Pro-inflammatory pattern of IgG1 Fc glycosylation in multiple sclerosis cerebrospinal fluid.

Background Immunoglobulin G (IgG) effector functions are regulated by the composition of glycans attached to a conserved N-glycosylation site in the Fc part. Intrathecal production of IgG, especially IgG1, is a hallmark of multiple sclerosis (MS), but nothing is known about IgG Fc glycosylation in MS and in cerebrospinal fluid (CSF) in general. Methods We applied mass spectrometry of tryptic Fc glycopeptides to analyze IgG Fc glycosylation (sialylation, galactosylation, fucosylation, and bisecting N-acetylglucosamine (GlcNAc)) in 48 paired CSF and serum samples from adult patients with MS or a first demyelinating event highly suggestive of MS (designated as MS cases), and from healthy volunteers and patients with other non-inflammatory diseases (control group). p values were adjusted for multiple testing. Results Our experiments revealed four main results. First, IgG1 glycosylation patterns were different in CSF vs. serum, in the MS group and even in control donors without intrathecal IgG synthesis. Second, in MS patients vs. controls, IgG1 glycosylation patterns were altered in CSF, but not in serum. Specifically, in CSF from the MS group, bisecting GlcNAc were elevated, and afucosylation and galactosylation were reduced. Elevated bisecting GlcNAc and reduced galactosylation are known to enhance IgG effector functions. Third, hypothesis-free regression analysis revealed that alterations of afucosylation and bisecting GlcNAc in CSF from MS cases peaked 2–3 months after the last relapse. Fourth, CSF IgG1 glycosylation correlated with the degree of intrathecal IgG synthesis and CSF cell count. Conclusions The CNS compartment as well as the inflammatory milieu in MS affect IgG1 Fc glycosylation. In MS, the CSF IgG1 glycosylation has features that enhance Fc effector functions

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

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Human immunoglobulin G (IgG) molecules are composed of two Fab portions and one Fc portion. The glycans attached to the Fc portions of IgG are known to modulate its biological activity as they influence interaction with both complement and various cellular Fc receptors. IgG glycosylation changes significantly with pregnancy, showing a vast increase in galactosylation and sialylation and a concomitant decrease in the incidence of bisecting GlcNAc. Maternal IgGs are actively transported to the fetus by the neonatal Fc receptor (FcRn) expressed in syncytiotrophoblasts in the placenta, providing the fetus and newborn with immunological protection. Two earlier reports described significant differences in total glycosylation between fetal and maternal IgG, suggesting a possible glycosylation-selective transport via the placenta. These results might suggest an alternative maternal transport pathway, since FcRn binding to IgG does not depend on Fc-glycosylation. These early studies were performed by releasing N-glycans from total IgG. Here, we chose for an alternative approach analyzing IgG Fc glycosylation at the glycopeptide level in an Fc-specific manner, providing glycosylation profiles for IgG1 and IgG4 as well as combined Fc glycosylation profiles of IgG2 and 3. The analysis of ten pairs of fetal and maternal IgG samples revealed largely comparable Fc glycosylation for all the analyzed subclasses. Average levels of galactosylation, sialylation, bisecting GlcNAc and fucosylation were very similar for the fetal and maternal IgGs. Our data suggest that the placental IgG transport is not Fc glycosylation selectiv

Association between Galactosylation of Immunoglobulin G and Improvement of Rheumatoid Arthritis during Pregnancy Is Independent of Sialylation

Rheumatoid arthritis (RA) is known to improve during pregnancy and to flare after delivery. Changes in the glycosylation of immunoglobulin G (IgG)’s fragment crystallizable (Fc) have been suggested to play a role herein. Recent animal studies indicate that not galactosylation but mainly sialylation is important in this respect. We aim to find new associations between IgG-Fc N-glycosylation and improvement of RA during pregnancy and the flare after delivery. Sera of RA patients (<i>n</i> = 251 pregnancies) and healthy controls (<i>n</i> = 32), all participating in a prospective cohort study on RA and pregnancy (PARA study), were collected before conception, during pregnancy, and after delivery. Using a recently developed fast and robust nanoRP-HPLC-sheath-flow-ESI–MS method the glycosylation of IgG Fc-glycopeptides was measured in a subclass specific manner, with relative standard deviations of <4% for the 8 most abundant IgG Fc glycopeptides during the entire measurement period of over 3 weeks. In patients and controls, several glycosylation changes were observed during pregnancy. In depth analysis of the association of these glycosylation changes with disease activity revealed that galactosylation, independent of sialylation, is associated with improvement of RA during pregnancy. Functional studies in human cell systems should be performed to obtain more insight into this matter

Association between Galactosylation of Immunoglobulin G and Improvement of Rheumatoid Arthritis during Pregnancy Is Independent of Sialylation

Rheumatoid arthritis (RA) is known to improve during pregnancy and to flare after delivery. Changes in the glycosylation of immunoglobulin G (IgG)’s fragment crystallizable (Fc) have been suggested to play a role herein. Recent animal studies indicate that not galactosylation but mainly sialylation is important in this respect. We aim to find new associations between IgG-Fc N-glycosylation and improvement of RA during pregnancy and the flare after delivery. Sera of RA patients (<i>n</i> = 251 pregnancies) and healthy controls (<i>n</i> = 32), all participating in a prospective cohort study on RA and pregnancy (PARA study), were collected before conception, during pregnancy, and after delivery. Using a recently developed fast and robust nanoRP-HPLC-sheath-flow-ESI–MS method the glycosylation of IgG Fc-glycopeptides was measured in a subclass specific manner, with relative standard deviations of <4% for the 8 most abundant IgG Fc glycopeptides during the entire measurement period of over 3 weeks. In patients and controls, several glycosylation changes were observed during pregnancy. In depth analysis of the association of these glycosylation changes with disease activity revealed that galactosylation, independent of sialylation, is associated with improvement of RA during pregnancy. Functional studies in human cell systems should be performed to obtain more insight into this matter

Association between Galactosylation of Immunoglobulin G and Improvement of Rheumatoid Arthritis during Pregnancy Is Independent of Sialylation

Rheumatoid arthritis (RA) is known to improve during pregnancy and to flare after delivery. Changes in the glycosylation of immunoglobulin G (IgG)’s fragment crystallizable (Fc) have been suggested to play a role herein. Recent animal studies indicate that not galactosylation but mainly sialylation is important in this respect. We aim to find new associations between IgG-Fc N-glycosylation and improvement of RA during pregnancy and the flare after delivery. Sera of RA patients (<i>n</i> = 251 pregnancies) and healthy controls (<i>n</i> = 32), all participating in a prospective cohort study on RA and pregnancy (PARA study), were collected before conception, during pregnancy, and after delivery. Using a recently developed fast and robust nanoRP-HPLC-sheath-flow-ESI–MS method the glycosylation of IgG Fc-glycopeptides was measured in a subclass specific manner, with relative standard deviations of <4% for the 8 most abundant IgG Fc glycopeptides during the entire measurement period of over 3 weeks. In patients and controls, several glycosylation changes were observed during pregnancy. In depth analysis of the association of these glycosylation changes with disease activity revealed that galactosylation, independent of sialylation, is associated with improvement of RA during pregnancy. Functional studies in human cell systems should be performed to obtain more insight into this matter

High-Throughput IgG Fc N‑Glycosylation Profiling by Mass Spectrometry of Glycopeptides

Age and sex dependence of subclass specific immunoglobulin G (IgG) Fc <i>N</i>-glycosylation was evaluated for 1709 individuals from two isolated human populations. IgGs were obtained from plasma by affinity purification using 96-well protein G monolithic plates and digested with trypsin. Fc <i>N</i>-glycopeptides were purified and analyzed by negative-mode MALDI-TOF-MS with 4-chloro-α-cyanocinnamic acid (Cl-CCA) matrix. Age-associated glycosylation changes were more pronounced in younger individuals (<57 years) than in older individuals (>57 years) and in females than in males. Galactosylation and sialylation decreased with increasing age and showed significant sex dependence. Interestingly, the most prominent drop in the levels of galactosylated and sialylated glycoforms in females was observed around the age of 45 to 60 years when females usually enter menopause. The incidence of bisecting <i>N</i>-acetylglucosamine increased in younger individuals and reached a plateau at older age. Furthermore, we compared the results to the total IgG <i>N</i>-glycosylation of the same populations recently analyzed by hydrophilic interaction liquid chromatography (HILIC). Significant differences were observed in the levels of galactosylation, bisecting <i>N</i>-acetylglucosamine and particularly sialylation, which were shown to be higher in HILIC analysis. Age and sex association of glycosylation features was, to a large extent, comparable between MALDI-TOF-MS and HILIC IgG glycosylation profiling

Fc-Glycosylation of IgG1 is Modulated by B-cell Stimuli*

We have recently shown that IgG1 directed against antigens thought to be involved in the pathogenesis of rheumatoid arthritis harbor different glycan moieties on their Fc-tail, as compared with total sera IgG1. Given the crucial roles of Fc-linked N-glycans for the structure and biological activity of IgG, Fc-glycosylation of antibodies is receiving considerable interest. However, so far little is known about the signals and factors that could influence the composition of these carbohydrate structures on secreted IgG produced by B lymphocytes. Here we show that both “environmental” factors, such as all-trans retinoic acid (a natural metabolite of vitamin A), as well as factors stimulating the innate immune system (i.e. CpG oligodeoxynucleotide, a ligand for toll-like receptor 9) or coming from the adaptive immune system (i.e. interleukin-21, a T-cell derived cytokine) can modulate IgG1 Fc-glycosylation. These factors affect Fc-glycan profiles in different ways. CpG oligodeoxynucleotide and interleukin-21 increase Fc-linked galactosylation and reduce bisecting N-acetylglucosamine levels, whereas all-trans retinoic acid significantly decreases galactosylation and sialylation levels. Moreover, these effects appeared to be stable and specific for secreted IgG1 as no parallel changes of the corresponding glycans in the cellular glycan pool were observed. Interestingly, several other cytokines and molecules known to affect B-cell biology and antibody production did not have an impact on IgG1 Fc-coupled glycan profiles. Together, these data indicate that different stimuli received by B cells during their activation and differentiation can modulate the Fc-linked glycosylation of secreted IgG1 without affecting the general cellular glycosylation machinery. Our study, therefore, furthers our understanding of the regulation of IgG1 glycosylation at the cellular level

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