Hepatocyte nuclear factor 1 alpha (HNF1A), hepatocyte nuclear factor 4 alpha (HNF4A), and forkhead box protein A2 (FOXA2) are key transcription factors that regulate a complex gene network in the liver, creating a regulatory transcriptional loop. The Encode and ChIP-Atlas databases identify the recognition sites of these transcription factors in many glycosyltransferase genes. Our in silico analysis of HNF1A, HNF4A, and FOXA2 binding to the 10 candidate glyco-genes studied in this work confirms a significant enrichment of these transcription factors specifically in the liver. Our previous studies identified HNF1A as a master regulator of fucosylation, glycan branching, and galactosylation of plasma glycoproteins. Here, we aimed to functionally validate the role of the three transcription factors on downstream glyco-gene transcriptional expression and the possible effect on glycan phenotype. We used the state-of-the-art clustered regularly interspaced short palindromic repeats/dead Cas9 (CRISPR/dCas9) molecular tool for the downregulation of the HNF1A, HNF4A, and FOXA2 genes in HepG2 cells—a human liver cancer cell line. The results show that the downregulation of all three genes individually and in pairs affects the transcriptional activity of many glyco-genes, although downregulation of glyco-genes was not always followed by an unambiguous change in the corresponding glycan structures. The effect is better seen as an overall change in the total HepG2 N-glycome, primarily due to the extension of biantennary glycans. We propose an alternative way to evaluate the N-glycome composition via estimating the overall complexity of the glycome by quantifying the number of monomers in each glycan structure. We also propose a model showing feedback loops with the mutual activation of HNF1A–FOXA2 and HNF4A–FOXA2 affecting glyco-genes and protein glycosylation in HepG2 cells.

Immunoglobulin G (IgG) N-glycosylation plays a crucial role in the development of inflammatory diseases. This study aimed to evaluate the diagnostic performance of IgG for gastrointestinal (GI) cancer subtypes. A total of 749 GI cancer patients were enrolled from the Cancer Hospital, Chinese Academy of Medical Sciences, including esophageal cancer (EC), gastric cancer (GC), colorectal cancer (CRC), and pancreatic cancer (PC) patients. Hydrophilic interaction liquid chromatography using ultra-performance liquid chromatography (HILIC-UPLC) was employed to analyze the composition of the plasma IgG N-glycome. The levels of circulating inflammatory cytokines were detected by means of a Bio-Plex Pro Human Th17 Cytokine Assay. Canonical correlation analysis (CCA) was used to explore the correlation between IgG N-glycosylation patterns and inflammatory cytokines. A Lasso algorithm, accompanied by a logistic regression model, was used to develop a glycan-based model for differentiating GI cancer patients from healthy individuals. The levels of sialylation and galactosylation were significantly decreased among EC, GC, CRC, and PC patients, whereas the abundance of glycans with bisecting Nacetylglucosamine (GlcNAc) was increased in GI cancer patients in comparison with the healthy controls. Moreover, only PC patients had a decreased level of fucosylation. The levels of interleukin 1β (IL-1β), IL- 31, and soluble CD40 ligand (sCD40L) were significantly higher in GI cancer patients than in the controls. In addition, the composition of IgG N-glycans was correlated with that of inflammatory cytokines (r = 0.556). The glycan-based models for diagnosing GI cancers exhibited an excellent performance, with areas under the receiver operating characteristic curves (AUCs) of 0.972 for EC, 0.871 for GC, 0.867 for CRC, and 0.907 for PC. Our findings demonstrate that IgG N-glycosylation plays an important role in modulating the pathogenesis of GI cancers. Serological IgG N-glycosylation is thus a potential candidate for noninvasively assisting in the clinical diagnosis of GI cancer subtypes.

Immunoglobulin G (IgG) is the most abundant plasma glycoprotein and a prominent humoral immune mediator. Glycan composition affects the affinity of IgG to ligands and consequent immune responses. The modification of IgG N-glycosylation is considered to be one of the various mechanisms by which sex hormones modulate the immune system. Although the menstrual cycle is the central sex hormone-related physiological process in most women of reproductive age, IgG N-glycosylation dynamics during the menstrual cycle have not yet been investigated. To fill this gap, we profiled the plasma IgG N-glycans of 70 healthy premenopausal women at 12 time points during their menstrual cycles (every 7 days for 3 months) using hydrophilic interaction ultra-performance liquid chromatography (HILIC-UPLC). We observed cyclic periodic changes in the N-glycosylation of IgG in association with the menstrual cycle phase and sex hormone concentration in plasma. On the integrated cohort level, the modeled average menstrual cycle effect on the abundance of IgG N-glycosylation traits was low for each trait, with the highest being 1.1% for agalactosylated N-glycans. However, intrapersonal changes were relatively high in some cases; for example, the largest difference between theminimum and maximum values during themenstrual cycle was up to 21% for sialylated N-glycans. Across all measurements, the menstrual cycle phase could explain up to 0.72% of the variation in the abundance of a single IgG glycosylation trait of monogalactosylation. In contrast, up to 99% of the variation in the abundance of digalactosylation could be attributed to interpersonal differences in IgG N-glycosylation. In conclusion, the average extent of changes in the IgG N-glycopattern that occur during the menstrual cycle is small; thus, the IgG N-glycoprofiling of women in large sample-size studies can be performed regardless of menstrual cycle phase.

Aberrant glycosylation is considered to be a hallmark of colorectal cancer (CRC), as demonstrated by various studies. While the N-glycosylation of cell lines and serum has been widely examined, the analysis of cancer-associated N-glycans from tissues has been hampered by the heterogeneity of tumors and the complexity of N-glycan structures. To overcome these obstacles, we present a study using laser capture microdissection that makes it possible to largely deconvolute distinct N-glycomic signatures originating from different regions of heterogeneous tissues including cancerous, stromal, and healthy mucosa cells. N-glycan alditols were analyzed by means of porous graphitized carbon liquid chromatography-electrospray ionization tandem mass spectrometry, enabling the differentiation and structural characterization of isomeric species. In total, 116 N-glycans were identified that showed profound differences in expression among cancer, stroma, and normal mucosa. In comparison with healthy mucosa, the cancer cells showed an increase in α2-6 sialylation and monoantennary N-glycans, as well as a decrease in bisected N-glycans. Moreover, specific sialylated and (sialyl-)LewisA/X antigen-carrying N-glycans were exclusively expressed in cancers. In comparison with cancer, the stroma showed lower levels of oligomannosidic and monoantennary N-glycans, LewisA/X epitopes, and sulfation, as well as increased expression of (core-)fucosylation and α2-3 sialylation. Our study reveals the distinct N-glycomic profiles of different cell types in CRC tumor and control tissues, proving the necessity of their separate analysis for the discovery of cancer-associated glycans.

As the roles of glycans in health and disease continue to be unraveled, it is becoming apparent that glycans'  immense complexity cannot be ignored. To fully delineate glycan structures, we developed an integrative approach combining a set of cost-effective, widespread, and easy-to-handle analytical methods. The key feature of our workflow is the exploitation of a removable fluorescent label—exemplified by 9-fluorenylmethyl chloroformate (Fmoc)—to bridge the gap between diverse glycoanalytical methods, especially multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (xCGE-LIF) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Through the detailed structural analysis of selected, dauntingly complex N-glycans from chicken ovalbumin, horse serum, and bovine transferrin, we illustrate the capabilities of the presented strategy. Moreover, this approach "visualizes" N-glycans that have been difficult to identify thus far—such as the sulfated glycans on human immunoglobulin A—including minute changes in glycan structures, potentially providing useful new targets for biomarker discovery.

Ketodeoxynononic acid (Kdn) is a rather uncommon class of sialic acid in mammals. However, associations have been found between elevated concentrations of free or conjugated Kdn in relation to human cancer progression. Hitherto, there has been a lack of conclusive evidence that Kdn occurs on (specific) human glycoproteins (conjugated Kdn). Here, we report for the first time that Kdn is expressed on prostate-specific antigen (PSA) N-linked glycans derived from human seminal plasma and urine. Interestingly, Kdn was found only in an α2,3-linkage configuration on an antennary galactose, indicating a highly specific biosynthesis. This unusual glycosylation feature was also identified in a urinary PSA cohort in relation to prostate cancer (PCa), although no differences were found between PCa and non-PCa patients. Further research is needed to investigate the occurrence, biosynthesis, biological role, and biomarker potential of both free and conjugated Kdn in humans.

Most human-secreted and membrane-bound proteins have covalently attached oligosaccharide chains, or glycans. Glycosylation influences the physical and chemical properties of proteins, as well as their biological functions. Unsurprisingly, alterations in protein glycosylation have been implicated in a growing number of human diseases, and glycans are increasingly being considered as potential therapeutic targets, an essential part of therapeutics, and biomarkers. Although glycosylation pathways are biochemically well-studied, little is known about the networks of genes that guide the cell- and tissue-specific regulation of these biochemical reactions in humans in vivo. The lack of a detailed understanding of the mechanisms regulating glycome variation and linking the glycome to human health and disease is slowing progress in clinical applications of human glycobiology. Two of the tools that can provide much sought-after knowledge of human in vivo glycobiology are human genetics and genomics, which offer a powerful data-driven agnostic approach for dissecting the biology of complex traits. This review summarizes the current state of human populational glycogenomics. In Section 1, we provide a brief overview of the N-glycan's structural organization, and in Section 2, we give a description of the major blood plasma glycoproteins. Next, in Section 3, we summarize, systemize, and generalize the results from current N-glycosylation genome-wide association studies (GWASs) that provide novel knowledge of the genetic regulation of the populational variation of glycosylation. Until now, such studies have been limited to an analysis of the human blood plasma N-glycome and the N-glycosylation of immunoglobulin G and transferrin. While these three glycomes make up a rather limited set compared with the enormous multitude of glycomes of different tissues and glycoproteins, the study of these three does allow for powerful analysis and generalization. Finally, in Section 4, we turn to genes in the established loci, paying particular attention to genes with strong support in Section 5. At the end of the review, in Sections 6 and 7, we describe special cases of interest in light of new discoveries, focusing on possible mechanisms of action and biological targets of genetic variation that have been implicated in human protein N-glycosylation.

The use of an altered immunoglobulin G (IgG) N-glycan pattern as an inflammation metric has been reported in subclinical atherosclerosis and metabolic disorders, both of which are important risk factors in cardiovascular health. However, the usable capacity of IgG N-glycosylation profiles for the risk stratification of cardiovascular diseases (CVDs) remains unknown. This study aimed to develop a cardiovascular aging index for tracking cardiovascular risk using IgG N-glycans. This cross-sectional investigation enrolled 1465 individuals aged 40–70 years from the Busselton Healthy and Ageing Study. We stepwise selected the intersection of altered N-glycans using feature-selection methods in machine learning (recursive feature elimination and penalized regression algorithms) and developed an IgG N-glycosylation cardiovascular age (GlyCage) index to reflect the deviation from calendar age attributable to cardiovascular risk. The strongest contributors to GlyCage index were fucosylated N-glycans with bisecting N-acetylglucosamine (GlcNAc) (glycan peak 6 (GP6), FA2B,) and digalactosylated N-glycans with bisecting GlcNAc (GP13, A2BG2). A one-unit increase of GlyCage was significantly associated with a higher Framingham ten-year cardiovascular risk (odds ratio (OR), 1.09; 95% confidence interval (95% CI): 1.05–1.13) and probability of CVDs (OR, 1.07; 95% CI: 1.01–1.13) independent of calendar age. Individuals with excessive GlyCage (exceeding a calendar age > 3 years) had an increased cardiovascular risk and probability of CVDs, with adjusted ORs of 2.22 (95% CI: 1.41–3.53) and 2.71 (95% CI: 1.25–6.41), respectively. The area under curve (AUC) values of discriminating high cardiovascular risk and events were 0.73 and 0.65 for GlyCage index, and 0.65 and 0.63 for calendar age. The GlyCage index developed in this study can thus be used to track cardiovascular health using IgG N-glycosylation profiles. The distance between GlyCage and calendar age independently indicates the cardiovascular risk, suggesting that IgG N-glycosylation plays a role in the pathogenesis of CVDs. The generalization of the observed associations and the predictive capability of GlyCage index require external and longitudinal validation in other populations.

Systemic lupus erythematosus (SLE) is a debilitating autoimmune disorder characterized by unknown pathogenesis and heterogeneous clinical manifestations. The current existing serum biomarkers for SLE have limited sensitivity or specificity, making early and precise diagnosis difficult. Here, we identified two N-glycans on serum immunoglobulin G (IgG) as excellent diagnostic biomarkers for SLE based on indepth glycomic analyses of 389 SLE patients and 304 healthy controls. These two N-glycan biomarkers are specific for diagnosing SLE, as no significant changes in these biomarkers were observed in other systemic autoimmune diseases that are easily confused with SLE, such as rheumatoid arthritis, primary Sjögren's syndrome, or systemic sclerosis. Notably, the two N-glycan biomarkers proved to be autoantibody-independent and all-stage patient suitable. The two N-glycan biomarkers are demonstrated to be located on the Fc region based on fragment-specific glycan analysis and glycopeptide analysis, suggesting their close correlation with disease activity. Enzyme analyses revealed dysregulation of a series of glycotransferases in SLE, which might be responsible for the observed glycan alteration. Our findings provide insights into efficient population screening based on serum IgG glycosylation and potential new pathogenic factors of SLE.

The aim of this study was to explore the role of serum N-glycomic-derived models in diagnosing significant liver fibrosis and cirrhosis in 285 chronic hepatitis B (CHB) patients with normal (< 40 IU·L^–1) alanine aminotransferase (ALT) levels. Liver biopsies were performed in all enrolled patients, and the stages of liver fibrosis were assessed using the Ishak scoring system. Serum N-glycan profiles were tested using DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). A total of nine N-glycan peaks were identified in serum samples for each subject. A machine learning method—namely, random forest (RF) analysis—was adopted to construct more ideal serum N-glycan models in order to distinguish significant liver fibrosis (≥ F3) and cirrhosis (≥ F5). The diagnostic value of the constructed N-glycan models and other fibrotic markers was evaluated. The liver biopsy results revealed that 63.86% (182/285) and 16.49% (47/285) of patients had significant liver fibrosis and cirrhosis, respectively, and 4.91% (14/285) of patients had significant inflammation. In distinguishing significant liver fibrosis, the diagnostic efficiency of the serum N-glycan RF model constructed for distinguishing significant liver fibrosis (≥ F3; RF-A model) was excellent (area under receiver operating characteristic (AUROC) curve: 0.94), and the coincidence rate of the serum N-glycan RF-A model compared with liver biopsy was 90.45%. In distinguishing liver cirrhosis, the diagnostic AUROC curve of the serum N-glycan RF model constructed for distinguishing liver cirrhosis (≥ F5; RF-B model) was 0.97, and the coincidence rate was 88.94%. The diagnostic efficiency of the constructed serum N-glycan models (RF-A and RF-B) was superior to that of liver stiffness measurement (LSM), the fibrosis index based on the four factors (FIB-4), and the aspartate aminotransferase-to-platelet ratio index (APRI). Serum N-glycan models are promising markers for the differentiation of significant liver fibrosis and cirrhosis in CHB patients with normal ALT levels.

Bidirectional causalityAlthough the association between immunoglobulin G (IgG) N-glycosylation and metabolic traits has been previously identified, the causal association between them remains unclear. In this work, we used Mendelian randomization (MR) analysis to integrate genome-wide association studies (GWASs) and quantitative trait loci (QTLs) data in order to investigate the bidirectional causal association of IgG Nglycosylation with metabolic traits. In the forward MR analysis, 59 (including nine putatively causal glycan peaks (GPs) for body mass index (BMI) (GP1, GP6, etc.) and seven for fasting plasma glucose (FPG) (GP1, GP5, etc.)) and 15 (including five putatively causal GPs for BMI (GP2, GP11, etc.) and four for FPG (GP1, GP10, etc.)) genetically determined IgG N-glycans were identified as being associated with metabolic traits in one- and two-sample MR studies, respectively, by integrating IgG N-glycan-QTL variants with GWAS results for metabolic traits (all P < 0.05). Accordingly, in the reverse MR analysis of the integrated metabolic-QTL variants with the GWAS results for IgG N-glycosylation traits, 72 (including one putatively causal metabolic trait for GP1 (high-density lipoprotein cholesterol (HDL-C)) and five for GP2 (FPG, systolic blood pressure (SBP), etc.)) and four (including one putatively causal metabolic trait for GP3 (HDL-C) and one for GP9 (HDL-C)) genetically determined metabolic traits were found to be related to the risk of IgG N-glycosylation in one- and two-sample MR studies, respectively (all P < 0.05). Notably, genetically determined associations of GP11 → BMI (fixed-effects model-Beta with standard error (SE): 0.106 (0.034) and 0.010 (0.005)) and HDL-C → GP9 (fixed-effects model-Beta with SE: –0.071 (0.022) and –0.306 (0.151)) were identified in both the one- and two-sample MR settings, which were further confirmed by a meta-analysis combining the one- and two-sample MR results (fixed-effects model-Beta with 95% confidence interval (95% CI): 0.0109 (0.0012, 0.0207) and –0.0759 (–0.1186, –0.0332), respectively). In conclusion, the comprehensively bidirectional MR analyses provide suggestive evidence of bidirectional causality between IgG N-glycosylation and metabolic traits, possibly revealing a new richness in the biological mechanism between IgG N-glycosylation and metabolic traits.

Consumption of boiled crayfish may lead to Haff disease (HD), which is considered to result from an unidentified toxin, although the etiology is still obscure. Profiling of the N-glycome in HD would assist in deciphering the underlying molecular mechanism of the disease, whereas HD-associated glycosylation has never been explored. Herein, we enrolled 90 serum samples with HD patients and healthy controls from the Wuhan Center for Disease Control & Prevention between 2019 and 2020. N-glycome profiles of both serum and serum-derived immunoglobulin G (IgG) in HD were characterized by means of high-throughput-based orthogonal mass spectrometry. It was observed that HD is associated with an increase in the core fucosylation and mono-galactosylation of total serum glycoproteins. The serum level of IgG was found to serve as a good indicator for HD patients. In addition, differential galactosylation and sialylation of IgG were strongly correlated with HD. It was notable that the changes in the galactosylation and sialylation of IgG1 and IgG2 were subclass specific. Interestingly, altered sialylation and galactosylation of IgG2 or IgG3/4 strongly correlated with clinical markers for HD. Our study reveals the association of differential IgG N-glycosylation with HD, providing new insight into the etiology of this rare disease.

The essential role of immunoglobulin G (IgG) in immune system regulation and combatting infectious diseases cannot be fully recognized without an understanding of the changes in its N-glycans attached to the asparagine 297 of the Fc domain that occur under such circumstances. These glycans impact the antibody stability, half-life, secretion, immunogenicity, and effector functions. Therefore, in this study, we analyzed and compared the total IgG glycome—at the level of individual glycan structures and derived glycosylation traits (sialylation, galactosylation, fucosylation, and bisecting N-acetylglucosamine (GlcNAc))—of 64 patients with influenza, 77 patients with coronavirus disease 2019 (COVID-19), and 56 healthy controls. Our study revealed a significant decrease in IgG galactosylation, sialylation, and bisecting GlcNAc (where the latter shows the most significant decrease) in deceased COVID-19 patients, whereas IgG fucosylation was increased. On the other hand, IgG galactosylation remained stable in influenza patients and COVID-19 survivors. IgG glycosylation in influenza patients was more time-dependent: In the first seven days of the disease, sialylation increased and fucosylation and bisecting GlcNAc decreased; in the next 21 days, sialylation decreased and fucosylation increased (while bisecting GlcNAc remained stable). The similarity of IgG glycosylation changes in COVID-19 survivors and influenza patients may be the consequence of an adequate immune response to enveloped viruses, while the observed changes in deceased COVID-19 patients may indicate its deviation.