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Introduction There is an increased need for the development of novel blood‐based biomarkers for early detection, prevention, or intervention in Alzheimer's disease (AD). This study sought to determine whether serum glycopeptide analysis holds potential for identifying novel diagnostics and prognostics of AD. Methods The study involved 195 participants, including 96 patients with an AD diagnosis and 99 controls with no cognitive deficit. Utilizing a validated analytical mass spectrometry method, we monitored the site‐specific glycosylation of 52 serum glycoproteins. Results Partial least‐squares discriminant analysis revealed that changes in overall sialylation and fucosylation of serum glycoproteins may be indicators of an AD disease state. Loss of fucosylation of immunoglobulin G1 (IgG1) and IgG2 was indicative of AD diagnosis. Individual glycopeptide analysis found separation between the AD patients and controls on complement proteins and apolipoprotein B. Discussion The results of this study suggest that serum glycoprofiling may be a promising approach for biomarker discovery.

Glycosylation has been found to be altered in the brains of individuals with Alzheimer’s disease (AD). However, it is unknown which specific glycosylation-related pathways are altered in AD dementia. Using publicly available RNA-seq datasets covering seven brain regions and including 1724 samples, we identified glycosylation-related genes ubiquitously changed in individuals with AD. Several differentially expressed glycosyltransferases found by RNA-seq were confirmed by qPCR in a different set of human medial temporal cortex (MTC) samples (n = 20 AD vs. 20 controls). N-glycan-related changes predicted by expression changes in these glycosyltransferases were confirmed by mass spectrometry (MS)-based N-glycan analysis in the MTC (n = 9 AD vs. 6 controls). About 80% of glycosylation-related genes were differentially expressed in at least one brain region of AD participants (adjusted p-values < 0.05). Upregulation of MGAT1 and B4GALT1 involved in complex N-linked glycan formation and galactosylation, respectively, were reflected by increased concentrations of corresponding N-glycans. Isozyme-specific changes were observed in expression of the polypeptide N-acetylgalactosaminyltransferase (GALNT) family and the alpha-N-acetylgalactosaminide alpha-2,6-sialyltransferase (ST6GALNAC) family of enzymes. Several glycolipid-specific genes (UGT8, PIGM) were upregulated. The critical transcription factors regulating the expression of N-glycosylation and elongation genes were predicted and found to include STAT1 and HSF5. The miRNA predicted to be involved in regulating N-glycosylation and elongation glycosyltransferases were has-miR-1-3p and has-miR-16-5p, respectively. Our findings provide an overview of glycosylation pathways affected by AD and potential regulators of glycosyltransferase expression that deserve further validation and suggest that glycosylation changes occurring in the brains of AD dementia individuals are highly pathway-specific and unique to AD.

A multi-glycomic method for characterizing the glycocalyx was employed to identify the difference between two-dimensional (2D) and three-dimensional (3D) culture models with two human colorectal cancer cell lines, HCT116 and HT29. 3D cell cultures are considered more representative of cancer due to their ability to mimic the microenvironment found in tumors. For this reason, they have become an important tool in cancer research. Cell-cell interactions increase in 3D models compared to 2D, indeed significant glycomic changes were observed for each cell line. Analyses included the N-glycome, O-glycome, glycolipidome, glycoproteome, and proteome providing the most extensive characterization of the glycocalyx between 3D and 2D thus far. The different glycoconjugates were affected in different ways. In the N-glycome, the 3D cells increased in high-mannose glycosylation and in core fucosylation. Glycolipids increased in sialylation. Specific glycoproteins were found to increase in the 3D cell, elucidating the pathways that are affected between the two models. The results show large structural and biological changes between the two models suggesting that the two are indeed very different potentially affecting individual outcomes in the study of diseases.

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