N-glycosylation profiling of Type 2 diabetes mellitus from baseline to follow-up: an observational study in a Ghanaian population

Evaluation of salivary glycopatterns based diagnostic models for prediction of diabetic vascular complications

Diabetic vascular complications (DVC) are the main cause of death in diabetic patients. However, there is a lack of effective biomarkers or convenient methods for early diagnosis of DVC. In this study, the salivary glycopatterns from 130 of healthy volunteers (HV), 139 patients with type 2 diabetes mellitus (T2DM) and 167 patients with DVC were case-by-case analyzed by using lectin microarrays. Subsequently, diagnostic models were developed using logistic regression and machine learning algorithms based on the data of lectin microarrays in training set. The performance of diagnostic models was evaluated in an independent blind cohort. The results of lectin microarrays indicated that the glycopatterns identified by 16 lectins (e.g. BS-I, PWM and EEL) were significantly altered in DVC patients compared with patients with T2DM, which suggested the alterations in salivary glycopatterns could reflect onset of DVC. Notably, K-Nearest Neighbor (KNN) model exhibited better performance for distinguishing DVC (accuracy: 0.939) than other models in blind cohort. The integrated classifier, which combined three machine learning models, exhibited a higher overall accuracy (≥ 0.933) than other models in blind cohort. Our study provided a cost-effective and non-invasive method for auxiliary diagnosis DVC based on the combination of salivary glycopatterns and machine learning algorithms.

Influence of plasma collection tubes on N-glycome in human blood samples

Background and aims

Quantitative analysis of plasma N-glycome is a promising method for identifying disease biomarkers. This study aimed to investigate the impact of using blood collection tubes with different anticoagulants on plasma N-glycome.

Materials and methods

We used a robust mass spectrometry method to profile plasma N-glycomes in two cohorts of healthy volunteers (cohort 1, n = 16; cohort 2, n = 53). The influence of three commonly used blood collection tubes on fully characterized N-glycomic profiles were explored.

Results

Principal component analysis revealed distinct clustering of blood samples based on the collection tubes. Pairwise comparisons demonstrated significant differences between EDTA and heparin plasma in 55 out of 82 quantified N-glycan traits, and between EDTA and citrate plasma in 62 out of 82 traits. These differences encompassed various N-glycan features, including glycan type, sialylation, galactosylation, fucosylation, and bisection. Trends in N-glycan variations in citrate and heparin plasma were largely consistent compared to EDTA plasma. In correlation analysis (EDTA vs. heparin; EDTA vs. citrate), Pearson's correlation coefficients were consistently higher than 0.7 for the majority of N-glycan traits.

Conclusion

Sample matrix variations impact plasma N-glycome measurements. Caution is crucial when comparing samples from different plasma collection tubes in glycomics projects.

Serum disease-specific IgG Fc glycosylation as potential biomarkers for nonproliferative diabetic retinopathy using mass spectrometry

OBJECTIVE

To explore the potential of serum disease-specific immunoglobulin G (DSIgG) glycosylation as a biomarker for the diagnosis of nonproliferative diabetic retinopathy (NPDR).

METHODS

A total of 387 consecutive diabetic patients presenting in an eye clinic without proliferative diabetic retinopathy (DR) were included and divided into those with nondiabetic retinopathy (NDR) (n = 181) and NPDR (n = 206) groups. Serum was collected from all patients for DSIgG separation. The enriched glycopeptides of the tryptic digests of DSIgG were detected using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS). Patients were randomly divided into discovery and validation sets (1:1). The differences in glycopeptide ratios between the groups were compared by using Student's t-test or the Mann-Whitney U test. The predictive ability of the model was assessed using the area under the receiver operating characteristic curve (AUC).

RESULTS

DSIgG1 G1FN/G0FN, G2N/G2, G2FN/G2N and DSIgG2 G1F/G0F, G1FN/G0FN, G2N/G1N, G2S/G2 were significantly different between NDR and NPDR patients (p < 0.05) in both the discovery and validation sets. The prediction model that was built comprising the seven glycopeptide ratios showed good NPDR prediction performance with an AUC of 0.85 in the discovery set and 0.87 in the validation set.

CONCLUSION

DSIgG Fc N-glycosylation ratios were associated with NPDR and can be used as potential biomarkers for the early diagnosis of DR.

High sensitivity glycomics in biomedicine

Many analytical challenges in biomedicine arise from the generally high heterogeneity and complexity of glycan- and glycoconjugate-containing samples, which are often only available in minute amounts. Therefore, highly sensitive workflows and detection methods are required. In this review mass spectrometric workflows and detection methods are evaluated for glycans and glycoproteins. Furthermore, glycomic methodologies and innovations that are tailored for enzymatic treatments, chemical derivatization, purification, separation, and detection at high sensitivity are highlighted. The discussion is focused on the analysis of mammalian N-linked and GalNAc-type O-linked glycans.

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.

Expression Profile of Immunoglobulin G Glycosylation in Children With Epilepsy in Han Nationality

Background

Epilepsy is a chronic brain disease that recurs during childhood, and more than half of adult epilepsy originates from childhood. Studies suggested that immunoglobulin G (IgG) glycosylation are closely related to neurological diseases. Here we analyzed the characteristics of the immunoglobulin glycosylation profile of children with epilepsy.

Methods

Patients were recruited in Taian, Shandong Province from December 2019 to March 2020. Serum IgG glycome composition was analyzed by hydrophilic interaction liquid chromatography with ultra-high-performance liquid chromatography approach.

Results

The proportion of fucosylated glycans in total IgG glycans was 93.72% in the epilepsy patients, which was significantly lower than that in the control group (94.94%). A lower level of total monogalactosylated and digalactosylated glycans were observed in the epilepsy patients group (30.76 and 40.14%) than that in the controls (36.17 and 42.69%). There was no significant difference between the two groups in bisected GlcNAc glycans and sialylated glycans.

Conclusion

The decrease of core fucosylation and galactosylation may promote the inflammatory reaction of the body and participate in the occurrence of epilepsy in children.

N-Glycosylation Profiling of Human Blood in Type 2 Diabetes by Capillary Electrophoresis: A Preliminary Study

Currently, diagnosing type 2 diabetes (T2D) is a great challenge. Thus, there is a need to find rapid, simple, and reliable analytical methods that can detect the disease at an early stage. The aim of this work was to shed light on the importance of sample collection options, sample preparation conditions, and the applied capillary electrophoresis bioanalytical technique, for a high-resolution determination of the N-glycan profile in human blood samples of patients with type 2 diabetes (T2D). To achieve the profile information of these complex oligosaccharides, linked by asparagine to hIgG in the blood, the glycoproteins of the samples needed to be cleaved, labelled, and purified with sufficient yield and selectivity. The resulting samples were analyzed by capillary electrophoresis, with laser-induced fluorescence detection. After separation parameter optimization, the capillary electrophoresis technique was implemented for efficient N-glycan profiling of whole blood samples from the diabetic patients. Our results revealed that there were subtle differences between the N-glycan profiles of the diabetic and control samples; in particular, two N-glycan structures were identified as potential glycobiomarkers that could reveal significant changes between the untreated/treated type 2 diabetic and control samples. By analyzing the resulting oligosaccharide profiles, clinically relevant information was obtained, revealing the differences between the untreated and HMG-CoA reductase-inhibitor-treated diabetic patients on changes in the N-glycan profile in the blood. In addition, the information from specific IgG N-glycosylation profiles in T2D could shed light on underlying inflammatory pathophysiological processes and lead to drug targets.