Interlaboratory evaluation of plasma N-glycan antennary fucosylation as a clinical biomarker for HNF1A-MODY using liquid chromatography methods

Highly-sensitive label-free deep profiling of N-glycans released from biomedically-relevant samples

Alterations of protein glycosylation can serve as sensitive and specific disease biomarkers. Labeling procedures for improved separation and detectability of oligosaccharides have several drawbacks, including incomplete derivatization, side-products, noticeable desialylation/defucosylation, sample loss, and interference with downstream analyses. Here, we develop a label-free workflow based on high sensitivity capillary zone electrophoresis-mass spectrometry (CZE-MS) for profiling of native underivatized released N-glycans. Our workflow provides a >45-fold increase in signal intensity compared to the conventional CZE-MS approaches used for N-glycan analysis. Qualitative and quantitative N-glycan profiling of purified human serum IgG, bovine serum fetuin, bovine pancreas ribonuclease B, blood-derived extracellular vesicle isolates, and total plasma results in the detection of >250, >400, >150, >310, and >520 N-glycans, respectively, using injected amounts equivalent to <25 ng of model protein and nL-levels of plasma-derived samples. Compared to reported results for biological samples of similar amounts and complexity, the number of identified N-glycans is increased up to ~15-fold, enabling highly sensitive analysis of sample amounts as low as sub-0.2 nL of plasma volume equivalents. Furthermore, highly sialylated N-glycans are identified and structurally characterized, and untreated sialic acid-linkage isomers are resolved in a single CZE-MS analysis.

Structure and function of microbial α-l-fucosidases: a mini review

Fucose is a monosaccharide commonly found in mammalian, insect, microbial and plant glycans. The removal of terminal α-l-fucosyl residues from oligosaccharides and glycoconjugates is catalysed by α-l-fucosidases. To date, glycoside hydrolases (GHs) with exo-fucosidase activity on α-l-fucosylated substrates (EC 3.2.1.51, EC 3.2.1.-) have been reported in the GH29, GH95, GH139, GH141 and GH151 families of the Carbohydrate Active Enzymes (CAZy) database. Microbes generally encode several fucosidases in their genomes, often from more than one GH family, reflecting the high diversity of naturally occuring fucosylated structures they encounter. Functionally characterised microbial α-l-fucosidases have been shown to act on a range of substrates with α-1,2, α-1,3, α-1,4 or α-1,6 fucosylated linkages depending on the GH family and microorganism. Fucosidases show a modular organisation with catalytic domains of GH29 and GH151 displaying a (β/α)8-barrel fold while GH95 and GH141 show a (α/α)6 barrel and parallel β-helix fold, respectively. A number of crystal structures have been solved in complex with ligands, providing structural basis for their substrate specificity. Fucosidases can also be used in transglycosylation reactions to synthesise oligosaccharides. This mini review provides an overview of the enzymatic and structural properties of microbial α-l-fucosidases and some insights into their biological function and biotechnological applications.

Serum metabolomics identified metabolite biomarkers and distinguished maturity-onset diabetes of the young from type 1 diabetes in the Chinese population

BACKGROUND

Maturity-onset diabetes of the young (MODY) patients have unique clinical manifestations and need individualized treatments. We identified novel serum metabolic biomarkers to distinguish MODY and explore the possible mechanism of the clinical manifestation and complications of MODY.

METHODS

Fasting serum samples were collected from MODY3 (n = 17), MODY2 (n = 33), type 1 diabetes (T1DM) (n = 34) and healthy individuals (n = 30), and were analyzed using the ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) metabolomic platform.

RESULTS

4 metabolites were found significantly fluctuated between groups, including glycerophosphocholine, LysoPC(18:2(9Z,12Z)), sphinganine and l-Phenylalanine. Glycerophosphocholine was selected as a diagnostic biomarker. The the area under the ROC curve (AUC) for distinguishing MODYs from healthy controls and differentiating MODY3 from T1DM reached 1.0. The combination of metabolites also gained good diagnostic value. The AUC of the combination of LysoPC(18:2(9Z,12Z)), sphinganine and l-Phenylalanine for discriminating MODY3 from T1DM was 0.983. Besides, the combination of clinical indices and metabolites helped to better differentiate the 2 MODY subtypes.

CONCLUSIONS

We identified the metabolic profiles of MODY2 and MODY3 and found promising biomarkers for distinguishing MODY from T1DM, which provides evidence for the pathogenesis and characteristic clinical manifestations of patients with MODY2 and MODY3.

Susceptibility of Human Plasma N-glycome to Low-Calorie and Different Weight-Maintenance Diets

Aberrant plasma protein glycosylation is associated with a wide range of diseases, including diabetes, cardiovascular, and immunological disorders. To investigate plasma protein glycosylation alterations due to weight loss and successive weight-maintenance diets, 1850 glycomes from participants of the Diogenes study were analyzed using Ultra-High-Performance Liquid Chromatography (UHPLC). The Diogenes study is a large dietary intervention study in which participants were subjected to a low-calorie diet (LCD) followed by one of five different weight-maintenance diets in a period of 6 months. The most notable alterations of the plasma glycome were 8 weeks after the subjects engaged in the LCD; a significant increase in low-branched glycan structures, accompanied by a decrease in high-branched glycan structures. After the LCD period, there was also a significant rise in N-glycan structures with antennary fucose. Interestingly, we did not observe significant changes between different diets, and almost all effects we observed immediately after the LCD period were annulled during the weight-maintenance diets period.

Deep learning explains the biology of branched glycans from single-cell sequencing data

Glycosylation is ubiquitous and often dysregulated in disease. However, the regulation and functional significance of various types of glycosylation at cellular levels is hard to unravel experimentally. Multi-omics, single-cell measurements such as SUGAR-seq, which quantifies transcriptomes and cell surface glycans, facilitate addressing this issue. Using SUGAR-seq data, we pioneered a deep learning model to predict the glycan phenotypes of cells (mouse T lymphocytes) from transcripts, with the example of predicting β1,6GlcNAc-branching across T cell subtypes (test set F1 score: 0.9351). Model interpretation via SHAP (SHapley Additive exPlanations) identified highly predictive genes, in part known to impact (i) branched glycan levels and (ii) the biology of branched glycans. These genes included physiologically relevant low-abundance genes that were not captured by conventional differential expression analysis. Our work shows that interpretable deep learning models are promising for uncovering novel functions and regulatory mechanisms of glycans from integrated transcriptomic and glycomic datasets.

Sialic Acid Derivatization of Fluorescently Labeled N-Glycans Allows Linkage Differentiation by Reversed-Phase Liquid Chromatography-Fluorescence Detection-Mass Spectrometry

Sialic acids have diverse biological roles, ranging from promoting up to preventing protein and cellular recognition in health and disease. The various functions of these monosaccharides are owed, in part, to linkage variants, and as a result, linkage-specific analysis of sialic acids is an important aspect of glycomic studies. This has been addressed by derivatization strategies using matrix-assisted laser desorption/ionization mass spectrometry (MS) or sialidase digestion arrays followed by liquid chromatography (LC)-MS. Despite this, these approaches are unable to simultaneously provide unambiguous assignment of sialic acid linkages and assess further isomeric glycan features within a single measurement. Thus, for the first time, we present the combination of procainamide fluorescent labeling with sialic acid linkage-specific derivatization via ethyl esterification and amidation for the analysis of released plasma N-glycans using reversed-phase (RP)LC-fluorescence detection (FD)-MS. As a result, α2,3- and α2,6-sialylated N-glycans, with the same mass prior to derivatization, are differentiated based on retention time, precursor mass, and fragmentation spectra, and additional sialylated isomers were also separated. Furthermore, improved glycan coverage and protocol precision were found via the novel application using a combined FD-MS quantification approach. Overall, this platform achieved unambiguous assignment of N-glycan sialic acid linkages within a single RPLC-FD-MS measurement, and by improving their retention on RPLC, this technique can be used for future investigations of released N-glycans as an additional or orthogonal method to current analytical approaches.

Development of an exoglycosidase plate-based assay for detecting α1-3,4 fucosylation biomarker in individuals with HNF1A-MODY

Maturity-onset diabetes of the young due to hepatocyte nuclear factor-1 alpha variants (HNF1A-MODY) causes monogenic diabetes. Individuals carrying damaging variants in HNF1A show decreased levels of α1-3,4 fucosylation, as demonstrated on antennary fucosylation of blood plasma N-glycans. The excellent diagnostic performance of this glycan biomarker in blood plasma N-glycans of individuals with HNF1A-MODY has been demonstrated using liquid chromatography methods. Here, we have developed a high-throughput exoglycosidase plate-based assay to measure α1-3,4 fucosylation levels in blood plasma samples. The assay has been optimized and its validity tested using 1000 clinical samples from a cohort of individuals with young-adult onset diabetes including cases with HNF1A-MODY. The α1-3,4 fucosylation levels in blood plasma showed a good differentiating power in identifying cases with damaging HNF1A variants, as demonstrated by receiver operating characteristic curve analysis with the AUC values of 0.87 and 0.95. This study supports future development of a simple diagnostic test to measure this glycan biomarker for application in a clinical setting.