Effects of allergic diseases and age on the composition of serum IgG glycome in children

Acknowledgements Glycan analysis was partly supported by European Commission GlycoBioM (contract #259869), IBD-BIOM (contract #305479), HighGlycan (contract #278535), MIMOmics (contract #305280), HTP-GlycoMet (contract #324400) and IntegraLife (contract #315997) grants. The SEATON cohort was partly funded by the UK Medical Research Council (contract #80219) and Asthma UK (contract #00/011 and 02/017) grants.Peer reviewedPublisher PD

Defining the genetic control of human blood plasma N-glycome using genome-wide association study

Glycosylation is a common post-translational modification of proteins. Glycosylation is associated with a number of human diseases. Defining genetic factors altering glycosylation may provide a basis for novel approaches to diagnostic and pharmaceutical applications. Here we report a genome-wide association study of the human blood plasma N-glycome composition in up to 3811 people measured by Ultra Performance Liquid Chromatography (UPLC) technology. Starting with the 36 original traits measured by UPLC, we computed an additional 77 derived traits leading to a total of 113 glycan traits. We studied associations between these traits and genetic polymorphisms located on human autosomes. We discovered and replicated 12 loci. This allowed us to demonstrate an overlap in genetic control between total plasma protein and IgG glycosylation. The majority of revealed loci contained genes that encode enzymes directly involved in glycosylation (FUT3/FUT6, FUT8, B3GAT1, ST6GAL1, B4GALT1, ST3GAL4, MGAT3 and MGAT5) and a known regulator of plasma protein fucosylation (HNF1A). However, we also found loci that could possibly reflect other more complex aspects of glycosylation process. Functional genomic annotation suggested the role of several genes including DERL3, CHCHD10, TMEM121, IGH and IKZF1. The hypotheses we generated may serve as a starting point for further functional studies in this research area

Association of Systemic Lupus Erythematosus With Decreased Immunosuppressive Potential of the IgG Glycome

OBJECTIVE: Glycans attached to the Fc portion of IgG are important modulators of IgG effector functions. Interindividual differences in IgG glycome composition are large and they associate strongly with different inflammatory and autoimmune diseases. IKZF1, HLA–DQ2A/B, and BACH2 genetic loci that affect IgG glycome composition show pleiotropy with systemic lupus erythematosus (SLE), indicating a potentially causative role of aberrant IgG glycosylation in SLE. We undertook this large multicenter case–control study to determine whether SLE is associated with altered IgG glycosylation. METHODS: Using ultra‐performance liquid chromatography analysis of released glycans, we analyzed the composition of the IgG glycome in 261 SLE patients and 247 matched controls of Latin American Mestizo origin (the discovery cohort) and in 2 independent replication cohorts of different ethnicity (108 SLE patients and 193 controls from Trinidad, and 106 SLE patients and 105 controls from China). RESULTS: Multiple statistically significant differences in IgG glycome composition were observed between patients and controls. The most significant changes included decreased galactosylation and sialylation of IgG (which regulate proinflammatory and antiinflammatory actions of IgG) as well as decreased core fucose and increased bisecting N‐acetylglucosamine (which affect antibody‐dependent cell‐mediated cytotoxicity). CONCLUSION: The IgG glycome in SLE patients is significantly altered in a way that decreases immunosuppressive action of circulating immunoglobulins. The magnitude of observed changes is associated with the intensity of the disease, indicating that aberrant IgG glycome composition or changes in IgG glycosylation may be an important molecular mechanism in SLE

NIST Interlaboratory Study on Glycosylation Analysis of Monoclonal Antibodies: Comparison of Results from Diverse Analytical Methods

Glycosylation is a topic of intense current interest in the development of biopharmaceuticals because it is related to drug safety and efficacy. This work describes results of an interlaboratory study on the glycosylation of the Primary Sample (PS) of NISTmAb, a monoclonal antibody reference material. Seventy-six laboratories from industry, university, research, government, and hospital sectors in Europe, North America, Asia, and Australia submit- Avenue, Silver Spring, Maryland 20993; 22Glycoscience Research Laboratory, Genos, Borongajska cesta 83h, 10 000 Zagreb, Croatia; 23Faculty of Pharmacy and Biochemistry, University of Zagreb, A. Kovacˇ ic´ a 1, 10 000 Zagreb, Croatia; 24Department of Chemistry, Georgia State University, 100 Piedmont Avenue, Atlanta, Georgia 30303; 25glyXera GmbH, Brenneckestrasse 20 * ZENIT / 39120 Magdeburg, Germany; 26Health Products and Foods Branch, Health Canada, AL 2201E, 251 Sir Frederick Banting Driveway, Ottawa, Ontario, K1A 0K9 Canada; 27Graduate School of Advanced Sciences of Matter, Hiroshima University, 1-3-1 Kagamiyama Higashi-Hiroshima 739–8530 Japan; 28ImmunoGen, 830 Winter Street, Waltham, Massachusetts 02451; 29Department of Medical Physiology, Jagiellonian University Medical College, ul. Michalowskiego 12, 31–126 Krakow, Poland; 30Department of Pathology, Johns Hopkins University, 400 N. Broadway Street Baltimore, Maryland 21287; 31Mass Spec Core Facility, KBI Biopharma, 1101 Hamlin Road Durham, North Carolina 27704; 32Division of Mass Spectrometry, Korea Basic Science Institute, 162 YeonGuDanji-Ro, Ochang-eup, Cheongwon-gu, Cheongju Chungbuk, 363–883 Korea (South); 33Advanced Therapy Products Research Division, Korea National Institute of Food and Drug Safety, 187 Osongsaengmyeong 2-ro Osong-eup, Heungdeok-gu, Cheongju-si, Chungcheongbuk-do, 363–700, Korea (South); 34Center for Proteomics and Metabolomics, Leiden University Medical Center, P.O. Box 9600, 2300 RC Leiden, The Netherlands; 35Ludger Limited, Culham Science Centre, Abingdon, Oxfordshire, OX14 3EB, United Kingdom; 36Biomolecular Discovery and Design Research Centre and ARC Centre of Excellence for Nanoscale BioPhotonics (CNBP), Macquarie University, North Ryde, Australia; 37Proteomics, Central European Institute for Technology, Masaryk University, Kamenice 5, A26, 625 00 BRNO, Czech Republic; 38Max Planck Institute for Dynamics of Complex Technical Systems, Sandtorstrasse 1, 39106 Magdeburg, Germany; 39Department of Biomolecular Sciences, Max Planck Institute of Colloids and Interfaces, 14424 Potsdam, Germany; 40AstraZeneca, Granta Park, Cambridgeshire, CB21 6GH United Kingdom; 41Merck, 2015 Galloping Hill Rd, Kenilworth, New Jersey 07033; 42Analytical R&D, MilliporeSigma, 2909 Laclede Ave. St. Louis, Missouri 63103; 43MS Bioworks, LLC, 3950 Varsity Drive Ann Arbor, Michigan 48108; 44MSD, Molenstraat 110, 5342 CC Oss, The Netherlands; 45Exploratory Research Center on Life and Living Systems (ExCELLS), National Institutes of Natural Sciences, 5–1 Higashiyama, Myodaiji, Okazaki 444–8787 Japan; 46Graduate School of Pharmaceutical Sciences, Nagoya City University, 3–1 Tanabe-dori, Mizuhoku, Nagoya 467–8603 Japan; 47Medical & Biological Laboratories Co., Ltd, 2-22-8 Chikusa, Chikusa-ku, Nagoya 464–0858 Japan; 48National Institute for Biological Standards and Control, Blanche Lane, South Mimms, Potters Bar, Hertfordshire EN6 3QG United Kingdom; 49Division of Biological Chemistry & Biologicals, National Institute of Health Sciences, 1-18-1 Kamiyoga, Setagaya-ku, Tokyo 158–8501 Japan; 50New England Biolabs, Inc., 240 County Road, Ipswich, Massachusetts 01938; 51New York University, 100 Washington Square East New York City, New York 10003; 52Target Discovery Institute, Nuffield Department of Medicine, University of Oxford, Roosevelt Drive, Oxford, OX3 7FZ, United Kingdom; 53GlycoScience Group, The National Institute for Bioprocessing Research and Training, Fosters Avenue, Mount Merrion, Blackrock, Co. Dublin, Ireland; 54Department of Chemistry, North Carolina State University, 2620 Yarborough Drive Raleigh, North Carolina 27695; 55Pantheon, 201 College Road East Princeton, New Jersey 08540; 56Pfizer Inc., 1 Burtt Road Andover, Massachusetts 01810; 57Proteodynamics, ZI La Varenne 20–22 rue Henri et Gilberte Goudier 63200 RIOM, France; 58ProZyme, Inc., 3832 Bay Center Place Hayward, California 94545; 59Koichi Tanaka Mass Spectrometry Research Laboratory, Shimadzu Corporation, 1 Nishinokyo Kuwabara-cho Nakagyo-ku, Kyoto, 604 8511 Japan; 60Children’s GMP LLC, St. Jude Children’s Research Hospital, 262 Danny Thomas Place Memphis, Tennessee 38105; 61Sumitomo Bakelite Co., Ltd., 1–5 Muromati 1-Chome, Nishiku, Kobe, 651–2241 Japan; 62Synthon Biopharmaceuticals, Microweg 22 P.O. Box 7071, 6503 GN Nijmegen, The Netherlands; 63Takeda Pharmaceuticals International Co., 40 Landsdowne Street Cambridge, Massachusetts 02139; 64Department of Chemistry and Biochemistry, Texas Tech University, 2500 Broadway, Lubbock, Texas 79409; 65Thermo Fisher Scientific, 1214 Oakmead Parkway Sunnyvale, California 94085; 66United States Pharmacopeia India Pvt. Ltd. IKP Knowledge Park, Genome Valley, Shamirpet, Turkapally Village, Medchal District, Hyderabad 500 101 Telangana, India; 67Alberta Glycomics Centre, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 68Department of Chemistry, University of Alberta, Edmonton, Alberta T6G 2G2 Canada; 69Department of Chemistry, University of California, One Shields Ave, Davis, California 95616; 70Horva´ th Csaba Memorial Laboratory for Bioseparation Sciences, Research Center for Molecular Medicine, Doctoral School of Molecular Medicine, Faculty of Medicine, University of Debrecen, Debrecen, Egyetem ter 1, Hungary; 71Translational Glycomics Research Group, Research Institute of Biomolecular and Chemical Engineering, University of Pannonia, Veszprem, Egyetem ut 10, Hungary; 72Delaware Biotechnology Institute, University of Delaware, 15 Innovation Way Newark, Delaware 19711; 73Proteomics Core Facility, University of Gothenburg, Medicinaregatan 1G SE 41390 Gothenburg, Sweden; 74Department of Medical Biochemistry and Cell Biology, University of Gothenburg, Institute of Biomedicine, Sahlgrenska Academy, Medicinaregatan 9A, Box 440, 405 30, Gothenburg, Sweden; 75Department of Clinical Chemistry and Transfusion Medicine, Sahlgrenska Academy at the University of Gothenburg, Bruna Straket 16, 41345 Gothenburg, Sweden; 76Department of Chemistry, University of Hamburg, Martin Luther King Pl. 6 20146 Hamburg, Germany; 77Department of Chemistry, University of Manitoba, 144 Dysart Road, Winnipeg, Manitoba, Canada R3T 2N2; 78Laboratory of Mass Spectrometry of Interactions and Systems, University of Strasbourg, UMR Unistra-CNRS 7140, France; 79Natural and Medical Sciences Institute, University of Tu¨ bingen, Markwiesenstrae 55, 72770 Reutlingen, Germany; 80Bijvoet Center for Biomolecular Research and Utrecht Institute for Pharmaceutical Sciences, Utrecht University, Padualaan 8, 3584 CH Utrecht, The Netherlands; 81Division of Bioanalytical Chemistry, Amsterdam Institute for Molecules, Medicines and Systems, Vrije Universiteit Amsterdam, de Boelelaan 1085, 1081 HV Amsterdam, The Netherlands; 82Department of Chemistry, Waters Corporation, 34 Maple Street Milford, Massachusetts 01757; 83Zoetis, 333 Portage St. Kalamazoo, Michigan 49007 Author’s Choice—Final version open access under the terms of the Creative Commons CC-BY license. Received July 24, 2019, and in revised form, August 26, 2019 Published, MCP Papers in Press, October 7, 2019, DOI 10.1074/mcp.RA119.001677 ER: NISTmAb Glycosylation Interlaboratory Study 12 Molecular & Cellular Proteomics 19.1 Downloaded from https://www.mcponline.org by guest on January 20, 2020 ted a total of 103 reports on glycan distributions. The principal objective of this study was to report and compare results for the full range of analytical methods presently used in the glycosylation analysis of mAbs. Therefore, participation was unrestricted, with laboratories choosing their own measurement techniques. Protein glycosylation was determined in various ways, including at the level of intact mAb, protein fragments, glycopeptides, or released glycans, using a wide variety of methods for derivatization, separation, identification, and quantification. Consequently, the diversity of results was enormous, with the number of glycan compositions identified by each laboratory ranging from 4 to 48. In total, one hundred sixteen glycan compositions were reported, of which 57 compositions could be assigned consensus abundance values. These consensus medians provide communityderived values for NISTmAb PS. Agreement with the consensus medians did not depend on the specific method or laboratory type. The study provides a view of the current state-of-the-art for biologic glycosylation measurement and suggests a clear need for harmonization of glycosylation analysis methods. Molecular & Cellular Proteomics 19: 11–30, 2020. DOI: 10.1074/mcp.RA119.001677.L

Significant Associations of IgG Glycan Structures With Chronic Graft-Versus-Host Disease Manifestations: Results of the Cross-Sectional NIH Cohort Study

Chronic graft-versus-host disease (cGvHD) is a systemic alloimmune and autoimmune disorder and a major late complication of allogeneic hematopoietic stem cell transplantation (alloHSCT). The disease is characterized by an altered homeostasis of the humoral immune response. Immunoglobulin G (IgG) glycoprotein is the main effector molecule of the humoral immune response. Changes in IgG glycosylation are associated with a number of autoimmune diseases. IgG glycosylation analysis was done by the means of liquid chromatography in the National Institutes of Health (NIH) cohort of 213 cGvHD patients. The results showed statistically significant differences with regards to cGvHD NIH joint/fascia and skin score, disease activity and intensity of systemic immunosuppression. ROC analysis confirmed that IgG glycosylation increases specificity and sensitivity of models using laboratory parameters and markers of inflammation associated with cGvHD (eosinophil count, complement components C3 and C4 and inflammation markers: albumin, CRP and thrombocyte count). This research shows that IgG glycosylation may play a significant role in cGvHD pathology. Further research could contribute to the understanding of the disease biology and lead to the clinical biomarker development to allow personalized approaches to chronic GvHD therapy

Glycosylation profile of IgG in moderate kidney dysfunction

Glycans constitute the most abundant and diverse form of the post-translational modifications, and animal studies have suggested the involvement of IgG glycosylation in mechanisms of renal damage. Here, we explored the associations between IgG glycans and renal function in 3274 individuals from the TwinsUK registry. We analyzed the correlation between renal function measured as eGFR and 76 N-glycan traits using linear regressions adjusted for covariates and multiple testing in the larger population. We replicated our results in 31 monozygotic twin pairs discordant for renal function. Results from both analyses were then meta-analyzed. Fourteen glycan traits were associated with renal function in the discovery sample (P<6.5×10(-4)) and remained significant after validation. Those glycan traits belong to three main glycosylation features: galactosylation, sialylation, and level of bisecting N-acetylglucosamine of the IgG glycans. These results show the role of IgG glycosylation in kidney function and provide novel insight into the pathophysiology of CKD and potential diagnostic and therapeutic targets

High-throughput Serum N-Glycomics: Method Comparison and Application to Study Rheumatoid Arthritis and Pregnancy-associated Changes

N-Glycosylation is a fundamentally important protein modification with a major impact on glycoprotein characteristics such as serum half-life and receptor interaction. More than half of the proteins in human serum are glycosylated, and the relative abundances of protein glycoforms often reflect alterations in health and disease. Several analytical methods are currently capable of analyzing the total serum N-glycosylation in a high-throughput manner. Here we evaluate and compare the performance of three high-throughput released N-glycome analysis methods. Included were hydrophilic-interaction ultra-high-performance liquid chromatography with fluorescence detection (HILIC-UHPLC-FLD) with 2-aminobenzamide labeling of the glycans, multiplexed capillary gel electrophoresis with laser-induced fluorescence detection (xCGE-LIF) with 8-aminopyrene-1,3,6-trisulfonic acid labeling, and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) with linkage-specific sialic acid esterification. All methods assessed the same panel of serum samples, which were obtained at multiple time points during the pregnancies and postpartum periods of healthy women and patients with rheumatoid arthritis (RA). We compared the analytical methods on their technical performance as well as on their ability to describe serum protein N-glycosylation changes throughout pregnancy, with RA, and with RA disease activity. Overall, the methods proved to be similar in their detection and relative quantification of serum protein N-glycosylation. However, the non-MS methods showed superior repeatability over MALDI-TOF-MS and allowed the best structural separation of low-complexity N-glycans. MALDI-TOF-MS achieved the highest throughput and provided compositional information on higher-complexity N-glycans. Consequentially, MALDI-TOF-MS could establish the linkage-specific sialylation differences within pregnancy and RA, whereas HILIC-UHPLC-FLD and xCGE-LIF demonstrated differences in α1,3- and α1,6-branch galactosylation. While the combination of methods proved to be the most beneficial for the analysis of total serum protein N-glycosylation, informed method choices can be made for the glycosylation analysis of single proteins or samples of varying complexity

Multiomics Profiling Reveals Signatures of Dysmetabolism in Urban Populations in Central India.

BACKGROUND Non-communicable diseases (NCDs) have become a major cause of morbidity and mortality in India. Perturbation of host-microbiome interactions may be a key mechanism by which lifestyle-related risk factors such as tobacco use, alcohol consumption, and physical inactivity may influence metabolic health. There is an urgent need to identify relevant dysmetabolic traits for predicting risk of metabolic disorders, such as diabetes, among susceptible Asian Indians where NCDs are a growing epidemic. METHODS Here, we report the first in-depth phenotypic study in which we prospectively enrolled 218 adults from urban and rural areas of Central India and used multiomic profiling to identify relationships between microbial taxa and circulating biomarkers of cardiometabolic risk. Assays included fecal microbiota analysis by 16S ribosomal RNA gene amplicon sequencing, quantification of serum short chain fatty acids by gas chromatography-mass spectrometry, and multiplex assaying of serum diabetic proteins, cytokines, chemokines, and multi-isotype antibodies. Sera was also analysed for -glycans and immunoglobulin G Fc -glycopeptides. RESULTS Multiple hallmarks of dysmetabolism were identified in urbanites and young overweight adults, the majority of whom did not have a known diagnosis of diabetes. Association analyses revealed several host-microbe and metabolic associations. CONCLUSIONS Host-microbe and metabolic interactions are differentially shaped by body weight and geographic status in Central Indians. Further exploration of these links may help create a molecular-level map for estimating risk of developing metabolic disorders and designing early interventions

Body mass index from age 15 years onwards and muscle mass, strength and quality in early old age: findings from the MRC National Survey of Health and Development

BACKGROUND: As more people live more of their lives obese, it is unclear what impact this will have on muscle mass, strength, and quality. We aimed to examine the associations of body mass index (BMI) from age 15 years onwards with low muscle mass, strength, and quality in early old age.METHODS: A total of 1,511 men and women from a British birth cohort study with BMI measured at 15, 20, 26, 36, 43, 53, and 60-64 years and dual-energy x-ray absorptiometry scans at 60-64 years were included. Four binary outcomes identified those in the bottom sex-specific 20% of (a) appendicular lean mass (ALM) index (kilogram per square meter), (b) ALM residuals (derived from sex-specific models in which ALM (kilogram) = ?0 + ?1 height [meter] + ?2 fat mass [kilogram]), (c) grip strength (kilogram), (d) muscle quality (grip strength [kilogram]/arm lean mass [kilogram]). Associations of BMI with each outcome were tested.RESULTS: Higher BMI from age 15 years was associated with lower odds of low ALM but higher odds of low muscle quality (per 1 SD increase in BMI at 36 years, odds ratio of low ALM residuals = 0.50 [95% CI: 0.43, 0.59], and muscle quality = 1.50 [1.29, 1.75]). Greater gains in BMI were associated with lower odds of low ALM index but higher odds of low muscle quality. BMI was not associated with grip strength.CONCLUSIONS: Given increases in the global prevalence of obesity, cross-cohort comparisons of sarcopenia need to consider our findings that greater gains in BMI are associated with higher muscle mass but not with grip strength and therefore with lower muscle quality