The author warmly acknowledges past and present members of the Proteome Informatics Group involved in developing the resources used in this tutorial, specifically, Julien Mariethoz and Catherine Hayes for GlyConnect, Fran&#231;ois Bonnardel for UniLectin, Davide Alocci, and Frederic Nikitin for the Octopus, and Thibault Robin for Compozitor and final touch on Octopus.
The development of the glyco@Expasy project is supported by the Swiss Federal Government through the State Secretariat for Education, Research and Innovation (SERI) and is currently complemented by the Swiss National Science Foundation (SNSF: 31003A_179249). ExPASy is maintained by the Swiss Institute of Bioinformatics and hosted at the Vital-IT Competency Center. The author also acknowledges Anne Imberty for outstanding cooperation on the UniLectin platform jointly supported by ANR PIA Glyco@Alps (ANR-15-IDEX-02), Alliance Campus Rhodanien Co-funds (http://campusrhodanien.unige-cofunds.ch) Labex Arcane/CBH-EUR-GS (ANR-17-EURE-0003).

<ol>
	<li>Spring Harbor Laboratory Press. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Essentials of Glycobiology. , (2015).</li><li>Gray, C. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Advancing+solutions+to+the+carbohydrate+sequencing+challenge.">Advancing solutions to the carbohydrate sequencing challenge.</a> Journal of the American Chemical Society. 141 (37), 14463-14479 (2019).</li><li>Tsuchiya, S., Yamada, I., Aoki-Kinoshita, K. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlycanFormatConverter:+a+conversion+tool+for+translating+the+complexities+of+glycans.">GlycanFormatConverter: a conversion tool for translating the complexities of glycans.</a> Bioinformatics. 35 (14), 2434-2440 (2018).</li><li>Fujita, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+international+glycan+repository+GlyTouCan+version+3.0.">The international glycan repository GlyTouCan version 3.0.</a> Nucleic Acids Research. 49, 1529-1533 (2021).</li><li>Alocci, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlyConnect:+glycoproteomics+goes+visual,+interactive,+and+analytical.">GlyConnect: glycoproteomics goes visual, interactive, and analytical.</a> Journal of Proteome Research. 18 (2), 664-677 (2019).</li><li>York, W. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlyGen:+computational+and+informatics+resources+for+glycoscience.">GlyGen: computational and informatics resources for glycoscience.</a> Glycobiology. 30 (2), 72-73 (2020).</li><li>Watanabe, Y., Aoki-Kinoshita, K. F., Ishihama, Y., Okuda, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=GlycoPOST+realizes+FAIR+principles+for+glycomics+mass+spectrometry+data.">GlycoPOST realizes FAIR principles for glycomics mass spectrometry data.</a> Nucleic Acids Research. 49, 1523-1528 (2020).</li><li>Campbell, M. P., Aoki-Kinoshita, K. F., Lisacek, F., York, W. S., Packer, N. H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoinformatics.">Glycoinformatics.</a> Essentials of Glycobiology. , (2015).</li><li>Cao, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Recent+advances+in+software+tools+for+more+generic+and+precise+intact+glycopeptide+analysis.">Recent advances in software tools for more generic and precise intact glycopeptide analysis.</a> Molecular &amp; Cellular Proteomics. 20, 100060 (2021).</li><li>Mariethoz, J., Hayes, C., Lisacek, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycan+compositions+with+Compozitor+to+enhance+glycopeptide+identification.">Glycan compositions with Compozitor to enhance glycopeptide identification.</a> Proteomics Data Analysis. 2361, 109-127 (2021).</li><li>Kawahara, R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Communityevaluation+of+glycoproteomics+informatics+solutions+reveals+high-performance+search+strategies+of+serum+glycopeptide+analysis.">Communityevaluation of glycoproteomics informatics solutions reveals high-performance search strategies of serum glycopeptide analysis.</a> Nature Methods. 18, 1304-1316 (2021).</li><li>Lisacek, F., Aoki-Kinoshita, K. F., Vora, J. K., Mazumder, R., Tiemeyer, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoinformatics+resources+integrated+through+the+GlySpace+Alliance.">Glycoinformatics resources integrated through the GlySpace Alliance.</a> Comprehensive Glycoscience. 1, 507-521 (2021).</li><li>Mariethoz, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycomics@ExPASy:+bridging+the+gap.">Glycomics@ExPASy: bridging the gap.</a> Molecular &amp; Cellular Proteomics. 17 (11), 2164-2176 (2018).</li><li>Duvaud, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Expasy,+the+swiss+bioinformatics+resource+portal,+as+designed+by+its+users.">Expasy, the swiss bioinformatics resource portal, as designed by its users.</a> Nucleic Acids Research. 49, 216-227 (2021).</li><li>The UniProt Consortium et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UniProt:+the+universal+protein+knowledgebase+in+2021.">UniProt: the universal protein knowledgebase in 2021.</a> Nucleic Acids Research. 49, 480-489 (2021).</li><li>Bonnardel, F., Perez, S., Lisacek, F., Imberty, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Structural+database+for+lectins+and+the+UniLectin+web+platform.">Structural database for lectins and the UniLectin web platform.</a> Lectin Purification and Analysis. 2132, 1-14 (2020).</li><li>Neelamegham, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Updates+to+the+symbol+nomenclature+for+glycans+guidelines.">Updates to the symbol nomenclature for glycans guidelines.</a> Glycobiology. 29 (9), 620-624 (2019).</li><li>Sharon, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=IUPAC-IUB+Joint+Commission+on+Biochemical+Nomenclature+(JCBN).+Nomenclature+of+glycoproteins,+glycopeptides+and+peptidoglycans:+JCBN+recommendations+1985.">IUPAC-IUB Joint Commission on Biochemical Nomenclature (JCBN). Nomenclature of glycoproteins, glycopeptides and peptidoglycans: JCBN recommendations 1985.</a> Glycoconjugate Journal. 3 (2), 123-133 (1986).</li><li>Harvey, D. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Proposal+for+a+standard+system+for+drawing+structural+diagrams+of+N-+and+O-linked+carbohydrates+and+related+compounds.">Proposal for a standard system for drawing structural diagrams of N- and O-linked carbohydrates and related compounds.</a> Proteomics. 9 (15), 3796-3801 (2009).</li><li>Song, E., Mayampurath, A., Yu, C. -. Y., Tang, H., Mechref, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycoproteomics:+identifying+the+glycosylation+of+prostate+specific+antigen+at+normal+and+high+isoelectric+points+by+LC-MS/MS.">Glycoproteomics: identifying the glycosylation of prostate specific antigen at normal and high isoelectric points by LC-MS/MS.</a> Journal of Proteome Research. 13 (12), 5570-5580 (2014).</li><li>Moran, A. B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Profiling+the+proteoforms+of+urinary+prostate-specific+antigen+by+capillary+electrophoresis+-+mass+spectrometry.">Profiling the proteoforms of urinary prostate-specific antigen by capillary electrophoresis - mass spectrometry.</a> Journal of Proteomics. 238, 104148 (2021).</li><li>Wang, W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High-throughput+glycopeptide+profiling+of+prostate-specific+antigen+from+seminal+plasma+by+MALDI-MS.">High-throughput glycopeptide profiling of prostate-specific antigen from seminal plasma by MALDI-MS.</a> Talanta. 222, 121495 (2021).</li><li>wwPDB consortium metal. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Protein+Data+Bank:+the+single+global+archive+for+3D+macromolecular+structure+data.">Protein Data Bank: the single global archive for 3D macromolecular structure data.</a> Nucleic Acids Research. 47, 520-528 (2019).</li><li>Sehnal, D., Grant, O. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Rapidly+display+glycan+symbols+in+3D+structures:+3D-SNFG+in+LiteMol.">Rapidly display glycan symbols in 3D structures: 3D-SNFG in LiteMol.</a> Journal of Proteome Research. 18 (2), 770-774 (2019).</li><li>Bonnardel, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UniLectin3D,+a+database+of+carbohydrate+binding+proteins+with+curated+information+on+3D+structures+and+interacting+ligands.">UniLectin3D, a database of carbohydrate binding proteins with curated information on 3D structures and interacting ligands.</a> Nucleic Acids Research. 47, 1236-1244 (2019).</li><li>Sehnal, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LiteMol+suite:+interactive+web-based+visualization+of+large-scale+macromolecular+structure+data.">LiteMol suite: interactive web-based visualization of large-scale macromolecular structure data.</a> Nature Methods. 14 (12), 1121-1122 (2017).</li><li>Salentin, S., Schreiber, S., Haupt, V. J., Adasme, M. F., Schroeder, M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PLIP:+fully+automated+protein-ligand+interaction+profiler.">PLIP: fully automated protein-ligand interaction profiler.</a> Nucleic Acids Research. 43, 443-447 (2015).</li><li>Robin, T., Mariethoz, J., Lisacek, F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Examining+and+fine-tuning+the+selection+of+glycan+compositions+with+GlyConnect+Compozitor.">Examining and fine-tuning the selection of glycan compositions with GlyConnect Compozitor.</a> Molecular &amp; Cellular Proteomics. 19 (10), 1602-1618 (2020).</li><li>Compagno, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Glycans+and+galectins+in+prostate+cancer+biology,+angiogenesis+and+metastasis.">Glycans and galectins in prostate cancer biology, angiogenesis and metastasis.</a> Glycobiology. 24 (10), 899-906 (2014).</li><li>Gentilini, L. D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stable+and+high+expression+of+Galectin-8+tightly+controls+metastatic+progression+of+prostate+cancer.">Stable and high expression of Galectin-8 tightly controls metastatic progression of prostate cancer.</a> Oncotarget. 8 (27), 44654-44668 (2017).</li><li>Schw&#228;mmle, V., Verano-Braga, T., Roepstorff, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+and+statistical+methods+for+high-throughput+analysis+of+post-translational+modifications+of+proteins.">Computational and statistical methods for high-throughput analysis of post-translational modifications of proteins.</a> Journal of Proteomics. 129, 3-15 (2015).</li><li>Khatri, K., Klein, J. A., Zaia, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Use+of+an+informed+search+space+maximizes+confidence+of+site-specific+assignment+of+glycoprotein+glycosylation.">Use of an informed search space maximizes confidence of site-specific assignment of glycoprotein glycosylation.</a> Analytical and Bioanalytical Chemistry. 409 (2), 607-618 (2017).</li><li>Sztain, T., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+glycan+gate+controls+opening+of+the+SARS-CoV-2+spike+protein.">A glycan gate controls opening of the SARS-CoV-2 spike protein.</a> Nature Chemistry. 13, 963-968 (2021).</li></ol>

The authors declare no conflicts of interest.

GlyConnect Octopus as a tool for revealing unexpected correlations 
GlyConnect Octopus was originally designed to query the database with a loose definition of glycans. Indeed, the literature often reports the main characteristics of glycans in a glycome such as being fucosylated or sialylated, being made of two or more antennae, etc. Furthermore, glycans whether N- or O-linked are classified in cores, as detailed in the reference manual Essentials of Glycobiology1, that are ...

Glycans, proteins to which they are attached (glycoproteins) and proteins to which they bind (lectins or carbohydrate-binding proteins) are the main molecular actors at the cell surface1. Despite this central role in cell-cell communication, large-scale studies, including glycomics, glycoproteomics, or glycan-interactomics data are still scarce compared to their counterpart in genomics and proteomics.
Until recently, methods for characterizing the branching structures of complex carbohydrates while still being conjugated to the carrier protein had not been developed. The biosynthesis of glycoproteins is a non-template-driven process in which the monosaccharide donors, the accepting glycoprotein substrates, and the glycosyltransferases and glycosidases play an interactive role. The resulting glycoproteins can bear complex structures with multiple branching points where each monosaccharide component can be one of the several types present in nature1. The non-template-driven process imposes biochemical analysis as the only option for generating oligosaccharide structural data. The analytical process of glycan structures attached to a native protein is often challenging as it requires sensitive, quantitative, and robust technologies to determine monosaccharide composition, linkages, and branching sequences2.
In this context, mass spectrometry (MS) is the most widely used technique in glycomics and glycoproteomics experiments. As time goes, these are carried out in higher throughput settings and data is now accumulating in databases. Glycan structures in various formats3, populate GlyTouCan4, the universal glycan data repository where each structure is associated with a stable identifier irrespective of the level of precision with which the glycan is defined (e.g., possibly missing linkage type or ambiguous composition). Very similar structures are collected but their minor differences are clearly reported. Glycoproteins are described and curated in GlyConnect5 and GlyGen6, two databases cross-referencing each other. MS data supporting structural pieces of evidence are increasingly stored in GlycoPOST7. For a wider coverage of online resources, chapter 52 of the reference manual, Essentials of Glycobiology, is dedicated to glycoinformatics8. Interestingly, glycopeptide identification software has proliferated in recent years9,10 though not to the benefit of reproducibility. The latter concern prompted the leaders of the HUPO GlycoProteomics Initiative (HGI) to set a software challenge in 2019. The MS data obtained from processing complex mixtures of N- and O-glycosylated human serum proteins in CID, ETD, and EThcD fragmentation modes, were made available to competitors whether software users or developers. The full report on the results of this challenge11 is only outlined here. To begin with, a spread of identifications was observed. It was mainly interpreted as caused by the diversity of methods implemented in search engines, of their settings, and how outputs were filtered, and peptide "counted". The experimental design may also have put some software and approaches at a (dis)advantage. Importantly, participants using the same software reported inconsistent results, thereby highlighting serious reproducibility issues. It was concluded by comparing different submissions that some software solutions perform better than others and some search strategies yield better results. This feedback is likely to guide the improvement of automated glycopeptide data analysis methods and will in turn, impact database content.
The expansion of glycoinformatics led to creating web portals that provide information and access to multiple similar or complementing resources. The most recent and up-to-date are described in a chapter of the Comprehensive Glycoscience book series12 and through cooperation, a solution to data sharing and information exchange is offered in an open access mode. One such portal was developed which was originally called Glycomics@ExPASy13 and renamed Glyco@Expasy, following the major overhaul of the Expasy platform14 that has hosted a large collection of tools and databases used across several -omics for decades, the most popular item being UniProt15-the universal protein knowledgebase. Glyco@Expasy offers a didactic discovery of the purpose and usage of databases and tools, based on a visual categorization and a display of their interdependencies. The following protocol illustrates procedures to explore glycomics and glycoproteomics data with a selection of resources from this portal that makes the connection between glycoproteomics and glycan-interactomics explicit via glycomics. As it is, glycomics experiments produce structures where monosaccharides are fully defined and linkages partially or fully determined, but their protein site attachment is poorly, if at all, characterized. In contrast, glycoproteomics experiments generate precise site attachment information but with a poor resolution of glycan structures, often limited to monosaccharide compositions. This information is pieced together in the GlyConnect database. Furthermore, search tools in GlyConnect can be used to detect potential glycan ligands which are described along with the proteins recognizing them in UniLectin16, linked to GlyConnect via glycans. The protocol presented here is divided into two sections to cover questions specific to N-linked and O-linked glycans and glycoproteins.

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bioinformatics resources for the study of glycan-mediated protein interactions

The Glyco@Expasy initiative was launched as a collection of interdependent databases and tools spanning several aspects of knowledge in glycobiology. In particular, it aims at highlighting interactions between glycoproteins (such as cell surface receptors) and carbohydrate-binding proteins mediated by glycans. Here, major resources of the collection are introduced through two illustrative examples centered on the N-glycome of the human Prostate Specific Antigen (PSA) and the O-glycome of human serum proteins. Through different database queries and with the help of visualization tools, this article shows how to explore and compare content in a continuum to gather and correlate otherwise scattered pieces of information. Collected data are destined to feed more elaborate scenarios of glycan function. Glycoinformatics introduced here is, therefore, proposed as a means to either strengthen, shape, or refute assumptions on the specificity of a protein glycome in a given context.

The first part of the protocol (section 1) showed how to investigate the specificity or the commonality of N-glycans attached on Asn-69 of the human Prostate Specific Antigen (PSA) using the GlyConnect platform. Tissue-dependent (urine and seminal fluid), as well as isoform-dependent (normal and high pI) variations in glycan expression, were emphasized using two visualizing tools (Figure 4 and Figure 5).
First, GlyCon...

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Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

This protocol illustrates how to explore, compare, and interpret human protein glycomes with online resources.

The Glyco@Expasy initiative was launched as a collection of interdependent databases and tools spanning several aspects of knowledge in glycobiology. In ...

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Biology

3.2K Views.  SIB Swiss Institute of Bioinformatics. This protocol illustrates how to explore, compare, and interpret human protein glycomes with online resources.

Video: Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

A Quantitative Glycomics and Proteomics Combined Purification Strategy

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define pathways, improve treatment, understand disease, and aid biomarker discovery. N-linked glycosylation is one of the most important and robust post-translational modifications on proteins and regulates critical cell functions such as signaling, adhesion, and enzymatic function. Analytical techniques to purify and analyze N-glycans have remained relatively static over the last decade. While accurate and effective, they commonly require significant expertise and resources. Though some high-throughput purification schemes have been developed, they have yet to find widespread adoption and often rely on the enrichment of glycopeptides. One promising method, developed by Thomas-Oates et al., filter aided N-glycan separation (FANGS), was qualitatively demonstrated on tissues. Herein, we adapted FANGS to plasma and coupled it to the individuality normalization when labeling with glycan hydrazide tags strategy in order to achieve accurate relative quantification by liquid chromatography mass spectrometry and enhanced electrospray ionization. Furthermore, we designed new functionality to the protocol by achieving tandem, shotgun proteomics and glycosylation site analysis on hen plasma. We showed that N-glycans purified on filter and derivatized by hydrophobic hydrazide tags were comparable in terms of abundance and class to those by solid phase extraction (SPE); the latter is considered a gold standard in the field. Importantly, the variability in the two protocols was not statistically different. Proteomic data that was collected in-line with glycomic data had the same depth compared to a standard trypsin digest. Peptide deamidation is minimized in the protocol, limiting non-specific deamidation detected at glycosylation motifs. This allowed for direct glycosylation site analysis, though the protocol can accommodate 18O site labeling as well. Overall, we demonstrated a new in-line high-throughput, unbiased, filter based protocol for quantitative glycomics and proteomics analysis.

A high-throughput protocol was developed for combined proteomics and glycomics purification and LC-MS/MS quantification in plasma. Deamidation analysis of N-linked glycosylation motifs was specific to deglycosylated sites. Accurate quantitation of N-glycans was achieved by coupling filter aided N-glycan separation to the individuality normalization when labeling with glycan hydrazide tags strategy.

There is a growing desire in the biological and clinical sciences to integrate and correlate multiple classes of biomolecules to unravel biology, define ...

Chemistry

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Synthesized in a non-template-driven process by enzymes called glycosyltransferases, glycans are key players in various significant intra- and extracellular events. Many pathological conditions, notably cancer, affect gene expression, which can in turn deregulate the relative abundance and activity levels of glycoside hydrolase and glycosyltransferase enzymes. Unique aberrant whole glycans resulting from deregulated glycosyltransferase(s) are often present in trace quantities within complex biofluids, making their detection difficult and sometimes stochastic. However, with proper sample preparation, one of the oldest forms of mass spectrometry (gas chromatography-mass spectrometry, GC-MS) can routinely detect the collection of branch-point and linkage-specific monosaccharides ("glycan nodes") present in complex biofluids. Complementary to traditional top-down glycomics techniques, the approach discussed herein involves the collection and condensation of each constituent glycan node in a sample into a single independent analytical signal, which provides detailed structural and quantitative information about changes to the glycome as a whole and reveals potentially deregulated glycosyltransferases. Improvements to the permethylation and subsequent liquid/liquid extraction stages provided herein enhance reproducibility and overall yield by facilitating minimal exposure of permethylated glycans to alkaline aqueous conditions. Modifications to the acetylation stage further increase the extent of reaction and overall yield. Despite their reproducibility, the overall yields of N-acetylhexosamine (HexNAc) partially permethylated alditol acetates (PMAAs) are shown to be inherently lower than their expected theoretical value relative to hexose PMAAs. Calculating the ratio of the area under the extracted ion chromatogram (XIC) for each individual hexose PMAA (or HexNAc PMAA) to the sum of such XIC areas for all hexoses (or HexNAcs) provides a new normalization method that facilitates relative quantification of individual glycan nodes in a sample. Although presently constrained in terms of its absolute limits of detection, this method expedites the analysis of clinical biofluids and shows considerable promise as a complementary approach to traditional top-down glycomics.

This article presents an enhanced form of a novel bottom-up glycomics technique designed to analyze the pooled compositional profile of glycans in unfractionated biofluids through the chemical breakdown of glycans into their constituent linkage-specific monosaccharides for detection by GC-MS. Potential applications include early detection of cancer and other glycan-affective disorders.

Synthesized in a non-template-driven process by enzymes called glycosyltransferases, glycans are key players in various significant intra- and ...

Profiling Anti-Neu5Gc IgG in Human Sera with a Sialoglycan Microarray Assay

Cells are covered with a cloak of carbohydrate chains (glycans) that is commonly altered in cancer and that includes variations in sialic acid (Sia) expression. These are acidic sugars that have a 9-carbon backbone and that cap vertebrate glycans on cell surfaces. Two of the major Sia forms in mammals are N-acetylneuraminic acid (Neu5Ac) and its hydroxylated form, N-glycolylneuraminic acid (Neu5Gc). Humans cannot produce endogenous Neu5Gc due to the inactivation of the gene encoding cytidine 5&#39;monophosphate-Neu5Ac (CMP-Neu5Ac) hydroxylase (CMAH). Foreign Neu5Gc is acquired by human cells through the dietary consumption of red meat and dairy and subsequently appears on diverse glycans on the cell surface, accumulating mostly on carcinomas. Consequently, humans have circulating anti-Neu5Gc antibodies that play diverse roles in cancer and other chronic inflammation-mediated diseases and that are becoming potential diagnostic and therapeutic targets. Here, we describe a high-throughput sialoglycan microarray assay to assess such anti-Neu5Gc antibodies in the human sera. Neu5Gc-containing glycans and their matched pairs of controls (Neu5Ac-containing glycans), each with a core primary amine, are covalently linked to epoxy-coated glass slides. We exemplify the printing of 56 slides in a 16-well format using a specific nano-printer capable of generating up to 896 arrays per print. Each slide can be used to screen 16 different human sera samples for the evaluation of anti-Neu5Gc antibody specificity, intensity, and diversity. The protocol describes the complexity of this robust tool and provides a basic guideline for those aiming to investigate the response to Neu5Gc dietary carbohydrate antigen in diverse clinical samples in an array format.

A sialoglycan microarray assay can be used to evaluate anti-Neu5Gc antibodies in human sera, making it a potential high-throughput diagnostic assay for cancer and other chronic inflammation-mediated human diseases.

Cells are covered with a cloak of carbohydrate chains (glycans) that is commonly altered in cancer and that includes variations in sialic acid (Sia) ...

Behavior

N-glycan Profiling of Glycoproteins by Hydrophilic Interaction Liquid Chromatography with Fluorescence and Mass Spectrometric Detection

Glycosylation is a vital modification found in proteins. N-glycan profiling of glycoproteins is required to detect novel biomarker candidates and determine glycan alterations in diseases. Most commercially available biopharmaceutical proteins are glycoproteins. The efficacy of these drugs is affected by glycosylation patterns. Therefore, an in-depth characterization method for the N-glycans is necessary. Here, we present a comprehensive approach for qualitative and quantitative analysis of N-glycans using hydrophilic interaction liquid chromatography equipped with fluorescence detection and tandem mass spectrometry (HILIC-FLD-MS/MS). N-glycans were released from glycoproteins with a facile method and labeled by a procainamide fluorophore tag in the strategy. Subsequently, the procainamide labeled N-glycans were analyzed by a HILIC-FLD-MS/MS technique. In this approach, N-glycan structures were confirmed by the tandem mass spectrometric analysis, whereas fluorescence detection was used for the quantitative analysis. An application for data analysis of the detected N-glycan peaks is described in the study. This protocol can be applied to any glycoprotein extracted from various species.

N-glycan Profiling of Glycoproteins by Hydrophilic Interaction Liquid Chromatography with Fluorescence and Mass Spectrometric Detection

N-glycan profiling of glycoproteins is essential for discovering novel biomarkers and understanding glycan functions in cellular events. Additionally, N-glycan analysis of protein biopharmaceuticals is very important for human use. In this current article, a high-throughput strategy for identifying and quantifying N-glycan structures was presented using the HILIC-FLD-MS/MS technique.

Glycosylation is a vital modification found in proteins. N-glycan profiling of glycoproteins is required to detect novel biomarker candidates and ...

Biochemistry

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal infectivity of pathogenic species. Due to this, there is increasing interest in characterizing bacterial glycosylation and a need for high-throughput analytical tools to identify these events. Although bottom-up proteomics readily enables the generation of rich glycopeptide data, the breadth and diversity of glycans observed in prokaryotic species make the identification of bacterial glycosylation events extremely challenging.
Traditionally, the manual determination of glycan compositions within bacterial proteomic datasets made this a largely bespoke analysis restricted to field-specific experts. Recently, open searching-based approaches have emerged as a powerful alternative for the identification of unknown modifications. By analyzing the frequency of unique modifications observed on peptide sequences, open searching techniques allow the identification of common glycans attached to peptides within complex samples. This article presents a streamlined workflow for the interpretation and analysis of glycoproteomic data, demonstrating how open searching techniques can be used to identify bacterial glycopeptides without prior knowledge of the glycan compositions.
Using this approach, glycopeptides within samples can rapidly be identified to understand glycosylation differences. Using Acinetobacter baumannii as a model, these approaches enable the comparison of glycan compositions between strains and the identification of novel glycoproteins. Taken together, this work demonstrates the versatility of open database-searching techniques for the identification of bacterial glycosylation, making the characterization of these highly diverse glycoproteomes easier than ever before.

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Open searching enables the identification of glycopeptides decorated with previously unknown glycan compositions. Within this article, a streamlined approach for undertaking open searching and subsequent glycan-focused glycopeptide searches are presented for bacterial samples using Acinetobacter baumannii as a model.

Protein glycosylation is increasingly recognized as a common modification within bacterial organisms, contributing to prokaryotic physiology and optimal ...

Nonspecific Proteolysis-Based Glycomics Strategy for Bacterial Glycoproteins

Protein glycosylation is one of the most common and complex post-translational modifications. Many techniques have been developed to characterize the specific roles of glycans, the relationship between their structures and their impact on the functions of proteins. A common method for glycan analysis is to employ exoglycosidase cleavage to release N-linked glycans from glycoproteins or glycopeptides using Peptide-N-Glycosidase F (PNGase F). However, the glycan-protein linkages in bacteria are different and there is no enzyme available to release glycans from bacterial glycoproteins. In addition, free glycans have also been described in mammalian cells, bacteria, yeast, plants, and fish. In this article, we present a method that can characterize the N-linked glycosylation system in Campylobacter jejuni by detecting asparagine (Asn)-linked and free glycans that are not attached to their target proteins. In this method, total proteins from C. jejuni were digested by Pronase E with a higher enzyme to protein ratio (2:1&#8722;3:1) and a longer incubation time (48&#8722;72 h). The resulted Asn-glycans and free glycans were then purified using porous graphitic carbon cartridges, permethylated, and analyzed by mass spectrometry.

This study presents a method for glycomics analysis of glycoproteins by a combination of pronase E digestion, permethylation, and mass spectrometry analysis. This method is capable of analyzing all types of N-linked glycans, including bacterial N-glycans.

Protein glycosylation is one of the most common and complex post-translational modifications. Many techniques have been developed to characterize the ...

Advanced NMR Techniques for Glycan-Protein Interaction Analysis

Disentangling Glycan-Protein Interactions: Nuclear Magnetic Resonance (NMR) to the Rescue

The interactions of glycans with proteins modulate many events related to health and disease. In fact, the establishment of these recognition events and their biological consequences are intimately related to the three-dimensional structures of both partners, as well as to their dynamic features and their presentation on the corresponding cell compartments. NMR techniques are unique to disentangle these characteristics and, indeed, diverse NMR-based methodologies have been developed and applied to monitor the binding events of glycans with their associate receptors. This protocol outlines the procedures to acquire, process and analyze two of the most powerful NMR methodologies employed in the NMR-glycobiology field, 1H-Saturation transfer difference (STD) and 1H,15N-Heteronuclear single quantum coherence (HSQC) titration experiments, which complementarily offer information from the glycan and protein perspective, respectively. Indeed, when combined they offer a powerful toolkit for elucidating both the structural and dynamic aspects of molecular recognition processes. This comprehensive approach enhances our understanding of glycan-protein interactions and contributes to advancing research in the chemical glycobiology field.

Author Spotlight: Unveiling the Structural and Dynamic Aspects of Glycan Molecular Recognition

Here, we present a protocol detailing the acquisition, processing, and analysis of a series of NMR experiments aimed at characterizing protein-glycan interactions in solution. Most common ligand-based and protein-based methodologies are outlined, which undoubtedly contribute to the fields of structural glycobiology and molecular recognition studies.

The interactions of glycans with proteins modulate many events related to health and disease. In fact, the establishment of these recognition events and ...

Profiling of Colorectal Cancer Tissues Using Glycomics-Guided Glycoproteomics

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven glycomics and glycoproteomics have emerged as powerful approaches to analyze liberated glycans and intact glycopeptides, respectively, offering a deeper understanding of the heterogeneous glycoproteome in the tumor microenvironment (TME). This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology, which firstly employs porous graphitized carbon-LC-MS/MS-based glycomics to elucidate the glycan structures and their quantitative distribution in the glycome of tumor tissues, cell populations, or bodily fluids being investigated. This allows for a comparative glycomics analysis to identify altered glycosylation across patient groups, disease stages, or conditions, and, importantly, serves to enhance the downstream glycoproteomics analysis of the same sample(s) by creating a library of known glycan structures, thus reducing the data search time and the glycoprotein misidentification rate. Focusing on N-glycoproteome profiling, the sample preparation steps for the glycomics-guided glycoproteomics method are detailed in this protocol, and key aspects of the data collection and analysis are discussed. The glycomics-guided glycoproteomics method provides quantitative information on the glycoproteins present in the TME and their glycosylation sites, site occupancy, and site-specific glycan structures. Representative results are presented from formalin-fixed paraffin-embedded tumor tissues from colorectal cancer patients, demonstrating that the method is sensitive and compatible with tissue sections commonly found in biobanks. Glycomics-guided glycoproteomics, therefore, offers a comprehensive view into the heterogeneity and dynamics of the glycoproteome in complex TMEs, generating robust biochemical data required to better understand the glycobiology of cancers.

This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology that offers comprehensive insights into the heterogeneous glycoproteome in complex tumor microenvironments required to better understand the glycobiology of cancers.

Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven ...

Cancer Research

Printed Glycan Array: A Sensitive Technique for the Analysis of the Repertoire of Circulating Anti-carbohydrate Antibodies in Small Animals

The repertoire of circulating anti-carbohydrate antibodies of a given individual is often associated with its immunological status. Not only the individual immune condition determines the success in combating internal and external potential threat signals, but also the existence of a particular pattern of circulating anti-glycan antibodies (and their serological level variation) could be a significant marker of the onset and progression of certain pathological conditions. Here, we describe a Printed Glycan Array (PGA)-based methodology that offers the opportunity to measure hundreds of glycan targets with very high sensitivity; using a minimal amount of sample, which is a common restriction present when small animals (rats, mice, hamster, etc.) are used as models to address aspects of human diseases. As a representative example of this approach, we show the results obtained from the analysis of the repertoire of natural anti-glycan antibodies in BALB/c mice. We demonstrate that each BALB/c mouse involved in the study, despite being genetically identical and maintained under the same conditions, develops a particular pattern of natural anti-carbohydrate antibodies. This work claims to expand the use of PGA technology to investigate repertoire (specificities) and the levels of circulating anti-carbohydrates antibodies, both in health and during any pathological condition.

This work shows the potential of printed glycan array (PGA) technology for the analysis of circulating anti-carbohydrate antibodies in small animals.

The repertoire of circulating anti-carbohydrate antibodies of a given individual is often associated with its immunological status. Not only the ...

Immunology and Infection

Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes such as cell adhesion, molecular trafficking, clearance, and signal transduction1-4. In eukaryotes, the most common glycosylation modifications in the secretory pathway are additions at consensus asparagine residues (N-linked); or at serine or threonine residues (O-linked) (Figure 1). Initiation of N-glycan synthesis is highly conserved in eukaryotes, while the end products can vary greatly among different species, tissues, or proteins. Some glycans remain unmodified ("high mannose N-glycans") or are further processed in the Golgi ("complex N-glycans"). Greater diversity is found for O-glycans, which start with a common N-Acetylgalactosamine (GalNAc) residue in animal cells but differ in lower organisms1.
The detailed analysis of the glycosylation of proteins is a field unto itself and requires extensive resources and expertise to execute properly. However a variety of available enzymes that remove sugars (glycosidases) makes possible to have a general idea of the glycosylation status of a protein in a standard laboratory setting. Here we illustrate the use of glycosidases for the analysis of a model glycoprotein: recombinant human chorionic gonadotropin beta (hCG&beta;), which carries two N-glycans and four O-glycans 5. The technique requires only simple instrumentation and typical consumables, and it can be readily adapted to the analysis of multiple glycoprotein samples.
Several enzymes can be used in parallel to study a glycoprotein. PNGase F is able to remove almost all types of N-linked glycans6,7. For O-glycans, there is no available enzyme that can cleave an intact oligosaccharide from the protein backbone. Instead, O-glycans are trimmed by exoglycosidases to a short core, which is then easily removed by O-Glycosidase. The Protein Deglycosylation Mix contains PNGase F, O-Glycosidase, Neuraminidase (sialidase), &beta;1-4 Galactosidase, and &beta;-N-Acetylglucosaminidase. It is used to simultaneously remove N-glycans and some O-glycans8 . Finally, the Deglycosylation Mix was supplemented with a mixture of other exoglycosidases (&alpha;-N-Acetylgalactosaminidase, &alpha;1-2 Fucosidase, &alpha;1-3,6 Galactosidase, and &beta;1-3 Galactosidase ), which help remove otherwise resistant monosaccharides that could be present in certain O-glycans.
SDS-PAGE/Coomasie blue is used to visualize differences in protein migration before and after glycosidase treatment. In addition, a sugar-specific staining method, ProQ Emerald-300, shows diminished signal as glycans are successively removed. This protocol is designed for the analysis of small amounts of glycoprotein (0.5 to 2 &mu;g), although enzymatic deglycosylation can be scaled up to accommodate larger quantities of protein as needed.

Using specific glycosidases to remove sugars from glycoproteins followed by SDS-PAGE is a valuable method to detect glycan modifications on protein samples and is a good choice for initial glycobiology studies. Changes following deglycosylation can be detected as shifts in gel mobility or by staining with glycan sensitive reagents.

Glycosylation, the addition of covalently linked sugars, is a major post-translational modification of proteins that can significantly affect processes ...

Bioinformatics Resources for the Study of Glycan-Mediated Protein Interactions

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A Quantitative Glycomics and Proteomics Combined Purification Strategy

Glycan Node Analysis: A Bottom-up Approach to Glycomics

Profiling Anti-Neu5Gc IgG in Human Sera with a Sialoglycan Microarray Assay

N-glycan Profiling of Glycoproteins by Hydrophilic Interaction Liquid Chromatography with Fluorescence and Mass Spectrometric Detection

The Application of Open Searching-based Approaches for the Identification of Acinetobacter baumannii O-linked Glycopeptides

Nonspecific Proteolysis-Based Glycomics Strategy for Bacterial Glycoproteins

Disentangling Glycan-Protein Interactions: Nuclear Magnetic Resonance (NMR) to the Rescue

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

Printed Glycan Array: A Sensitive Technique for the Analysis of the Repertoire of Circulating Anti-carbohydrate Antibodies in Small Animals

Identification and Characterization of Protein Glycosylation using Specific Endo- and Exoglycosidases