Peptide-N-glycosidase F or A treatment and procainamide-labeling for identification and quantification of N-glycans in two types of mammalian glycoproteins using UPLC and LC-MS/MS

Analysis of N- and O-linked site-specific glycosylation by ion mobility mass spectrometry: State of the art and future directions

Glycosylation, the major post-translational modification of proteins, significantly increases the diversity of proteoforms. Glycans are involved in a variety of pivotal structural and functional roles of proteins, and changes in glycosylation are profoundly connected to the progression of numerous diseases. Mass spectrometry (MS) has emerged as the gold standard for glycan and glycopeptide analysis because of its high sensitivity and the wealth of fragmentation information that can be obtained. Various separation techniques have been employed to resolve glycan and glycopeptide isomers at the front end of the MS. However, differentiating structures of isobaric and isomeric glycopeptides constitutes a challenge in MS-based characterization. Many reports described the use of various ion mobility-mass spectrometry (IM-MS) techniques for glycomic analyses. Nevertheless, very few studies have focused on N- and O-linked site-specific glycopeptidomic analysis. Unlike glycomics, glycoproteomics presents a multitude of inherent challenges in microheterogeneity, which are further exacerbated by the lack of dedicated bioinformatics tools. In this review, we cover recent advances made towards the growing field of site-specific glycosylation analysis using IM-MS with a specific emphasis on the MS techniques and capabilities in resolving isomeric peptidoglycan structures. Furthermore, we discuss commonly used software that supports IM-MS data analysis of glycopeptides.

The role of glycosylation in non-productive adsorption of cellulase to lignin isolated from pretreated corn stover

Glycosylation, a general post-translational modification for fungal cellulase, has been shown to affect cellulase binding to its substrate. However, the exact impact of glycosylation on cellulase-lignin interaction remain unclear. Here, we demonstrated that the lignin isolated from tetrahydrofuran-pretreated corn stover exhibits strong adsorption capability to cellulase due to its negatively charged and porous structure. For the cellulases with varying glycosylation levels, the less-glycosylated protein showed high adsorption capability to lignin, and that trend was observed for the main cellulase components secreted by Penicillium oxilicum, including endoglucanase PoCel5B, cellobiohydrolase PoCel7A-2, and β-glucosidase PoBgl1. Additionally, N-glycan sites and motifs were examined using mass spectrometry, and protein structures with N-glycans were constructed, where PoBgl1 and PoCel7A-2 contained 13 and 1 glycosylated sites respectively. The results of molecular dynamics simulations indicated that the N-glycans impacted on the solvent-accessible surface area and secondary structure of protein, and the binding conformation of lignin fragment on cellulase, resulting in a decrease in binding energy (14 kcal/mol for PoBgl1 and 13 kcal/mol for PoCel7A-2), particularly for van der Waals and electrostatic interaction. Those findings suggested that glycosylation negatively impacted the lignin-cellulase interaction, providing a theoretical basis for the rational engineering of enzymes to reduce lignin-enzyme interaction.

Labeling of Mucin-Type O-Glycans for Quantification Using Liquid Chromatography and Fluorescence Detection

O-glycosylation is a common post-translational modification that is essential for the defensive properties of mucus barriers. Incomplete and altered O-glycosylation is often linked to severe diseases, such as cancer, cystic fibrosis, and chronic obstructive pulmonary disease. Originating from a nontemplate-driven biosynthesis, mucin-type O-glycan structures are very complex. They are often present as heterogeneous mixtures containing multiple isomers. Therefore, the analysis of complex O-glycan mixtures usually requires hyphenation of orthogonal techniques such as liquid chromatography (LC), ion mobility spectrometry, and mass spectrometry (MS). However, MS-based techniques are mainly qualitative. Moreover, LC separation of O-glycans often lacks reproducibility and requires sophisticated data treatment and analysis. Here we present a mucin-type O-glycomics analysis workflow that utilizes hydrophilic interaction liquid chromatography for separation and fluorescence labeling for detection and quantification. In combination with mass spectrometry, a detailed analysis on the relative abundance of specific mucin-type O-glycan compositions and features, such as fucose, sialic acids, and sulfates, is performed. Furthermore, the average number of monosaccharide units of O-glycans in different samples was determined. To demonstrate universal applicability, the method was tested on mucins from different tissue types and mammals, such as bovine submaxillary mucins, porcine gastric mucins, and human milk mucins. To account for day-to-day retention time shifts in O-glycan separations and increase the comparability between different instruments and laboratories, we included fluorescently labeled dextran ladders in our workflow. In addition, we set up a library of glucose unit values for all identified O-glycans, which can be used to simplify the identification process of glycans in future analyses.

LeGenD: determining N-glycoprofiles using an explainable AI-leveraged model with lectin profiling

Glycosylation affects many vital functions of organisms. Therefore, its surveillance is critical from basic science to biotechnology, including biopharmaceutical development and clinical diagnostics. However, conventional glycan structure analysis faces challenges with throughput and cost. Lectins offer an alternative approach for analyzing glycans, but they only provide glycan epitopes and not full glycan structure information. To overcome these limitations, we developed LeGenD, a lectin and AI-based approach to predict N-glycan structures and determine their relative abundance in purified proteins based on lectin-binding patterns. We trained the LeGenD model using 309 glycoprofiles from 10 recombinant proteins, produced in 30 glycoengineered CHO cell lines. Our approach accurately reconstructed experimentally-measured N-glycoprofiles of bovine Fetuin B and IgG from human sera. Explanatory AI analysis with SHapley Additive exPlanations (SHAP) helped identify the critical lectins for glycoprofile predictions. Our LeGenD approach thus presents an alternative approach for N-glycan analysis.

Profiling of aberrant sialylated N-glycans in hepatocellular carcinoma by liquid chromatography mass spectrometry

BACKGROUND

Aberrant sialylation is closely associated with the tumorigenesis, progression, and metastasis, and may be of importance for disease diagnosis. However, the analysis of altered expression of sialylated glycans (SGs) in blood is particularly challenging due to the low content and poor ionization efficiency of sialylated glycans in mass spectrometry.

METHODS

An analytical strategy based on enrichment of SGs, liquid chromatography-high resolution mass spectrometric detection, and automatic glycan annotation was developed to profile the sialylated N-glycome in serum. The enrichment of sialylated glycans was accomplished using cationic cotton via electrostatic and hydrogen interaction. Using partial least squares-discriminant analysis (PLS-DA), the approach was applied for nontarget screening and profiling of aberrant sialylated N-glycans in hepatocellular carcinoma (HCC).

RESULTS

55 SGs were identified in human serum, and three important SGs (SG35, SG45, and SG46) were screened to have good diagnostic specificity for HCC. Their areas under the receiver operating characteristic (ROC) curve (AUC) were higher than α-fetoprotein (AFP)'s (AUC = 0.85), at 0.88, 0.87, and 0.91, respectively. When three SGs are combined, the diagnostic specificity for HCC may increase to 94 %. The fact that SGs biomarkers are sensitive to AFP-Negative HCC is very noteworthy.

CONCLUSIONS

The method significantly advanced the search for sialylated glycan-based cancer biomarkers. In comparison to traditional indicators like AFP and imaging tools, SGs showed a higher diagnostic sensitivity for HCC.

Label-Free Liquid Chromatography-Mass Spectrometry Quantitation of Relative N- and O-Glycan Concentrations in Human Milk in Japan

Human milk is abundant in carbohydrates and includes human milk oligosaccharides (HMOs) and N/O-glycans conjugated to proteins. HMO compositions and concentrations vary in individuals according to the maternal secretor status based on the fucosyltransferase 2 genotype; however, the profile of N/O-glycans remains uninvestigated because of the analytical complexity. Herein, we applied a label-free chromatography-mass spectrometry (LC-MS) technique to elucidate the variation in the composition and concentration of N/O-glycans in human milk. We used label-free LC-MS to relatively quantify 16 N-glycans and 12 O-glycans in 200 samples of Japanese human milk (1-2 months postpartum) and applied high performance anion exchange chromatography with pulsed amperometric detection to absolutely quantify the concentrations of 11 representative HMOs. Cluster analysis of the quantitative data revealed that O-glycans and several HMOs were classified according to the presence or absence of fucose linked to galactose while N-glycans were classified into a different group from O-glycans and HMOs. O-glycans and HMOs with fucose linked to galactose were more abundant in human milk from secretor mothers than from nonsecretor mothers. Thus, secretor status influenced the composition and concentration of HMOs and O-glycans but not those of N-glycans in human milk.