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Wang Y, Lei K, Zhao L, Zhang Y. Clinical glycoproteomics: methods and diseases. MedComm (Beijing) 2024; 5:e760. [PMID: 39372389 PMCID: PMC11450256 DOI: 10.1002/mco2.760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2024] [Revised: 09/08/2024] [Accepted: 09/10/2024] [Indexed: 10/08/2024] Open
Abstract
Glycoproteins, representing a significant proportion of posttranslational products, play pivotal roles in various biological processes, such as signal transduction and immune response. Abnormal glycosylation may lead to structural and functional changes of glycoprotein, which is closely related to the occurrence and development of various diseases. Consequently, exploring protein glycosylation can shed light on the mechanisms behind disease manifestation and pave the way for innovative diagnostic and therapeutic strategies. Nonetheless, the study of clinical glycoproteomics is fraught with challenges due to the low abundance and intricate structures of glycosylation. Recent advancements in mass spectrometry-based clinical glycoproteomics have improved our ability to identify abnormal glycoproteins in clinical samples. In this review, we aim to provide a comprehensive overview of the foundational principles and recent advancements in clinical glycoproteomic methodologies and applications. Furthermore, we discussed the typical characteristics, underlying functions, and mechanisms of glycoproteins in various diseases, such as brain diseases, cardiovascular diseases, cancers, kidney diseases, and metabolic diseases. Additionally, we highlighted potential avenues for future development in clinical glycoproteomics. These insights provided in this review will enhance the comprehension of clinical glycoproteomic methods and diseases and promote the elucidation of pathogenesis and the discovery of novel diagnostic biomarkers and therapeutic targets.
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Affiliation(s)
- Yujia Wang
- Department of General Practice Ward/International Medical Center Ward General Practice Medical Center and Institutes for Systems Genetics West China Hospital Sichuan University Chengdu China
| | - Kaixin Lei
- Department of General Practice Ward/International Medical Center Ward General Practice Medical Center and Institutes for Systems Genetics West China Hospital Sichuan University Chengdu China
| | - Lijun Zhao
- Department of General Practice Ward/International Medical Center Ward General Practice Medical Center and Institutes for Systems Genetics West China Hospital Sichuan University Chengdu China
| | - Yong Zhang
- Department of General Practice Ward/International Medical Center Ward General Practice Medical Center and Institutes for Systems Genetics West China Hospital Sichuan University Chengdu China
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2
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Wen W, Hu X, Liu J, Zeng F, Xu Y, Yuan Y, Gao C, Sun X, Cheng B, Wang J, Hu X, Xiao RP, Chen X, Zhang X. RIP3 regulates doxorubicin-induced intestinal mucositis via FUT2-mediated α-1,2-fucosylation. Inflamm Res 2024; 73:1781-1801. [PMID: 39180691 DOI: 10.1007/s00011-024-01932-2] [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] [Received: 06/03/2024] [Revised: 07/24/2024] [Accepted: 08/05/2024] [Indexed: 08/26/2024] Open
Abstract
OBJECTIVE Intestinal mucositis is one of the common side effects of anti-cancer chemotherapy. However, the molecular mechanisms involved in mucositis development remain incompletely understood. In this study, we investigated the function of receptor-interacting protein kinase 3 (RIP3/RIPK3) in regulating doxorubicin-induced intestinal mucositis and its potential mechanisms. METHODS Intestinal mucositis animal models were induced in mice for in vivo studies. Rat intestinal cell line IEC-6 was used for in vitro studies. RNA‑seq was used to explore the transcriptomic changes in doxorubicin-induced intestinal mucositis. Intact glycopeptide characterization using mass spectrometry was applied to identify α-1,2-fucosylated proteins associated with mucositis. RESULTS Doxorubicin treatment increased RIP3 expression in the intestine and caused severe intestinal mucositis in the mice, depletion of RIP3 abolished doxorubicin-induced intestinal mucositis. RIP3-mediated doxorubicin-induced mucositis did not depend on mixed lineage kinase domain-like (MLKL) but on α-1,2-fucosyltransferase 2 (FUT2)-catalyzed α-1,2-fucosylation on inflammation-related proteins. Deficiency of MLKL did not affect intestinal mucositis, whereas inhibition of α-1,2-fucosylation by 2-deoxy-D-galactose (2dGal) profoundly attenuated doxorubicin-induced inflammation and mucositis. CONCLUSIONS RIP3-FUT2 pathway is a central node in doxorubicin-induced intestinal mucositis. Targeting intestinal RIP3 and/or FUT2-mediated α-1,2-fucosylation may provide potential targets for preventing chemotherapy-induced intestinal mucositis.
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Affiliation(s)
- Wei Wen
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
- PKU-Nanjing Institute of Translational Medicine, Nanjing, 211800, China
| | - Xiaomin Hu
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
- State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, 100730, China
| | - Jialin Liu
- College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
- Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, 100871, China
| | - Fanxin Zeng
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
- Department of Clinical Research Center, Dazhou Central Hospital, Dazhou, 635000, China
| | - Yihua Xu
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Ye Yuan
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Chunyan Gao
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Xueting Sun
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Bo Cheng
- College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China
- Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, 100871, China
| | - Jue Wang
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Xinli Hu
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China
| | - Rui-Ping Xiao
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China.
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China.
- PKU-Nanjing Institute of Translational Medicine, Nanjing, 211800, China.
- State Key Laboratory of Biomembrane and Membrane Biotechnology, Peking University, Beijing, 100871, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
| | - Xing Chen
- College of Chemistry and Molecular Engineering, Peking University, Beijing, 100871, China.
- Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, 100871, China.
- Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, 100871, China.
- Synthetic and Functional Biomolecules Center, Peking University, Beijing, 100871, China.
- Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China.
| | - Xiuqin Zhang
- Institute of Molecular Medicine, College of Future Technology, Peking University, Beijing, 100871, China.
- Beijing Key Laboratory of Cardiometabolic Molecular Medicine, Peking University, Beijing, 100871, China.
- PKU-Nanjing Institute of Translational Medicine, Nanjing, 211800, China.
- National Biomedical Imaging Center, School of Future Technology, Peking University, Beijing, 100871, China.
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3
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Zeng WF, Yan G, Zhao HH, Liu C, Cao W. Uncovering missing glycans and unexpected fragments with pGlycoNovo for site-specific glycosylation analysis across species. Nat Commun 2024; 15:8055. [PMID: 39277585 PMCID: PMC11401942 DOI: 10.1038/s41467-024-52099-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Accepted: 08/23/2024] [Indexed: 09/17/2024] Open
Abstract
Precision mapping of site-specific glycans using mass spectrometry is vital in glycoproteomics. However, the diversity of glycan compositions across species often exceeds database capacity, hindering the identification of rare glycans. Here, we introduce pGlycoNovo, a software within the pGlyco3 software environment, which employs a glycan first-based full-range Y-ion dynamic searching strategy. pGlycoNovo enables de novo identification of intact glycopeptides with rare glycans by considering all possible monosaccharide combinations, expanding the glycan search space to 16~1000 times compared to non-open search methods, while maintaining accuracy, sensitivity and speed. Reanalysis of SARS Covid-2 spike protein glycosylation data revealed 230 additional site-specific N-glycans and 30 previously unreported O-glycans. pGlycoNovo demonstrated high complementarity to six other tools and superior search speed. It enables characterization of site-specific N-glycosylation across five evolutionarily distant species, contributing to a dataset of 32,549 site-specific glycans on 4602 proteins, including 2409 site-specific rare glycans, and uncovering unexpected glycan fragments.
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Affiliation(s)
- Wen-Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- Center for Infectious Disease Research & School of Engineering, Westlake University, Hangzhou, China
| | - Guoquan Yan
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Huan-Huan Zhao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
- NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, China
| | - Chao Liu
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Weiqian 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.
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4
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Ahmad I, Mashwani ZUR, Younas Z, Yousaf T, Raish M, Arif M. Chemometric Modeling Revealed Oleic and Linoleic Acids as Varietal Biomarkers for Six Sesame Varieties-In Vitro and UHPLC Analyses. ACS OMEGA 2024; 9:37213-37224. [PMID: 39246474 PMCID: PMC11375699 DOI: 10.1021/acsomega.4c04519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 07/15/2024] [Accepted: 07/18/2024] [Indexed: 09/10/2024]
Abstract
Pakistan once considered self-sufficient for edible oil production now became the major importer with 88.6% imports and producing only the minor portion. Scientific negligence in oil seed crops led to a dramatic decrease in edible oil production depending mainly on only the imports. Sesamum indicum L., "Queen of Oil seeds" with 50-55% oil, is cultivated in various geographical regions of Pakistan, but farmers are not considering this crop because of insufficient knowledge, poor crop management practices, and low yielding varieties. This study was conducted to check the nutritional, biochemical, antioxidant, and yield potentials of six major varieties, i.e., TS-5, TH-6, Til-18, NIAB-Mil, NIAB-Pearl, and NS-16, and to compare the nutritionals, oil quality, and oil yield potential of these varieties. Field experiment was conducted, and various crop growth biomarkers were analyzed. Chlorophyll content and superoxide dismutase activity were found to be highest in NIAB-Mil followed by NIAB-Pearl and comparable to those of Til-18, while APX, Cat, and GPX activity was found to be highest in Til-18 with 25.6 and 5.9 and 6.02 mg/g, respectively. Seed antioxidant parameters showed a mixed response, but NIAB-Mil, NIAB pearl, and Til-18 were found to be highest in all antioxidant parameters. UHPLC analysis of seed oil resulted in a total of 14 triacylglycerols (TAGs), and principal component analysis and OPLS-Da analysis showed seven TAG biomarkers responsible for the separation of sesame varieties. Til-18 was found to be highest in oil content (53.3%) more abundant with oleic acid, while NIAB-Pearl, NIAB-Mil, and NS-16 were found to be abundant with linoleic acid, both considered as potential TAG biomarkers for sesame oil separation. This study concluded that, in general, Til-18 variety is more resistant with high nutritional status, high antioxidant activity, and oil yielding variety, followed by NIAB-Mil and NIAB-Pearl.
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Affiliation(s)
- Ilyas Ahmad
- Department of Food Science and Nutrition, College of Food, Agriculture and Natural Resources, University of Minnesota, Twin Cities, Minneapolis, Minnesota 55455, United States
- Department of Botany, PMAS Arid Agriculture University, Rawalpindi, Punjab 46300, Pakistan
| | - Zia-Ur-Rehman Mashwani
- Department of Botany, PMAS Arid Agriculture University, Rawalpindi, Punjab 46300, Pakistan
- Pakistan Academy of Sciences, Islamabad 44010, Pakistan
| | - Zohaib Younas
- Department of Botany, PMAS Arid Agriculture University, Rawalpindi, Punjab 46300, Pakistan
| | - Tayyaba Yousaf
- Department of Botany, PMAS Arid Agriculture University, Rawalpindi, Punjab 46300, Pakistan
| | - Mohammad Raish
- Department of Pharmaceutics, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia
| | - Muhammad Arif
- College of Agriculture, Guizhou University, Guiyang, Guizhou 550025, China
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5
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Qin S, Tian Z. Deep structure-level N-glycan identification using feature-induced structure diagnosis integrated with a deep learning model. Anal Bioanal Chem 2024:10.1007/s00216-024-05505-4. [PMID: 39212697 DOI: 10.1007/s00216-024-05505-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2024] [Revised: 07/30/2024] [Accepted: 08/14/2024] [Indexed: 09/04/2024]
Abstract
Being a widely occurring protein post-translational modification, N-glycosylation features unique multi-dimensional structures including sequence and linkage isomers. There have been successful bioinformatics efforts in N-glycan structure identification using N-glycoproteomics data; however, symmetric "mirror" branch isomers and linkage isomers are largely unresolved. Here, we report deep structure-level N-glycan identification using feature-induced structure diagnosis (FISD) integrated with a deep learning model. A neural network model is integrated to conduct the identification of featured N-glycan motifs and boosts the process of structure diagnosis and distinction for linkage isomers. By adopting publicly available N-glycoproteomics datasets of five mouse tissues (17,136 intact N-glycopeptide spectrum matches) and a consideration of 23 motif features, a deep learning model integrated with a convolutional autoencoder and a multilayer perceptron was trained to be capable of predicting N-glycan featured motifs in the MS/MS spectra with previously identified compositions. In the test of the trained model, a prediction accuracy of 0.8 and AUC value of 0.95 were achieved; 5701 previously unresolved N-glycan structures were assigned by matched structure-diagnostic ions; and by using an explainable learning algorithm, two new fragmentation features of m/z = 674.25 and m/z = 835.28 were found to be significant to three N-glycan structure motifs with fucose, NeuAc, and NeuGc, proving the capability of FISD to discover new features in the MS/MS spectra.
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Affiliation(s)
- Suideng Qin
- School of Chemical Science & Engineering, Shanghai Key Laboratory of Chemical Assessment and Sustainability, Tongji University, Shanghai, 200092, China
| | - Zhixin Tian
- School of Chemical Science & Engineering, Shanghai Key Laboratory of Chemical Assessment and Sustainability, Tongji University, Shanghai, 200092, China.
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6
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Klein J, Carvalho L, Zaia J. Expanding N-glycopeptide identifications by modeling fragmentation, elution, and glycome connectivity. Nat Commun 2024; 15:6168. [PMID: 39039063 PMCID: PMC11263600 DOI: 10.1038/s41467-024-50338-5] [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: 01/26/2021] [Accepted: 07/08/2024] [Indexed: 07/24/2024] Open
Abstract
Accurate glycopeptide identification in mass spectrometry-based glycoproteomics is a challenging problem at scale. Recent innovation has been made in increasing the scope and accuracy of glycopeptide identifications, with more precise uncertainty estimates for each part of the structure. We present a dynamically adapting relative retention time model for detecting and correcting ambiguous glycan assignments that are difficult to detect from fragmentation alone, a layered approach to glycopeptide fragmentation modeling that improves N-glycopeptide identification in samples without compromising identification quality, and a site-specific method to increase the depth of the glycoproteome confidently identifiable even further. We demonstrate our techniques on a set of previously published datasets, showing the performance gains at each stage of optimization. These techniques are provided in the open-source glycomics and glycoproteomics platform GlycReSoft available at https://github.com/mobiusklein/glycresoft .
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Affiliation(s)
- Joshua Klein
- Program for Bioinformatics, Boston University, Boston, MA, US.
| | - Luis Carvalho
- Program for Bioinformatics, Boston University, Boston, MA, US
- Department of Math and Statistics, Boston University, Boston, MA, US
| | - Joseph Zaia
- Program for Bioinformatics, Boston University, Boston, MA, US.
- Department of Biochemistry and Cell Biology, Boston University, Boston, MA, US.
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7
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Chongsaritsinsuk J, Rangel-Angarita V, Mahoney KE, Lucas TM, Enny OM, Katemauswa M, Malaker SA. Quantification and site-specific analysis of co-occupied N- and O-glycopeptides. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.06.602348. [PMID: 39005468 PMCID: PMC11245114 DOI: 10.1101/2024.07.06.602348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/16/2024]
Abstract
Protein glycosylation is a complex post-translational modification that is generally classified as N- or O-linked. Site-specific analysis of glycopeptides is accomplished with a variety of fragmentation methods, depending on the type of glycosylation being investigated and the instrumentation available. For instance, collisional dissociation methods are frequently used for N-glycoproteomic analysis with the assumption that one N-sequon exists per tryptic peptide. Alternatively, electron-based methods are indispensable for O-glycosite localization. However, the presence of simultaneously N- and O-glycosylated peptides could suggest the necessity of electron-based fragmentation methods for N-glycoproteomics, which is not commonly performed. Thus, we quantified the prevalence of N- and O-glycopeptides in mucins and other glycoproteins. A much higher frequency of co-occupancy within mucins was detected whereas only a negligible occurrence occurred within non-mucin glycoproteins. This was demonstrated from analyses of recombinant and/or purified proteins, as well as more complex samples. Where co-occupancy occurred, O-glycosites were frequently localized to the Ser/Thr within the N-sequon. Additionally, we found that O-glycans in close proximity to the occupied Asn were predominantly unelaborated core 1 structures, while those further away were more extended. Overall, we demonstrate electron-based methods are required for robust site-specific analysis of mucins, wherein co-occupancy is more prevalent. Conversely, collisional methods are generally sufficient for analyses of other types of glycoproteins.
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8
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Baerenfaenger M, Post MA, Zijlstra F, van Gool AJ, Lefeber DJ, Wessels HJCT. Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow. Anal Chem 2024; 96:8956-8964. [PMID: 38776126 PMCID: PMC11154686 DOI: 10.1021/acs.analchem.3c05874] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/10/2024] [Accepted: 05/11/2024] [Indexed: 06/05/2024]
Abstract
Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.
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Affiliation(s)
- Melissa Baerenfaenger
- Department
of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
- Division
of BioAnalytical Chemistry, AIMMS Amsterdam Institute of Molecular
and Life Sciences, Vrije Universiteit Amsterdam, Amsterdam 1081 HZ, Netherlands
| | - Merel A. Post
- Department
of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
| | - Fokje Zijlstra
- Translational
Metabolic Laboratory, Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
| | - Alain J. van Gool
- Translational
Metabolic Laboratory, Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
| | - Dirk J. Lefeber
- Department
of Neurology, Donders Institute for Brain, Cognition, and Behavior, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
- Translational
Metabolic Laboratory, Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
| | - Hans J. C. T. Wessels
- Translational
Metabolic Laboratory, Department of Human Genetics, Radboud University Medical Center, Nijmegen 6525 GA, Netherlands
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9
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Cao W. Advancing mass spectrometry-based glycoproteomic software tools for comprehensive site-specific glycoproteome analysis. Curr Opin Chem Biol 2024; 80:102442. [PMID: 38460452 DOI: 10.1016/j.cbpa.2024.102442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 02/18/2024] [Accepted: 02/19/2024] [Indexed: 03/11/2024]
Abstract
Glycoproteome analysis at a site-specific level and proteome scale stands out as a highly promising approach for gaining insights into the intricate roles of glycans in biological systems. Recent years have witnessed an upsurge in the development of innovative methodologies tailored for precisely this purpose. Breakthroughs in mass spectrometry-based glycoproteomic techniques, enabling the identification, quantification, and systematic exploration of site-specific glycans, have significantly enhanced our capacity to comprehensively and thoroughly characterize glycoproteins. In this short review, we delve into novel tools in advancing site-specific glycoproteomic analysis and summarize pertinent studies published in the past two years. Lastly, we discuss the ongoing challenges and outline future prospects in the field, considering both the analytical strategies of mass spectrometry and the tools employed for data interpretation.
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Affiliation(s)
- Weiqian Cao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai, 200433, China.
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10
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Girgis M, Petruncio G, Russo P, Peyton S, Paige M, Campos D, Sanda M. Analysis of N- and O-linked site-specific glycosylation by ion mobility mass spectrometry: State of the art and future directions. Proteomics 2024; 24:e2300281. [PMID: 38171879 DOI: 10.1002/pmic.202300281] [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] [Received: 07/18/2023] [Revised: 11/22/2023] [Accepted: 12/05/2023] [Indexed: 01/05/2024]
Abstract
Glycosylation, the major post-translational modification of proteins, significantly increases the diversity of proteoforms. Glycans are involved in a variety of pivotal structural and functional roles of proteins, and changes in glycosylation are profoundly connected to the progression of numerous diseases. Mass spectrometry (MS) has emerged as the gold standard for glycan and glycopeptide analysis because of its high sensitivity and the wealth of fragmentation information that can be obtained. Various separation techniques have been employed to resolve glycan and glycopeptide isomers at the front end of the MS. However, differentiating structures of isobaric and isomeric glycopeptides constitutes a challenge in MS-based characterization. Many reports described the use of various ion mobility-mass spectrometry (IM-MS) techniques for glycomic analyses. Nevertheless, very few studies have focused on N- and O-linked site-specific glycopeptidomic analysis. Unlike glycomics, glycoproteomics presents a multitude of inherent challenges in microheterogeneity, which are further exacerbated by the lack of dedicated bioinformatics tools. In this review, we cover recent advances made towards the growing field of site-specific glycosylation analysis using IM-MS with a specific emphasis on the MS techniques and capabilities in resolving isomeric peptidoglycan structures. Furthermore, we discuss commonly used software that supports IM-MS data analysis of glycopeptides.
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Affiliation(s)
- Michael Girgis
- Department of Bioengineering, College of Engineering & Computing, George Mason University, Fairfax, Virginia, USA
- Center for Molecular Engineering, George Mason University, Manassas, Virginia, USA
| | - Gregory Petruncio
- Center for Molecular Engineering, George Mason University, Manassas, Virginia, USA
- Department of Chemistry & Biochemistry, College of Science, George Mason University, Fairfax, Virginia, USA
| | - Paul Russo
- Center for Applied Proteomics and Molecular Medicine, George Mason University, Manassas, Virginia, USA
| | - Steven Peyton
- Center for Molecular Engineering, George Mason University, Manassas, Virginia, USA
| | - Mikell Paige
- Center for Molecular Engineering, George Mason University, Manassas, Virginia, USA
- Department of Chemistry & Biochemistry, College of Science, George Mason University, Fairfax, Virginia, USA
| | - Diana Campos
- Max-Planck-Institut fuer Herz- und Lungenforschung, Bad Nauheim, Germany
| | - Miloslav Sanda
- Max-Planck-Institut fuer Herz- und Lungenforschung, Bad Nauheim, Germany
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11
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Kellman BP, Mariethoz J, Zhang Y, Shaul S, Alteri M, Sandoval D, Jeffris M, Armingol E, Bao B, Lisacek F, Bojar D, Lewis NE. Decoding glycosylation potential from protein structure across human glycoproteins with a multi-view recurrent neural network. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.05.15.594334. [PMID: 38798633 PMCID: PMC11118808 DOI: 10.1101/2024.05.15.594334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Glycosylation is described as a non-templated biosynthesis. Yet, the template-free premise is antithetical to the observation that different N-glycans are consistently placed at specific sites. It has been proposed that glycosite-proximal protein structures could constrain glycosylation and explain the observed microheterogeneity. Using site-specific glycosylation data, we trained a hybrid neural network to parse glycosites (recurrent neural network) and match them to feasible N-glycosylation events (graph neural network). From glycosite-flanking sequences, the algorithm predicts most human N-glycosylation events documented in the GlyConnect database and proposed structures corresponding to observed monosaccharide composition of the glycans at these sites. The algorithm also recapitulated glycosylation in Enhanced Aromatic Sequons, SARS-CoV-2 spike, and IgG3 variants, thus demonstrating the ability of the algorithm to predict both glycan structure and abundance. Thus, protein structure constrains glycosylation, and the neural network enables predictive in silico glycosylation of uncharacterized or novel protein sequences and genetic variants.
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Affiliation(s)
- Benjamin P. Kellman
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Augment Biologics, La Jolla, CA 92092
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
| | - Julien Mariethoz
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
| | - Yujie Zhang
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
| | - Sigal Shaul
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Alteri
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Daniel Sandoval
- Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA 92093, USA
| | - Mia Jeffris
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
| | - Erick Armingol
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Bokan Bao
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
| | - Frederique Lisacek
- Proteome Informatics Group, Swiss Institute of Bioinformatics, CH-1227 Geneva, Switzerland
- Computer Science Department & Section of Biology, University of Geneva, route de Drize 7, CH-1227, Geneva, Switzerland
| | - Daniel Bojar
- Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg 41390, Sweden
- Department of Chemistry and Molecular Biology, University of Gothenburg, Gothenburg 41390, Sweden
| | - Nathan E. Lewis
- Department of Pediatrics, University of California, San Diego, La Jolla, CA 92093, USA
- Department of Bioengineering, University of California, San Diego, La Jolla, CA 92093, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, La Jolla, CA 92093, USA
- Ragon Institute of MGH, MIT, and Harvard, Cambridge, MA, USA
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12
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Kang T, Budhraja R, Kim J, Joshi N, Garapati K, Pandey A. Global O-glycoproteome enrichment and analysis enabled by a combinatorial enzymatic workflow. CELL REPORTS METHODS 2024; 4:100744. [PMID: 38582075 PMCID: PMC11046030 DOI: 10.1016/j.crmeth.2024.100744] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Revised: 01/25/2024] [Accepted: 03/18/2024] [Indexed: 04/08/2024]
Abstract
A comprehensive analysis of site-specific protein O-glycosylation is hindered by the absence of a consensus O-glycosylation motif, the diversity of O-glycan structures, and the lack of a universal enzyme that cleaves attached O-glycans. Here, we report the development of a robust O-glycoproteomic workflow for analyzing complex biological samples by combining four different strategies: removal of N-glycans, complementary digestion using O-glycoprotease (IMPa) with/without another protease, glycopeptide enrichment, and mass spectrometry with fragmentation of glycopeptides using stepped collision energy. Using this workflow, we cataloged 474 O-glycopeptides on 189 O-glycosites derived from 79 O-glycoproteins from human plasma. These data revealed O-glycosylation of several abundant proteins that have not been previously reported. Because many of the proteins that contained unannotated O-glycosylation sites have been extensively studied, we wished to confirm glycosylation at these sites in a targeted fashion. Thus, we analyzed selected purified proteins (kininogen-1, fetuin-A, fibrinogen, apolipoprotein E, and plasminogen) in independent experiments and validated the previously unknown O-glycosites.
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Affiliation(s)
- Taewook Kang
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Rohit Budhraja
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Jinyong Kim
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA
| | - Neha Joshi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Kishore Garapati
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal, Karnataka 576104, India
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 55905, USA; Manipal Academy of Higher Education, Manipal, Karnataka 576104, India; Center for Individualized Medicine, Mayo Clinic, Rochester, MN 55905, USA.
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13
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Garapati K, Budhraja R, Saraswat M, Kim J, Joshi N, Sachdeva GS, Jain A, Ligezka AN, Radenkovic S, Ramarajan MG, Udainiya S, Raymond K, He M, Lam C, Larson A, Edmondson AC, Sarafoglou K, Larson NB, Freeze HH, Schultz MJ, Kozicz T, Morava E, Pandey A. A complement C4-derived glycopeptide is a biomarker for PMM2-CDG. JCI Insight 2024; 9:e172509. [PMID: 38587076 PMCID: PMC7615924 DOI: 10.1172/jci.insight.172509] [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] [Received: 05/24/2023] [Accepted: 02/15/2024] [Indexed: 04/09/2024] Open
Abstract
BACKGROUNDDiagnosis of PMM2-CDG, the most common congenital disorder of glycosylation (CDG), relies on measuring carbohydrate-deficient transferrin (CDT) and genetic testing. CDT tests have false negatives and may normalize with age. Site-specific changes in protein N-glycosylation have not been reported in sera in PMM2-CDG.METHODSUsing multistep mass spectrometry-based N-glycoproteomics, we analyzed sera from 72 individuals to discover and validate glycopeptide alterations. We performed comprehensive tandem mass tag-based discovery experiments in well-characterized patients and controls. Next, we developed a method for rapid profiling of additional samples. Finally, targeted mass spectrometry was used for validation in an independent set of samples in a blinded fashion.RESULTSOf the 3,342 N-glycopeptides identified, patients exhibited decrease in complex-type N-glycans and increase in truncated, mannose-rich, and hybrid species. We identified a glycopeptide from complement C4 carrying the glycan Man5GlcNAc2, which was not detected in controls, in 5 patients with normal CDT results, including 1 after liver transplant and 2 with a known genetic variant associated with mild disease, indicating greater sensitivity than CDT. It was detected by targeted analysis in 2 individuals with variants of uncertain significance in PMM2.CONCLUSIONComplement C4-derived Man5GlcNAc2 glycopeptide could be a biomarker for accurate diagnosis and therapeutic monitoring of patients with PMM2-CDG and other CDGs.FUNDINGU54NS115198 (Frontiers in Congenital Disorders of Glycosylation: NINDS; NCATS; Eunice Kennedy Shriver NICHD; Rare Disorders Consortium Disease Network); K08NS118119 (NINDS); Minnesota Partnership for Biotechnology and Medical Genomics; Rocket Fund; R01DK099551 (NIDDK); Mayo Clinic DERIVE Office; Mayo Clinic Center for Biomedical Discovery; IA/CRC/20/1/600002 (Center for Rare Disease Diagnosis, Research and Training; DBT/Wellcome Trust India Alliance).
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Affiliation(s)
- Kishore Garapati
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Rohit Budhraja
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mayank Saraswat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Jinyong Kim
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Neha Joshi
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Gunveen S. Sachdeva
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Anu Jain
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | | | | | - Madan Gopal Ramarajan
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Savita Udainiya
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Institute of Bioinformatics, International Technology Park, Bangalore, India
- Manipal Academy of Higher Education (MAHE), Manipal, India
| | - Kimiyo Raymond
- Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Miao He
- Department of Pathology and Laboratory Medicine, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Christina Lam
- Center for Integrative Brain Research, Seattle Children’s Research Institute, Seattle, Washington, USA
- Division of Genetic Medicine, Department of Pediatrics, University of Washington School of Medicine, Seattle, Washington, USA
| | | | - Andrew C. Edmondson
- Division of Human Genetics, Department of Pediatrics, Children’s Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Kyriakie Sarafoglou
- Division of Pediatric Endocrinology, Department of Pediatrics, University of Minnesota Medical School, Minneapolis, Minnesota, USA
- Department of Experimental and Clinical Pharmacology, University of Minnesota School of Pharmacy, Minneapolis, Minnesota, USA
| | - Nicholas B. Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Hudson H. Freeze
- Sanford Children’s Health Research Center, Sanford Burnham Prebys Medical Discovery Institute, La Jolla, California, USA
| | - Matthew J. Schultz
- Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Tamas Kozicz
- Department of Clinical Genomics and
- Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Anatomy, University of Pécs Medical School, Pécs, Hungary
- Department of Genomics and Genetic Sciences, Icahn School of Medicine at Mount Sinai Hospital, New York, New York, USA
| | - Eva Morava
- Department of Clinical Genomics and
- Biochemical Genetics Laboratory, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Anatomy, University of Pécs Medical School, Pécs, Hungary
- Department of Genomics and Genetic Sciences, Icahn School of Medicine at Mount Sinai Hospital, New York, New York, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
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14
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Nalehua MR, Zaia J. A critical evaluation of ultrasensitive single-cell proteomics strategies. Anal Bioanal Chem 2024; 416:2359-2369. [PMID: 38358530 DOI: 10.1007/s00216-024-05171-6] [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] [Received: 09/25/2023] [Revised: 01/20/2024] [Accepted: 01/23/2024] [Indexed: 02/16/2024]
Abstract
Success of mass spectrometry characterization of the proteome of single cells allows us to gain a greater understanding than afforded by transcriptomics alone but requires clear understanding of the tradeoffs between analytical throughput and precision. Recent advances in mass spectrometry acquisition techniques, including updated instrumentation and sample preparation, have improved the quality of peptide signals obtained from single cell data. However, much of the proteome remains uncharacterized, and higher throughput techniques often come at the expense of reduced sensitivity and coverage, which diminish the ability to measure proteoform heterogeneity, including splice variants and post-translational modifications, in single cell data analysis. Here, we assess the growing body of ultrasensitive single-cell approaches and their tradeoffs as researchers try to balance throughput and precision in their experiments.
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Affiliation(s)
| | - Joseph Zaia
- Bioinformatics Program, Boston University, Boston, MA, USA.
- Department of Biochemistry and Cell Biology, Boston University, Boston, MA, USA.
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15
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Yang Y, Fang Q. Prediction of glycopeptide fragment mass spectra by deep learning. Nat Commun 2024; 15:2448. [PMID: 38503734 PMCID: PMC10951270 DOI: 10.1038/s41467-024-46771-1] [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] [Received: 09/13/2023] [Accepted: 03/11/2024] [Indexed: 03/21/2024] Open
Abstract
Deep learning has achieved a notable success in mass spectrometry-based proteomics and is now emerging in glycoproteomics. While various deep learning models can predict fragment mass spectra of peptides with good accuracy, they cannot cope with the non-linear glycan structure in an intact glycopeptide. Herein, we present DeepGlyco, a deep learning-based approach for the prediction of fragment spectra of intact glycopeptides. Our model adopts tree-structured long-short term memory networks to process the glycan moiety and a graph neural network architecture to incorporate potential fragmentation pathways of a specific glycan structure. This feature is beneficial to model explainability and differentiation ability of glycan structural isomers. We further demonstrate that predicted spectral libraries can be used for data-independent acquisition glycoproteomics as a supplement for library completeness. We expect that this work will provide a valuable deep learning resource for glycoproteomics.
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Affiliation(s)
- Yi Yang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
| | - Qun Fang
- ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, 311200, China.
- Department of Chemistry, Zhejiang University, Hangzhou, 310058, China.
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16
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Bartholow T, Burroughs PW, Elledge SK, Byrnes JR, Kirkemo LL, Garda V, Leung KK, Wells JA. Photoproximity Labeling from Single Catalyst Sites Allows Calibration and Increased Resolution for Carbene Labeling of Protein Partners In Vitro and on Cells. ACS CENTRAL SCIENCE 2024; 10:199-208. [PMID: 38292613 PMCID: PMC10823516 DOI: 10.1021/acscentsci.3c01473] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/07/2023] [Accepted: 12/08/2023] [Indexed: 02/01/2024]
Abstract
The cell surface proteome (surfaceome) plays a pivotal role in virtually all extracellular biology, and yet we are only beginning to understand the protein complexes formed in this crowded environment. Recently, a high-resolution approach (μMap) was described that utilizes multiple iridium-photocatalysts attached to a secondary antibody, directed to a primary antibody of a protein of interest, to identify proximal neighbors by light-activated conversion of a biotin-diazirine to a highly reactive carbene followed by LC/MS (Geri, J. B.; Oakley, J. V.; Reyes-Robles, T.; Wang, T.; McCarver, S. J.; White, C. H.; Rodriguez-Rivera, F. P.; Parker, D. L.; Hett, E. C.; Fadeyi, O. O.; Oslund, R. C.; MacMillan, D. W. C. Science2020, 367, 1091-1097). Here we calibrated the spatial resolution for carbene labeling using site-specific conjugation of a single photocatalyst to a primary antibody drug, trastuzumab (Traz), in complex with its structurally well-characterized oncogene target, HER2. We observed relatively uniform carbene labeling across all amino acids, and a maximum distance of ∼110 Å from the fixed photocatalyst. When targeting HER2 overexpression cells, we identified 20 highly enriched HER2 neighbors, compared to a nonspecific membrane tethered catalyst. These studies identify new HER2 interactors and calibrate the radius of carbene photoprobe labeling for the surfaceome.
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Affiliation(s)
- Thomas
G. Bartholow
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Paul W.W. Burroughs
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Susanna K. Elledge
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - James R. Byrnes
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Lisa L. Kirkemo
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Virginia Garda
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - Kevin K. Leung
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
| | - James A. Wells
- Department
of Pharmaceutical Chemistry, University
of California San Francisco, San Francisco, California 94158, United States
- Department
of Cellular & Molecular Pharmacology, University of California San Francisco, San Francisco, California 94158, United States
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17
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Li R, Xia C, Wu S, Downs MJ, Tong H, Tursumamat N, Zaia J, Costello CE, Lin C, Wei J. Direct and Detailed Site-Specific Glycopeptide Characterization by Higher-Energy Electron-Activated Dissociation Tandem Mass Spectrometry. Anal Chem 2024; 96:1251-1258. [PMID: 38206681 PMCID: PMC10885852 DOI: 10.1021/acs.analchem.3c04484] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Glycosylation is widely recognized as the most complex post-translational modification due to the widespread presence of macro- and microheterogeneities, wherein its biological consequence is closely related to both the glycosylation sites and the glycan fine structures. Yet, efficient site-specific detailed glycan characterization remains a significant analytical challenge. Here, utilizing an Orbitrap-Omnitrap platform, higher-energy electron-activated dissociation (heExD) tandem mass spectrometry (MS/MS) revealed extraordinary efficacy for the structural characterization of intact glycopeptides. HeExD produced extensive fragmentation within both the glycan and the peptide, including A-/B-/C-/Y-/Z-/X-ions from the glycan motif and a-/b-/c-/x-/y-/z-type peptide fragments (with or without the glycan). The intensity of cross-ring cleavage and backbone fragments retaining the intact glycan was highly dependent on the electron energy. Among the four electron energy levels investigated, electronic excitation dissociation (EED) provided the most comprehensive structural information, yielding a complete series of glycosidic fragments for accurate glycan topology determination, a wealth of cross-ring fragments for linkage definition, and the most extensive peptide backbone fragments for accurate peptide sequencing and glycosylation site localization. The glycan fragments observed in the EED spectrum correlated well with the fragmentation patterns observed in EED MS/MS of the released glycans. The advantages of EED over higher-energy collisional dissociation (HCD), stepped collision energy HCD (sceHCD), and electron-transfer/higher-energy collisional dissociation (EThcD) were demonstrated for the characterization of a glycopeptide bearing a biantennary disialylated glycan. EED can produce a complete peptide backbone and glycan sequence coverage even for doubly protonated precursors. The exceptional performance of heExD MS/MS, particularly EED MS/MS, in site-specific detailed glycan characterization on an Orbitrap-Omnitrap hybrid instrument presents a novel option for in-depth glycosylation analysis.
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Affiliation(s)
- Ruiqing Li
- Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education, School of Pharmaceutical Sciences, National Key Laboratory of Innovative Immunotherapy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Chaoshuang Xia
- Center for Biomedical Mass Spectrometry, Boston University Chobanian & Avedisian School of Medicine, 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Shuye Wu
- Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education, School of Pharmaceutical Sciences, National Key Laboratory of Innovative Immunotherapy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Margaret J Downs
- Center for Biomedical Mass Spectrometry, Boston University Chobanian & Avedisian School of Medicine, 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Haowei Tong
- School of Life Science, Shanghai Jiao Tong University, Shanghai, 800 Dongchuan Road, Shanghai 200240, China
| | - Nafisa Tursumamat
- Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education, School of Pharmaceutical Sciences, National Key Laboratory of Innovative Immunotherapy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Joseph Zaia
- Center for Biomedical Mass Spectrometry, Boston University Chobanian & Avedisian School of Medicine, 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Catherine E Costello
- Center for Biomedical Mass Spectrometry, Boston University Chobanian & Avedisian School of Medicine, 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Cheng Lin
- Center for Biomedical Mass Spectrometry, Boston University Chobanian & Avedisian School of Medicine, 670 Albany Street, Boston, Massachusetts 02118, United States
| | - Juan Wei
- Engineering Research Center of Cell & Therapeutic Antibody, Ministry of Education, School of Pharmaceutical Sciences, National Key Laboratory of Innovative Immunotherapy, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
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18
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Adalia R, Patel S, Paiva A, Kaufman T, Zamora I, Cai X, Sanjuan G, Shou WZ. Development of a Predictive Multiple Reaction Monitoring (MRM) Model for High-Throughput ADME Analyses Using Learning-to-Rank (LTR) Techniques. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:131-139. [PMID: 38014625 DOI: 10.1021/jasms.3c00363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
Multiple Reaction Monitoring (MRM) is an important MS/MS technique commonly used in drug discovery and development, allowing for the selective and sensitive quantification of compounds in complex matrices. However, compound optimization can be resource intensive and requires experimental determination of product ions for each compound. In this study, we developed a Learning-to-Rank (LTR) model to predict the product ions directly from compound structures, eliminating the requirement for MRM optimization experiments. Experimentally determined MRM conditions for 5757 compounds were used to develop the model. Using the MassChemSite software, theoretical fragments and their mass-to-charge ratios were generated, which were then matched to the experimental product ions to create a data set. Each possible fragment was ranked based on its intensity in the experimental data. Different LTR models were built on a training split. Hyperparameter selection was performed using 5-fold cross validation. The models were evaluated using the Normalized Discounted Cumulative Gain at top k (NDCG@k) and the Coverage at top k (Coverage@k) metrics. Finally, the model was applied to predict MRM conditions for a prospective set of 235 compounds in high-throughput Caco-2 permeability and metabolic stability assays, and quantification results were compared to those obtained with experimentally acquired MRM conditions. The LTR model achieved a NDCG@5 of 0.732 and Coverage@5 of 0.841 on the validation split, and its predictions led to 97% of biologically equivalent results in the Caco-2 permeability and metabolic stability assays.
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Affiliation(s)
- Ramon Adalia
- Lead Molecular Design S.L., 08172 Sant Cugat de Valles, Spain
- Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
| | - Shivani Patel
- Lead Discovery and Optimization, Bristol-Myers Squibb, Princeton, New Jersey 08648, United States
| | - Anthony Paiva
- Lead Discovery and Optimization, Bristol-Myers Squibb, Princeton, New Jersey 08648, United States
| | - Tierni Kaufman
- Lead Discovery and Optimization, Bristol-Myers Squibb, Princeton, New Jersey 08648, United States
| | - Ismael Zamora
- Lead Molecular Design S.L., 08172 Sant Cugat de Valles, Spain
| | - Xianmei Cai
- Lead Discovery and Optimization, Bristol-Myers Squibb, Princeton, New Jersey 08648, United States
| | - Gemma Sanjuan
- Universitat Autònoma de Barcelona, 08193 Barcelona, Spain
| | - Wilson Z Shou
- Lead Discovery and Optimization, Bristol-Myers Squibb, Princeton, New Jersey 08648, United States
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19
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Zhu Y. Plasma/Serum Proteomics based on Mass Spectrometry. Protein Pept Lett 2024; 31:192-208. [PMID: 38869039 PMCID: PMC11165715 DOI: 10.2174/0109298665286952240212053723] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2023] [Revised: 01/22/2024] [Accepted: 01/31/2024] [Indexed: 06/14/2024]
Abstract
Human blood is a window of physiology and disease. Examination of biomarkers in blood is a common clinical procedure, which can be informative in diagnosis and prognosis of diseases, and in evaluating treatment effectiveness. There is still a huge demand on new blood biomarkers and assays for precision medicine nowadays, therefore plasma/serum proteomics has attracted increasing attention in recent years. How to effectively proceed with the biomarker discovery and clinical diagnostic assay development is a question raised to researchers who are interested in this area. In this review, we comprehensively introduce the background and advancement of technologies for blood proteomics, with a focus on mass spectrometry (MS). Analyzing existing blood biomarkers and newly-built diagnostic assays based on MS can shed light on developing new biomarkers and analytical methods. We summarize various protein analytes in plasma/serum which include total proteome, protein post-translational modifications, and extracellular vesicles, focusing on their corresponding sample preparation methods for MS analysis. We propose screening multiple protein analytes in the same set of blood samples in order to increase success rate for biomarker discovery. We also review the trends of MS techniques for blood tests including sample preparation automation, and further provide our perspectives on their future directions.
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Affiliation(s)
- Yiying Zhu
- Department of Chemistry, Tsinghua University, Beijing, China
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20
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Min Y, Wu J, Hou W, Li X, Zhao X, Guan X, Qian X, Hao C, Ying W. Differential analysis of core-fucosylated glycoproteomics enabled by single-step truncation of N-glycans. Glycoconj J 2023; 40:541-549. [PMID: 37542637 DOI: 10.1007/s10719-023-10130-x] [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: 04/07/2023] [Revised: 06/14/2023] [Accepted: 07/19/2023] [Indexed: 08/07/2023]
Abstract
Alpha-1,6 fucosylation of N-glycans (core fucosylation, CF) represents a unique form of N-glycans and is widely involved in disease progression. In order to accurately identify CF glycoproteins, several approaches have been developed based on sequential cleavage with different glycosidases to truncate the N-glycans. Since multi-step sample treatments may introduce quantitation bias and affect the practicality of these approaches in large-scale applications. Here, we systematically evaluated the performance of the single-step treatment of intact glycopeptides by endoglycosidase F3 for CF glycoproteome. The single-step truncation (SST) strategy demonstrated higher quantitative stability and higher efficiency compared with previous approaches. The strategy was further practiced on both cell lines and serum samples. The dysregulation of CF glycopeptides between preoperative and postoperative serum from patients with pancreatic ductal adenocarcinoma was revealed, and the CF modifications of BCHE_N369, CDH5_N112 and SERPIND1_N49 were found to be potential prognostic markers. This study thus provides an efficient solution for large-scale quantitative analysis of the CF glycoproteome.
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Affiliation(s)
- Yao Min
- School of Basic Medical Science, Anhui Medical University, Hefei, 230032, China
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Jianhui Wu
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, 102206, China
| | - Wenhao Hou
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Xiaoyu Li
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Xinyuan Zhao
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Xiaoya Guan
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, 102206, China
| | - Xiaohong Qian
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China
| | - Chunyi Hao
- Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Hepato-Pancreato-Biliary Surgery, Peking University Cancer Hospital & Institute, Beijing, 102206, China.
| | - Wantao Ying
- School of Basic Medical Science, Anhui Medical University, Hefei, 230032, China.
- State Key Laboratory of Proteomics, Beijing Proteome Research Center, National Center for Protein Sciences (Beijing), Beijing, 102206, China.
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21
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Liu J, Cheng B, Fan X, Zhou X, Wang J, Zhou W, Li H, Zeng W, Yang P, Chen X. Click-iG: Simultaneous Enrichment and Profiling of Intact N-linked, O-GalNAc, and O-GlcNAcylated Glycopeptides. Angew Chem Int Ed Engl 2023; 62:e202303410. [PMID: 37431278 DOI: 10.1002/anie.202303410] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 07/12/2023]
Abstract
Proteins are ubiquitously modified with glycans of varied chemical structures through distinct glycosidic linkages, making the landscape of protein glycosylation challenging to map. Profiling of intact glycopeptides with mass spectrometry (MS) has recently emerged as a powerful tool for revealing matched information of the glycosylation sites and attached glycans (i.e., intact glycosites), but is largely limited to individual glycosylation types. Herein, we describe Click-iG, which integrates metabolic labeling of glycans with clickable unnatural sugars, an optimized MS method, and a tailored version of pGlyco3 software to enable simultaneous enrichment and profiling of three types of intact glycopeptides: N-linked, mucin-type O-linked, and O-GlcNAcylated glycopeptides. We demonstrate the utility of Click-iG by the identification of thousands of intact glycosites in cell lines and living mice. From the mouse lung, heart, and spleen, a total of 2053 intact N-glycosites, 262 intact O-GalNAc glycosites, and 1947 O-GlcNAcylation sites were identified. Click-iG-enabled comprehensive coverage of the protein glycosylation landscape lays the foundation for interrogating crosstalk between different glycosylation pathways.
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Affiliation(s)
- Jialin Liu
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
- Institute of Biomedical Sciences and Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Bo Cheng
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Xinqi Fan
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Xinyue Zhou
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Jiankun Wang
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Wen Zhou
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Hengyu Li
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
| | - Wenfeng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) and Institute of Computing Technology, CAS, Beijing, 100190, China
| | - Pengyuan Yang
- Institute of Biomedical Sciences and Department of Chemistry, Fudan University, Shanghai, 200433, China
| | - Xing Chen
- College of Chemistry and Molecular Engineering, Beijing National Laboratory for Molecular Sciences, Peking-Tsinghua Center for Life Sciences, Synthetic and Functional Biomolecules Center, and Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, 100871, China
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22
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Bartholow TG, Burroughs P, Elledge SK, Byrnes JR, Kirkemo LL, Garda V, Leung KK, Wells JA. Site-specific proximity labeling at single residue resolution for identification of protein partners in vitro and on cells. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.27.550738. [PMID: 37546992 PMCID: PMC10402114 DOI: 10.1101/2023.07.27.550738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
The cell surface proteome, or surfaceome, is encoded by more than 4000 genes, but we are only beginning to understand the complexes they form. Rapid proximity labeling around specific membrane targets allows for capturing weak and transient interactions expected in the crowded and dynamic environment of the surfaceome. Recently, a high-resolution approach called μMap has been described (Geri, J. B., Oakley, J. V., Reyes-Robles, T., Wang, T., McCarver, S. J., White, C. H., Rodriguez-Rivera, F. P., Parker, D. L., Hett, E. C., Fadeyi, O. O., Oslund, R. C., and MacMillan, D. W. C. (2020) Science 367 , 1091-1097) in which an iridium (Ir)-based photocatalyst is attached to a specific antibody to target labeling of neighbors utilizing light-activated generation of carbenes from diazirine compounds via Dexter Energy Transfer (DET). Here we studied and optimized the spatial resolution for the method using an oncoprotein complex between the antibody drug, trastuzumab (Traz), and its target HER2. A set of eight single site-specific Ir-catalytic centers were engineered into Traz to study intra- and inter-molecular labeling in vitro and on cells by mass spectrometry. From this structurally well-characterized complex we observed a maximum distance of ∼110 Å for labeling. Labeling occurred almost uniformly over the full range of amino acids, unlike the residue specific labeling of other techniques. To examine on cell labeling that is specific to HER2 as opposed to simply being on the membrane, we compared the labeling patterns for the eight Traz-catalyst species to random labeling of membrane proteins using a metabolically integrated fatty acid catalyst. Our results identified 20 high confidence HER2 neighbors, many novel, that were more than 6-fold enriched compared to the non-specific membrane tethered catalyst. These studies define distance labeling parameters from single-site catalysts placed directly on the membrane target of interest, and more accurately compare to non-specific labeling to identify membrane complexes with higher confidence.
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23
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Campos D, Girgis M, Yang Q, Zong G, Goldman R, Wang LX, Sanda M. "Ghost" Fragment Ions in Structure and Site-Specific Glycoproteomics Analysis. Anal Chem 2023; 95:10145-10148. [PMID: 37382290 PMCID: PMC10339278 DOI: 10.1021/acs.analchem.3c02207] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 06/26/2023] [Indexed: 06/30/2023]
Abstract
Mass spectrometry (MS) can unlock crucial insights into the intricate world of glycosylation analysis. Despite its immense potential, the qualitative and quantitative analysis of isobaric glycopeptide structures remains one of the most daunting hurdles in the field of glycoproteomics. The ability to distinguish between these complex glycan structures poses a significant challenge, hindering our ability to accurately measure and understand the role of glycoproteins in biological systems. A few recent publications described the use of collision energy (CE) modulation to improve structural elucidation, especially for qualitative purposes. Different linkages of glycan units usually demonstrate different stabilities under CID/HCD fragmentation conditions. Fragmentation of the glycan moiety produces low molecular weight ions (oxonium ions) that can serve as a structure-specific signature for specific glycan moieties; however, the specificity of these fragments has never been examined closely. Here, we particularly focused on N-glycoproteomics analysis and investigated fragmentation specificity using synthetic stable isotope-labeled N-glycopeptide standards. These standards were isotopically labeled at the reducing terminal GlcNAc, which allowed us to resolve fragments produced by the oligomannose core moiety and fragments generated from outer antennary structures. Our research identified the potential for false-positive structure assignments due to the occurrence of "Ghost" fragments resulting from single glyco unit rearrangement or mannose core fragmentation within the collision cell. To mitigate this issue, we have established a minimal intensity threshold for these fragments to prevent misidentification of structure-specific fragments in glycoproteomics analysis. Our findings provide a crucial step forward in the quest for more accurate and reliable glycoproteomics measurements.
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Affiliation(s)
- Diana Campos
- Max-Planck-Institut
fuer Herz- und Lungenforschung, Ludwigstrasse 43, 61231 Bad Nauheim, Germany
| | - Michael Girgis
- Department
of Bioengineering, College of Engineering and Computing, George Mason University, Fairfax, Virginia 22030, United States
| | - Qiang Yang
- GlycoT
Therapeutics, College Park, Maryland 20742, United States
| | - Guanghui Zong
- Department
of Chemistry and Biochemistry, University
of Maryland, College
Park, Maryland 20742, United States
| | - Radoslav Goldman
- Department
of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, D.C. 20057, United States
- Clinical
and Translational Glycoscience Research Center, Georgetown University, Washington, D.C. 20057, United States
| | - Lai-Xi Wang
- Department
of Chemistry and Biochemistry, University
of Maryland, College
Park, Maryland 20742, United States
| | - Miloslav Sanda
- Max-Planck-Institut
fuer Herz- und Lungenforschung, Ludwigstrasse 43, 61231 Bad Nauheim, Germany
- Department
of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University, Washington, D.C. 20057, United States
- Clinical
and Translational Glycoscience Research Center, Georgetown University, Washington, D.C. 20057, United States
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24
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Sun W, Zhang Q, Zhang X, Tran NH, Ziaur Rahman M, Chen Z, Peng C, Ma J, Li M, Xin L, Shan B. Glycopeptide database search and de novo sequencing with PEAKS GlycanFinder enable highly sensitive glycoproteomics. Nat Commun 2023; 14:4046. [PMID: 37422459 PMCID: PMC10329677 DOI: 10.1038/s41467-023-39699-5] [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] [Received: 09/26/2022] [Accepted: 06/19/2023] [Indexed: 07/10/2023] Open
Abstract
Here we present GlycanFinder, a database search and de novo sequencing tool for the analysis of intact glycopeptides from mass spectrometry data. GlycanFinder integrates peptide-based and glycan-based search strategies to address the challenge of complex fragmentation of glycopeptides. A deep learning model is designed to capture glycan tree structures and their fragment ions for de novo sequencing of glycans that do not exist in the database. We performed extensive analyses to validate the false discovery rates (FDRs) at both peptide and glycan levels and to evaluate GlycanFinder based on comprehensive benchmarks from previous community-based studies. Our results show that GlycanFinder achieved comparable performance to other leading glycoproteomics softwares in terms of both FDR control and the number of identifications. Moreover, GlycanFinder was also able to identify glycopeptides not found in existing databases. Finally, we conducted a mass spectrometry experiment for antibody N-linked glycosylation profiling that could distinguish isomeric peptides and glycans in four immunoglobulin G subclasses, which had been a challenging problem to previous studies.
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Affiliation(s)
- Weiping Sun
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Qianqiu Zhang
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Xiyue Zhang
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Ngoc Hieu Tran
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
| | - M Ziaur Rahman
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Zheng Chen
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Chao Peng
- BaizhenBio Inc., Wuhan, China
- Wuhan BioBank, Wuhan, China
| | - Jun Ma
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada
| | - Ming Li
- David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada.
- Henan Academy of Sciences, Zhengzhou, Henan, China.
| | - Lei Xin
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada.
| | - Baozhen Shan
- Bioinformatics Solutions Inc., Waterloo, Ontario, Canada.
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25
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Yu H, Li M, Shu J, Dang L, Wu X, Wang Y, Wang X, Chang X, Bao X, Zhu B, Ren X, Chen W, Li Y. Characterization of aberrant glycosylation associated with osteoarthritis based on integrated glycomics methods. Arthritis Res Ther 2023; 25:102. [PMID: 37308935 PMCID: PMC10258941 DOI: 10.1186/s13075-023-03084-w] [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] [Received: 04/12/2023] [Accepted: 06/03/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Osteoarthritis (OA) is the most common form of arthritis, affecting millions of aging people. Investigation of abnormal glycosylation is essential for the understanding of pathological mechanisms of OA. METHODS The total protein was isolated from OA (n = 13) and control (n = 11) cartilages. Subsequently, glycosylation alterations of glycoproteins in OA cartilage were investigated by lectin microarrays and intact glycopeptides analysis. Finally, the expression of glycosyltransferases involved in the synthesis of altered glycosylation was assessed by qPCR and GEO database. RESULTS Our findings revealed that several glycopatterns, such as α-1,3/6 fucosylation and high-mannose type of N-glycans were altered in OA cartilages. Notably, over 27% of identified glycopeptides (109 glycopeptides derived from 47 glycoproteins mainly located in the extracellular region) disappeared or decreased in OA cartilages, which is related to the cartilage matrix degradation. Interestingly, the microheterogeneity of N-glycans on fibronectin and aggrecan core protein was observed in OA cartilage. Our results combined with GEO data indicated that the pro-inflammatory cytokines altered the expression of glycosyltransferases (ALG3, ALG5, MGAT4C, and MGAT5) which may contribute to the alterations in glycosylation. CONCLUSION Our study revealed the abnormal glycopatterns and heterogeneities of site-specific glycosylation associated with OA. To our knowledge, it is the first time that the heterogeneity of site-specific N-glycans was reported in OA cartilage. The results of gene expression analysis suggested that the expression of glycosyltransferases was impacted by pro-inflammatory cytokines, which may facilitate the degradation of protein and accelerate the process of OA. Our findings provide valuable information for the understanding of molecular mechanisms in the pathogenesis of OA.
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Affiliation(s)
- Hanjie Yu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Mingxiu Li
- Department of Foot and Ankle Surgery, Honghui Hospital, Xi'an Jiaotong University, 76 Nanguo Road, Xi'an, 710054, Shaanxi Province, China
| | - Jian Shu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Liuyi Dang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Xin Wu
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Yuzi Wang
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Xuan Wang
- The Second Clinical Medical College of Shaanxi University of Chinese Medicine, Xianyang, China
| | - Xin Chang
- Department of Foot and Ankle Surgery, Honghui Hospital, Xi'an Jiaotong University, 76 Nanguo Road, Xi'an, 710054, Shaanxi Province, China
| | - Xiaojuan Bao
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Bojing Zhu
- College of Life Science, Northwest University, Xi'an, China
| | - Xiameng Ren
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Wentian Chen
- Laboratory for Functional Glycomics, College of Life Sciences, Northwest University, Xi'an, China
| | - Yi Li
- Department of Foot and Ankle Surgery, Honghui Hospital, Xi'an Jiaotong University, 76 Nanguo Road, Xi'an, 710054, Shaanxi Province, China.
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26
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Campos D, Girgis M, Yang Q, Zong G, Goldman R, Wang LX, Sanda M. "Ghost" fragment ions in structure and site-specific glycoproteomics analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.05.17.541150. [PMID: 37292769 PMCID: PMC10245710 DOI: 10.1101/2023.05.17.541150] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Mass spectrometry (MS) can unlock crucial insights into the intricate world of glycosylation analysis. Despite its immense potential, the qualitative and quantitative analysis of isobaric glycopeptide structures remains one of the most daunting hurdles in the field of glycoproteomics. The ability to distinguish between these complex glycan structures poses a significant challenge, hindering our ability to accurately measure and understand the role of glycoproteins in biological systems. A few recent publications described the use of collision energy (CE) modulation to improve structural elucidation, especially for qualitative purposes. Different linkages of glycan units usually demonstrate different stabilities under CID/HCD fragmentation conditions. Fragmentation of the glycan moiety produces low molecular weight ions (oxonium ions) that can serve as a structure-specific signature for specific glycan moieties, however, specificity of these fragments has never been examined closely. Here, we investigated fragmentation specificity using synthetic stable isotope-labelled glycopeptide standards. These standards were isotopically labelled at the reducing terminal GlcNAc, which allowed us to resolve fragments produced by oligomannose core moiety and fragments generated from outer antennary structures. Our research identified the potential for false positive structure assignments due to the occurrence of "Ghost" fragments resulting from single glyco unit rearrangement or mannose core fragmentation within the collision cell. To mitigate this issue, we have established a minimal intensity threshold for these fragments to prevent the misidentification of structure-specific fragments in glycoproteomics analysis. Our findings provide a crucial step forward in the quest for more accurate and reliable glycoproteomics measurements. Graphical abstract
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27
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Čaval T, Alisson-Silva F, Schwarz F. Roles of glycosylation at the cancer cell surface: opportunities for large scale glycoproteomics. Theranostics 2023; 13:2605-2615. [PMID: 37215580 PMCID: PMC10196828 DOI: 10.7150/thno.81760] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 04/13/2023] [Indexed: 05/24/2023] Open
Abstract
Cell surface glycosylation has a variety of functions, and its dysregulation in cancer contributes to impaired signaling, metastasis and the evasion of the immune responses. Recently, a number of glycosyltransferases that lead to altered glycosylation have been linked to reduced anti-tumor immune responses: B3GNT3, which is implicated in PD-L1 glycosylation in triple negative breast cancer, FUT8, through fucosylation of B7H3, and B3GNT2, which confers cancer resistance to T cell cytotoxicity. Given the increased appreciation of the relevance of protein glycosylation, there is a critical need for the development of methods that allow for an unbiased interrogation of cell surface glycosylation status. Here we provide an overview of the broad changes in glycosylation at the surface of cancer cell and describe selected examples of receptors with aberrant glycosylation leading to functional changes, with emphasis on immune checkpoint inhibitors, growth-promoting and growth-arresting receptors. Finally, we posit that the field of glycoproteomics has matured to an extent where large-scale profiling of intact glycopeptides from the cell surface is feasible and is poised for discovery of new actionable targets against cancer.
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28
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Wu D, Guo M, Robinson CV. Connecting single-nucleotide polymorphisms, glycosylation status, and interactions of plasma serine protease inhibitors. Chem 2023; 9:665-681. [PMID: 38455847 PMCID: PMC10914678 DOI: 10.1016/j.chempr.2022.11.018] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2022] [Revised: 10/06/2022] [Accepted: 11/22/2022] [Indexed: 12/23/2022]
Abstract
Understanding the combined impacts of genetic variances and post-translational modifications requires new approaches. Here, we delineate proteoforms of plasma serine protease inhibitors and relate specific proteoforms to their interactions in complexes through the use of native mass spectrometry (MS). First, we dissect the proteoform repertoire of an acute-phase plasma protein, serine protease inhibitor A1 (SERPINA1), resolving four SERPINA1 variants (M1V, M1A, M2, and M3) with common single-nucleotide polymorphisms (SNPs). Investigating the glycosylation status of these variants and their ability to form complexes with a serine protease, elastase, we find that fucosylation stabilizes the interaction of the SERPINA1 M1V variant through its core fucosylation on Asn271. In contrast, antennary fucosylation on Asn271 destabilizes SERPINA1-elastase interactions. We unveil the same opposing effects of core and antennary fucosylation on SERPINA3 interactions with chymotrypsin. Together, our native MS results highlight the modulating effects of fucosylation with different linkages on glycoprotein interactions.
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Affiliation(s)
- Di Wu
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
| | - Manman Guo
- Botnar Research Centre, NIHR Biomedical Research Unit Oxford, Nuffield Department of Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Carol V. Robinson
- Department of Chemistry, University of Oxford, Oxford OX1 3QZ, UK
- Kavli Institute for Nanoscience Discovery, University of Oxford, Oxford OX1 3QU, UK
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29
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Sun F, Suttapitugsakul S, Wu R. Systematic characterization of extracellular glycoproteins using mass spectrometry. MASS SPECTROMETRY REVIEWS 2023; 42:519-545. [PMID: 34047389 PMCID: PMC8627532 DOI: 10.1002/mas.21708] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 05/10/2021] [Accepted: 05/13/2021] [Indexed: 05/13/2023]
Abstract
Surface and secreted glycoproteins are essential to cells and regulate many extracellular events. Because of the diversity of glycans, the low abundance of many glycoproteins, and the complexity of biological samples, a system-wide investigation of extracellular glycoproteins is a daunting task. With the development of modern mass spectrometry (MS)-based proteomics, comprehensive analysis of different protein modifications including glycosylation has advanced dramatically. This review focuses on the investigation of extracellular glycoproteins using MS-based proteomics. We first discuss the methods for selectively enriching surface glycoproteins and investigating protein interactions on the cell surface, followed by the application of MS-based proteomics for surface glycoprotein dynamics analysis and biomarker discovery. We then summarize the methods to comprehensively study secreted glycoproteins by integrating various enrichment approaches with MS-based proteomics and their applications for global analysis of secreted glycoproteins in different biological samples. Collectively, MS significantly expands our knowledge of extracellular glycoproteins and enables us to identify extracellular glycoproteins as potential biomarkers for disease detection and drug targets for disease treatment.
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Affiliation(s)
| | | | - Ronghu Wu
- School of Chemistry and Biochemistry and the Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, USA
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30
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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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31
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Yang Y, Qiao L. Profiling Serum Intact N-Glycopeptides Using Data-Independent Acquisition Mass Spectrometry. Methods Mol Biol 2023; 2628:365-391. [PMID: 36781798 DOI: 10.1007/978-1-0716-2978-9_24] [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: 02/15/2023]
Abstract
Large-scale profiling of intact glycopeptides is critical but challenging in glycoproteomics. Data-independent acquisition (DIA) mass spectrometry is an emerging technology with deep proteome coverage as well as accurate quantitative capability for large-scale proteomics studies and has also been applied to the field of glycoproteomics. In this protocol, we describe how to analyze data from a DIA experiment for profiling serum intact N-glycopeptides. We present a comprehensive data analysis workflow using GproDIA, including glycopeptide spectral library building, chromatographic feature extraction from the DIA data, and feature scoring with appropriate statistical control of error rates. We anticipate that this method could provide a powerful tool to explore the serum glycoproteome.
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Affiliation(s)
- Yi Yang
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.,ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University, Hangzhou, China
| | - Liang Qiao
- Department of Chemistry and Shanghai Stomatological Hospital, Fudan University, Shanghai, China.
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32
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Polasky DA, Nesvizhskii AI. Recent advances in computational algorithms and software for large-scale glycoproteomics. Curr Opin Chem Biol 2023; 72:102238. [PMID: 36525809 DOI: 10.1016/j.cbpa.2022.102238] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2022] [Revised: 11/12/2022] [Accepted: 11/14/2022] [Indexed: 12/15/2022]
Abstract
Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.
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Affiliation(s)
- Daniel A Polasky
- University of Michigan Department of Pathology, Ann Arbor, MI, USA.
| | - Alexey I Nesvizhskii
- University of Michigan Department of Pathology, Ann Arbor, MI, USA; University of Michigan Department of Computational Medicine and Bioinformatics, Ann Arbor, MI, USA.
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Mukherjee S, Jankevics A, Busch F, Lubeck M, Zou Y, Kruppa G, Heck AJR, Scheltema RA, Reiding KR. Oxonium Ion-Guided Optimization of Ion Mobility-Assisted Glycoproteomics on the timsTOF Pro. Mol Cell Proteomics 2023; 22:100486. [PMID: 36549589 PMCID: PMC9853368 DOI: 10.1016/j.mcpro.2022.100486] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Revised: 11/15/2022] [Accepted: 12/09/2022] [Indexed: 12/23/2022] Open
Abstract
Spatial separation of ions in the gas phase, providing information about their size as collisional cross-sections, can readily be achieved through ion mobility. The timsTOF Pro (Bruker Daltonics) series combines a trapped ion mobility device with a quadrupole, collision cell, and a time-of-flight analyzer to enable the analysis of ions at great speed. Here, we show that the timsTOF Pro is capable of physically separating N-glycopeptides from nonmodified peptides and producing high-quality fragmentation spectra, both beneficial for glycoproteomics analyses of complex samples. The glycan moieties enlarge the size of glycopeptides compared with nonmodified peptides, yielding a clear cluster in the mobilogram that, next to increased dynamic range from the physical separation of glycopeptides and nonmodified peptides, can be used to make an effective selection filter for directing the mass spectrometer to analytes of interest. We designed an approach where we (1) focused on a region of interest in the ion mobilogram and (2) applied stepped collision energies to obtain informative glycopeptide tandem mass spectra on the timsTOF Pro:glyco-polygon-stepped collision energy-parallel accumulation serial fragmentation. This method was applied to selected glycoproteins, human plasma- and neutrophil-derived glycopeptides. We show that the achieved physical separation in the region of interest allows for improved extraction of information from the samples, even at shorter liquid chromatography gradients of 15 min. We validated our approach on human neutrophil and plasma samples of known makeup, in which we captured the anticipated glycan heterogeneity (paucimannose, phosphomannose, high mannose, hybrid and complex glycans) from plasma and neutrophil samples at the expected abundances. As the method is compatible with off-the-shelve data acquisition routines and data analysis software, it can readily be applied by any laboratory with a timsTOF Pro and is reproducible as demonstrated by a comparison between two laboratories.
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Affiliation(s)
- Soumya Mukherjee
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Andris Jankevics
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | | | | | - Yang Zou
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Gary Kruppa
- Bruker Daltonik GmbH & Co KG, Bremen, Germany
| | - Albert J R Heck
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands
| | - Richard A Scheltema
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands.
| | - Karli R Reiding
- Biomolecular Mass Spectrometry and Proteomics, Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, University of Utrecht, Utrecht, The Netherlands; Netherlands Proteomics Center, Utrecht, The Netherlands.
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Wu RQ, Lao XM, Chen DP, Qin H, Mu M, Cao WJ, Deng J, Wan CC, Zhan WY, Wang JC, Xu L, Chen MS, Gao Q, Zheng L, Wei Y, Kuang DM. Immune checkpoint therapy-elicited sialylation of IgG antibodies impairs antitumorigenic type I interferon responses in hepatocellular carcinoma. Immunity 2023; 56:180-192.e11. [PMID: 36563676 DOI: 10.1016/j.immuni.2022.11.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Revised: 08/24/2022] [Accepted: 11/28/2022] [Indexed: 12/24/2022]
Abstract
The reinvigoration of anti-tumor T cells in response to immune checkpoint blockade (ICB) therapy is well established. Whether and how ICB therapy manipulates antibody-mediated immune response in cancer environments, however, remains elusive. Using tandem mass spectrometric analysis of modification of immunoglobulin G (IgG) from hepatoma tissues, we identified a role of ICB therapy in catalyzing IgG sialylation in the Fc region. Effector T cells triggered sialylation of IgG via an interferon (IFN)-γ-ST6Gal-I-dependent pathway. DC-SIGN+ macrophages represented the main target cells of sialylated IgG. Upon interacting with sialylated IgG, DC-SIGN stimulated Raf-1-elicited elevation of ATF3, which inactivated cGAS-STING pathway and eliminated subsequent type-I-IFN-triggered antitumorigenic immunity. Although enhanced IgG sialylation in tumors predicted improved therapeutic outcomes for patients receiving ICB therapy, impeding IgG sialylation augmented antitumorigenic T cell immunity after ICB therapy. Thus, targeting antibody-based negative feedback action of ICB therapy has potential for improving efficacy of cancer immunotherapies.
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Affiliation(s)
- Rui-Qi Wu
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Xiang-Ming Lao
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Dong-Ping Chen
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Hongqiang Qin
- CAS Key Laboratory of Separation Science for Analytical Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian, Liaoning 116023, China
| | - Ming Mu
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Wen-Jie Cao
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Jia Deng
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Chao-Chao Wan
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Wan-Yu Zhan
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Jun-Cheng Wang
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Li Xu
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Min-Shan Chen
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Qiang Gao
- Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai 200032, China
| | - Limin Zheng
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China
| | - Yuan Wei
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China.
| | - Dong-Ming Kuang
- Guangdong Province Key Laboratory of Pharmaceutical Functional Genes, MOE Key Laboratory of Gene Function and Regulation, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, and Sun Yat-sen University Cancer Center, Sun Yat-sen University, Guangzhou, Guangdong 510060, China.
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Xie X, Yang J, Du H, Chen J, Sanganyado E, Gong Y, Du H, Chen W, Liu Z, Liu X. Golgi fucosyltransferase 1 reveals its important role in α-1,4-fucose modification of N-glycan in CRISPR/Cas9 diatom Phaeodactylum tricornutum. Microb Cell Fact 2023; 22:6. [PMID: 36611199 PMCID: PMC9826595 DOI: 10.1186/s12934-022-02000-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Accepted: 12/17/2022] [Indexed: 01/09/2023] Open
Abstract
Phaeodactylum tricornutum (Pt) is a critical microbial cell factory to produce a wide spectrum of marketable products including recombinant biopharmaceutical N-glycoproteins. N-glycosylation modification of proteins is important for their activity, stability, and half-life, especially some special modifications, such as fucose-modification by fucosyltransferase (FucT). Three PtFucTs were annotated in the genome of P. tricornutum, PtFucT1 was located on the medial/trans-Golgi apparatus and PtFucT2-3 in the plastid stroma. Algal growth, biomass and photosynthesis efficiency were significantly inhibited in a knockout mutant of PtFucT1 (PtFucT1-KO). PtFucT1 played a role in non-core fucose modification of N-glycans. The knockout of PtFucT1 might affect the activity of PtGnTI in the complex and change the complex N-glycan to mannose type N-glycan. The study provided critical information for understanding the mechanism of protein N-glycosylation modification and using microalgae as an alternative ecofriendly cell factory to produce biopharmaceuticals.
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Affiliation(s)
- Xihui Xie
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Jianchao Yang
- grid.495347.8Yantai Academy of Agricultural Sciences, Yantai, 265500 Shandong China
| | - Hong Du
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Jichen Chen
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Edmond Sanganyado
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Yangmin Gong
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Hua Du
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Weizhou Chen
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
| | - Zhengyi Liu
- grid.9227.e0000000119573309Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai, 264003 Shandong China
| | - Xiaojuan Liu
- grid.263451.70000 0000 9927 110XGuangdong Provincial Key Laboratory of Marine Biotechnology, Guangdong Provincial Key Laboratory of Marine Disaster Prediction and Prevention, Institute of Marine Sciences, STU-UNIVPM Joint Algal Research Center, College of Sciences, Shantou University, Shantou, 515063 Guangdong China
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36
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Sun Z, Fu B, Wang G, Zhang L, Xu R, Zhang Y, Lu H. High-throughput site-specific N-glycoproteomics reveals glyco-signatures for liver disease diagnosis. Natl Sci Rev 2023; 10:nwac059. [PMID: 36879659 PMCID: PMC9985154 DOI: 10.1093/nsr/nwac059] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2021] [Revised: 03/15/2022] [Accepted: 03/15/2022] [Indexed: 11/13/2022] Open
Abstract
The glycoproteome has emerged as a prominent target for screening biomarkers, as altered glycosylation is a hallmark of cancer cells. In this work, we incorporated tandem mass tag labeling into quantitative glycoproteomics by developing a chemical labeling-assisted complementary dissociation method for the multiplexed analysis of intact N-glycopeptides. Benefiting from the complementary nature of two different mass spectrometry dissociation methods for identification and multiplex labeling for quantification of intact N-glycopeptides, we conducted the most comprehensive site-specific and subclass-specific N-glycosylation profiling of human serum immunoglobulin G (IgG) to date. By analysing the serum of 90 human patients with varying severities of liver diseases, as well as healthy controls, we identified that the combination of IgG1-H3N5F1 and IgG4-H4N3 can be used for distinguishing between different stages of liver diseases. Finally, we used targeted parallel reaction monitoring to successfully validate the expression changes of glycosylation in liver diseases in a different sample cohort that included 45 serum samples.
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Affiliation(s)
- Zhenyu Sun
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Bin Fu
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Guoli Wang
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Lei Zhang
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
| | - Ruofan Xu
- Eleanor Roosevelt College, University of California San Diego, La Jolla, CA92093, USA
| | - Ying Zhang
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
| | - Haojie Lu
- Shanghai Cancer Center and Institutes of Biomedical Sciences, Fudan University, Shanghai 200032, China
- Department of Chemistry and NHC Key Laboratory of Glycoconjugates Research, Fudan University, Shanghai 200032, China
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Budhraja R, Saraswat M, De Graef D, Ranatunga W, Ramarajan MG, Mousa J, Kozicz T, Pandey A, Morava E. N-glycoproteomics reveals distinct glycosylation alterations in NGLY1-deficient patient-derived dermal fibroblasts. J Inherit Metab Dis 2023; 46:76-91. [PMID: 36102038 PMCID: PMC10092224 DOI: 10.1002/jimd.12557] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 09/10/2022] [Accepted: 09/12/2022] [Indexed: 01/19/2023]
Abstract
Congenital disorders of glycosylation are genetic disorders that occur due to defects in protein and lipid glycosylation pathways. A deficiency of N-glycanase 1, encoded by the NGLY1 gene, results in a congenital disorder of deglycosylation. The NGLY1 enzyme is mainly involved in cleaving N-glycans from misfolded, retro-translocated glycoproteins in the cytosol from the endoplasmic reticulum before their proteasomal degradation or activation. Despite the essential role of NGLY1 in deglycosylation pathways, the exact consequences of NGLY1 deficiency on global cellular protein glycosylation have not yet been investigated. We undertook a multiplexed tandem mass tags-labeling-based quantitative glycoproteomics and proteomics analysis of fibroblasts from NGLY1-deficient individuals carrying different biallelic pathogenic variants in NGLY1. This quantitative mass spectrometric analysis detected 8041 proteins and defined a proteomic signature of differential expression across affected individuals and controls. Proteins that showed significant differential expression included phospholipid phosphatase 3, stromal cell-derived factor 1, collagen alpha-1 (IV) chain, hyaluronan and proteoglycan link protein 1, and thrombospondin-1. We further detected a total of 3255 N-glycopeptides derived from 550 glycosylation sites of 407 glycoproteins by multiplexed N-glycoproteomics. Several extracellular matrix glycoproteins and adhesion molecules showed altered abundance of N-glycopeptides. Overall, we observed distinct alterations in specific glycoproteins, but our data revealed no global accumulation of glycopeptides in the patient-derived fibroblasts, despite the genetic defect in NGLY1. Our findings highlight new molecular and system-level insights for understanding NGLY1-CDDG.
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Affiliation(s)
- Rohit Budhraja
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Mayank Saraswat
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Diederik De Graef
- Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Wasantha Ranatunga
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
| | - Madan G Ramarajan
- Institute of Bioinformatics, International Technology Park, Bangalore, Karnataka, India
- Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
| | - Jehan Mousa
- Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Tamas Kozicz
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
| | - Akhilesh Pandey
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India
- Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Eva Morava
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota, USA
- Department of Clinical Genomics, Mayo Clinic, Rochester, Minnesota, USA
- Department of Medical Genetics and Department of Biophysics, University of Pecs Medical School, Pecs, Hungary
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Kong S, Gong P, Zeng WF, Jiang B, Hou X, Zhang Y, Zhao H, Liu M, Yan G, Zhou X, Qiao X, Wu M, Yang P, Liu C, Cao W. pGlycoQuant with a deep residual network for quantitative glycoproteomics at intact glycopeptide level. Nat Commun 2022; 13:7539. [PMID: 36477196 PMCID: PMC9729625 DOI: 10.1038/s41467-022-35172-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Accepted: 11/17/2022] [Indexed: 12/12/2022] Open
Abstract
Large-scale intact glycopeptide identification has been advanced by software tools. However, tools for quantitative analysis remain lagging behind, which hinders exploring the differential site-specific glycosylation. Here, we report pGlycoQuant, a generic tool for both primary and tandem mass spectrometry-based intact glycopeptide quantitation. pGlycoQuant advances in glycopeptide matching through applying a deep learning model that reduces missing values by 19-89% compared with Byologic, MSFragger-Glyco, Skyline, and Proteome Discoverer, as well as a Match In Run algorithm for more glycopeptide coverage, greatly expanding the quantitative function of several widely used search engines, including pGlyco 2.0, pGlyco3, Byonic and MSFragger-Glyco. Further application of pGlycoQuant to the N-glycoproteomic study in three different metastatic HCC cell lines quantifies 6435 intact N-glycopeptides and, together with in vitro molecular biology experiments, illustrates site 979-core fucosylation of L1CAM as a potential regulator of HCC metastasis. We expected further applications of the freely available pGlycoQuant in glycoproteomic studies.
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Affiliation(s)
- Siyuan Kong
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Pengyun Gong
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Wen-Feng Zeng
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China
- Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Biyun Jiang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xinhang Hou
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Yang Zhang
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Huanhuan Zhao
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Mingqi Liu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Guoquan Yan
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xinwen Zhou
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Xihua Qiao
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China
| | - Mengxi Wu
- Shanghai Fifth People's Hospital and Institutes of Biomedical Sciences, Fudan University, Shanghai, China
| | - Pengyuan 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
| | - Chao Liu
- School of Engineering Medicine & School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
- Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS), Institute of Computing Technology, CAS, Beijing, China.
| | - Weiqian 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.
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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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40
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Chang D, Zaia J. Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data. MASS SPECTROMETRY REVIEWS 2022; 41:922-937. [PMID: 33764573 DOI: 10.1002/mas.21692] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/10/2020] [Revised: 12/24/2020] [Accepted: 03/11/2021] [Indexed: 05/18/2023]
Abstract
Advances in mass spectrometry instrumentation, methods development, and bioinformatics have greatly improved the ease and accuracy of site-specific, quantitative glycoproteomics analysis. Data-dependent acquisition is the most popular method for identification and quantification of glycopeptides; however, complete coverage of glycosylation site glycoforms remains elusive with this method. Targeted acquisition methods improve the precision and accuracy of quantification, but at the cost of throughput and discoverability. Data-independent acquisition (DIA) holds great promise for more complete and highly quantitative site-specific glycoproteomics analysis, while maintaining the ability to discover novel glycopeptides without prior knowledge. We review additional features that can be used to increase selectivity and coverage to the DIA workflow: retention time modeling, which would simplify the interpretation of complex tandem mass spectra, and ion mobility separation, which would maximize the sampling of all precursors at a giving chromatographic retention time. The instrumentation and bioinformatics to incorporate these features into glycoproteomics analysis exist. These improvements in quantitative, site-specific analysis will enable researchers to assess glycosylation similarity in related biological systems, answering new questions about the interplay between glycosylation state and biological function.
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Affiliation(s)
- Deborah Chang
- Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts, USA
| | - Joseph Zaia
- Department of Biochemistry, Boston University School of Medicine, Boston, Massachusetts, USA
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41
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Luo M, Mao Y, Zeng W, Zheng S, Li H, Hu J, Xie X, Zhang Y. Site-specific N-glycosylation characterization of micro monoclonal immunoglobulins based on EThcD-sceHCD-MS/MS. Front Immunol 2022; 13:1013990. [PMID: 36189210 PMCID: PMC9520751 DOI: 10.3389/fimmu.2022.1013990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 09/02/2022] [Indexed: 11/28/2022] Open
Abstract
Monoclonal immunoglobulin produced by clonal plasma cells is the main cause in multiple myeloma and monoclonal gammopathy of renal significance. Because of the complicated purification method and the low stoichiometry of purified protein and glycans, site-specific N-glycosylation characterization for monoclonal immunoglobulin is still challenging. To profile the site-specific N-glycosylation of monoclonal immunoglobulins is of great interest. Therefore, in this study, we presented an integrated workflow for micro monoclonal IgA and IgG purification from patients with multiple myeloma in the HYDRASYS system, in-agarose-gel digestion, LC-MS/MS analysis without intact N-glycopeptide enrichment, and compared the identification performance of different mass spectrometry dissociation methods (EThcD-sceHCD, sceHCD, EThcD and sceHCD-pd-ETD). The results showed that EThcD-sceHCD was a better choice for site-specific N-glycosylation characterization of micro in-agarose-gel immunoglobulins (~2 μg) because it can cover more unique intact N-glycopeptides (37 and 50 intact N-glycopeptides from IgA1 and IgG2, respectively) and provide more high-quality spectra than sceHCD, EThcD and sceHCD-pd-ETD. We demonstrated the benefits of the alternative strategy in site-specific N-glycosylation characterizing micro monoclonal immunoglobulins obtained from bands separated by electrophoresis. This work could promote the development of clinical N-glycoproteomics and related immunology.
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Affiliation(s)
- Mengqi Luo
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Yonghong Mao
- Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Wenjuan Zeng
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Shanshan Zheng
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Huixian Li
- Department of Nephrology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
| | - Juanjuan Hu
- Department of Laboratory Medicine, Institute of Clinical Laboratory Medicine of People’s Liberation Army (PLA), Xijing Hospital, Fourth Military Medical University, Xi’an, China
| | - Xinfang Xie
- Department of Nephrology, The First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, China
- *Correspondence: Yong Zhang, ; Xinfang Xie,
| | - Yong Zhang
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yong Zhang, ; Xinfang Xie,
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42
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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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43
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Ren W, Bian Q, Cai Y. Mass spectrometry-based N-glycosylation analysis in kidney disease. Front Mol Biosci 2022; 9:976298. [PMID: 36072428 PMCID: PMC9442644 DOI: 10.3389/fmolb.2022.976298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2022] [Accepted: 07/18/2022] [Indexed: 11/14/2022] Open
Abstract
Kidney disease is a global health concern with an enormous expense. It is estimated that more than 10% of the population worldwide is affected by kidney disease and millions of patients would progress to death prematurely and unnecessarily. Although creatinine detection and renal biopsy are well-established tools for kidney disease diagnosis, they are limited by several inevitable defects. Therefore, diagnostic tools need to be upgraded, especially for the early stage of the disease and possible progression. As one of the most common post-translational modifications of proteins, N-glycosylation plays a vital role in renal structure and function. Deepening research on N-glycosylation in kidney disease provides new insights into the pathophysiology and paves the way for clinical application. In this study, we reviewed recent N-glycosylation studies on several kidney diseases. We also summarized the development of mass spectrometric methods in the field of N-glycoproteomics and N-glycomics.
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Affiliation(s)
- Weifu Ren
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- Department of Nephrology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Qi Bian
- Department of Nephrology, Changhai Hospital, Naval Medical University, Shanghai, China
| | - Yan Cai
- Shanghai Institute of Precision Medicine, Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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44
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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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45
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Mao Y, Su T, Lin T, Yang H, Zhao Y, Zhang Y, Dai X. Comprehensive Plasma N-Glycoproteome Profiling Based on EThcD-sceHCD-MS/MS. Front Chem 2022; 10:920009. [PMID: 35795219 PMCID: PMC9251008 DOI: 10.3389/fchem.2022.920009] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 05/09/2022] [Indexed: 01/05/2023] Open
Abstract
Glycoproteins are involved in a variety of biological processes. More than one-third of the plasma protein biomarkers of tumors approved by the FDA are glycoproteins, and could improve the diagnostic specificity and/or sensitivity. Therefore, it is of great significance to perform the systematic characterization of plasma N-glycoproteome. In previous studies, we developed an integrated method based on the combinatorial peptide ligand library (CPLL) and stepped collision energy/higher energy collisional dissociation (sceHCD) for comprehensive plasma N-glycoproteome profiling. Recently, we presented a new fragmentation method, EThcD-sceHCD, which outperformed sceHCD in the accuracy of identification. Herein, we integrated the combinatorial peptide ligand library (CPLL) into EThcD-sceHCD and compared the performance of different mass spectrometry dissociation methods (EThcD-sceHCD, EThcD, and sceHCD) in the intact N-glycopeptide analysis of prostate cancer plasma. The results illustrated that EThcD-sceHCD was better than EThcD and sceHCD in the number of identified intact N-glycopeptides (two-folds). A combination of sceHCD and EThcD-sceHCD methods can cover almost all glycoproteins (96.4%) and intact N-glycopeptides (93.6%), indicating good complementarity between the two. Our study has great potential for medium- and low-abundance plasma glycoprotein biomarker discovery.
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Affiliation(s)
- Yonghong Mao
- Institute of Thoracic Oncology, West China Hospital, Sichuan University, Chengdu, China
| | - Tao Su
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Tianhai Lin
- Department of Urology, Institute of Urology, West China Hospital, Sichuan University, Chengdu, China
| | - Hao Yang
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
| | - Yang Zhao
- Mass Spectrometry Engineering Technology Research Center, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
- *Correspondence: Yang Zhao, ; Yong Zhang, ; Xinhua Dai,
| | - Yong Zhang
- Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China
- *Correspondence: Yang Zhao, ; Yong Zhang, ; Xinhua Dai,
| | - Xinhua Dai
- Mass Spectrometry Engineering Technology Research Center, Center for Advanced Measurement Science, National Institute of Metrology, Beijing, China
- *Correspondence: Yang Zhao, ; Yong Zhang, ; Xinhua Dai,
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46
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Wang Y, Yang C, Hu H, Chen C, Yan M, Ling F, Wang KC, Wang X, Deng Z, Zhou X, Zhang F, Lin S, Du Z, Zhao K, Xiao X. Directed evolution of adeno-associated virus 5 capsid enables specific liver tropism. MOLECULAR THERAPY. NUCLEIC ACIDS 2022; 28:293-306. [PMID: 35474733 PMCID: PMC9010518 DOI: 10.1016/j.omtn.2022.03.017] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2021] [Accepted: 03/18/2022] [Indexed: 02/07/2023]
Abstract
Impressive achievements in clinical trials to treat hemophilia establish a milestone in the development of gene therapy. It highlights the significance of AAV-mediated gene delivery to liver. AAV5 is a unique serotype featured by low neutralizing antibody prevalence. Nevertheless, its liver infectivity is relatively weak. Consequently, it is vital to exploit novel AAV5 capsid mutants with robust liver tropism. To this aim, we performed AAV5-NNK library and barcode screening in mice, from which we identified one capsid variant, called AAVzk2. AAVzk2 displayed a similar yield but divergent post-translational modification sites compared with wild-type serotypes. Mice intravenously injected with AAVzk2 demonstrated a stronger liver transduction than AAV5, roughly comparable with AAV8 and AAV9, with undetectable transduction of other tissues or organs such as heart, lung, spleen, kidney, brain, and skeletal muscle, indicating a liver-specific tropism. Further studies showed a superior human hepatocellular transduction of AAVzk2 to AAV5, AAV8 and AAV9, whereas the seroreactivity of AAVzk2 was as low as AAV5. Overall, we provide a novel AAV serotype that facilitates a robust and specific liver gene delivery to a large population, especially those unable to be treated by AAV8 and AAV9.
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Affiliation(s)
- Yuqiu Wang
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Chen Yang
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Hanyang Hu
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Chen Chen
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Mengdi Yan
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Feixiang Ling
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Kathy Cheng Wang
- Department of Biology, New York University, 24 Waverly Pl, New York, NY 10003, USA
| | - Xintao Wang
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Zhe Deng
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Xinyue Zhou
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Feixu Zhang
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Sen Lin
- Department of Ophthalmology, Daping Hospital, Army Medical Center of PLA, Army Medical University, Chongqing 400042, China
| | - Zengmin Du
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
| | - Kai Zhao
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- Corresponding author Kai Zhao, School of Bioengineering and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
| | - Xiao Xiao
- School of Bioengineering, East China University of Science and Technology, Shanghai 200237, China
- School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
- Corresponding author Xiao Xiao, School of Bioengineering and School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China.
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47
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Fan X, Song Q, Sun DE, Hao Y, Wang J, Wang C, Chen X. Cell-type-specific labeling and profiling of glycans in living mice. Nat Chem Biol 2022; 18:625-633. [PMID: 35513511 DOI: 10.1038/s41589-022-01016-4] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2021] [Accepted: 03/15/2022] [Indexed: 11/09/2022]
Abstract
Metabolic labeling of glycans with clickable unnatural sugars has enabled glycan analysis in multicellular systems. However, cell-type-specific labeling of glycans in vivo remains challenging. Here we develop genetically encoded metabolic glycan labeling (GeMGL), a cell-type-specific strategy based on a bump-and-hole pair of an unnatural sugar and its matching engineered enzyme. N-pentynylacetylglucosamine (GlcNAl) serves as a bumped analog of N-acetylglucosamine (GlcNAc) that is specifically incorporated into glycans of cells expressing a UDP-GlcNAc pyrophosphorylase mutant, AGX2F383G. GeMGL with the 1,3-di-O-propionylated GlcNAl (1,3-Pr2GlcNAl) and AGX2F383G pair was demonstrated in cell cocultures, and used for specific labeling of glycans in mouse xenograft tumors. By generating a transgenic mouse line with AGX2F383G expressed under a cardiomyocyte-specific promoter, we performed specific imaging of cardiomyocyte glycans in the heart and identified 582 cardiomyocyte O-GlcNAcylated proteins with no interference from other cardiac cell types. GeMGL will facilitate cell-type-specific glycan imaging and glycoproteomics in various tissues and disease models.
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Affiliation(s)
- Xinqi Fan
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Qitao Song
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China
| | - De-En Sun
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Yi Hao
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Jingyang Wang
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Chunting Wang
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China.,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China
| | - Xing Chen
- College of Chemistry and Molecular Engineering, Peking University, Beijing, China. .,Beijing National Laboratory for Molecular Sciences, Peking University, Beijing, China. .,Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, China. .,Synthetic and Functional Biomolecules Center, Peking University, Beijing, China. .,Key Laboratory of Bioorganic Chemistry and Molecular Engineering of Ministry of Education, Peking University, Beijing, China.
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48
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Tabang DN, Wang D, Li L. A Spin-Tip Enrichment Strategy for Simultaneous Analysis of N-Glycopeptides and Phosphopeptides from Human Pancreatic Tissues. J Vis Exp 2022:10.3791/63735. [PMID: 35604151 PMCID: PMC9186302 DOI: 10.3791/63735] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/26/2024] Open
Abstract
Mass spectrometry can provide deep coverage of post-translational modifications (PTMs), although enrichment of these modifications from complex biological matrices is often necessary due to their low stoichiometry in comparison to non-modified analytes. Most enrichment workflows of PTMs on peptides in bottom-up proteomics workflows, where proteins are enzymatically digested before the resulting peptides are analyzed, only enrich one type of modification. It is the entire complement of PTMs, however, that leads to biological functions, and enrichment of a single type of PTM may miss such crosstalk of PTMs. PTM crosstalk has been observed between protein glycosylation and phosphorylation, the two most common PTMs in human proteins and also the two most studied PTMs using mass spectrometry workflows. Using the simultaneous enrichment strategy described herein, both PTMs are enriched from post-mortem human pancreatic tissue, a complex biological matrix. Dual-functional Ti(IV)-immobilized metal affinity chromatography is used to separate various forms of glycosylation and phosphorylation simultaneously in multiple fractions in a convenient spin tip-based method, allowing downstream analyses of potential PTM crosstalk interactions. This enrichment workflow for glyco- and phosphopeptides can be applied to various sample types to achieve deep profiling of multiple PTMs and identify potential target molecules for future studies.
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Affiliation(s)
| | - Danqing Wang
- Department of Chemistry, University of Wisconsin-Madison
| | - Lingjun Li
- Department of Chemistry, University of Wisconsin-Madison; School of Pharmacy, University of Wisconsin-Madison;
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49
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Pujić I, Perreault H. Recent advancements in glycoproteomic studies: Glycopeptide enrichment and derivatization, characterization of glycosylation in SARS CoV2, and interacting glycoproteins. MASS SPECTROMETRY REVIEWS 2022; 41:488-507. [PMID: 33393161 DOI: 10.1002/mas.21679] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 06/12/2023]
Abstract
Proteomics studies allow for the determination of the identity, amount, and interactions of proteins under specific conditions that allow the biological state of an organism to ultimately change. These conditions can be either beneficial or detrimental. Diseases are due to detrimental changes caused by either protein overexpression or underexpression caused by as a result of a mutation or posttranslational modifications (PTM), among other factors. Identification of disease biomarkers through proteomics can be potentially used as clinical information for diagnostics. Common biomarkers to look for include PTM. For example, aberrant glycosylation of proteins is a common marker and will be a focus of interest in this review. A common way to analyze glycoproteins is by glycoproteomics involving mass spectrometry. Due to factors such as micro- and macroheterogeneity which result in a lower abundance of each version of a glycoprotein, it is difficult to obtain meaningful results unless rigorous sample preparation procedures are in place. Microheterogeneity represents the diversity of glycans at a single site, whereas macroheterogeneity depicts glycosylation levels at each site of a protein. Enrichment and derivatization of glycopeptides help to overcome these limitations. Over the time range of 2016 to 2020, several methods have been proposed in the literature and have contributed to drastically improve the outcome of glycosylation analysis, as presented in the sampling surveyed in this review. As a current topic in 2020, glycoproteins carried by pathogens can also cause disease and this is seen with SARS CoV2, causing the COVID-19 pandemic. This review will discuss glycoproteomic studies of the spike glycoprotein and interacting proteins such as the ACE2 receptor.
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Affiliation(s)
- Ivona Pujić
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Hélène Perreault
- Chemistry Department, University of Manitoba, Winnipeg, Manitoba, Canada
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50
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Escobar EE, Wang S, Goswami R, Lanzillotti MB, Li L, McLellan JS, Brodbelt JS. Analysis of Viral Spike Protein N-Glycosylation Using Ultraviolet Photodissociation Mass Spectrometry. Anal Chem 2022; 94:5776-5784. [PMID: 35388686 PMCID: PMC9272412 DOI: 10.1021/acs.analchem.1c04874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Characterization of protein glycosylation by tandem mass spectrometry remains challenging owing to the vast diversity of oligosaccharides bound to proteins, the variation in monosaccharide linkage patterns, and the lability of the linkage between the glycan and protein. Here, we have adapted an HCD-triggered-ultraviolet photodissociation (UVPD) approach for the simultaneous localization of glycosites and full characterization of both glycan compositions and intersaccharide linkages, the latter provided by extensive cross-ring cleavages enabled by UVPD. The method is applied to study glycan compositions based on analysis of glycopeptides from proteolytic digestion of recombinant human coronaviruse spike proteins from SARS-CoV-2 and HKU1. UVPD reveals unique intersaccharide linkage information and is leveraged to localize N-linked glycoforms with confidence.
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Affiliation(s)
- Edwin E Escobar
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Shuaishuai Wang
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | | | - Michael B Lanzillotti
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Lei Li
- Department of Chemistry, Georgia State University, Atlanta, Georgia 30303, United States
| | - Jason S McLellan
- Department of Molecular Biosciences, The University of Texas at Austin, Austin, Texas 78712, United States
| | - Jennifer S Brodbelt
- Department of Chemistry, The University of Texas at Austin, Austin, Texas 78712, United States
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