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... Show moreGlycomics 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. Show less
The development of medical interventions for the preservation of disease-free longevity would be facilitated by markers that predict healthy aging. Altered protein N-glycosylation patterns have... Show moreThe development of medical interventions for the preservation of disease-free longevity would be facilitated by markers that predict healthy aging. Altered protein N-glycosylation patterns have been found with increasing age and several disease states. Here we investigate whether glycans derived from the total glycoprotein pool in plasma mark familial longevity and distinguish healthy from unhealthy aging. Total plasma N-glycan profiles of 2396 middle aged participants in the Leiden Longevity Study (LLS) were obtained by glycan release, labeling and subsequent HPLC analysis with fluorescence detection. After normalization and batch correction, several regression strategies were applied to evaluate associations between glycan patterns, familial longevity and healthy aging. Two N-glycan features (LC-7 and LC-8) were identified to be more abundant in plasma of the offspring of long-lived individuals as compared to controls. These results were not confounded by the altered lipid status or glucose homeostasis of the offspring. Furthermore, a decrease in levels of LC-8 was associated with the occurrence of myocardial infarction (p = 0.049, coefficient = -0.065), indicating that plasma glycosylation patterns do not only mark familial longevity, but may also reflect healthy aging. In conclusion, we describe two glycan features, of which increased levels mark familial longevity while decreased levels of one of these features mark the presence of cardiovascular disease. Show less
Background: Markers for longevity that reflect the health condition and predict healthy aging are extremely scarce. Such markers are, however, valuable in aging research. It has been shown... Show moreBackground: Markers for longevity that reflect the health condition and predict healthy aging are extremely scarce. Such markers are, however, valuable in aging research. It has been shown previously that the N-glycosylation pattern of human immunoglobulin G (IgG) is age-dependent. Here we investigate whether N-linked glycans reflect early features of human longevity. Methodology/Principal Findings: The Leiden Longevity Study (LLS) consists of nonagenarian sibling pairs, their offspring, and partners of the offspring serving as control. IgG subclass specific glycosylation patterns were obtained from 1967 participants in the LLS by MALDI-TOF-MS analysis of tryptic IgG Fc glycopeptides. Several regression strategies were applied to evaluate the association of IgG glycosylation with age, sex, and longevity. The degree of galactosylation of IgG decreased with increasing age. For the galactosylated glycoforms the incidence of bisecting GlcNAc increased as a function of age. Sex-related differences were observed at ages below 60 years. Compared to males, younger females had higher galactosylation, which decreased stronger with increasing age, resulting in similar galactosylation for both sexes from 60 onwards. In younger participants (<60 years of age), but not in the older age group (>60 years), decreased levels of non-galactosylated glycoforms containing a bisecting GlcNAc reflected early features of longevity. Conclusions/Significance: We here describe IgG glycoforms associated with calendar age at all ages and the propensity for longevity before middle age. As modulation of IgG effector functions has been described for various IgG glycosylation features, a modulatory effect may be expected for the longevity marker described in this study. Show less
