glycosphingolipids. Because perturbations in glycosylation could result in dysfunction of cells and are related to diseases, the analysis of surface glycosylation is critical for understanding pathogenic mechanisms and can further lead to biomarker discovery. Different mass spectrometry-based techniques have been developed for glycan analysis, ranging from highly specific, targeted approaches to more comprehensive profiling studies. In this review, we summarized the work conducted for extensive analysis of cell membrane glycosylation, particularly those employing liquid chromatography with mass spectrometry (LC-MS) in combination with various sample preparation techniques.
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Affiliation(s)
- Qiongyu Li
- Department of Chemistry, University of California, Davis, CA 95616, USA
| | - Yixuan Xie
- Department of Chemistry, University of California, Davis, CA 95616, USA
| | - Maurice Wong
- Department of Chemistry, University of California, Davis, CA 95616, USA
| | - Carlito B Lebrilla
- Department of Chemistry, University of California, Davis, CA 95616, USA.
- Department of Biochemistry, University of California, Davis, CA 95616, USA.
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44
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Abstract
Glycosylation is one of the most ubiquitous and complex post-translational modifications (PTMs). It plays pivotal roles in various biological processes. Studies at the glycopeptide level are typically considered as a downstream work resulting from enzymatic digested glycoproteins. Less attention has been focused on glycosylated endogenous signaling peptides due to their low abundance, structural heterogeneity and the lack of enabling analytical tools. Here, protocols are presented to isolate and characterize glycosylated neuropeptides utilizing nanoflow liquid chromatography coupled with mass spectrometry (LC-MS). We first demonstrate how to extract neuropeptides from raw tissues and perform further separation/cleanup before MS analysis. Then we describe hybrid MS methods for glycosylated neuropeptide profiling and site-specific analysis. We also include recommendations for data analysis to identify glycosylated neuropeptides in crustaceans where a complete neuropeptide database is still lacking. Other strategies and future directions are discussed to provide readers with alternative approaches and further unravel biological complexity rendered by glycosylation.
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Affiliation(s)
- Yang Liu
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, United States
| | - Qinjingwen Cao
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, United States
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison, Madison, WI, United States; School of Pharmacy, University of Wisconsin-Madison, Madison, WI, United States.
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45
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Shipman JT, Su X, Hua D, Desaire H. DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator. J Proteome Res 2019; 18:2896-2902. [PMID: 31129958 DOI: 10.1021/acs.jproteome.9b00203] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .
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Affiliation(s)
- Joshua T Shipman
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Xiaomeng Su
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - David Hua
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
| | - Heather Desaire
- Department of Chemistry , University of Kansas , Lawrence , Kansas 66045 , United States
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46
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Xiao K, Tian Z. GPSeeker Enables Quantitative Structural N-Glycoproteomics for Site- and Structure-Specific Characterization of Differentially Expressed N-Glycosylation in Hepatocellular Carcinoma. J Proteome Res 2019; 18:2885-2895. [DOI: 10.1021/acs.jproteome.9b00191] [Citation(s) in RCA: 48] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Kaijie Xiao
- School of Chemical Science & Engineering and Shanghai Key Laboratory of Chemical Assessment and Sustainability, Tongji University, Shanghai 200092, China
| | - Zhixin Tian
- School of Chemical Science & Engineering and Shanghai Key Laboratory of Chemical Assessment and Sustainability, Tongji University, Shanghai 200092, China
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47
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Zhu J, Chen Z, Zhang J, An M, Wu J, Yu Q, Skilton SJ, Bern M, Sen KI, Li L, Lubman DM. Differential Quantitative Determination of Site-Specific Intact N-Glycopeptides in Serum Haptoglobin between Hepatocellular Carcinoma and Cirrhosis Using LC-EThcD-MS/MS. J Proteome Res 2019; 18:359-371. [PMID: 30370771 PMCID: PMC6465142 DOI: 10.1021/acs.jproteome.8b00654] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Intact N-glycopeptide analysis remains challenging due to the complexity of glycopeptide structures, low abundance of glycopeptides in protein digests, and difficulties in data interpretation/quantitation. Herein, we developed a workflow that involved advanced methodologies, the EThcD-MS/MS fragmentation method and data interpretation software, for differential analysis of the microheterogeneity of site-specific intact N-glycopeptides of serum haptoglobin between early hepatocellular carcinoma (HCC) and liver cirrhosis. Haptoglobin was immunopurified from 20 μL of serum in patients with early HCC, liver cirrhosis, and healthy controls, respectively, followed by trypsin/GluC digestion, glycopeptide enrichment, and LC-EThcD-MS/MS analysis. Identification and differential quantitation of site-specific N-glycopeptides were performed using a combination of Byonic and Byologic software. In total, 93, 87, and 68 site-specific N-glycopeptides were identified in early HCC, liver cirrhosis, and healthy controls, respectively, with high confidence. The increased variety of N-glycopeptides in liver diseases compared to healthy controls was due to increased branching with hyper-fucosylation and sialylation. Differential quantitation analysis showed that 5 site-specific N-glycopeptides on sites N184 and N241 were significantly elevated in early HCC compared to cirrhosis ( p < 0.05) and normal controls ( p ≤ 0.001). The result demonstrates that the workflow provides a strategy for detailed profiles of N-glycopeptides of patient samples as well as for relative quantitation to determine the level changes in site-specific N-glycopeptides between disease states.
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Affiliation(s)
- Jianhui Zhu
- 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
| | - Jie Zhang
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Mingrui An
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Jing Wu
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
| | - Qing Yu
- School of Pharmacy, University of Wisconsin—Madison, Madison, Wisconsin 53705, United States
| | - St. John Skilton
- Protein Metrics Incorporated, San Carlos, California 94070, United States
| | - Marshall Bern
- Protein Metrics Incorporated, San Carlos, California 94070, United States
| | - K. Ilker Sen
- Protein Metrics Incorporated, San Carlos, California 94070, 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
| | - David M. Lubman
- Department of Surgery, University of Michigan Medical Center, Ann Arbor, Michigan 48109, United States
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48
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Yu A, Zhao J, Peng W, Banazadeh A, Williamson SD, Goli M, Huang Y, Mechref Y. Advances in mass spectrometry-based glycoproteomics. Electrophoresis 2018; 39:3104-3122. [PMID: 30203847 PMCID: PMC6375712 DOI: 10.1002/elps.201800272] [Citation(s) in RCA: 70] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Revised: 09/03/2018] [Accepted: 09/03/2018] [Indexed: 12/13/2022]
Abstract
Protein glycosylation, an important PTM, plays an essential role in a wide range of biological processes such as immune response, intercellular signaling, inflammation, and host-pathogen interaction. Aberrant glycosylation has been correlated with various diseases. However, studying protein glycosylation remains challenging because of low abundance, microheterogeneities of glycosylation sites, and poor ionization efficiency of glycopeptides. Therefore, the development of sensitive and accurate approaches to characterize protein glycosylation is crucial. The identification and characterization of protein glycosylation by MS is referred to as the field of glycoproteomics. Methods such as enrichment, metabolic labeling, and derivatization of glycopeptides in conjunction with different MS techniques and bioinformatics tools, have been developed to achieve an unequivocal quantitative and qualitative characterization of glycoproteins. This review summarizes the recent developments in the field of glycoproteomics over the past 6 years (2012 to 2018).
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Affiliation(s)
- Aiying Yu
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Jingfu Zhao
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Wenjing Peng
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Alireza Banazadeh
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Seth D. Williamson
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Mona Goli
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Yifan Huang
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
| | - Yehia Mechref
- Department of Chemistry and Biochemistry, Texas Tech University, Lubbock, Texas 79409, United States
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Gabriele C, Cantiello F, Nicastri A, Crocerossa F, Russo GI, Cicione A, Vartolomei MD, Ferro M, Morgia G, Lucarelli G, Cuda G, Damiano R, Gaspari M. High-throughput detection of low abundance sialylated glycoproteins in human serum by TiO 2 enrichment and targeted LC-MS/MS analysis: application to a prostate cancer sample set. Anal Bioanal Chem 2018; 411:755-763. [PMID: 30483857 DOI: 10.1007/s00216-018-1497-5] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 11/03/2018] [Accepted: 11/13/2018] [Indexed: 12/22/2022]
Abstract
Glycopeptide enrichment can be a strategy to allow the detection of peptides belonging to low abundance proteins in complex matrixes such as blood serum or plasma. Though several glycopeptide enrichment protocols have shown excellent sensitivities in this respect, few reports have demonstrated the applicability of these methods to relatively large sample cohorts. In this work, a fast protocol based on TiO2 enrichment and highly sensitive mass spectrometric analysis by Selected Reaction Monitoring (SRM) has been applied to a cohort of serum samples from prostate cancer and benign prostatic hyperplasia patients in order to detect low abundance proteins in a single LC-MS/MS analysis in nanoscale format, without immunodepletion or peptide fractionation. A peptide library of over 700 formerly N-glycosylated peptides was created by data dependent analysis. Then, 16 medium to low abundance proteins were selected for detection by single injection LC-MS/MS based on selected-reaction monitoring. Results demonstrated the consistent detection of the low-level proteins under investigation. Following label-free quantification, four proteins (Adipocyte plasma membrane-associated protein, Periostin, Cathepsin D and Lysosome-associated membrane glycoprotein 2) were found significantly increased in prostate cancer sera compared to the control group. Graphical abstract ᅟ.
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Affiliation(s)
- Caterina Gabriele
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Francesco Cantiello
- Urology Unit, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy.
| | - Annalisa Nicastri
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Fabio Crocerossa
- Urology Unit, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Giorgio Ivan Russo
- Urology Section, Department of Surgery, University of Catania, 95131, Catania, Italy
| | - Antonio Cicione
- Urology Unit, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Mihai D Vartolomei
- Department of Urology, European Institute of Oncology, 20141, Milan, Italy.,Department of Cell and Molecular Biology, University of Medicine, Pharmacy, Sciences and Technology, 540139, Targu Mures, Romania
| | - Matteo Ferro
- Department of Urology, European Institute of Oncology, 20141, Milan, Italy
| | - Giuseppe Morgia
- Urology Section, Department of Surgery, University of Catania, 95131, Catania, Italy
| | - Giuseppe Lucarelli
- Urology, Andrology & Kidney Transplantation Unit, Department of Emergency & Organ Transplantation, University of Bari, 70121, Bari, Italy
| | - Giovanni Cuda
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Rocco Damiano
- Urology Unit, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy
| | - Marco Gaspari
- Department of Experimental and Clinical Medicine, Magna Graecia University of Catanzaro, Campus "S. Venuta", Viale Europa, Loc. Germaneto, 88100, Catanzaro, Italy.
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50
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Pioch M, Hoffmann M, Pralow A, Reichl U, Rapp E. glyXtoolMS: An Open-Source Pipeline for Semiautomated Analysis of Glycopeptide Mass Spectrometry Data. Anal Chem 2018; 90:11908-11916. [DOI: 10.1021/acs.analchem.8b02087] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Affiliation(s)
- Markus Pioch
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
| | - Marcus Hoffmann
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
| | - Alexander Pralow
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- glyXera GmbH, 39120, Magdeburg, Germany
| | - Udo Reichl
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- Bioprocess Engineering, Otto-von-Guericke University, 39106, Magdeburg, Germany
| | - Erdmann Rapp
- Bioprocess Engineering, Max Planck Institute for Dynamics of Complex Technical Systems, 39106, Magdeburg, Germany
- glyXera GmbH, 39120, Magdeburg, Germany
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