Glycosylation alterations in serum of Alzheimer's disease patients show widespread changes in N-glycosylation of proteins related to immune function, inflammation, and lipoprotein metabolism

Integration of RNAseq transcriptomics and N-glycomics reveal biosynthetic pathways and predict structure-specific N-glycan expression

The processes involved in protein N-glycosylation represent new therapeutic targets for diseases but their stepwise and overlapping biosynthetic processes make it challenging to identify the specific glycogenes involved. In this work, we aimed to elucidate the interactions between glycogene expression and N-glycan abundance by constructing supervised machine-learning models for each N-glycan composition. Regression models were trained to predict N-glycan abundance (response variable) from glycogene expression (predictors) using paired LC-MS/MS N-glycomic and 3'-TagSeq transcriptomic datasets from cells derived from multiple tissue origins and treatment conditions. The datasets include cells from several tissue origins - B cell, brain, colon, lung, muscle, prostate - encompassing nearly 400 N-glycan compounds and over 160 glycogenes filtered from an 18 000-gene transcriptome. Accurate models (validation R 2 > 0.8) predicted N-glycan abundance across cell types, including GLC01 (lung cancer), CCD19-Lu (lung fibroblast), and Tib-190 (B cell). Model importance scores ranked glycogene contributions to N-glycan predictions, revealing significant glycogene associations with specific N-glycan types. The predictions were consistent across input cell quantities, unlike LC-MS/MS glycomics which showed inconsistent results. This suggests that the models can reliably predict N-glycosylation even in samples with low cell amounts and by extension, single-cell samples. These findings can provide insights into cellular N-glycosylation machinery, offering potential therapeutic strategies for diseases linked to aberrant glycosylation, such as cancer, and neurodegenerative and autoimmune disorders.

Serum N-Glycomics with Nano-LC-QToF LC-MS/MS Reveals N-Glycan Biomarkers for Glioblastoma, Meningioma, and High-Grade Meningioma

Alteration of glycosylation in cancer cells leads to the expression of tumor-associated glycans, which can be used as biomarkers for diagnosis and prognostic prediction of diseases. In this study, we used nano-LC-QToF to identify serum N-glycan biomarkers for the detection of brain tumors. We observed an increase in sialylated N-glycans and a decrease in fucosylated N-glycans in the serum of patients with glioblastoma (GBM) and meningioma (MG) compared to healthy individuals. In GBM, a combination of increased serum sialylated N-glycan (6_4_0_2 compound) and decreased fucosylated N-glycan (4_4_1_0 compound) was identified as the most appropriate panel, with an area under the curve (AUC) of 0.8660, 78.95% sensitivity, 84.21% specificity, and 82.89% accuracy. For MG, a combination of decreased 6_6_2_0 and 5_5_2_0 compounds and increased 4_4_1_1 compound achieved an AUC of 0.9260, 82.35% sensitivity, 78.57% specificity, and 80.26% accuracy for diagnosis of MG. Additionally, an increase in 5_5_1_0 and 4_3_0_0 compounds combined with a decrease in 7_7_4_3 was associated with high-grade MG (WHO grades II-III). In conclusion, we identified serum N-glycan profiles associated with brain tumors, highlighting their potential as biomarkers for the diagnosis and prognosis of these diseases.

Glycosylation in aging and neurodegenerative diseases

Aging, a complex biological process, involves the progressive decline of physiological functions across various systems, leading to increased susceptibility to neurodegenerative diseases. In society, demographic aging imposes significant economic and social burdens due to these conditions. This review specifically examines the association of protein glycosylation with aging and neurodegenerative diseases. Glycosylation, a critical post-translational modification, influences numerous aspects of protein function that are pivotal in aging and the pathophysiology of diseases such as Alzheimer's disease, Parkinson's disease, and other neurodegenerative conditions. We highlight the alterations in glycosylation patterns observed during aging, their implications in the onset and progression of neurodegenerative diseases, and the potential of glycosylation profiles as biomarkers for early detection, prognosis, and monitoring of these age-associated conditions, and delve into the mechanisms of glycosylation. Furthermore, this review explores their role in regulating protein function and mediating critical biological interactions in these diseases. By examining the changes in glycosylation profiles associated with each part, this review underscores the potential of glycosylation research as a tool to enhance our understanding of aging and its related diseases.

N-Glycan profile of the cell membrane as a probe for lipopolysaccharide-induced microglial neuroinflammation uncovers the effects of common fatty acid supplementation

Altered N-glycosylation of proteins on the cell membrane is associated with several neurodegenerative diseases. Microglia are an ideal model for studying glycosylation and neuroinflammation, but whether aberrant N-glycosylation in microglia can be restored by diet remains unknown. Herein, we profiled the N-glycome, proteome, and glycoproteome of the human microglia following lipopolysaccharide (LPS) induction to probe the impact of dietary and gut microbe-derived fatty acids-oleic acid, lauric acid, palmitic acid, valeric acid, butyric acid, isobutyric acid, and propionic acid-on neuroinflammation using liquid chromatography-tandem mass spectrometry. LPS changed N-glycosylation in the microglial glycocalyx altering high mannose and sialofucosylated N-glycans, suggesting the dysregulation of mannosidases, fucosyltransferases, and sialyltransferases. The results were consistent as we observed the restoration effect of the fatty acids, especially oleic acid, on the LPS-treated microglia, specifically on the high mannose and sialofucosylated glycoforms of translocon-associated proteins, SSRA and SSRB along with the cell surface proteins, CD63 and CD166. In addition, proteomic analysis and in silico modeling substantiated the potential of fatty acids in reverting the effects of LPS on microglial N-glycosylation. Our results showed that N-glycosylation is likely affected by diet by restoring alterations following LPS challenge, which may then influence the disease state.

Immunoglobulin G glycosylation and its alterations in aging-related diseases

Immunoglobulin G (IgG) is an important serum glycoprotein and a major component of antibodies. Glycans on IgG affect the binding of IgG to the Fc receptor or complement C1q, which in turn affects the biological activity and biological function of IgG. Altered glycosylation patterns on IgG emerge as important biomarkers in the aging process and age-related diseases. Key aging-related alterations observed in IgG glycosylation include reductions in galactosylation and sialylation, alongside increases in agalactosylation, and bisecting GlcNAc. Understanding the role of IgG glycosylation in aging-related diseases offers insights into disease mechanisms and provides opportunities for the development of diagnostic and therapeutic strategies. This review summarizes five aspects of IgG: an overview of IgG, IgG glycosylation, IgG glycosylation with inflammation mediation, IgG glycan changes with normal aging, as well as the relevance of IgG glycan changes to aging-related diseases. This review provides a reference for further investigation of the regulatory mechanisms of IgG glycosylation in aging-related diseases, as well as for evaluating the potential of IgG glycosylation changes as markers of aging and aging-related diseases.

Recent advances in N-glycan biomarker discovery among human diseases

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

The effects of immortalization on the N-glycome and proteome of CDK4-transformed lung cancer cells

Biological experiments are often conducted in vitro using immortalized cells due to their accessibility and ease of propagation compared to primary cells and live animals. However, immortalized cells may present different proteomic and glycoproteomic characteristics from the primary cell source due to the introduction of genes that enhance proliferation (e.g. CDK4) or enable telomere lengthening. To demonstrate the changes in phenotype upon CDK4-transformation, we performed LC-MS/MS glycomic and proteomic characterizations of a human lung cancer primary cell line (DTW75) and a CDK4-transformed cell line (GL01) derived from DTW75. We observed that the primary and CDK4-transformed cells expressed significantly different levels of sialylated, fucosylated, and sialofucosylated N-glycans. Specifically, the primary cells expressed higher levels of hybrid- and complex-type sialylated N-glycans, while CDK4-transformed cells expressed higher levels of complex-type fucosylated and sialofucosylated N-glycans. Further, we compared the proteomic differences between the cell lines and found that CDK4-transformed cells expressed higher levels of RNA-binding and adhesion proteins. Further, we observed that the CDK4-transformed cells changed N-glycosylation after 31 days in cell culture, with a decrease in high-mannose and increase in fucosylated, sialylated, and sialofucosylated N-glycans. Identifying these changes between primary and CDK4-transformed cells will provide useful insight when adapting cell lines that more closely resemble in vivo physiological conditions.

Integrating transcriptomics, glycomics and glycoproteomics to characterize hepatitis B virus-associated hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) ranks as the third most common cause of cancer related death globally, representing a substantial challenge to global healthcare systems. In China, the primary risk factor for HCC is the hepatitis B virus (HBV). Aberrant serum glycoconjugate levels have long been linked to the progression of HBV-associated HCC (HBV-HCC). Nevertheless, few study systematically explored the dysregulation of glycoconjugates in the progression of HBV-associated HCC and their potency as the diagnostic and prognostic biomarker.

METHODS

An integrated strategy that combined transcriptomics, glycomics, and glycoproteomics was employed to comprehensively investigate the dynamic alterations in glyco-genes, N-glycans, and glycoproteins in the progression of HBV- HCC.

RESULTS

Bioinformatic analysis of Gene Expression Omnibus (GEO) datasets uncovered dysregulation of fucosyltransferases (FUTs) in liver tissues from HCC patients compared to adjacent tissues. Glycomic analysis indicated an elevated level of fucosylated N-glycans, especially a progressive increase in fucosylation levels on IgA1 and IgG2 determined by glycoproteomic analysis.

CONCLUSIONS

The findings indicate that the abnormal fucosylation plays a pivotal role in the progression of HBV-HCC. Systematic and integrative multi-omic analysis is anticipated to facilitate the discovery of aberrant glycoconjugates in tumor progression.

DiLeu Isobaric Labeling Coupled with Limited Proteolysis Mass Spectrometry for High-Throughput Profiling of Protein Structural Changes in Alzheimer's Disease

High-throughput quantitative analysis of protein conformational changes has a profound impact on our understanding of the pathological mechanisms of Alzheimer's disease (AD). To establish an effective workflow enabling quantitative analysis of changes in protein conformation within multiple samples simultaneously, here we report the combination of N,N-dimethyl leucine (DiLeu) isobaric tag labeling with limited proteolysis mass spectrometry (DiLeu-LiP-MS) for high-throughput structural protein quantitation in serum samples collected from AD patients and control donors. Twenty-three proteins were discovered to undergo structural changes, mapping to 35 unique conformotypic peptides with significant changes between the AD group and the control group. Seven out of 23 proteins, including CO3, CO9, C4BPA, APOA1, APOA4, C1R, and APOA, exhibited a potential correlation with AD. Moreover, we found that complement proteins (e.g., CO3, CO9, and C4BPA) related to AD exhibited elevated levels in the AD group compared to those in the control group. These results provide evidence that the established DiLeu-LiP-MS method can be used for high-throughput structural protein quantitation, which also showed great potential in achieving large-scale and in-depth quantitative analysis of protein conformational changes in other biological systems.

N-Glycan and Glycopeptide Serum Biomarkers in Philippine Lung Cancer Patients Identified Using Liquid Chromatography-Tandem Mass Spectrometry

Aberrant glycosylation has been extensively reported in cancer, with fundamental changes in the glycosylation patterns of cell-surface and secreted proteins largely occurring during cancer progression. As such, serum glycan and glycopeptide biomarkers have been discovered using mass spectrometry and proposed for cancer detection. Here, we report for the first time potential serum N-glycan and glycopeptide biomarkers for Philippine lung cancer patients. The N-glycan and glycoprotein profiles of a cohort (n = 26 patients, n = 22 age- and gender-matched) of lung cancer patients were analyzed and compared to identify potential N-glycan and glycopeptide serum biomarkers using nano-QToF-MS/MS and ultra-high-performance liquid chromatography coupled with triple quadrupole mass spectrometry dynamic multiple monitoring methods, respectively. Statistical analyses identified differential N-glycan and glycopeptide abundances. The N-glycans were mostly sialylated and sialofucosylated branched structures. The glycopeptides involved proteins in complement and coagulation cascades (p _adj = 6.418 × 10^-4), innate immunity (p _adj = 6.094 × 10^-3), acute inflammatory response (p _adj = 6.404 × 10^-5), defense response (p _adj = 2.082 × 10^-4), complement activation pathways (p _adj = 1.895 × 10^-2), and immunoglobulin-mediated immune response pathways (p _adj = 4.818 × 10^-2). Biomarker models were constructed using serum N-glycans [area under the curve (AUC) = 0.775; 95% CI: 0.617-0.931] and glycopeptides (AUC = 0.959; 95% CI: 0.85-1.0), with glycopeptides having higher accuracies than N-glycans. The results suggest that in the Philippine lung cancer patient sera, specific N-glycans and site-specific glycans are differentially expressed between cases and controls. This report represents the first serum glycan and glycopeptide biomarkers of Philippine lung cancer patients, further demonstrating the utility of mass spectrometry-based glycomic and glycoproteomic methods.

Identification and immune characteristics of molecular subtypes related to protein glycosylation in Alzheimer's disease

BACKGROUND

Protein glycosylation has been confirmed to be involved in the pathological mechanisms of Alzheimer's disease (AD); however, there is still a lack of systematic analysis of the immune processes mediated by protein glycosylation-related genes (PGRGs) in AD.

MATERIALS AND METHODS

Transcriptomic data of AD patients were obtained from the Gene Expression Omnibus database and divided into training and verification datasets. The core PGRGs of the training set were identified by weighted gene co-expression network analysis, and protein glycosylation-related subtypes in AD were identified based on k-means unsupervised clustering. Protein glycosylation scores and neuroinflammatory levels of different subtypes were compared, and functional enrichment analysis and drug prediction were performed based on the differentially expressed genes (DEGs) between the subtypes. A random forest model was used to select important DEGs as diagnostic markers between subtypes, and a line chart model was constructed and verified in other datasets. We evaluated the differences in immune cell infiltration between the subtypes through the single-sample gene set enrichment analysis, analyzed the correlation between core diagnostic markers and immune cells, and explored the expression regulation network of the core diagnostic markers.

RESULTS

Eight core PGRGs were differentially expressed between the training set and control samples. AD was divided into two subtypes with significantly different biological processes, such as vesicle-mediated transport in synapses and neuroactive ligand-receptor interactions. The high protein glycosylation subtype had a higher level of neuroinflammation. Riluzole and sulfasalazine were found to have potential clinical value in this subtype. A reliable construction line chart model was constructed based on nine diagnostic markers, and SERPINA3 was identified as the core diagnostic marker. There were significant differences in immune cell infiltration between the two subtypes. SERPINA3 was found to be closely related to immune cells, and the expression of SERPINA3 in AD was found to be regulated by a competing endogenous RNA network that involves eight long non-coding RNAs and seven microRNAs.

CONCLUSION

Protein glycosylation and its corresponding immune process play an important role in the occurrence and development of AD. Understanding the role of PGRGs in AD may provide a new potential therapeutic target for AD.