An Integrated Glycosylation Signature of Rheumatoid Arthritis

Rheumatoid arthritis (RA) Is a highly prevalent autoimmune disease that affects the joints but also various other organs. The disease is characterized by autoantibodies that are often already observed pre-disease. Since the 1980s, it has been known that antibody glycosylation is different in RA as compared to control individuals. While the literature on glycosylation changes in RA is dominated by reports on serum or plasma immunoglobulin G (IgG), our recent studies have indicated that the glycosylation changes observed for immunoglobulin A (IgA) and total serum N-glycome (TSNG) may be similarly prominent, and useful in differentiating between the RA patients and controls, or as a proxy of the disease activity. In this study, we integrated and compared the RA glycosylation signatures of IgG, IgA and TSNG, all determined in the pregnancy-induced amelioration of rheumatoid arthritis (PARA) cohort. We assessed the association of the altered glycosylation patterns with the disease, autoantibody positivity and disease activity. Our analyses indicated a common, composite glycosylation signature of RA that was independent of the autoantibody status.</p

Software-Assisted Data Processing Workflow for Intact Glycoprotein Mass Spectrometry

Intact protein analysis by mass spectrometry is important for several applications such as assessing post-translational modifications and biotransformation. In particular, intact protein analysis allows the detection of proteoforms that are commonly missed by other approaches such as proteolytic digestion followed by bottom-up analysis. Two quantification methods are mainly used for intact protein data quantification, namely the extracted ion and deconvolution approaches. However, a consensus with regard to a single best practice for intact protein data processing is lacking. Furthermore, many data processing tools are not fit-for-purpose and, as a result, the analysis of intact proteins is laborious and lacks the throughput required to be implemented for the analysis of clinical cohorts. Therefore, in this study, we investigated the application of a software-assisted data analysis and processing workflow in order to streamline intact protein integration, annotation, and quantification via deconvolution. In addition, the assessment of orthogonal data sets generated via middle-up and bottom-up analysis enabled the cross-validation of cleavage proteoform assignments present in seminal prostate-specific antigen (PSA). Furthermore, deconvolution quantification of PSA from patients' urine revealed results that were comparable with manually performed quantification based on extracted ion electropherograms. Overall, the presented workflow allows fast and efficient processing of intact protein data. The raw data is available on MassIVE using the identifier MSV000086699.</p

Serum N-Glycosylation RPLC-FD-MS Assay to Assess Colorectal Cancer Surgical Interventions.

A newly developed analytical strategy was applied to profile the total serum N-glycome of 64 colorectal cancer (CRC) patients before and after surgical intervention. In this cohort, it was previously found that serum N-glycome alterations in CRC were associated with patient survival. Here, fluorescent labeling of serum N-glycans was applied using procainamide and followed by sialic acid derivatization specific for α2,6- and α2,3-linkage types via ethyl esterification and amidation, respectively. This strategy allowed efficient separation of specific positional isomers on reversed-phase liquid chromatography-fluorescence detection-mass spectrometry (RPLC-FD-MS) and complemented the previous glycomics data based on matrix-assisted laser desorption/ionization (MALDI)-MS that did not include such separations. The results from comparing pre-operative CRC to post-operative samples were in agreement with studies that identified a decrease in di-antennary structures with core fucosylation and an increase in sialylated tri- and tetra-antennary N-glycans in CRC patient sera. Pre-operative abundances of N-glycans showed good performance for the classification of adenocarcinoma and led to the revisit of the previous MALDI-MS dataset with regard to histological and clinical data. This strategy has the potential to monitor patient profiles before, during, and after clinical events such as treatment, therapy, or surgery and should also be further explored. </p

Specific (sialyl-)Lewis core 2 O-glycans differentiate colorectal cancer from healthy colon epithelium

Cells are covered with a dense layer of carbohydrates, some of which are solely present on neoplastic cells. The so-called tumor-associated carbohydrate antigens (TACAs) are increasingly recognized as promising targets for immunotherapy. These carbohydrates differ from those of the surrounding non-cancerous tissues and contribute to the malignant phenotype of the cancer cells by promoting proliferation, metastasis, and immunosuppression. However, due to tumor tissue heterogeneity and technological limitations, TACAs are insufficiently explored. Methods: A workflow was established to decode the colorectal cancer (CRC)-associated O-linked glycans from approximately 20,000 cell extracts. Extracts were obtained through laser capture microdissection of formalin fixed paraffin embedded tissues of both primary tumors and metastatic sites, and compared to healthy colon mucosa from the same patients. The released O-glycans were analyzed by porous graphitized carbon liquid chromatography-tandem mass spectrometry in negative ion mode. Results: Distinctive O-glycosylation features were found in cancerous, stromal and normal colon mucosal regions. Over 100 O-linked glycans were detected in cancerous regions with absence in normal mucosa. From those, six core 2 O-glycans were exclusively found in more than 33% of the cancers, carrying the terminal (sialyl-)LewisX/A antigen. Moreover, two O-glycans were present in 72% of the analyzed cancers and 94% of the investigated cancers expressed at least one of these two O-glycans. In contrast, normal colon mucosa predominantly expressed core 3 O-glycans, carrying α2-6-linked sialylation, (sulfo-)LewisX/A and Sda antigens. Conclusion: In this study, we present a novel panel of highly specific TACAs, based upon differences in the glycomic profiles between CRC and healthy colon mucosa. These TACAs are promising new targets for development of innovative cancer immune target therapies and lay the foundation for the targeted treatment of CRC

Neutron-encoded diubiquitins to profile linkage selectivity of deubiquitinating enzymes

Deubiquitinating enzymes are key regulators in the ubiquitin system and an emerging class of drug targets. These proteases disassemble polyubiquitin chains and many deubiquitinases show selectivity for specific polyubiquitin linkages. However, most biochemical insights originate from studies of single diubiquitin linkages in isolation, whereas in cells all linkages coexist. To better mimick this diubiquitin substrate competition, we develop a multiplexed mass spectrometry-based deubiquitinase assay that can probe all ubiquitin linkage types simultaneously to quantify deubiquitinase activity in the presence of all potential diubiquitin substrates. For this, all eight native diubiquitins are generated and each linkage type is designed with a distinct molecular weight by incorporating neutron-encoded amino acids. Overall, 22 deubiquitinases are profiled, providing a three-dimensional overview of deubiquitinase linkage selectivity over time and enzyme concentration.</p

Integrative epigenome-wide analysis demonstrates that DNA methylation may mediate genetic risk in inflammatory bowel disease.

Epigenetic alterations may provide important insights into gene-environment interaction in inflammatory bowel disease (IBD). Here we observe epigenome-wide DNA methylation differences in 240 newly-diagnosed IBD cases and 190 controls. These include 439 differentially methylated positions (DMPs) and 5 differentially methylated regions (DMRs), which we study in detail using whole genome bisulphite sequencing. We replicate the top DMP (RPS6KA2) and DMRs (VMP1, ITGB2 and TXK) in an independent cohort. Using paired genetic and epigenetic data, we delineate methylation quantitative trait loci; VMP1/microRNA-21 methylation associates with two polymorphisms in linkage disequilibrium with a known IBD susceptibility variant. Separated cell data shows that IBD-associated hypermethylation within the TXK promoter region negatively correlates with gene expression in whole-blood and CD8 þ T cells, but not other cell types. Thus, site-specific DNA methylation changes in IBD relate to underlying genotype and associate with cell-specific alteration in gene expression

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

In-Depth Glycoproteomic Assay of Urinary Prostatic Acid Phosphatase

Prostate-specific antigen (PSA) is a well-known clinical biomarker in prostate cancer (PCa) diagnosis, but a better test is still needed, as the serum-level-based PSA quantification exhibits limited specificity and comes with poor predictive value. Prior to PSA, prostatic acid phosphatase (PAP) was used, but it was replaced by PSA because PSA improved the early detection of PCa. Upon revisiting PAP and its glycosylation specifically, it appears to be a promising new biomarker candidate. Namely, previous studies have indicated that PAP glycoforms differ between PCa and non-PCa individuals. However, an in-depth characterization of PAP glycosylation is still lacking. In this study, we established an in-depth glycoproteomic assay for urinary PAP by characterizing both the micro- and macroheterogeneity of the PAP glycoprofile. For this purpose, PAP samples were analyzed by capillary electrophoresis coupled to mass spectrometry after affinity purification from urine and proteolytic digestion. The developed urinary PAP assay was applied on a pooled DRE (digital rectal examination) urine sample from nine individuals. Three glycosylation sites were characterized, namely N94, N220, and N333, via N-glycopeptide analysis. Taking sialic acid linkage isomers into account, a total of 63, 27, and 4 N-glycan structures were identified, respectively. The presented PAP glycoproteomic assay will enable the determination of potential glycomic biomarkers for the early detection and prognosis of PCa in cohort studies.</p

In-Depth Glycoproteomic Assay of Urinary Prostatic Acid Phosphatase

Prostate-specific antigen (PSA) is a well-known clinical biomarker in prostate cancer (PCa) diagnosis, but a better test is still needed, as the serum-level-based PSA quantification exhibits limited specificity and comes with poor predictive value. Prior to PSA, prostatic acid phosphatase (PAP) was used, but it was replaced by PSA because PSA improved the early detection of PCa. Upon revisiting PAP and its glycosylation specifically, it appears to be a promising new biomarker candidate. Namely, previous studies have indicated that PAP glycoforms differ between PCa and non-PCa individuals. However, an in-depth characterization of PAP glycosylation is still lacking. In this study, we established an in-depth glycoproteomic assay for urinary PAP by characterizing both the micro- and macroheterogeneity of the PAP glycoprofile. For this purpose, PAP samples were analyzed by capillary electrophoresis coupled to mass spectrometry after affinity purification from urine and proteolytic digestion. The developed urinary PAP assay was applied on a pooled DRE (digital rectal examination) urine sample from nine individuals. Three glycosylation sites were characterized, namely N-94, N-220, and N-333, via N-glycopeptide analysis. Taking sialic acid linkage isomers into account, a total of 63, 27, and 4 N-glycan structures were identified, respectively. The presented PAP glycoproteomic assay will enable the determination of potential glycomic biomarkers for the early detection and prognosis of PCa in cohort studies.Proteomic