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Xu Y, Wang Y, Höti N, Clark DJ, Chen SY, Zhang H. The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection. Mass Spectrom Rev 2023; 42:822-843. [PMID: 34766650 PMCID: PMC9095761 DOI: 10.1002/mas.21748] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Revised: 10/07/2021] [Accepted: 10/24/2021] [Indexed: 05/02/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.
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Affiliation(s)
- Yuanwei Xu
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Yuefan Wang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Naseruddin Höti
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - David J Clark
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Shao-Yung Chen
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
| | - Hui Zhang
- Department of Pathology, School of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
- Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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2
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Abstract
Artificial intelligence (AI) methods have been and are now being increasingly integrated in prediction software implemented in bioinformatics and its glycoscience branch known as glycoinformatics. AI techniques have evolved in the past decades, and their applications in glycoscience are not yet widespread. This limited use is partly explained by the peculiarities of glyco-data that are notoriously hard to produce and analyze. Nonetheless, as time goes, the accumulation of glycomics, glycoproteomics, and glycan-binding data has reached a point where even the most recent deep learning methods can provide predictors with good performance. We discuss the historical development of the application of various AI methods in the broader field of glycoinformatics. A particular focus is placed on shining a light on challenges in glyco-data handling, contextualized by lessons learnt from related disciplines. Ending on the discussion of state-of-the-art deep learning approaches in glycoinformatics, we also envision the future of glycoinformatics, including development that need to occur in order to truly unleash the capabilities of glycoscience in the systems biology era.
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Affiliation(s)
- Daniel Bojar
- Department
of Chemistry and Molecular Biology, University
of Gothenburg, Gothenburg 41390, Sweden
- Wallenberg
Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg 41390, Sweden
| | - 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
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3
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Abstract
Mucin-domain glycoproteins comprise a class of proteins whose densely O-glycosylated mucin domains adopt a secondary structure with unique biophysical and biochemical properties. The canonical family of mucins is well-known to be involved in various diseases, especially cancer. Despite this, very little is known about the site-specific molecular structures and biological activities of mucins, in part because they are extremely challenging to study by mass spectrometry (MS). Here, we summarize recent advancements toward this goal, with a particular focus on mucin-domain glycoproteins as opposed to general O-glycoproteins. We summarize proteolytic digestion techniques, enrichment strategies, MS fragmentation, and intact analysis, as well as new bioinformatic platforms. In particular, we highlight mucin directed technologies such as mucin-selective proteases, tunable mucin platforms, and a mucinomics strategy to enrich mucin-domain glycoproteins from complex samples. Finally, we provide examples of targeted mucin-domain glycoproteomics that combine these techniques in comprehensive site-specific analyses of proteins. Overall, this Review summarizes the methods, challenges, and new opportunities associated with studying enigmatic mucin domains.
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Affiliation(s)
- Valentina Rangel-Angarita
- Department of Chemistry, Yale University, 275 Prospect Street, New Haven, Connecticut 06511, United States
| | - Stacy A. Malaker
- Department of Chemistry, Yale University, 275 Prospect Street, New Haven, Connecticut 06511, United States
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Oliveira T, Thaysen-Andersen M, Packer NH, Kolarich D. The Hitchhiker's guide to glycoproteomics. Biochem Soc Trans 2021; 49:1643-1662. [PMID: 34282822 PMCID: PMC8421054 DOI: 10.1042/bst20200879] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 06/03/2021] [Accepted: 06/23/2021] [Indexed: 02/06/2023]
Abstract
Protein glycosylation is one of the most common post-translational modifications that are essential for cell function across all domains of life. Changes in glycosylation are considered a hallmark of many diseases, thus making glycoproteins important diagnostic and prognostic biomarker candidates and therapeutic targets. Glycoproteomics, the study of glycans and their carrier proteins in a system-wide context, is becoming a powerful tool in glycobiology that enables the functional analysis of protein glycosylation. This 'Hitchhiker's guide to glycoproteomics' is intended as a starting point for anyone who wants to explore the emerging world of glycoproteomics. The review moves from the techniques that have been developed for the characterisation of single glycoproteins to technologies that may be used for a successful complex glycoproteome characterisation. Examples of the variety of approaches, methodologies, and technologies currently used in the field are given. This review introduces the common strategies to capture glycoprotein-specific and system-wide glycoproteome data from tissues, body fluids, or cells, and a perspective on how integration into a multi-omics workflow enables a deep identification and characterisation of glycoproteins - a class of biomolecules essential in regulating cell function.
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Affiliation(s)
- Tiago Oliveira
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
| | | | - Nicolle H. Packer
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
- Department of Molecular Sciences, Macquarie University, Sydney, New South Wales, Australia
- ARC Centre of Excellence for Nanoscale BioPhotonics, Griffith University, QLD and Macquarie University, NSW, Australia
| | - Daniel Kolarich
- Institute for Glycomics, Griffith University, Gold Coast Campus, Gold Coast, Queensland, Australia
- ARC Centre of Excellence for Nanoscale BioPhotonics, Griffith University, QLD and Macquarie University, NSW, Australia
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5
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Abstract
N-linked glycosylation is a ubiquitous protein modification that is capable of modulating protein structure, function and interactions. Many proteins in the brain associated with the synapse and important for synaptic transmission are highly glycosylated and their glycosylation could be important for learning and memory related molecular processes and synaptic plasticity. In the present study, we extend the knowledge of the synaptic glycome and glycoproteome by performing glycan- and intact glycopeptide-focused analyses of isolated rat nerve terminals (synaptosomes) by LC-MS/MS. Overall, glycomics identified a total of 41 N-glycans in isolated synaptosomes. Sialylated N-glycans represented only 7% of the total abundance of the rat synaptosome N-glycome with oligomannose, neutral hybrid and complex type N-glycans being the most abundant structures. Using detergent extraction of the active zone proteins from the synaptosomes revealed a change in the active zone glycan abundance in comparison with the rest of the synaptosome glycan content. Characterization of intact sialylated N-linked glycopeptides enriched by titanium dioxide chromatography revealed more than 85% selectivity of sialylated species and the presence of NeuGc on active zone proteins. In addition, both disialic and trisialic acid modified glycans were present on synaptic glycoproteins, although oxonium ion profiling revealed that trisialic units were only present on glycoproteins in the detergent soluble fraction. However, correct identification of intact sialylated N-linked glycopeptides using the Byonic program failed, most likely due to the lack of peptide backbone fragmentation during tandem mass spectrometry.
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Affiliation(s)
- Inga Matthies
- Department of Biochemistry and Molecular Biology, University of Southern Denmark, Denmark
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Gaunitz S, Tjernberg LO, Schedin-Weiss S. What Can N-glycomics and N-glycoproteomics of Cerebrospinal Fluid Tell Us about Alzheimer Disease? Biomolecules 2021; 11:858. [PMID: 34207636 PMCID: PMC8226827 DOI: 10.3390/biom11060858] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2021] [Revised: 05/28/2021] [Accepted: 06/04/2021] [Indexed: 12/18/2022] Open
Abstract
Proteomics-large-scale studies of proteins-has over the last decade gained an enormous interest for studies aimed at revealing proteins and pathways involved in disease. To fully understand biological and pathological processes it is crucial to also include post-translational modifications in the "omics". To this end, glycomics (identification and quantification of glycans enzymatically or chemically released from proteins) and glycoproteomics (identification and quantification of peptides/proteins with the glycans still attached) is gaining interest. The study of protein glycosylation requires a workflow that involves an array of sample preparation and analysis steps that needs to be carefully considered. Herein, we briefly touch upon important steps such as sample preparation and preconcentration, glycan release, glycan derivatization and quantification and advances in mass spectrometry that today are the work-horse for glycomics and glycoproteomics studies. Several proteins related to Alzheimer disease pathogenesis have altered protein glycosylation, and recent glycomics studies have shown differences in cerebrospinal fluid as well as in brain tissue in Alzheimer disease as compared to controls. In this review, we discuss these techniques and how they have been used to shed light on Alzheimer disease and to find glycan biomarkers in cerebrospinal fluid.
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Affiliation(s)
- Stefan Gaunitz
- Department of Clinical Chemistry, Karolinska University Hospital, 14186 Stockholm, Sweden;
| | - Lars O. Tjernberg
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164 Solna, Sweden;
| | - Sophia Schedin-Weiss
- Division of Neurogeriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, 17164 Solna, Sweden;
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