Characterization of Site-Specific <i>N</i>-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC–MS/MS

Lee, Ju‐Yeon; Lee, Hyun Kyoung; Park, Gun Wook; Hwang, Heeyoun; Jeong, Hoi Keun; Yun, Ki Na; Ji, Eun Sun; Kim, Kwang Hoe; Kim, Jun Seok; Kim, Jong Won; Yun, Sung Ho; Choi, Chi Won; Kim, Kyung Sik; Lim, Jong Sun; Jeong, Seul Ki; Paik, Young Ki; Lee, Soo-Youn; Park, Ji Sook; Kim, Su Yeon; Choi, Young Jin; Kim, Yong In; Seo, Jawon; Cho, Je Yoel; Oh, Myoung Jin; Seo, Nari; An, Hyun Joo; Kim, Jin Young; Yoo, Jong Shin

doi:10.1021/acs.jproteome.5b01159

Cited by 36 publications

(49 citation statements)

References 76 publications

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“…was performed using the ZIC-HILIC kit according to the manufacturer's instructions, with minor modifications 35 . Rehydrated human plasma (30 μg) was diluted with 50 μL ZIC binding buffer.…”

Section: Glycopeptide Enrichment Prior To Lc-ms/ms Analysis Of the Hmentioning

confidence: 99%

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

Hwang

Jeong

Lee

et al. 2020

Sci Rep

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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

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Section: Glycopeptide Enrichment Prior To Lc-ms/ms Analysis Of the Hmentioning

confidence: 99%

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

Hwang

Jeong

Lee

et al. 2020

Sci Rep

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“…As with all analyses of the released TSNG, the here-reported differences may originate from changes in protein glycosylation, or from differences in the relative abundances of glycoproteins in serum. Nonetheless, the changing glycosylation phenotypes seem to reflect immune modulation of either IgG-Fc (predominantly nonsialylated FA2) (31,64), IgG-Fab, and other plasma-cell-derived immunoglobulins (highly sialylated FA2) (31,64) or acute-phase glycoproteins such as alpha-1-antitrypsin and alpha-1-acid glycoprotein (tri-and tetraantennary species) (31,65,67,68). In addition, the previously reported MALDI-TOF-MS association of A3FGS with DAS28(3)-CRP was reproduced (62), although its protein of origin is as of yet unclear.…”

Section: Discussionmentioning

confidence: 99%

High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes

Reiding

Bondt

Hennig

et al. 2019

Molecular & Cellular Proteomics

106

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N-Glycosylation is a fundamentally important protein modification with a major impact on glycoprotein characteristics such as serum half-life and receptor interaction. More than half of the proteins in human serum are glycosylated, and the relative abundances of protein glycoforms often reflect alterations in health and disease. Several analytical methods are currently capable of analyzing the total serum N-glycosylation in a highthroughput manner. Here we evaluate and compare the performance of three high-throughput released N-glycome analysis methods. Included were hydrophilic-interaction ultra-highperformance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLD) with 2-aminobenzamide labeling of the glycans, multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (xCGE-LIF) with 8-aminopyrene-1,3,6-trisulfonic acid labeling, and matrix-assisted laser desorption/ionization time-offlight mass spectrometry (MALDI-TOF-MS) with linkagespecific sialic acid esterification. All methods assessed the same panel of serum samples, which were obtained at multiple time points during the pregnancies and postpartum periods of healthy women and patients with rheumatoid arthritis (RA). We compared the analytical methods on their technical performance as well as on their ability to describe serum protein N-glycosylation changes throughout pregnancy, with RA, and with RA disease activity. Overall, the methods proved to be similar in their detection and relative quantification of serum protein N-gly-From the ‡Center for Proteomics and Metabolomics,

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“…However, the ability to analyze the glycopeptides has remained a challenge. In recent work, there have been a number of techniques developed to deal with this problem (Table ), including collision‐induced dissociation (CID)/electron‐transfer dissociation (ETD) (Alley et al, ), CID/higher‐energy collisional dissociation (HCD) (Segu & Mechref, ; Lee et al, ), stepped HCD (Liu et al, ; Yin et al, ), and electron‐transfer/higher‐energy collision dissociation (EThcD) (Yu et al, ; Chen et al, ; Glover et al, ), for example. The use of CID/ETD MS to analyze glycopeptides has been recently reviewed by Mechref (Mechref, ).…”

Section: Methodsmentioning

confidence: 99%

Glycoproteomic markers of hepatocellular carcinoma‐mass spectrometry based approaches

Zhu¹,

Warner²,

Parikh³

et al. 2018

Mass Spectrometry Reviews

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Hepatocellular carcinoma (HCC) is the third most‐common cause of cancer‐related death worldwide. Most cases of HCC develop in patients that already have liver cirrhosis and have been recommended for surveillance for an early onset of HCC. Cirrhosis is the final common pathway for several etiologies of liver disease, including hepatitis B and C, alcohol, and increasingly non‐alcoholic fatty liver disease. Only 20–30% of patients with HCC are eligible for curative therapy due primarily to inadequate early‐detection strategies. Reliable, accurate biomarkers for HCC early detection provide the highest likelihood of curative therapy and survival; however, current early‐detection methods that use abdominal ultrasound and serum alpha fetoprotein are inadequate due to poor adherence and limited sensitivity and specificity. There is an urgent need for convenient and highly accurate validated biomarkers for HCC early detection. The theme of this review is the development of new methods to discover glycoprotein‐based markers for detection of HCC with mass spectrometry approaches. We outline the non‐mass spectrometry based methods that have been used to discover HCC markers including immunoassays, capillary electrophoresis, 2‐D gel electrophoresis, and lectin‐FLISA assays. We describe the development and results of mass spectrometry‐based assays for glycan screening based on either MALDI‐MS or ESI analysis. These analyses might be based on the glycan content of serum or on glycan screening for target molecules from serum. We describe some of the specific markers that have been developed as a result, including for proteins such as Haptoglobin, Hemopexin, Kininogen, and others. We discuss the potential role for other technologies, including PGC chromatography and ion mobility, to separate isoforms of glycan markers. Analyses of glycopeptides based on new technologies and innovative softwares are described and also their potential role in discovery of markers of HCC. These technologies include new fragmentation methods such as EThcD and stepped HCD, which can identify large numbers of glycopeptide structures from serum. The key role of lectin extraction in various assays for intact glycopeptides or their truncated versions is also described, where various core‐fucosylated and hyperfucosylated glycopeptides have been identified as potential markers of HCC. Finally, we describe the role of LC‐MRMs or lectin‐FLISA MRMs as a means to validate these glycoprotein markers from patient samples. These technological advancements in mass spectrometry have the potential to lead to novel biomarkers to improve the early detection of HCC.

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Characterization of Site-Specific N-Glycopeptide Isoforms of α-1-Acid Glycoprotein from an Interlaboratory Study Using LC–MS/MS

Cited by 36 publications

References 76 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

High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes

Glycoproteomic markers of hepatocellular carcinoma‐mass spectrometry based approaches

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