N-glycan microheterogeneity regulates interactions of plasma proteins

Wu, Di; Struwe, Weston B.; Harvey, David J.; Ferguson, Michael A. J.; Robinson, Carol V.

doi:10.1073/pnas.1807439115

Cited by 105 publications

(138 citation statements)

References 56 publications

(75 reference statements)

Supporting

Mentioning

132

Contrasting

Order By: Relevance

“…Eight N-glycopeptides form alpha-2-HS-glycoprotein, ceruloplasmin, haptoglobin, kininogeN-1, and vitronectin, were confirmed with the study of liver-secreted N-glycoproteins, where bi-and tri-antennary glycopeptides were most common ones from HCC plasma 22,25 . Fucosylation of AGP and haptoglobin inhibits the biding with drug and hemoglobin, respectively 48 . We also classified 20 fucosylated N-glycopeptides from complement components C7, IgA2, IgJ, IgM, alpha-1-antichymotrypsin, alpha-2-HS-glycoprotein, AGP, apolipoprotein D, ceruloplasmin, hemopexin, and phospholipid transfer protein as core fucosylated.…”

Section: Resultsmentioning

confidence: 99%

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Hwang

Jeong

Lee

et al. 2020

Sci Rep

View full text Add to dashboard Cite

Protein glycosylation is known to be involved in biological progresses such as cell recognition, growth, differentiation, and apoptosis. Fucosylation of glycoproteins plays an important role for structural stability and function of N-linked glycoproteins. Although many of biological and clinical studies of protein fucosylation by fucosyltransferases has been reported, structural classification of fucosylated N-glycoproteins such as core or outer isoforms remains a challenge. Here, we report for the first time the classification of N-glycopeptides as core-and outer-fucosylated types using tandem mass spectrometry (MS/MS) and machine learning algorithms such as the deep neural network (DNN) and support vector machine (SVM). Training and test sets of more than 800 MS/MS spectra of N-glycopeptides from the immunoglobulin gamma and alpha 1-acid-glycoprotein standards were selected for classification of the fucosylation types using supervised learning models. The bestperforming model had an accuracy of more than 99% against manual characterization and area under the curve values greater than 0.99, which were calculated by probability scores from target and decoy datasets. Finally, this model was applied to classify fucosylated N-glycoproteins from human plasma. A total of 82N-glycopeptides, with 54 core-, 24 outer-, and 4 dual-fucosylation types derived from 54 glycoproteins, were commonly classified as the same type in both the DNN and SVM. Specifically, outer fucosylation was dominant in tri-and tetra-antennary N-glycopeptides, while core fucosylation was dominant in the mono-, bi-antennary and hybrid types of N-glycoproteins in human plasma. Thus, the machine learning methods can be combined with MS/MS to distinguish between different isoforms of fucosylated N-glycopeptides. Protein glycosylation is one of the most common post-translational modifications related to protein structure, stability, trafficking, and proteiN-protein interactions 1,2 Protein glycosylation is divided into O-or N-glycosylation according to the amino acid binding groups, which include the hydroxyl side chains of serine (S) or threonine (T) and the carboxy-amido nitrogen of asparagine (N) residues, respectively. The heterogeneity and complexity of N-glycosylation are due to the various combinations of four kinds of carbohydrate blocks, including N-acetylhexosamine (HexNAc; e.g., N-acetylglucosamine, N-acetylgalactosamine), hexose (Hex; e.g., glucose, galactose, mannose), fucose (Fuc), and sialic acid (Sia; N-acetylneuraminic acid). These combinations are made by their corresponding glycosyltransferases in the endoplasmic reticulum and Golgi apparatus 1. Various diseases, including cancer, involve the fucosylation of human N-glycosylation. This is due to two kinds of

show abstract

Section: Resultsmentioning

confidence: 99%

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Hwang

Jeong

Lee

et al. 2020

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The glycoform AFP‐L3, but not L1 or L2, is the marker for hepatocellular carcinoma. Wu and coworkers have reported that N‐glycan heterogeneity of α1‐acid glycoprotein (AGP) and haptoglobin (Hp) will regulate the interactions of plasma proteins. Meanwhile, it has been reported that phosphorylation of Hsp90 can affect its interaction with co‐chaperones or client proteins .…”

Section: Discussionmentioning

confidence: 99%

A novel pan‐cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic

Liu

Zhang

et al. 2019

Cancer Science

View full text Add to dashboard Cite

A sensitive and specific diagnosis biomarker, in principle scalable to most cancer types, is needed to reduce the prevalent cancer mortality. Meanwhile, the investigation of diagnosis determinants of a biomarker will facilitate the interpretation of its screening results in clinic. Here we design a large‐scale (1558 enrollments), multicenter (multiple hospitals), and cross‐validation (two datasets) clinic study to validate plasma Hsp90α quantified by ELISA as a pan‐cancer biomarker. ROC curve shows the optimum diagnostic cutoff is 69.19 ng/mL in discriminating various cancer patients from all controls (AUC 0.895, sensitivity 81.33% and specificity 81.65% in test cohort; AUC 0.893, sensitivity 81.72% and specificity 81.03% in validation cohort). Similar results are noted in detecting early‐stage cancer patients. Plasma Hsp90α maintains also broad‐spectrum for cancer subtypes, especially with 91.78% sensitivity and 91.96% specificity in patients with AFP‐limited liver cancer. In addition, we demonstrate levels of plasma Hsp90α are determined by ADAM10 expression, which will affect Hsp90α content in exosomes. Furthermore, Western blotting and PRM‐based quantitative proteomics identify that partial false ELISA‐negative patients secret high levels of plasma Hsp90α. Mechanism analysis reveal that TGFβ‐PKCγ gene signature defines a distinct pool of hyperphosphorylated Hsp90α at Theronine residue. In clinic, a mechanistically relevant population of false ELISA‐negative patients express also higher levels of PKCγ. In sum, plasma Hsp90α is a novel pan‐cancer diagnosis biomarker, and cancer diagnosis with plasma Hsp90α is particularly effective in those patients with high expression of ADAM10, but may be insufficient to detect the patients with low ADAM10 and those with hyperphosphorylated Hsp90α.

show abstract

“…Differences in glycan composition contribute greatly to glycosite heterogeneity, but structural isomers also contribute, such as conformational isomers (α vs β) or linkage differences (e.g., α2,3-linked vs. α2,6-linked sialic acid). Microheterogeneity can vary glycosite by glycosite or can be somewhat uniform across a given glycoprotein, and differences in glycan structures at a single glycosite can alter biological relevance (31)(32)(33)(34)(35)(36). The term "metaheterogeneity" has recently been proposed to describe these levels of variation across glycosites of a given protein (37).…”

Section: Introductionmentioning

confidence: 99%

A Pragmatic Guide to Enrichment Strategies for Mass Spectrometry–Based Glycoproteomics

Riley

Bertozzi

Pitteri

2021

Molecular & Cellular Proteomics

165

158

View full text Add to dashboard Cite

Glycosylation is a prevalent, yet heterogeneous modification with a broad range of implications in molecular biology. This heterogeneity precludes enrichment strategies that can be universally beneficial for all glycan classes. Thus, choice of enrichment strategy has profound implications on experimental outcomes. Here we review common enrichment strategies used in modern mass spectrometry (MS)-based glycoproteomic experiments, including lectins and other affinity chromatographies, hydrophilic interaction chromatography (HILIC) and its derivatives, porous graphitic carbon (PGC), reversible and irreversible chemical coupling strategies, and chemical biology tools that often leverage bioorthogonal handles. Interest in glycoproteomics continues to surge as MS instrumentation and software improve, so this review aims to help equip researchers with necessary information to choose appropriate enrichment strategies that best complement these efforts.

show abstract

N-glycan microheterogeneity regulates interactions of plasma proteins

Cited by 105 publications

References 56 publications

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

Machine Learning Classifies Core and Outer Fucosylation of N-Glycoproteins Using Mass Spectrometry

A novel pan‐cancer biomarker plasma heat shock protein 90alpha and its diagnosis determinants in clinic

A Pragmatic Guide to Enrichment Strategies for Mass Spectrometry–Based Glycoproteomics

Contact Info

Product

Resources

About