An integrated approach for comprehensive profiling and quantitation of IgG-Fc glycopeptides with application to rheumatoid arthritis

Integrative Proteomics and N-Glycoproteomics Analyses of Rheumatoid Arthritis Synovium Reveal Immune-Associated Glycopeptides

Rheumatoid arthritis (RA) is a typical autoimmune disease characterized by synovial inflammation, synovial tissue hyperplasia, and destruction of bone and cartilage. Protein glycosylation plays key roles in the pathogenesis of RA but in-depth glycoproteomics analysis of synovial tissues is still lacking. Here, by using a strategy to quantify intact N-glycopeptides, we identified 1260 intact N-glycopeptides from 481 N-glycosites on 334 glycoproteins in RA synovium. Bioinformatics analysis revealed that the hyper-glycosylated proteins in RA were closely linked to immune responses. By using DNASTAR software, we identified 20 N-glycopeptides whose prototype peptides were highly immunogenic. We next calculated the enrichment scores of nine types of immune cells using specific gene sets from public single-cell transcriptomics data of RA and revealed that the N-glycosylation levels at some sites, such as IGSF10_N2147, MOXD2P_N404, and PTCH2_N812, were significantly correlated with the enrichment scores of certain immune cell types. Furthermore, we showed that aberrant N-glycosylation in the RA synovium was related to increased expression of glycosylation enzymes. Collectively, this work presents, for the first time, the N-glycoproteome of RA synovium and describes immune-associated glycosylation, providing novel insights into RA pathogenesis.

Affinity Purification Coupled to Stable Isotope Dilution LC-MS/MS Analysis to Discover IgG4 Glycosylation Profiles for Autoimmune Pancreatitis

Type 1 autoimmune pancreatitis (AIP) is categorized as an IgG4-related disease (IgG4-RD), where a high concentration of plasma IgG4 is one of the common biomarkers among patients. IgG Fc-glycosylation has been reported to be potential biosignatures for diseases. However, human IgG3 and IgG4 Fc-glycopeptides from populations in Asia were found to be isobaric ions when using LC-MS/MS as an analytical tool. In this study, an analytical workflow that coupled affinity purification and stable isotope dilution LC-MS/MS was developed to dissect IgG4 glycosylation profiles for autoimmune pancreatitis. Comparing the IgG4 and glycosylation profiles among healthy controls, patients with pancreatic ductal adenocarcinoma (PDAC), and AIP, the IgG4 glycosylations from the AIP group were found to have more digalactosylation (compared to PDAC) and less monogalactosylation (compared to HC). In addition, higher fucosylation and sialylation profiles were also discovered for the AIP group. The workflow is efficient and selective for IgG4 glycopeptides, and can be used for clinical biosignature discovery.

Fc Glycosylation Characterization of Human Immunoglobulins G Using Immunocapture and LC-MS

Immunoglobulins G (IgG) are proteins produced by the immune system of higher life forms that play a central role in the defense against microbial pathogens. IgG bind pathogens with the hypervariable Fab component and mediate a diversity of effector functions by binding to immune effector cells via their crystallizable (Fc) component. All IgG Fc carry a polymorphic N-glycan that regulates its binding properties and thereby its effector functions. The glycosylation profile of IgG Fc is modulated by physiological and pathological conditions, including infectious diseases and inflammatory disorders. Characterization of IgG Fc glycosylation profiles is a promising approach to understand the pathogenesis of diseases involving the immune system and to develop novel biomarkers of disease activity. Measuring the proportion of the different IgG Fc glycoforms remains an analytical challenge, that requires a sensitive and reproducible analytical approach.This chapter describes an optimized approach for the preparation and the analysis of Fc N-glycans from total serum or plasma IgG using magnetic beads, RapiFluor MS label©, and LC-MS.

Recent Advances in Analytical Approaches for Glycan and Glycopeptide Quantitation

Growing implications of glycosylation in physiological occurrences and human disease have prompted intensive focus on revealing glycomic perturbations through absolute and relative quantification. Empowered by seminal methodologies and increasing capacity for detection, identification, and characterization, the past decade has provided a significant increase in the number of suitable strategies for glycan and glycopeptide quantification. Mass-spectrometry-based strategies for glycomic quantitation have grown to include metabolic incorporation of stable isotopes, deposition of mass difference and mass defect isotopic labels, and isobaric chemical labeling, providing researchers with ample tools for accurate and robust quantitation. Beyond this, workflows have been designed to harness instrument capability for label-free quantification, and numerous software packages have been developed to facilitate reliable spectrum scoring. In this review, we present and highlight the most recent advances in chemical labeling and associated techniques for glycan and glycopeptide quantification.

Sialylated Immunoglobulins for the Treatment of Immuno-Inflammatory Diseases

Immunoglobulins are the potent effector proteins of the humoral immune response. In the course of evolution, immunoglobulins have formed extremely diverse types of molecular structures with antigen-recognizing, antigen-binding, and effector functions embedded in a single molecule. Polysaccharide moiety of immunoglobulins plays the essential role in immunoglobulin functioning. There is growing evidence that the carbohydrate composition of immunoglobulin-linked glycans, and especially their terminal sialic acid residues, provide a key effect on the effector functions of immunoglobulins. Possibly, sialylation of Fc glycan is a common mechanism of IgG anti-inflammatory action in vivo. Thus, the post-translational modification (glycosylation) of immunoglobulins opens up significant possibilities in the diagnosis of both immunological and inflammatory disorders and in their therapies. This review is focused on the analysis of glycosylation of immunoglobulins, which can be a promising addition to improve existing strategies for the diagnosis and treatment of various immuno-inflammatory diseases.

Development of efficient on-bead protein elution process coupled to ultra-high performance liquid chromatography-tandem mass spectrometry to determine immunoglobulin G subclass and glycosylation for discovery of bio-signatures in pancreatic disease

Type 1 autoimmune pancreatitis (AIP) is a kind of IgG4-related disease in which higher IgG4 and total IgG levels have been found in patient serum. Due to the similar imaging features and laboratory parameters between AIP and pancreatic ductal adenocarcinoma (PDAC), a differential diagnosis is still challenging. Since IgG profiles can be potential bio-signatures for disease, we developed and validated a method which coupled on-bead enzymatic protein elution process to an efficient UHPLC-MS/MS method to determine IgG subclass and glycosylation. A stable-isotope labeled IgG was incorporated as internal standard to achieve accurate quantification. For calibration curves, the correlation coefficients for total IgG and the four IgG subclasses were higher than 0.995. Intraday (n = 5) and interday (n = 3) precisions of the peak area ratios of LLOQ, low, medium, and high QC samples were all less than 6.6% relative standard deviation (% RSD), and the accuracies were between 93.5 and 114.9%. Calibration curves, precision, and accuracy were also evaluated for 26 IgG glycopeptides. The method was applied to samples from healthy controls and patients with AIP and PDAC. Distinct IgG patterns were discovered among the groups, and 7 glycopeptides showed high potential in differentiating AIP and PDAC. The results demonstrated that the developed method is suitable for multi-feature analysis of human IgG, and the discovered IgG profiles can be used as bio-signatures for AIP and PDAC.