Plasma N-glycome shows continuous deterioration as the diagnosis of insulin resistance approaches

Predicting glycan structure from tandem mass spectrometry via deep learning

Glycans constitute the most complicated post-translational modification, modulating protein activity in health and disease. However, structural annotation from tandem mass spectrometry (MS/MS) data is a bottleneck in glycomics, preventing high-throughput endeavors and relegating glycomics to a few experts. Trained on a newly curated set of 500,000 annotated MS/MS spectra, here we present CandyCrunch, a dilated residual neural network predicting glycan structure from raw liquid chromatography-MS/MS data in seconds (top-1 accuracy: 90.3%). We developed an open-access Python-based workflow of raw data conversion and prediction, followed by automated curation and fragment annotation, with predictions recapitulating and extending expert annotation. We demonstrate that this can be used for de novo annotation, diagnostic fragment identification and high-throughput glycomics. For maximum impact, this entire pipeline is tightly interlaced with our glycowork platform and can be easily tested at https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb . We envision CandyCrunch to democratize structural glycomics and the elucidation of biological roles of glycans.

Plasma protein N-glycome composition associates with postprandial lipaemic response

BACKGROUND

A dysregulated postprandial metabolic response is a risk factor for chronic diseases, including type 2 diabetes mellitus (T2DM). The plasma protein N-glycome is implicated in both lipid metabolism and T2DM risk. Hence, we first investigate the relationship between the N-glycome and postprandial metabolism and then explore the mediatory role of the plasma N-glycome in the relationship between postprandial lipaemia and T2DM.

METHODS

We included 995 individuals from the ZOE-PREDICT 1 study with plasma N-glycans measured by ultra-performance liquid chromatography at fasting and triglyceride, insulin, and glucose levels measured at fasting and following a mixed-meal challenge. Linear mixed models were used to investigate the associations between plasma protein N-glycosylation and metabolic response (fasting, postprandial (Cmax), or change from fasting). A mediation analysis was used to further explore the relationship of the N-glycome in the prediabetes (HbA1c = 39-47 mmol/mol (5.7-6.5%))-postprandial lipaemia association.

RESULTS

We identified 36 out of 55 glycans significantly associated with postprandial triglycerides (Cmax β ranging from -0.28 for low-branched glycans to 0.30 for GP26) after adjusting for covariates and multiple testing (padjusted < 0.05). N-glycome composition explained 12.6% of the variance in postprandial triglycerides not already explained by traditional risk factors. Twenty-seven glycans were also associated with postprandial glucose and 12 with postprandial insulin. Additionally, 3 of the postprandial triglyceride-associated glycans (GP9, GP11, and GP32) also correlate with prediabetes and partially mediate the relationship between prediabetes and postprandial triglycerides.

CONCLUSIONS

This study provides a comprehensive overview of the interconnections between plasma protein N-glycosylation and postprandial responses, demonstrating the incremental predictive benefit of N-glycans. We also suggest a considerable proportion of the effect of prediabetes on postprandial triglycerides is mediated by some plasma N-glycans.

NMR-based metabolomic profiling identifies inflammation and muscle-related metabolites as predictors of incident type 2 diabetes mellitus beyond glucose: the Di@bet.es study

AIMS

The aim of this study was to combine nuclear magnetic resonance-based metabolomics and machine learning to find a glucose-independent molecular signature associated with future type 2 diabetes mellitus development in a subgroup of individuals from the Di@bet.es study.

METHODS

The study group included 145 individuals developing type 2 diabetes mellitus during the 8-year follow-up, 145 individuals matched by age, sex and BMI who did not develop diabetes during the follow-up but had equal glucose concentrations to those who did and 145 controls matched by age and sex. A metabolomic analysis of serum was performed to obtain the lipoprotein and glycoprotein profiles and 15 low molecular weight metabolites. Several machine learning-based models were trained.

RESULTS

Logistic regression performed the best classification between individuals developing type 2 diabetes during the follow-up and glucose-matched individuals. The area under the curve was 0.628, and its 95% confidence interval was 0.510-0.746. Glycoprotein-related variables, creatinine, creatine, small HDL particles and the Johnson-Neyman intervals of the interaction of Glyc A and Glyc B were statistically significant.

CONCLUSIONS

The model highlighted a relevant contribution of inflammation (glycosylation pattern and HDL) and muscle (creatinine and creatine) in the development of type 2 diabetes as independent factors of hyperglycemia.

Not-So-Sweet Dreams: Plasma and IgG N-Glycome in the Severe Form of the Obstructive Sleep Apnea

Obstructive sleep apnea (OSA) is a prevalent disease associated with increased risk for cardiovascular and metabolic diseases and shortened lifespan. The aim of this study was to explore the possibility of using N-glycome as a biomarker for the severe form of OSA. Seventy subjects who underwent a whole-night polysomnography/polygraphy and had apnea-hypopnea index (AHI) over 30 were compared to 23 controls (AHI under 5). Plasma samples were used to extract 39 glycan peaks using ultra-high-performance liquid chromatography (UPLC) and 27 IgG peaks using capillary gel electrophoresis (CGE). We also measured glycan age, a molecular proxy for biological aging. Three plasma and one IgG peaks were significant in a multivariate model controlling for the effects of age, sex, and body mass index. These included decreased GP24 (disialylated triantennary glycans as major structure) and GP28 (trigalactosylated, triantennary, disialylated, and trisialylated glycans), and increased GP32 (trisialylated triantennary glycan). Only one IgG glycan peak was significantly increased (P26), which contains biantennary digalactosylated glycans with core fucose. Patients with severe OSA exhibited accelerated biological aging, with a median of 6.9 years more than their chronological age (p < 0.001). Plasma N-glycome can be used as a biomarker for severe OSA.

Advances in protein glycosylation and its role in tissue repair and regeneration

After tissue damage, a series of molecular and cellular events are initiated to promote tissue repair and regeneration to restore its original structure and function. These events include inter-cell communication, cell proliferation, cell migration, extracellular matrix differentiation, and other critical biological processes. Glycosylation is the crucial conservative and universal post-translational modification in all eukaryotic cells [1], with influential roles in intercellular recognition, regulation, signaling, immune response, cellular transformation, and disease development. Studies have shown that abnormally glycosylation of proteins is a well-recognized feature of cancer cells, and specific glycan structures are considered markers of tumor development. There are many studies on gene expression and regulation during tissue repair and regeneration. Still, there needs to be more knowledge of complex carbohydrates' effects on tissue repair and regeneration, such as glycosylation. Here, we present a review of studies investigating protein glycosylation in the tissue repair and regeneration process.

The N-Glycosylation of Total Plasma Proteins and IgG in Atrial Fibrillation

Atrial fibrillation is a disease with a complex pathophysiology, whose occurrence and persistence are caused not only by aberrant electrical signaling in the heart, but by the development of a susceptible heart substrate. These changes, such as the accumulation of adipose tissue and interstitial fibrosis, are characterized by the presence of inflammation. N-glycans have shown great promise as biomarkers in different diseases, specifically those involving inflammatory changes. To assess the changes in the N-glycosylation of the plasma proteins and IgG in atrial fibrillation, we analyzed the N-glycosylation of 172 patients with atrial fibrillation, before and six months after a pulmonary vein isolation procedure, with 54 cardiovascularly healthy controls. An analysis was performed using ultra-high-performance liquid chromatography. We found one oligomannose N-glycan structure from the plasma N-glycome and six IgG N-glycans, mainly revolving around the presence of bisecting N-acetylglucosamine, that were significantly different between the case and control groups. In addition, four plasma N-glycans, mostly oligomannose structures and a derived trait that was related to them, were found to be different in the patients who experienced an atrial fibrillation recurrence during the six-month follow-up. IgG N-glycosylation was extensively associated with the CHA2DS2-VASc score, confirming its previously reported associations with the conditions that make up the score. This is the first study looking at the N-glycosylation patterns in atrial fibrillation and warrants further investigation into the prospect of glycans as biomarkers for atrial fibrillation.

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.

Immunoglobulin G N-Glycosylation Signatures in Incident Type 2 Diabetes and Cardiovascular Disease

OBJECTIVE

N-glycosylation is a functional posttranslational modification of immunoglobulins (Igs). We hypothesized that specific IgG N-glycans are associated with incident type 2 diabetes and cardiovascular disease (CVD).

RESEARCH DESIGN AND METHODS

We performed case-cohort studies within the population-based European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam cohort (2,127 in the type 2 diabetes subcohort [741 incident cases]; 2,175 in the CVD subcohort [417 myocardial infarction and stroke cases]). Relative abundances of 24 IgG N-glycan peaks (IgG-GPs) were measured by ultraperformance liquid chromatography, and eight glycosylation traits were derived based on structural similarity. End point-associated IgG-GPs were preselected with fractional polynomials, and prospective associations were estimated in confounder-adjusted Cox models. Diabetes risk associations were validated in three independent studies.

RESULTS

After adjustment for confounders and multiple testing correction, IgG-GP7, IgG-GP8, IgG-GP9, IgG-GP11, and IgG-GP19 were associated with type 2 diabetes risk. A score based on these IgG-GPs was associated with a higher diabetes risk in EPIC-Potsdam and independent validation studies (843 total cases, 3,149 total non-cases, pooled estimate per SD increase 1.50 [95% CI 1.37-1.64]). Associations of IgG-GPs with CVD risk differed between men and women. In women, IgG-GP9 was inversely associated with CVD risk (hazard ratio [HR] per SD 0.80 [95% CI 0.65-0.98]). In men, a weighted score based on IgG-GP19 and IgG-GP23 was associated with higher CVD risk (HR per SD 1.47 [95% CI 1.20-1.80]). In addition, several derived traits were associated with cardiometabolic disease incidence.

CONCLUSIONS

Selected IgG N-glycans are associated with cardiometabolic risk beyond classic risk factors, including clinical biomarkers.

High-Throughput Glycomic Methods

Glycomics aims to identify the structure and function of the glycome, the complete set of oligosaccharides (glycans), produced in a given cell or organism, as well as to identify genes and other factors that govern glycosylation. This challenging endeavor requires highly robust, sensitive, and potentially automatable analytical technologies for the analysis of hundreds or thousands of glycomes in a timely manner (termed high-throughput glycomics). This review provides a historic overview as well as highlights recent developments and challenges of glycomic profiling by the most prominent high-throughput glycomic approaches, with N-glycosylation analysis as the focal point. It describes the current state-of-the-art regarding levels of characterization and most widely used technologies, selected applications of high-throughput glycomics in deciphering glycosylation process in healthy and disease states, as well as future perspectives.

In a pursuit of optimal glycan fluorescent label for negative MS mode for high-throughput N-glycan analysis

Over the past few decades, essential role of glycosylation in protein functioning has become widely recognized, rapidly advancing glycan analysis techniques. Because free glycan’s lack chromophore or fluorophore properties, and do not ionize well, they are often derivatized to facilitate their separation or detection, and to enhance the sensitivity of the analysis. Released glycan’s are usually derivatized using a fluorescent tag, which enables their optical detection in LC profiling. Some fluorescent labels can also promote ionization efficiency, thus facilitating MS detection. For this reason, there is a need to design fluorophores that will contribute more to the fluorescence and ionization of glycan’s and the need to quantify these contributions to improve glycan analysis methods. In this paper we focused on negative MS mode as these methods are more informative than methods involving positive MS mode, allowing for a less ambiguous elucidation of detailed glycan structures. Additionally, traditional glycan labels in negative mode MS usually result with diminished sensitivity compared to positive mode, thus making selection of appropriate label even more important for successful high-throughput analysis. Therefore, eleven fluorescent labels of different chemo-physical properties were chosen to study the influence of label hydrophobicity and presence of a negative charge on glycan ionization in negative MS mode. N-glycans released from IgG sample were labeled with one of the eleven labels, purified with HILIC-SPE and analyzed with HILIC-UPLC-FLR-MS. To make evaluation of studied labels performance more objective, analysis was performed in two laboratories and at two mobile phase pH (4.4 and 7.4). Although there was a notable trend of more hydrophobic labels having bigger signal intensities in one laboratory, we observed no such trend in the other laboratory. The results show that MS parameters and intrinsic configuration of the spectrometer have even bigger effect on the final ESI response of the labeled-glycan ionization in negative MS mode that the labels themselves. With this in mind, further research and development of fluorophores that will be suitable for high-throughput glycan analysis in the negative MS mode are proposed.

Multi-block data integration analysis for identifying and validating targeted N-glycans as biomarkers for type II diabetes mellitus

Plasma N-glycan profiles have been shown to be defective in type II diabetes Mellitus (T2DM) and holds a promise to discovering biomarkers. The study comprised 232 T2DM patients and 219 healthy individuals. N-glycans were analysed by high-performance liquid chromatography. The multivariate integrative framework, DIABLO was employed for the statistical analysis. N-glycan groups (GPs 34, 32, 26, 31, 36 and 30) were significantly expressed in T2DM in component 1 and GPs 38 and 20 were related to T2DM in component 2. Four clusters were observed based on the correlation of the expressive signatures of the 39 N-glycans across T2DM and controls. Cluster A, B, C and D had 16, 16, 4 and 3 N-glycans respectively, of which 11, 8, 1 and 1 were found to express differently between controls and T2DM in a univariate analysis \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(p < 0.05)$$\end{document}(p<0.05). Multi-block analysis revealed that trigalactosylated (G3), triantennary (TRIA), high branching (HB) and trisialylated (S3) expressed significantly highly in T2DM than healthy controls. A bipartite relevance network revealed that HB, monogalactosylated (G1) and G3 were central in the network and observed more connections, highlighting their importance in discriminating between T2DM and healthy controls. Investigation of these N-glycans can enhance the understanding of T2DM.

Comparison of self-sampling blood collection for N-glycosylation analysis

Objective

Self-sampling of capillary blood provides easier sample collection, handling, and shipping compared to more invasive blood sampling via venepuncture. Recently, other means of capillary blood collection were introduced to the market, such as Neoteryx sticks and Noviplex cards. We tested the comparability of these two self-sampling methods, alongside dried blood spots (DBS), with plasma acquired from venepunctured blood in N-glycoprofiling of total proteins. We have also tested the intra-day repeatability of the three mentioned self-sampling methods. Capillary blood collection with Neoteryx, Noviplex and DBS was done following the manufacturers’ instructions and N-glycoprofiling of released, fluorescently labelled N-glycans was performed with ultra-performance liquid chromatography.

Results

Comparability with plasma was assessed by calculating the relative deviance, which was 0.674 for DBS, 0.092 for Neoteryx sticks, and 0.069 for Noviplex cards. In repeatability testing, similar results were obtained, with Noviplex cards and Neoteryx sticks performing substantially better than DBS (CVs = 4.831% and 7.098%, compared to 14.305%, respectively). Our preliminary study on the use of Neoteryx and Noviplex self-sampling devices in glycoanalysis demonstrates their satisfactory performance in both the comparability and repeatability testing, however, they should be further tested in larger collaborations and cohorts.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13104-022-05958-9.