Alterations in plasma protein N-glycosylation after caloric restriction and bariatric surgery.

BACKGROUND: Protein glycosylation is an enzymatic process known to reflect an individual's physiologic state and changes thereof. The impact of metabolic interventions on plasma protein N-glycosylation has only been sparsely investigated. OBJECTIVE: To examine alterations in plasma protein N-glycosylation following changes in caloric intake and bariatric surgery. SETTING: University of Texas Southwestern Medical Center, US and Oxford University Hospitals, UK. METHODS: This study included 2 independent patient cohorts that recruited 10 and 37 individuals with obesity undergoing a period of caloric restriction followed by bariatric surgery. In both cohorts, clinical data were collated, and the composition of plasma protein N-glycome was analyzed chromatographically. Linear mixed models adjusting for age, sex, and multiple testing (false discovery rate <.05) were used to investigate longitudinal changes in glycosylation features and metabolic clinical markers. RESULTS: A low-calorie diet resulted in a decrease in high-branched trigalactosylated and trisialylated plasma N-glycans and a concomitant increase in low-branched N-glycans in both cohorts. Participants from one cohort additionally underwent a washout period during which caloric intake and body weight increased, resulting in reversal of the initial low-calorie diet-related changes in the plasma N-glycome. Immediate postoperative follow-up revealed the same pattern of N-glycosylation changes in both cohorts-an increase in complex, high-branched, antennary fucosylated, extensively galactosylated and sialylated N-glycans and a substantial decline in simpler, low-branched, core fucosylated, bisected, agalactosylated, and asialylated glycans. A 12-month postoperative monitoring in one cohort showed that N-glycan complexity declines while low branching increases. CONCLUSIONS: Plasma protein N-glycosylation undergoes extensive alterations following caloric restriction and bariatric surgery. These comprehensive changes may reflect the varying inflammatory status of the individual following dietary and surgical interventions and subsequent weight loss.

Mapping of the gene network that regulates glycan clock of ageing.

Glycans are an essential structural component of immunoglobulin G (IgG) that modulate its structure and function. However, regulatory mechanisms behind this complex posttranslational modification are not well known. Previous genome-wide association studies (GWAS) identified 29 genomic regions involved in regulation of IgG glycosylation, but only a few were functionally validated. One of the key functional features of IgG glycosylation is the addition of galactose (galactosylation), a trait which was shown to be associated with ageing. We performed GWAS of IgG galactosylation (N=13,705) and identified 16 significantly associated loci, indicating that IgG galactosylation is regulated by a complex network of genes that extends beyond the galactosyltransferase enzyme that adds galactose to IgG glycans. Gene prioritization identified 37 candidate genes. Using a recently developed CRISPR/dCas9 system we manipulated gene expression of candidate genes in the in vitro IgG expression system. Upregulation of three genes, EEF1A1, MANBA and TNFRSF13B, changed the IgG glycome composition, which confirmed that these three genes are involved in IgG galactosylation in this in vitro expression system.

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 ß 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.

Associations between plasma protein, IgG and IgA N-glycosylation and metabolic health markers in pregnancy and gestational diabetes.

BACKGROUND: Monitoring human circulating N-glycome could provide valuable insight into an individual's metabolic status. Therefore, we examined if aberrant carbohydrate metabolism in gestational diabetes mellitus (GDM) associates with alterations in plasma protein, immunoglobulin G (IgG) and immunoglobulin A (IgA) N-glycosylation. METHODS: Plasma protein, IgG and IgA N-glycans were enzymatically released, purified and chromatographically profiled in 48 pregnant women with normal glucose tolerance and 41 pregnant women with GDM, all sampled at 24-28 weeks of gestation. Linear mixed models adjusting for age and multiple testing (FDR<0.05) were used to investigate the associations between glycosylation features, metabolic markers and GDM status. RESULTS: Fasting insulin exhibited significant associations to numerous glycan traits, including plasma protein galactosylation, sialylation, branching, core fucosylation and bisection, to IgG core fucosylated, bisected (FA2B) and afucosylated disialylated (A2G2S2) glycan and to IgA trisialylated triantennary (A3G3S3) glycan (padj range: 4.37x10-05-4.94x10-02). Insulin resistance markers HOMA2-IR and HOMA2-%B were mostly associated to the same glycan structures as fasting insulin. Both markers showed positive association with high-branched plasma glycans (padj = 1.12x10-02 and 2.03x10-03) and negative association with low-branched plasma glycans (padj = 1.21x10-02 and 2.05x10-03). Additionally, HOMA2-%B index was significantly correlated with glycosylation features describing IgG sialylation. Multiple plasma protein IgG and IgA glycans showed significant associations with total cholesterol and triglyceride levels. None of the tested glycan traits showed a significant difference between GDM and normoglycemic pregnancies. CONCLUSION: Markers of glucose homeostasis and lipid metabolism in pregnancy show extensive associations to various N-glycosylation features. However, plasma protein, IgG and IgA N-glycans were not able to differentiate pregnant women with and without GDM, possibly due to numerous physiological changes accompanying pregnancy, which confound the impact of GDM on protein glycosylation.

Integrated glycomics and genetics analyses reveal a potential role for N-glycosylation of plasma proteins and IgGs, as well as the complement system, in the development of type 1 diabetes.

AIMS/HYPOTHESIS: We previously demonstrated that N-glycosylation of plasma proteins and IgGs is different in children with recent-onset type 1 diabetes compared with their healthy siblings. To search for genetic variants contributing to these changes, we undertook a genetic association study of the plasma protein and IgG N-glycome in type 1 diabetes. METHODS: A total of 1105 recent-onset type 1 diabetes patients from the Danish Registry of Childhood and Adolescent Diabetes were genotyped at 183,546 genetic markers, testing these for genetic association with variable levels of 24 IgG and 39 plasma protein N-glycan traits. In the follow-up study, significant associations were validated in 455 samples. RESULTS: This study confirmed previously known plasma protein and/or IgG N-glycosylation loci (candidate genes MGAT3, MGAT5 and ST6GAL1, encoding beta-1,4-mannosyl-glycoprotein 4-beta-N-acetylglucosaminyltransferase, alpha-1,6-mannosylglycoprotein 6-beta-N-acetylglucosaminyltransferase and ST6 beta-galactoside alpha-2,6-sialyltransferase 1 gene, respectively) and identified novel associations that were not previously reported for the general European population. First, novel genetic associations of IgG-bound glycans were found with SNPs on chromosome 22 residing in two genomic intervals close to candidate gene MGAT3; these include core fucosylated digalactosylated disialylated IgG N-glycan with bisecting N-acetylglucosamine (GlcNAc) (pdiscovery=7.65 × 10-12, preplication=8.33 × 10-6 for the top associated SNP rs5757680) and core fucosylated digalactosylated glycan with bisecting GlcNAc (pdiscovery=2.88 × 10-10, preplication=3.03 × 10-3 for the top associated SNP rs137702). The most significant genetic associations of IgG-bound glycans were those with MGAT3. Second, two SNPs in high linkage disequilibrium (missense rs1047286 and synonymous rs2230203) located on chromosome 19 within the protein coding region of the complement C3 gene (C3) showed association with the oligomannose plasma protein N-glycan (pdiscovery=2.43 × 10-11, preplication=8.66 × 10-4 for the top associated SNP rs1047286). CONCLUSIONS/INTERPRETATION: This study identified novel genetic associations driving the distinct N-glycosylation of plasma proteins and IgGs identified previously at type 1 diabetes onset. Our results highlight the importance of further exploring the potential role of N-glycosylation and its influence on complement activation and type 1 diabetes susceptibility.

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.

Fucosylated AGP glycopeptides as biomarkers of HNF1A-Maturity onset diabetes of the young.

AIMS: We previously demonstrated that antennary fucosylated N-glycans on plasma proteins are regulated by HNF1A and can identify cases of Maturity-Onset Diabetes of the Young caused by HNF1A variants (HNF1A-MODY). Based on literature data, we further postulated that N-glycans with best diagnostic value mostly originate from alpha-1-acid glycoprotein (AGP). In this study we analyzed fucosylation of AGP in subjects with HNF1A-MODY and other types of diabetes aiming to evaluate its diagnostic potential. METHODS: A recently developed LC-MS method for AGP N-glycopeptide analysis was utilized in two independent cohorts: a) 466 subjects with different diabetes subtypes to test the fucosylation differences, b) 98 selected individuals to test the discriminative potential for pathogenic HNF1A variants. RESULTS: Our results showed significant reduction in AGP fucosylation associated to HNF1A-MODY when compared to other diabetes subtypes. Additionally, ROC curve analysis confirmed significant discriminatory potential of individual fucosylated AGP glycopeptides, where the best performing glycopeptide had an AUC of 0.94 (95% CI 0.90-0.99). CONCLUSIONS: A glycopeptide based diagnostic tool would be beneficial for patient stratification by providing information about the functionality of HNF1A. It could assist the interpretation of DNA sequencing results and be a useful addition to the differential diagnostic process.

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.

Protein Glycosylation in Diabetes.

Diabetes mellitus is a group of metabolic disorders characterized by the presence of hyperglycaemia. Due to its high prevalence and substantial heterogeneity, many studies have been investigating markers that could identify predisposition for the disease development, differentiate between the various subtypes, establish early diagnosis, predict complications or represent novel therapeutic targets. N-glycans, complex oligosaccharide molecules covalently linked to proteins, emerged as potential markers and functional effectors of various diabetes subtypes, appearing to have the capacity to meet these requirements. For instance, it has been shown that N-glycome changes in patients with type 2 diabetes and that N-glycans can even identify individuals with an increased risk for its development. Moreover, genome-wide association studies identified glycosyltransferase genes as candidate causal genes for both type 1 and type 2 diabetes. N-glycans have also been suggested to have a major role in preventing the impairment of glucose-stimulated insulin secretion by modulating cell surface expression of glucose transporters. In this chapter we aimed to describe four major diabetes subtypes: type 1, type 2, gestational and monogenic diabetes, giving an overview of suggested role for N-glycosylation in their development, diagnosis and management.

Extensive weight loss reduces glycan age by altering IgG N-glycosylation.

BACKGROUND: Obesity, a major global health problem, is associated with increased cardiometabolic morbidity and mortality. Protein glycosylation is a frequent posttranslational modification, highly responsive to inflammation and ageing. The prospect of biological age reduction, by changing glycosylation patterns through metabolic intervention, opens many possibilities. We have investigated whether weight loss interventions affect inflammation- and ageing-associated IgG glycosylation changes, in a longitudinal cohort of bariatric surgery patients. To support potential findings, BMI-related glycosylation changes were monitored in a longitudinal twins cohort. METHODS: IgG N-glycans were chromatographically profiled in 37 obese patients, subjected to low-calorie diet, followed by bariatric surgery, across multiple timepoints. Similarly, plasma-derived IgG N-glycan traits were longitudinally monitored in 1680 participants from the TwinsUK cohort. RESULTS: Low-calorie diet induced a marked decrease in the levels of IgG N-glycans with bisecting GlcNAc, whose higher levels are usually associated with ageing and inflammatory conditions. Bariatric surgery resulted in extensive alterations of the IgG N-glycome that accompanied progressive weight loss during 1-year follow-up. We observed a significant increase in digalactosylated and sialylated glycans, and a substantial decrease in agalactosylated and core fucosylated IgG N-glycans (adjusted p value range 7.38 × 10-04-3.94 × 10-02). This IgG N-glycan profile is known to be associated with a younger biological age and reflects an enhanced anti-inflammatory IgG potential. Loss of BMI over a 20 year period in the TwinsUK cohort validated a weight loss-associated agalactosylation decrease (adjusted p value 1.79 × 10-02) and an increase in digalactosylation (adjusted p value 5.85 × 10-06). CONCLUSIONS: Altogether, these findings highlight that weight loss substantially affects IgG N-glycosylation, resulting in reduced glycan and biological age.

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

Antennary fucosylation alterations in plasma glycoproteins have been previously proposed and tested as a biomarker for differentiation of maturity onset diabetes of the young (MODY) patients carrying a functional mutation in the HNF1A gene. Here, we developed a novel LC-based workflow to analyze blood plasma N-glycan fucosylation in 320 diabetes cases with clinical features matching those at risk of HNF1A-MODY. Fucosylation levels measured in two independent research centers by using similar LC-based methods were correlated to evaluate the interlaboratory performance of the biomarker. The interlaboratory study showed good correlation between fucosylation levels measured for the 320 cases in the two centers with the correlation coefficient (r) of up to 0.88 for a single trait A3FG3S2. The improved chromatographic separation allowed the identification of six single glycan traits and a derived antennary fucosylation trait that were able to differentiate individuals carrying pathogenic mutations from benign or no HNF1A mutation cases, as determined by the area under the curve (AUC) of up to 0.94. The excellent (r = 0.88) interlaboratory performance of the glycan biomarker for HNF1A-MODY further supports the development of a clinically relevant diagnostic test measuring antennary fucosylation levels.

N-glycans as functional effectors of genetic and epigenetic disease risk.

N-glycosylation is a frequent modification of proteins, essential for all domains of life. N-glycan biosynthesis is a dynamic, complex, non-templated process, wherein specific glycoforms are modulated by various microenvironmental cues, cellular signals and local availability of dedicated enzymes and sugar precursors. This intricate regulatory network comprises hundreds of proteins, whose activity is dependent on both sequence of implicated genes and the regulation of their expression. In this regard, variation in N-glycosylation patterns stems from either gene polymorphisms or from stable epigenetic regulation of gene expression in different individuals. Moreover, epigenome alters in response to various environmental factors, representing a direct link between environmental exposure and changes in gene expression, that are subsequently reflected through altered N-glycosylation. N-glycosylation itself has a fundamental role in numerous biological processes, ranging from protein folding, cellular homeostasis, adhesion and immune regulation, to the effector functions in multiple diseases. Moreover, specific modification of the glycan structure can modulate glycoprotein's biological function or direct the faith of the entire cell, as seen on the examples of antibodies and T cells, respectively. Since immunoglobulin G is one of the most profoundly studied glycoproteins in general, the focus of this review will be on its N-glycosylation changes and their functional implications. By deepening the knowledge on the mechanistic roles that certain glycoforms exert in differential pathological processes, valuable insight into molecular perturbations occurring during disease development could be obtained. The prospect of resolving the exact biological pathways involved offers a potential for the development of new therapeutic interventions and molecular tools that would aid in prognosis, early referral and timely treatment of multiple disease conditions.

Replication of 15 loci involved in human plasma protein N-glycosylation in 4802 samples from four cohorts.

Human protein glycosylation is a complex process, and its in vivo regulation is poorly understood. Changes in glycosylation patterns are associated with many human diseases and conditions. Understanding the biological determinants of protein glycome provides a basis for future diagnostic and therapeutic applications. Genome-wide association studies (GWAS) allow to study biology via a hypothesis-free search of loci and genetic variants associated with a trait of interest. Sixteen loci were identified by three previous GWAS of human plasma proteome N-glycosylation. However, the possibility that some of these loci are false positives needs to be eliminated by replication studies, which have been limited so far. Here, we use the largest set of samples so far (4802 individuals) to replicate the previously identified loci. For all but one locus, the expected replication power exceeded 95%. Of the 16 loci reported previously, 15 were replicated in our study. For the remaining locus (near the KREMEN1 gene), the replication power was low, and hence, replication results were inconclusive. The very high replication rate highlights the general robustness of the GWAS findings as well as the high standards adopted by the community that studies genetic regulation of protein glycosylation. The 15 replicated loci present a good target for further functional studies. Among these, eight loci contain genes encoding glycosyltransferases: MGAT5, B3GAT1, FUT8, FUT6, ST6GAL1, B4GALT1, ST3GAL4 and MGAT3. The remaining seven loci offer starting points for further functional follow-up investigation into molecules and mechanisms that regulate human protein N-glycosylation in vivo.

A strategy to incorporate prior knowledge into correlation network cutoff selection.

Correlation networks are frequently used to statistically extract biological interactions between omics markers. Network edge selection is typically based on the statistical significance of the correlation coefficients. This procedure, however, is not guaranteed to capture biological mechanisms. We here propose an alternative approach for network reconstruction: a cutoff selection algorithm that maximizes the overlap of the inferred network with available prior knowledge. We first evaluate the approach on IgG glycomics data, for which the biochemical pathway is known and well-characterized. Importantly, even in the case of incomplete or incorrect prior knowledge, the optimal network is close to the true optimum. We then demonstrate the generalizability of the approach with applications to untargeted metabolomics and transcriptomics data. For the transcriptomics case, we demonstrate that the optimized network is superior to statistical networks in systematically retrieving interactions that were not included in the biological reference used for optimization.

Glycosylation Alterations in Multiple Sclerosis Show Increased Proinflammatory Potential.

Multiple sclerosis (MS) is an inflammatory autoimmune disorder affecting the central nervous system (CNS), with unresolved aetiology. Previous studies have implicated N-glycosylation, a highly regulated enzymatic attachment of complex sugars to targeted proteins, in MS pathogenesis. We investigated individual variation in N-glycosylation of the total plasma proteome and of IgG in MS. Both plasma protein and IgG N-glycans were chromatographically profiled and quantified in 83 MS cases and 88 age- and sex-matched controls. Comparing levels of glycosylation features between MS cases and controls revealed that core fucosylation (p = 6.96 × 10-3) and abundance of high-mannose structures (p = 1.48 × 10-2) were the most prominently altered IgG glycosylation traits. Significant changes in plasma protein N-glycome composition were observed for antennary fucosylated, tri- and tetrasialylated, tri- and tetragalactosylated, high-branched N-glycans (p-value range 1.66 × 10-2-4.28 × 10-2). Classification performance of N-glycans was examined by ROC curve analysis, resulting in an AUC of 0.852 for the total plasma N-glycome and 0.798 for IgG N-glycome prediction models. Our results indicate that multiple aspects of protein glycosylation are altered in MS, showing increased proinflammatory potential. N-glycan alterations showed substantial value in classification of the disease status, nonetheless, additional studies are warranted to explore their exact role in MS development and utility as biomarkers.

Global variability of the human IgG glycome.

Immunoglobulin G (IgG) is the most abundant serum antibody which structural characteristics and effector functions are modulated through the attachment of various sugar moieties called glycans. Composition of the IgG N-glycome changes with age of an individual and in different diseases. Variability of IgG glycosylation within a population is well studied and is known to be affected by both genetic and environmental factors. However, global inter-population differences in IgG glycosylation have never been properly addressed. Here we present population-specific N-glycosylation patterns of IgG, analyzed in 5 different populations totaling 10,482 IgG glycomes, and of IgG's fragment crystallizable region (Fc), analyzed in 2,579 samples from 27 populations sampled across the world. Country of residence associated with many N-glycan features and the strongest association was with monogalactosylation where it explained 38% of variability. IgG monogalactosylation strongly correlated with the development level of a country, defined by United Nations health and socioeconomic development indicators, and with the expected lifespan. Subjects from developing countries had low levels of IgG galactosylation, characteristic for inflammation and ageing. Our results suggest that citizens of developing countries may be exposed to environmental factors that can cause low-grade chronic inflammation and the apparent increase in biological age.

Systematic Evaluation of Normalization Methods for Glycomics Data Based on Performance of Network Inference.

Glycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, here, we quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms: Liquid Chromatography - ElectroSpray Ionization - Mass Spectrometry (LC-ESI-MS), Ultra High Performance Liquid Chromatography with Fluorescence Detection (UHPLC-FLD), and Matrix Assisted Laser Desorption Ionization - Furier Transform Ion Cyclotron Resonance - Mass Spectrometry (MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the 'Probabilistic Quotient' method followed by log-transformation, irrespective of the measurement platform. This recommendation is further supported by an additional analysis, where we ranked normalization methods based on their statistical associations with age, a factor known to associate with glycomics measurements.

Plasma N-Glycans as Emerging Biomarkers of Cardiometabolic Risk: A Prospective Investigation in the EPIC-Potsdam Cohort Study.

OBJECTIVE: Plasma protein N-glycan profiling integrates information on enzymatic protein glycosylation, which is a highly controlled ubiquitous posttranslational modification. Here we investigate the ability of the plasma N-glycome to predict incidence of type 2 diabetes and cardiovascular diseases (CVDs; i.e., myocardial infarction and stroke). RESEARCH DESIGN AND METHODS: Based on the prospective European Prospective Investigation of Cancer (EPIC)-Potsdam cohort (n = 27,548), we constructed case-cohorts including a random subsample of 2,500 participants and all physician-verified incident cases of type 2 diabetes (n = 820; median follow-up time 6.5 years) and CVD (n = 508; median follow-up time 8.2 years). Information on the relative abundance of 39 N-glycan groups in baseline plasma samples was generated by chromatographic profiling. We selected predictive N-glycans for type 2 diabetes and CVD separately, based on cross-validated machine learning, nonlinear model building, and construction of weighted prediction scores. This workflow for CVD was applied separately in men and women. RESULTS: The N-glycan-based type 2 diabetes score was strongly predictive for diabetes risk in an internal validation cohort (weighted C-index 0.83, 95% CI 0.78-0.88), and this finding was externally validated in the Finland Cardiovascular Risk Study (FINRISK) cohort. N-glycans were moderately predictive for CVD incidence (weighted C-indices 0.66, 95% CI 0.60-0.72, for men; 0.64, 95% CI 0.55-0.73, for women). Information on the selected N-glycans improved the accuracy of established and clinically applied risk prediction scores for type 2 diabetes and CVD. CONCLUSIONS: Selected N-glycans improve type 2 diabetes and CVD prediction beyond established risk markers. Plasma protein N-glycan profiling may thus be useful for risk stratification in the context of precisely targeted primary prevention of cardiometabolic diseases.

Heritability of Human Plasma N-Glycome.

The N-glycosylation profile of total human plasma proteins could be a useful biomarker for various pathological states. Reliable high-throughput methods for such profiling have been developed. However, studies of relative importance of genetic and environmental factors in regulating plasma N-glycome are scarce. The aim of our study was to determine the role of genetic factors in phenotypic variation of plasma N-glycan profile through the estimates of its heritability. Thirty-nine total plasma N-glycome traits were analyzed in 2816 individuals from the TwinsUK data set. For the majority of the traits, high heritability estimates (>50%) were obtained pointing at a significant contribution of genetic factors in plasma N-glycome variation, especially for glycans mostly attached to immunoglobulins. We have also found several structures with higher environmental contribution to their variation.