{"id":23159,"date":"2025-03-19T13:40:10","date_gmt":"2025-03-19T13:40:10","guid":{"rendered":"https:\/\/clinlabint.com\/?p=23159"},"modified":"2025-03-19T13:40:10","modified_gmt":"2025-03-19T13:40:10","slug":"deep-quantitative-glycoprofiling-new-method-for-fast-sensitive-and-in-depth-mass-spectrometry-of-protein-glycosylation","status":"publish","type":"post","link":"https:\/\/clinlabint.com\/deep-quantitative-glycoprofiling-new-method-for-fast-sensitive-and-in-depth-mass-spectrometry-of-protein-glycosylation\/","title":{"rendered":"Deep quantitative glycoprofiling: new method for fast, sensitive and in-depth mass spectrometry of protein glycosylation"},"content":{"rendered":"
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Deep quantitative glycoprofiling: new method for fast, sensitive and in-depth mass spectrometry of protein glycosylation<\/h1>\/ in Protein Analysis, Proteomics & Mass Spectrometry<\/a>, Featured Articles<\/a> <\/span><\/span><\/header>\n<\/div><\/section>
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What is protein glycosylation?<\/strong><\/h3>\n

There are several types of post-translational modifications (PTMs) that proteins can undergo. Phosphorylation, methylation and acetylation all involve the addition of small chemical groups which affect function. Ubiquitylation involves the addition of the small peptide, ubiquitin, which targets proteins for degradation. Glycosylation is one of the more complex forms of PTM, involves the addition of a variety of complex sugars (glycans) and occurs mainly in the endoplasmic reticulum and Golgi apparatus [1,2]. Glycosylation generally occurs as two main types: N-linked and O-linked.<\/h3>\n

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N-linked glycosylation starts with the addition of N-acetylglucosamine onto asparagine (N) amino acids in the recognition sequence N-X-S\/T (where X is any amino acid except proline). Subsequently, commonly added glycans include mannose, galactose, fucose and sialic acid forming highly branched glycan chains. N-linked glycosylation is divided into three main subtypes: complex, mixed (or hybrid), and high mannose.<\/p>\n

In eukaryotes, O-linked glycosylation is divided into two main types: O-N-acetylgalactosamine (O-GalNAc) and O-N-acetylglucosamine (O-GlcNAc). O-GalNAc starts with the addition of N-acetylgalactosamine, typically onto serine or threonine amino acids, generally on proteins destined for secretion. Subsequently, galactose and N-acetylglucosamine are added, to form a variety of \u2018core\u2019 structures. O-GlcNAc starts with the addition of N-acetylglucosamine onto serine and threonine amino acids, more usually on proteins destined to remain in the cytoplasm and nucleus of the cell. This form of glycosylation is more dynamic \u2013 as no other glycans are added onto the core structure, the N-acetylglucosamine moiety can be added and removed in a process that has a lot of similarities to (and possibly a relationship with) phosphorylation.<\/p>\n

Biological role of glycosylation<\/h4>\n

Protein glycosylation is important in a number of biological processes. In some proteins it plays a part in correct protein folding, protein structure stabilization and quality control, as well as protein degradation and aggregation. Glycosylation has a role in cell\u2013matrix interactions, which affects cell adhesion. It has a critical role in immunity, particularly immune evasion by pathogens. It is also crucial in cell signalling, including Notch, JAK\/STAT, TGF-\u03b2 and Wnt\/\u03b2-catenin signalling pathways. Deregulation of glycosylation has been linked to many pathologies, including cancer and neuronal disorders.<\/p>\n<\/div><\/section>
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Glycosylation can be important in maintaining proper protein folding<\/em><\/p>\n<\/div><\/section>
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Analysis of glycosylation<\/h4>\n

The reasons to study protein glycosylation are obvious from the role it plays in the variety of biological processes alluded to above. However, analysis of glycosylation is challenging, not least because of its diversity, as almost every aspect of glycosylation can be varied, including macro-
\nheterogeneity (glycosylation at different sites) and microheterogeneity (the types of glycans added, whether it involves unbranched or branched chains of glycans, and the length of the oligosaccharide chains) at the same site. Over the past 20 years, mass spectrometry has emerged as one of the main techniques for the analysis of glycoproteins [3], although this has not been without its challenges. One of the main difficulties has been with sample enrichment strategies, which have suffered from limitations such as glycan bias and low enrichment specificity.<\/p>\n

Improved mass spectrometry strategy<\/h4>\n

In their recent paper, Potel et al. describe their new enrichment and prefractionation strategy (deep quantitative glycoprofiling; DQGlyco) that has allowed substantial increases in coverage of the glycoproteome [4]. Their workflow uses silica beads with phenylboronic acid (PBA) to bind glycopeptides via the diol moiety, while using a sample lysis buffer with a high concentration of chaotropic salts and organic solvent to precipitate and remove nucleic acids that would also bind to PBA. Their method for sample preparation was fast (1 hour for 96 samples in a 96-well plate), efficiently removed RNA, and enabled significant enrichment of N-glycopeptides. Additionally, they adjusted the first-level mass spectrometry range to preferentially target glycopeptides, which are higher in mass compared to non-modified peptides that non-specifically bind to PBA beads. Their DQGlyco methodology markedly improved the detected glycoproteome coverage when compared to previous studies without the need for prefractionation.<\/p>\n

Potel et al. used their method to study the effect of the gut microbiota on glycosylation signatures observed in the brain, finding that mice colonized with different gut bacteria had different brain glycosylation patterns compared to germ-free mice. Their method allowed them to identify almost 180 000 unique N-glycopeptides, opening an avenue to study the impact of the gut microbiota on brain protein function. This was in addition to other work to characterize surface-exposed glycoforms and glycoform solubility. As they say, \u201cThis approach holds substantial promise for identifying disease-specific glycosylation patterns [which] could, in the future, support the identification of biomarkers\u201d.<\/p>\n

References<\/strong><\/em>
\n1. He M, Zhou X, Wang X. Glycosylation: mechanisms, biological functions and clinical implications. Signal Transduct Target Ther 2024;9(1):194 (https:\/\/www.nature.com\/articles\/s41392-024-01886-1<\/a>).<\/em>
\n2. Schoberer J, Shin YJ, Vavra U, Veit C, Strasser R. Analysis of protein glycosylation in the ER. Methods Mol Biol 2024;2772:221\u2013238 <\/em>
\n(https:\/\/link.springer.com\/protocol\/10.1007\/978-1-0716-3710-4_16<\/a>).<\/em>
\n3. Bagdonaite I, Malaker SA, Polasky DA et al. Glycoproteomics. Nat Rev Methods Primers 2022;2:48 (https:\/\/doi.org\/10.1038\/s43586-022-00128-4<\/a>).<\/em>
\n4. Potel CM, Burtscher ML, Garrido-Rodriguez M et al. Uncovering protein glycosylation dynamics and heterogeneity using deep quantitative glycoprofiling (DQGlyco). Nat Struct Mol Biol 2025;doi:10.1038\/s41594-025-01485-w (https:\/\/www.nature.com\/articles\/s41594-025-01485-w<\/a>).<\/em><\/p>\n<\/div><\/section>
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