Characterization of N-linked intact glycopeptide signatures of plasma IgGs from patients with prostate carcinoma and benign prostatic hyperplasia for diagnosis pre-stratification

Deep learning enhanced the diagnostic merit of serum glycome for multiple cancers

Protein glycosylation is associated with the pathogenesis of various cancers. The utilization of certain glycans in cancer diagnosis models holds promise, yet their accuracy is not always guaranteed. Here, we investigated the utility of deep learning techniques, specifically random forests combined with transfer learning, in enhancing serum glycome's discriminative power for cancer diagnosis (including ovarian cancer, non-small cell lung cancer, gastric cancer, and esophageal cancer). We started with ovarian cancer and demonstrated that transfer learning can achieve superior performance in data-disadvantaged cohorts (AUROC >0.9), outperforming the approach of PLS-DA. We identified a serum glycan-biomarker panel including 18 serum N-glycans and 4 glycan derived traits, most of which were featured with sialylation. Furthermore, we validated advantage of the transfer learning scheme across other cancer groups. These findings highlighted the superiority of transfer learning in improving the performance of glycans-based cancer diagnosis model and identifying cancer biomarkers, providing a new high-fidelity cancer diagnosis venue.

Methods for quantification of glycopeptides by liquid separation and mass spectrometry

Recent advances in analytical techniques provide the opportunity to quantify even low-abundance glycopeptides derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Herein, we discuss the sample preparation procedures and the mass spectrometry (MS) strategies that have facilitated glycopeptide quantification, as well as the standards used for glycopeptide quantification. For sample preparation, various glycopeptide enrichment methods are summarized including the columns used for glycopeptide separation in liquid chromatography separation. For MS analysis strategies, MS1 level-based quantification and MS2 level-based quantification are described, either with or without labeling, where we have covered isotope labeling, TMT/iTRAQ labeling, data dependent acquisition, data independent acquisition, multiple reaction monitoring, and parallel reaction monitoring. The strengths and weaknesses of these methods are compared, particularly those associated with the figures of merit that are important for clinical biomarker studies and the pathological and functional studies of glycoproteins in various diseases. Possible future developments for glycopeptide quantification are discussed.

Progress of proteomics-driven precision medicine: From a glycosylation view

Currently, cancer is one of the leading causes of death worldwide, partially owing to the lack of early diagnosis methods and effective therapies. With the rapid development of various omics, the precision medicine strategy becomes a promising way to increase the survival rates by considering individual differences. Glycosylation is one of the most essential protein post-translational modifications and plays important roles in a variety of biological processes. Therefore, it is highly possible to acquire understanding of the molecular mechanisms as well as discover novel potential markers for diagnosis and prognosis based on glycoproteomics research. This review summarizes the recent glycoproteomics studies about N-glycosylation of several cancer types, mainly in the past 5 years. We also highlight corresponding mass spectrometry-based analytical methods to give a brief overview on the main techniques applied in glycoproteomics.

Comparative N-Glycoproteomics Analysis of Clinical Samples Via Different Mass Spectrometry Dissociation Methods

Site-specific N-glycosylation characterization requires intact N-glycopeptide analysis based on suitable tandem mass spectrometry (MS/MS) method. Electron-transfer/higher-energy collisional dissociation (EThcD), stepped collision energy/higher-energy collisional dissociation (sceHCD), higher-energy collisional dissociation-product-dependent electron-transfer dissociation (HCD-pd-ETD), and a hybrid mass spectrometry fragmentation method EThcD-sceHCD have emerged as valuable approaches for glycoprotein analysis. However, each of them incurs some compromise, necessitating the systematic performance comparisons when applied to the analysis of complex clinical samples (e.g., plasma, urine, cells, and tissues). Herein, we compared the performance of EThcD-sceHCD with those previous approaches (EThcD, sceHCD, HCD-pd-ETD, and sceHCD-pd-ETD) in the intact N-glycopeptide analysis, and determined its applicability for clinical N-glycoproteomic study. The intact N-glycopeptides of distinct samples, namely, plasma from prostate cancer (PCa) patients, urine from immunoglobulin A nephropathy (IgAN) patients, human hepatocarcinoma cell line (HepG2), and thyroid tissues from thyroid cancer (TC) patients were analyzed by these methods. We found that EThcD-sceHCD outperformed other methods in the balance of depth and accuracy of intact N-glycopeptide identification, and sceHCD and EThcD-sceHCD have good complementarity. EThcD-sceHCD holds great potential for biomarker discovery from clinical samples.

IgG N-glycans

Glycosylation, one of the most common post-translational modifications in mammalian cells, impacts many biological processes such as cell adhesion, proliferation and differentiation. As the most abundant glycoprotein in human serum, immunoglobulin G (IgG) plays a vital role in immune response and protection. There is a growing body of evidence suggests that IgG structure and function are modulated by attached glycans, especially N-glycans, and aberrant glycosylation is associated with disease states. In this chapter, we review IgG glycan repertoire and function, strategies for profiling IgG N-glycome and recent studies. Mass spectrometry (MS) based techniques are the most powerful tools for profiling IgG glycome. IgG glycans can be divided into high-mannose, biantennary complex and hybrid types, modified with mannosylation, core-fucosylation, galactosylation, bisecting GlcNAcylation, or sialylation. Glycosylation of IgG affects antibody half-life and their affinity and avidity for antigens, regulates crystallizable fragment (Fc) structure and Fcγ receptor signaling, as well as antibody effector function. Because of their critical roles, IgG N-glycans appear to be promising biomarkers for various disease states. Specific IgG glycosylation can convert a pro-inflammatory response to an anti-inflammatory activity. Accordingly, IgG glycoengineering provides a powerful approach to potentially develop effective drugs and treat disease. Based on the understanding of the functional role of IgG glycans, the development of vaccines with enhanced capacity and long-term protection are possible in the near future.

Systems analysis of plasma IgG intact N-glycopeptides from patients with chronic kidney diseases via EThcD-sceHCD-MS/MS

Immunoglobulin G (IgG) molecules modulate an immune response. However, site-specific N-glycosylation signatures of plasma IgG in patients with chronic kidney disease (CKD) remain unclear. This study aimed to propose a novel method to explore the N-glycosylation pattern of IgG and to compare it with reported methods. We separated human plasma IgG from 58 healthy controls (HC) and 111 patients with CKD. Purified IgG molecules were digested by trypsin. Tryptic peptides without enrichment of intact N-glycopeptides were analyzed using a combination of electron-transfer/higher-energy collisional dissociation (EThcD) and stepped collision energy/higher-energy collisional dissociation (sceHCD) mass spectrometry (EThcD-sceHCD-MS/MS). This resulted in higher spectral quality, more informative fragment ions, higher Byonic score, and nearly twice the depth of intact N-glycopeptide identification than sceHCD or EThcD alone. Site-specific N-glycosylation mapping revealed that intact N-glycopeptides were differentially expressed in HC and CKD patients; thus, it can be a diagnostic tool. This study provides a method for the determination of glycosylation patterns in CKD and a framework for understanding the role of IgG in the pathophysiology of CKD. Data are available via ProteomeXchange with identifier PXD027174.

Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins

The glycoprotein spike (S) on the surface of severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a determinant for viral invasion and host immune response. Herein, we characterized the site-specific N-glycosylation of S protein at the level of intact glycopeptides. All 22 potential N-glycosites were identified in the S-protein protomer and were found to be preserved among the 753 SARS-CoV-2 genome sequences. The glycosites exhibited glycoform heterogeneity as expected for a human cell-expressed protein subunit. We identified masses that correspond to 157 N-glycans, primarily of the complex type. In contrast, the insect cell-expressed S protein contained 38 N-glycans, completely of the high-mannose type. Our results revealed that the glycan types were highly determined by the differential processing of N-glycans among human and insect cells, regardless of the glycosites' location. Moreover, the N-glycan compositions were conserved among different sizes of subunits. Our study indicates that the S protein N-glycosylation occurs regularly at each site, albeit the occupied N-glycans were diverse and heterogenous. This N-glycosylation landscape and the differential N-glycan patterns among distinct host cells are expected to shed light on the infection mechanism and present a positive view for the development of vaccines and targeted drugs.

Site-specific N-glycosylation Characterization of Recombinant SARS-CoV-2 Spike Proteins

The glycoprotein spike (S) on the surface of severe acute respiratory syndrome coronavirus (SARS-CoV-2) is a determinant for viral invasion and host immune response. Herein, we characterized the site-specific N-glycosylation of S protein at the level of intact glycopeptides. All 22 potential N-glycosites were identified in the S-protein protomer and were found to be preserved among the 753 SARS-CoV-2 genome sequences. The glycosites exhibited glycoform heterogeneity as expected for a human cell-expressed protein subunit. We identified masses that correspond to 157 N-glycans, primarily of the complex type. In contrast, the insect cell-expressed S protein contained 38 N-glycans, completely of the high-mannose type. Our results revealed that the glycan types were highly determined by the differential processing of N-glycans among human and insect cells, regardless of the glycosites' location. Moreover, the N-glycan compositions were conserved among different sizes of subunits. Our study indicates that the S protein N-glycosylation occurs regularly at each site, albeit the occupied N-glycans were diverse and heterogenous. This N-glycosylation landscape and the differential N-glycan patterns among distinct host cells are expected to shed light on the infection mechanism and present a positive view for the development of vaccines and targeted drugs.