Florent Clerc1, Mislav Novokmet2, Viktoria Dotz1, Karli R Reiding1, Noortje de Haan1, Guinevere S M Kammeijer1, Hans Dalebout1, Marco R Bladergroen1, Frano Vukovic2, Erdmann Rapp3, Stephan R Targan4, Gildardo Barron4, Natalia Manetti5, Anna Latiano6, Dermot P B McGovern4, Vito Annese7, Gordan Lauc2, Manfred Wuhrer8. 1. Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Leiden, The Netherlands. 2. Genos Glycoscience Research Laboratory, Zagreb, Croatia. 3. Max Planck Institute for Dynamics of Complex Technical Systems, Magdeburg, Germany; glyXera GmbH, Magdeburg, Germany. 4. F. Widjaja Foundation Inflammatory Bowel and Immunobiology Research Institute, Cedars-Sinai Medical Center, Los Angeles, California. 5. Unit of Gastroenterology SOD2 (Strutture Organizzative Dipartimentali), Azienda Ospedaliero Universitaria (AOU) Careggi, Florence, Italy. 6. Unit of Gastroenterology, IRCCS-CSS (Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo della Sofferenza) Hospital, San Giovanni Rotondo, Italy. 7. Unit of Gastroenterology SOD2 (Strutture Organizzative Dipartimentali), Azienda Ospedaliero Universitaria (AOU) Careggi, Florence, Italy; Unit of Gastroenterology, IRCCS-CSS (Istituto di Ricovero e Cura a Carattere Scientifico-Casa Sollievo della Sofferenza) Hospital, San Giovanni Rotondo, Italy. 8. Center for Proteomics and Metabolomics, Leiden University Medical Center (LUMC), Leiden, The Netherlands. Electronic address: m.wuhrer@lumc.nl.
Abstract
BACKGROUND & AIMS: Biomarkers are needed for early detection of Crohn's disease (CD) and ulcerative colitis (UC) or to predict patient outcomes. Glycosylation is a common and complex posttranslational modification of proteins that affects their structure and activity. We compared plasma N-glycosylation profiles between patients with CD or UC and healthy individuals (controls). METHODS: We analyzed the total plasma N-glycomes of 2635 patients with inflammatory bowel diseases and 996 controls by mass spectrometry with a linkage-specific sialic acid derivatization technique. Plasma samples were acquired from 2 hospitals in Italy (discovery cohort, 1989 patients with inflammatory bowel disease [IBD] and 570 controls) and 1 medical center in the United States (validation cohort, 646 cases of IBD and 426 controls). Sixty-three glycoforms met our criteria for relative quantification and were extracted from the raw data with the software MassyTools. Common features shared by the glycan compositions were combined in 78 derived traits, including the number of antennae of complex-type glycans and levels of fucosylation, bisection, galactosylation, and sialylation. Associations of plasma N-glycomes with age, sex, CD, UC, and IBD-related parameters such as disease location, surgery and medication, level of C-reactive protein, and sedimentation rate were tested by linear and logistic regression. RESULTS: Plasma samples from patients with IBD had a higher abundance of large-size glycans compared with controls, a decreased relative abundance of hybrid and high-mannose structures, lower fucosylation, lower galactosylation, and higher sialylation (α2,3- and α2,6-linked). We could discriminate plasma from patients with CD from that of patients with UC based on higher bisection, lower galactosylation, and higher sialylation (α2,3-linked). Glycosylation patterns were associated with disease location and progression, the need for a more potent medication, and surgery. These results were replicated in a large independent cohort. CONCLUSIONS: We performed high-throughput analysis to compare total plasma N-glycomes of individuals with vs without IBD and to identify patterns associated with disease features and the need for treatment. These profiles might be used in diagnosis and for predicting patients' responses to treatment.
BACKGROUND & AIMS: Biomarkers are needed for early detection of Crohn's disease (CD) and ulcerative colitis (UC) or to predict patient outcomes. Glycosylation is a common and complex posttranslational modification of proteins that affects their structure and activity. We compared plasma N-glycosylation profiles between patients with CD or UC and healthy individuals (controls). METHODS: We analyzed the total plasma N-glycomes of 2635 patients with inflammatory bowel diseases and 996 controls by mass spectrometry with a linkage-specific sialic acid derivatization technique. Plasma samples were acquired from 2 hospitals in Italy (discovery cohort, 1989 patients with inflammatory bowel disease [IBD] and 570 controls) and 1 medical center in the United States (validation cohort, 646 cases of IBD and 426 controls). Sixty-three glycoforms met our criteria for relative quantification and were extracted from the raw data with the software MassyTools. Common features shared by the glycan compositions were combined in 78 derived traits, including the number of antennae of complex-type glycans and levels of fucosylation, bisection, galactosylation, and sialylation. Associations of plasma N-glycomes with age, sex, CD, UC, and IBD-related parameters such as disease location, surgery and medication, level of C-reactive protein, and sedimentation rate were tested by linear and logistic regression. RESULTS: Plasma samples from patients with IBD had a higher abundance of large-size glycans compared with controls, a decreased relative abundance of hybrid and high-mannose structures, lower fucosylation, lower galactosylation, and higher sialylation (α2,3- and α2,6-linked). We could discriminate plasma from patients with CD from that of patients with UC based on higher bisection, lower galactosylation, and higher sialylation (α2,3-linked). Glycosylation patterns were associated with disease location and progression, the need for a more potent medication, and surgery. These results were replicated in a large independent cohort. CONCLUSIONS: We performed high-throughput analysis to compare total plasma N-glycomes of individuals with vs without IBD and to identify patterns associated with disease features and the need for treatment. These profiles might be used in diagnosis and for predicting patients' responses to treatment.
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