Advertisement

Human Plasma N-glycosylation as Analyzed by Matrix-Assisted Laser Desorption/Ionization-Fourier Transform Ion Cyclotron Resonance-MS Associates with Markers of Inflammation and Metabolic Health*

  • Karli R. Reiding
    Affiliations
    Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • L. Renee Ruhaak
    Affiliations
    Department of Clinical Chemistry and Laboratory Medicine, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Hae-Won Uh
    Affiliations
    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Said el Bouhaddani
    Affiliations
    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Erik B. van den Akker
    Affiliations
    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands

    Pattern Recognition & Bioinformatics, Delft University of Technology, 2600 GA Delft, The Netherlands
    Search for articles by this author
  • Rosina Plomp
    Affiliations
    Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Liam A. McDonnell
    Affiliations
    Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Jeanine J. Houwing-Duistermaat
    Affiliations
    Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands

    Department of Statistics, University of Leeds, LS2 9JT Leeds, United Kingdom
    Search for articles by this author
  • P. Eline Slagboom
    Affiliations
    Department of Molecular Epidemiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Marian Beekman
    Affiliations
    Department of Molecular Epidemiology, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Manfred Wuhrer
    Correspondence
    To whom correspondence should be addressed:Center for Proteomics and Metabolomics, Leiden University Medical Center, Albinusdreef 2, Leiden 2300 RC. Tel.:31-71-5268744; E-mail:.
    Affiliations
    Center for Proteomics and Metabolomics, Leiden University Medical Center, 2300 RC Leiden, The Netherlands
    Search for articles by this author
  • Author Footnotes
    * This work was supported by the European Union Seventh Framework Programme projects HighGlycan (278535), MIMOmics (305280), and IDEAL (259679). In addition, financial support was provided by the Innovation-Oriented Research Program on Genomics (SenterNovem IGE05007), the Centre for Medical Systems Biology and the Netherlands Consortium for Healthy Aging (grant 050-060-810), all in the framework of the Netherlands Genomics Initiative, the Netherlands Organization for Scientific Research (NWO) and by BBMRI-NL, a research infrastructure financed by the Dutch government (NWO 184.021.007).
    This article contains supplemental material.

      Abstract

      Glycosylation is an abundant co- and post-translational protein modification of importance to protein processing and activity. Although not template-defined, glycosylation does reflect the biological state of an organism and is a high-potential biomarker for disease and patient stratification. However, to interpret a complex but informative sample like the total plasma N-glycome, it is important to establish its baseline association with plasma protein levels and systemic processes. Thus far, large-scale studies (n >200) of the total plasma N-glycome have been performed with methods of chromatographic and electrophoretic separation, which, although being informative, are limited in resolving the structural complexity of plasma N-glycans. MS has the opportunity to contribute additional information on, among others, antennarity, sialylation, and the identity of high-mannose type species.
      Here, we have used matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-MS to study the total plasma N-glycome of 2144 healthy middle-aged individuals from the Leiden Longevity Study, to allow association analysis with markers of metabolic health and inflammation. To achieve this, N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-aminobenzoic acid, and following purification analyzed by negative ion mode intermediate pressure MALDI-FTICR-MS. In doing so, we achieved the relative quantification of 61 glycan compositions, ranging from Hex4HexNAc2 to Hex7HexNAc6dHex1Neu5Ac4, as well as that of 39 glycosylation traits derived thereof. Next to confirming known associations of glycosylation with age and sex by MALDI-FTICR-MS, we report novel associations with C-reactive protein (CRP), interleukin 6 (IL-6), body mass index (BMI), leptin, adiponectin, HDL cholesterol, triglycerides (TG), insulin, gamma-glutamyl transferase (GGT), alanine aminotransferase (ALT), and smoking. Overall, the bisection, galactosylation, and sialylation of diantennary species, the sialylation of tetraantennary species, and the size of high-mannose species proved to be important plasma characteristics associated with inflammation and metabolic health.
      Glycosylation is a ubiquitous co- and post-translational protein modification of functional relevance to the processing and activity of the conjugate. Examples include quality control during protein folding, regulation of circulatory half-life, and modulation of receptor interactions by either providing the recognition motif or by affecting protein conformation (
      • Varki A.
      Biological roles of oligosaccharides: all of the theories are correct.
      ,
      • Varki A.
      • Cummings R.D.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Aebi M.
      • Darvill A.
      • Kinoshita T.
      • Packer N.H.
      • Prestegard J.J.
      • Schnaar R.L.
      • Seeberger P.H.
      ,
      • Xu C.
      • Ng D.T.
      Glycosylation-directed quality control of protein folding.
      ,
      • Kontermann R.E.
      Strategies for extended serum half-life of protein therapeutics.
      ,
      • Yang W.H.
      • Aziz P.V.
      • Heithoff D.M.
      • Mahan M.J.
      • Smith J.W.
      • Marth J.D.
      An intrinsic mechanism of secreted protein aging and turnover.
      ,
      • Ferrara C.
      • Grau S.
      • Jager C.
      • Sondermann P.
      • Brunker P.
      • Waldhauer I.
      • Hennig M.
      • Ruf A.
      • Rufer A.C.
      • Stihle M.
      • Umana P.
      • Benz J.
      Unique carbohydrate-carbohydrate interactions are required for high affinity binding between FcgammaRIII and antibodies lacking core fucose.
      ,
      • Varki A.
      • Gagneux P.
      Multifarious roles of sialic acids in immunity.
      ). Consequentially, glycosylation has been associated with a multitude of diseases and states thereof, among which the progression and metastasis of cancer and the remission of rheumatoid arthritis (
      • Pinho S.S.
      • Reis C.A.
      Glycosylation in cancer: mechanisms and clinical implications.
      ,
      • Arnold J.N.
      • Saldova R.
      • Hamid U.M.
      • Rudd P.M.
      Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation.
      ,
      • Axford J.S.
      Glycosylation and rheumatic disease.
      ,
      • Bondt A.
      • Selman M.H.
      • Deelder A.M.
      • Hazes J.M.
      • Willemsen S.P.
      • Wuhrer M.
      • Dolhain R.J.
      Association between galactosylation of immunoglobulin G and improvement of rheumatoid arthritis during pregnancy is independent of sialylation.
      ). Because the process of glycosylation is not template-defined, glycosylation integrates a large series of cellular conditions such as glycosidase/glycosyltransferase abundance and activity, endoplasmic reticulum (ER)/Golgi localization and nucleotide sugar availability, and reflects the intricate biological state of an organism (
      • Varki A.
      Biological roles of oligosaccharides: all of the theories are correct.
      ,
      • Varki A.
      • Cummings R.D.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Aebi M.
      • Darvill A.
      • Kinoshita T.
      • Packer N.H.
      • Prestegard J.J.
      • Schnaar R.L.
      • Seeberger P.H.
      ,
      • Moremen K.W.
      • Tiemeyer M.
      • Nairn A.V.
      Vertebrate protein glycosylation: diversity, synthesis and function.
      ). To establish glycosylation as biomarker for (early detection of) disease and patient stratification, analysis of an easily obtainable biofluid such as plasma is of great interest (
      • Maverakis E.
      • Kim K.
      • Shimoda M.
      • Gershwin M.E.
      • Patel F.
      • Wilken R.
      • Raychaudhuri S.
      • Ruhaak L.R.
      • Lebrilla C.B.
      Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: a critical review.
      ,
      • Ruhaak L.R.
      • Miyamoto S.
      • Lebrilla C.B.
      Developments in the identification of glycan biomarkers for the detection of cancer.
      ). Observed effects in a total plasma N-glycome (TPNG)
      The abbreviations used are: TPNG, total plasma N-glycome; MALDI, matrix-assisted laser dissorption/ionization; FTICR, Fourier transform ion cyclotron resonance; HILIC, hydrophilic-interaction liquid chromatography.
      1The abbreviations used are: TPNG, total plasma N-glycome; MALDI, matrix-assisted laser dissorption/ionization; FTICR, Fourier transform ion cyclotron resonance; HILIC, hydrophilic-interaction liquid chromatography.
      , i.e. the released N-glycans from all plasma proteins, are highly informative but difficult to comprehend because of the complex contributions from relative protein glycoforms and overall glycoprotein abundances (
      • Klein A.
      Human total serum N-glycome.
      ,
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ).
      To interpret the TPNG in a context of human health and disease it is of importance to establish the behavior of N-glycans, and groups of N-glycans, in relation to plasma protein levels and systemic processes such as inflammation and metabolism. Previous studies of suitable size (n > 200) have performed this to various degrees, finding plasma N-glycans to be highly associated with e.g. age, sex, inflammation, body mass index (BMI), cholesterol and lipid levels (
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Koeleman C.A.
      • Hokke C.H.
      • Westendorp R.G.
      • Wuhrer M.
      • Houwing-Duistermaat J.J.
      • Slagboom P.E.
      • Deelder A.M.
      Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity.
      ,
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Hokke C.H.
      • Westendorp R.G.
      • Houwing-Duistermaat J.
      • Wuhrer M.
      • Deelder A.M.
      • Slagboom P.E.
      Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health.
      ,
      • Lu J.P.
      • Knezevic A.
      • Wang Y.X.
      • Rudan I.
      • Campbell H.
      • Zou Z.K.
      • Lan J.
      • Lai Q.X.
      • Wu J.J.
      • He Y.
      • Song M.S.
      • Zhang L.
      • Lauc G.
      • Wang W.
      Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations.
      ,
      • Knezevic A.
      • Gornik O.
      • Polasek O.
      • Pucic M.
      • Redzic I.
      • Novokmet M.
      • Rudd P.M.
      • Wright A.F.
      • Campbell H.
      • Rudan I.
      • Lauc G.
      Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma N-glycans.
      ,
      • Kristic J.
      • Vuckovic F.
      • Menni C.
      • Klaric L.
      • Keser T.
      • Beceheli I.
      • Pucic-Bakovic M.
      • Novokmet M.
      • Mangino M.
      • Thaqi K.
      • Rudan P.
      • Novokmet N.
      • Sarac J.
      • Missoni S.
      • Kolcic I.
      • Polasek O.
      • Rudan I.
      • Campbell H.
      • Hayward C.
      • Aulchenko Y.
      • Valdes A.
      • Wilson J.F.
      • Gornik O.
      • Primorac D.
      • Zoldos V.
      • Spector T.
      • Lauc G.
      Glycans are a novel biomarker of chronological and biological ages.
      ,
      • Vanhooren V.
      • Desmyter L.
      • Liu X.E.
      • Cardelli M.
      • Franceschi C.
      • Federico A.
      • Libert C.
      • Laroy W.
      • Dewaele S.
      • Contreras R.
      • Chen C.
      N-glycomic changes in serum proteins during human aging.
      ,
      • Igl W.
      • Polasek O.
      • Gornik O.
      • Knezevic A.
      • Pucic M.
      • Novokmet M.
      • Huffman J.
      • Gnewuch C.
      • Liebisch G.
      • Rudd P.M.
      • Campbell H.
      • Wilson J.F.
      • Rudan I.
      • Gyllensten U.
      • Schmitz G.
      • Lauc G.
      Glycomics meets lipidomics–associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations.
      ,
      • Knezevic A.
      • Polasek O.
      • Gornik O.
      • Rudan I.
      • Campbell H.
      • Hayward C.
      • Wright A.
      • Kolcic I.
      • O'Donoghue N.
      • Bones J.
      • Rudd P.M.
      • Lauc G.
      Variability, heritability and environmental determinants of human plasma N-glycome.
      ). However, these studies have either been performed on single proteins, immunoglobulin G (IgG) being a particularly well-studied example, or predominantly by methods of liquid chromatographic and electrophoretic separation (e.g. (ultra)-high-performance liquid chromatography (U)HPLC and capillary gel electrophoresis with laser-induced fluorescence detection (CGE-LIF)). Although of high analytical value—the techniques can separate analytes that are the same in monosaccharide composition (e.g. Hex4HexNAc4dHex1) but differ in glycan structure (e.g. α1,3-branch galactosylation versus α1,6-branch galactosylation)—the complexity of the TPNG means that observed signals generally comprise a variety of distinct compositions (
      • Trbojevic Akmacic I.
      • Ventham N.T.
      • Theodoratou E.
      • Vuckovic F.
      • Kennedy N.A.
      • Kristic J.
      • Nimmo E.R.
      • Kalla R.
      • Drummond H.
      • Stambuk J.
      • Dunlop M.G.
      • Novokmet M.
      • Aulchenko Y.
      • Gornik O.
      • Campbell H.
      • Pucic Bakovic M.
      • Satsangi J.
      • Lauc G.
      • IBD-BIOM Consortium
      Inflammatory bowel disease associates with proinflammatory potential of the immunoglobulin G glycome.
      ,
      • Novokmet M.
      • Lukic E.
      • Vuckovic F.
      • Ethuric Z.
      • Keser T.
      • Rajsl K.
      • Remondini D.
      • Castellani G.
      • Gasparovic H.
      • Gornik O.
      • Lauc G.
      Changes in IgG and total plasma protein glycomes in acute systemic inflammation.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ,
      • Ruhaak L.R.
      • Uh H.W.
      • Deelder A.M.
      • Dolhain R.E.
      • Wuhrer M.
      Total plasma N-glycome changes during pregnancy.
      ). Mass spectrometry provides orthogonal information from these methodologies, as it does not distinguish isomers but instead unambiguously evaluates glycans on a compositional level (
      • Harvey D.J.
      Matrix-assisted laser desorption/ionization mass spectrometry of carbohydrates.
      ,
      • Canis K.
      • McKinnon T.A.
      • Nowak A.
      • Haslam S.M.
      • Panico M.
      • Morris H.R.
      • Laffan M.A.
      • Dell A.
      Mapping the N-glycome of human von Willebrand factor.
      ,
      • Reiding K.R.
      • Blank D.
      • Kuijper D.M.
      • Deelder A.M.
      • Wuhrer M.
      High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification.
      ). To date, a large mass spectrometric TPNG study remains to be performed for revealing associations with markers of inflammation and metabolic health (
      • Kang P.
      • Madera M.
      • Alley Jr, W.R.
      • Goldman R.
      • Mechref Y.
      • Novotny M.V.
      Glycomic alterations in the highly-abundant and lesser-abundant blood serum protein fractions for patients diagnosed with hepatocellular carcinoma.
      ,
      • Borelli V.
      • Vanhooren V.
      • Lonardi E.
      • Reiding K.R.
      • Capri M.
      • Libert C.
      • Garagnani P.
      • Salvioli S.
      • Franceschi C.
      • Wuhrer M.
      Plasma N-glycome signature of Down Syndrome.
      ,
      • Jansen B.C.
      • Bondt A.
      • Reiding K.R.
      • Lonardi E.
      • de Jong C.J.
      • Falck D.
      • Kammeijer G.S.
      • Dolhain R.J.
      • Rombouts Y.
      • Wuhrer M.
      Pregnancy-associated serum N-glycome changes studied by high-throughput MALDI-TOF-MS.
      ).
      Here, we have used high-resolution intermediate-pressure matrix-assisted laser desorption/ionization (MALDI)-Fourier transform ion cyclotron resonance (FTICR)-mass spectrometry (MS) to profile the total plasma N-glycosylation of 2144 middle-aged individuals of the Leiden Longevity Study (LLS) (
      • Schoenmaker M.
      • de Craen A.J.
      • de Meijer P.H.
      • Beekman M.
      • Blauw G.J.
      • Slagboom P.E.
      • Westendorp R.G.
      Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study.
      ). Although MALDI-MS is reported to lead to underestimation of sialylated glycan species because of in-source and metastable decay, a phenomenon particularly visible with reflectron-based MALDI-time-of-flight (TOF)-MS, the intermediate pressure of the here-presented method prevents the residue loss and allows for the repeatable analysis of species carrying up to four sialic acids (
      • Asakawa D.
      • Calligaris D.
      • Zimmerman T.A.
      • De Pauw E.
      In-source decay during matrix-assisted laser desorption/ionization combined with the collisional process in an FTICR mass spectrometer.
      ,
      • Powell A.K.
      • Harvey D.J.
      Stabilization of sialic acids in N-linked oligosaccharides and gangliosides for analysis by positive ion matrix-assisted laser desorption/ionization mass spectrometry.
      ,
      • Selman M.H.
      • McDonnell L.A.
      • Palmblad M.
      • Ruhaak L.R.
      • Deelder A.M.
      • Wuhrer M.
      Immunoglobulin G glycopeptide profiling by matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry.
      ,
      • Lee H.
      • An H.J.
      • Lerno Jr, L.A.
      • German J.B.
      • Lebrilla C.B.
      Rapid profiling of bovine and human milk gangliosides by matrix-assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry.
      ,
      • Park Y.
      • Lebrilla C.B.
      Application of Fourier transform ion cyclotron resonance mass spectrometry to oligosaccharides.
      ,
      • O'Connor P.B.
      • Mirgorodskaya E.
      • Costello C.E.
      High pressure matrix-assisted laser desorption/ionization Fourier transform mass spectrometry for minimization of ganglioside fragmentation.
      ). The 61 plasma N-glycan compositions we detected by the method, as well as 39 glycosylation traits mathematically derived thereof, showed to highly associate with not only age and sex, but also with clinical markers of inflammation, liver function, cholesterol, insulin, and lipid metabolism.

      EXPERIMENTAL PROCEDURES

       Participants

      The LLS, described in detail previously (
      • Schoenmaker M.
      • de Craen A.J.
      • de Meijer P.H.
      • Beekman M.
      • Blauw G.J.
      • Slagboom P.E.
      • Westendorp R.G.
      Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study.
      ,
      • Westendorp R.G.
      • van Heemst D.
      • Rozing M.P.
      • Frolich M.
      • Mooijaart S.P.
      • Blauw G.J.
      • Beekman M.
      • Heijmans B.T.
      • de Craen A.J.
      • Slagboom P.E.
      • Leiden Longevity Study Group
      Nonagenarian siblings and their offspring display lower risk of mortality and morbidity than sporadic nonagenarians: The Leiden Longevity Study.
      ), is a family-based study comprising 1671 offspring of 421 nonagenarians sibling pairs of Dutch descent, and the 744 partners of these offspring. A total of 2144 individuals with clinical blood parameters available were included in the current analysis. The study protocol was approved by the Leiden University Medical Center ethical committee and an informed consent was signed by all participants prior to participation in the study.
      All standard blood measurements were performed in nonfasting venous blood samples using fully automated equipment. Glucose, high-sensitivity C-reactive protein (hsCRP), triglyceride (TG), total cholesterol, and high-density lipoprotein cholesterol (HDL) levels were measured on the Hitachi Modular P800 (Roche Diagnostics, Mannheim, Germany). Free triiodothyronine (T3) levels were measured on the Modular E170 (Roche Diagnostics). Low-density lipoprotein cholesterol (LDL) levels were calculated using the Friedewald formula (
      • Friedewald W.T.
      • Levy R.I.
      • Fredrickson D.S.
      Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.
      ), and set to missing if plasma TG levels exceeded 4.52 mmol/L. Insulin levels were measured on the Immulite 2500 (DPC, Los Angeles, CA). Specific sandwich enzyme-linked immunosorbent assays (ELISA) were used for the determination of adiponectin (R&D Systems Europe, Abingdon, UK), leptin (Diagnostics Biochem Canada, Dorchester, Canada) and interleukin 6 (IL-6) levels (Sanquin Reagents, Amsterdam, The Netherlands). Alanine aminotransferase (ALT) and aspartate aminotransferase (AST) levels were measured using the NADH (with P-5′-P) methodology (Modular P800, Roche Diagnostics), and gamma-glutamyl transferase (GGT) levels using the l-gamma-glutamyl-3-carboxy-4-nitroanilide substrate methodology (Modular P800, Roche Diagnostics). Dehydroepiandrosterone sulfate (DHEA-S) levels were measured with an Architect delayed one-step immunoassay (Abbot, Wiesbaden, Germany). Hypertension was defined as having a systolic blood pressure > 140 and a diastolic blood pressure > 90. Antihypertensive medication included diuretics, beta-blockers, calcium channel blockers, and agents acting on the renin-angiotensin system. Cytomegalovirus (CMV) serostatus was determined by ELISA using the CMV-IgG ELISA PKS assay (Medac, Wedel, Germany).

       N-glycan Preparation

      N-glycans from total plasma proteins from participants of the LLS were released, labeled with 2-aminobenzoic acid (2-AA) (Sigma-Aldrich, Steinheim, Germany) to allow negative mode mass spectrometric detection, and purified using hydrophilic-interaction liquid chromatography (HILIC)-solid-phase extraction (SPE) as previously described (
      • Ruhaak L.R.
      • Huhn C.
      • Waterreus W.J.
      • de Boer A.R.
      • Neususs C.
      • Hokke C.H.
      • Deelder A.M.
      • Wuhrer M.
      Hydrophilic interaction chromatography-based high-throughput sample preparation method for N-glycan analysis from total human plasma glycoproteins.
      ). Specifically, 20 μl of 2% sodium dodecyl sulfate (SDS) (US BioChem, Cleveland, OH) was added to 10 μl plasma, randomly distributed across twenty-seven 96-well plates, followed by protein denaturation for 10 min at 60 °C and subsequent neutralization of the SDS by 10 μl 4% Nonidet P-40 substitute (Nonidet P-40) (Sigma-Aldrich). Then, after addition of 0.5 mU peptide-N-glycosidase F (PNGase F; Roche Diagnostics) in 10 μl 5x phosphate buffered saline solution, the N-glycans were released overnight at 37 °C. Without intermediate purification, the N-glycans were labeled for 2 h at 65 °C with the addition of 50 μl 48 mg/ml 2-AA 63 mg/ml NaCNBH3 (Merck, Darmstadt, Germany) in a 10:3 (v/v) mixture of dimethyl sulfoxide (DMSO; Sigma-Aldrich) and glacial acetic acid (Merck). HILIC-SPE was subsequently performed using 40 mg microcrystalline cellulose (Merck) in 96-well 0.45 μm GHP-filter plates (Pall, Ann Arbor, MI). All wells of the filter plate were washed using water and subsequently equilibrated using 80:20 (v/v) acetonitrile (ACN; Biosolve, Valkenswaard, The Netherlands)/water. The labeled N-glycan samples were then applied to the wells in 80% ACN, and the wells were washed using ACN/water (80:20 v/v). Purified 2-AA labeled N-glycans were eluted in 0.8-ml-deep well collection plates (ABgene via Westburg, Leusden, The Netherlands) using 400 μl water.

       Carbon-SPE

      Prior to analysis by MALDI-FTICR-MS, samples were additionally desalted using carbon SPE. To achieve this, 100 μl of graphitic porous carbon (Grace, Deerfield, IL) was applied to each well of an OF1100 96-well polypropylene filter plate with a 10 μm polyethylene frit (Orochem Technologies, Lombard, IL) using a 96-well column loader (Millipore, Billerica, MA). The stationary phase was activated and conditioned with 2 × 200 μl ACN/water (80:20 v/v) and 3 × 100 μl 0.1% trifluoroacetic acid (TFA; Sigma-Aldrich) in water, respectively. Of the 2-AA labeled N-glycans, 100 μl were loaded into the wells and washed using 3 × 100 μl 0.1% TFA in water. Slight vacuum was applied to facilitate the procedure. The 2-AA labeled N-glycans were eluted into a V-bottom microtiter plate (Nunc, Roskilde, Denmark) using 3 × 30 μl of freshly prepared ACN/water (80:20 v/v) containing 0.1% TFA by centrifugation at 500 rpm (154 mm rotational diameter).

       MALDI-FTICR-MS Analysis

      One μl of 2-AA labeled N-glycans was spotted in quadruplicate on a 384-AnchorChip target plate (Bruker Daltonics, Bremen, Germany) and air-dried. Subsequently, 1 μl of 2,5-dihydroxybenzoic acid (2,5-DHB; Bruker Daltonics) matrix (20 mg/ml in ACN/water; 50:50 (v/v)) was applied to the spots and left to dry. To generate microcrystals, 2 × 1 μl of ethanol was applied to the spots for recrystallization prior to mass spectrometric analysis.
      The 9.4 T FTICR APEX-ultra mass spectrometer was equipped with a dual electrospray ionization (ESI)/MALDI ion source (Apollo II) incorporating a quadrupole mass filter and a smartbeam laser system. Before analysis, the instrument was externally calibrated by peptide calibration standard (Bruker Daltonics). All experiments used a laser spot size of ∼150 μm, laser fluence slightly above threshold, and a laser repetition rate of 200 Hz. To allow the semiquantitative analysis for the range of expected N-glycans, all samples were analyzed using two methods: one optimized for lower m/z ions (approximately m/z 1000 to 2500) and another optimized for higher m/z ions (approximately m/z 2200 to 4000). The quadrupole was operated in rf-only mode with the selection masses set to m/z 1650 and 2500 for low-mass and high-mass measurements respectively. A customized experiment sequence (pulse program) was used, in which the multiple ICR-fill parameter was reconfigured to approximate a “random-walk” functionality (
      • Selman M.H.
      • McDonnell L.A.
      • Palmblad M.
      • Ruhaak L.R.
      • Deelder A.M.
      • Wuhrer M.
      Immunoglobulin G glycopeptide profiling by matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry.
      ). Briefly, the ions produced from 50 laser shots were accumulated in a hexapole and then transferred through the rf-only quadrupole to the collision cell. The sample stage was then moved 200 μm, and fresh sample interrogated with the next 50 laser shots. This cycle was performed nine times, effectively accumulating ions from 450 laser shots in the collision cell. The accumulated ions were then transferred to the ICR cell for a mass analysis scan. Each spectrum is the sum of eight such scans. All data were acquired using ApexControl 3.0.0 software (Bruker Daltonics) in expert mode, controlled by Hystar 3.8 software (Bruker Daltonics) for automatic measurement. In total, 20,736 spectra were recorded, these being for each biological sample a quadruplicate of low- as well as high-mass measurements.

       Data processing

      Following acquisition, representative low- and high-mass spectra were internally calibrated in DataAnalysis 4.2 (Bruker Daltonics) using a set of expected glycan masses (supplemental Table S1). Using the calibrated spectra, 37 glycan compositions were manually assigned within the low-mass measurements (H4N2 to H5N4S1), and 25 within the high-mass measurements (H5N4S1 to H7N6F1S4), using mass and parts-per-million (ppm) errors to validate the assignments (supplemental Table S1) (H = hexose; n = N-acetylhexosamine; F = deoxyhexose (fucose); S = N-acetylneuraminic acid). In addition, peak widths were assessed per composition to allow precise area integration (supplemental Table S1). The resulting 61 compositions (H5N4S1 being present in both the low- and high-mass spectra) were in agreement with previously reported observations, as well as knowledge of the biological synthesis of N-glycans (
      • Varki A.
      • Cummings R.D.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Aebi M.
      • Darvill A.
      • Kinoshita T.
      • Packer N.H.
      • Prestegard J.J.
      • Schnaar R.L.
      • Seeberger P.H.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ,
      • Nairn A.V.
      • York W.S.
      • Harris K.
      • Hall E.M.
      • Pierce J.M.
      • Moremen K.W.
      Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes.
      ,
      • Freeze H.H.
      Genetic defects in the human glycome.
      ).
      To achieve repeated extraction of the list of glycan compositions from the 20,736 MALDI-FTICR-MS measurements (10,368 low-mass and 10,368 high-mass), the spectra were converted to simple text based format (x,y) using msconvert from ProteoWizard 3.0.5622 (
      • Chambers M.C.
      • Maclean B.
      • Burke R.
      • Amodei D.
      • Ruderman D.L.
      • Neumann S.
      • Gatto L.
      • Fischer B.
      • Pratt B.
      • Egertson J.
      • Hoff K.
      • Kessner D.
      • Tasman N.
      • Shulman N.
      • Frewen B.
      • Baker T.A.
      • Brusniak M.Y.
      • Paulse C.
      • Creasy D.
      • Flashner L.
      • Kani K.
      • Moulding C.
      • Seymour S.L.
      • Nuwaysir L.M.
      • Lefebvre B.
      • Kuhlmann F.
      • Roark J.
      • Rainer P.
      • Detlev S.
      • Hemenway T.
      • Huhmer A.
      • Langridge J.
      • Connolly B.
      • Chadick T.
      • Holly K.
      • Eckels J.
      • Deutsch E.W.
      • Moritz R.L.
      • Katz J.E.
      • Agus D.B.
      • MacCoss M.
      • Tabb D.L.
      • Mallick P.
      A cross-platform toolkit for mass spectrometry and proteomics.
      ). The raw mass spectrometric data has been made publicly available in the MassIVE repository (massive.ucsd.edu) titled “MALDI-FTICR-MS total plasma N-glycomics” with ID: MSV000080307. Spectrum calibration, analyte integration and spectrum curation was performed using MassyTools 0.1.5.0 (
      • Jansen B.C.
      • Reiding K.R.
      • Bondt A.
      • Hipgrave Ederveen A.L.
      • Palmblad M.
      • Falck D.
      • Wuhrer M.
      MassyTools: A high-throughput targeted data processing tool for relative quantitation and quality control developed for glycomic and glycoproteomic MALDI-MS.
      ). In short, spectra were calibrated by applying the least variance second degree polynomial fit through a set of calibration masses (supplemental Table S1). During this step, spectra were excluded from further analysis when not all calibrants were detected with intensities at least 3-fold higher than the maximum signal deviation within the local noise (MinMax; roughly corresponding to a root-mean-square (RMS) signal-to-noise ratio (S/N) of 9). This led to the exclusion of 470 low-mass and 372 high-mass spectra. Glycan compositions from the analyte list were subsequently integrated by summing and grouping the areas of 95% of the theoretical isotopic envelope belonging to that composition. Prior to summation, each isotope was integrated using the peak widths previously established, and an equal width local background (within a window of 50 Thomson) was subtracted from these. To further ensure data quality, spectra were removed if more than 5% of the total analyte area was below S/N 3 (MinMax), which led to the additional exclusion of 305 low-mass and 424 high-mass spectra. After additional curation of clinical samples with no available information on age and sex, we retained a total of 16,346 spectra (8194 low-mass and 8152 high-mass), yielding glycosylation information on 2144 individuals.
      To arrive at one set of glycan values per individual, the replicate spectra for the low- and high-mass spectra were averaged for that individual. In case of low-mass spectra, 1878 averages were constructed from 4 spectra, 170 from 3, 76 from 2 and 20 from 1 (supplemental Fig. S1). For the high-mass spectra, 1857 averages were constructed from 4 spectra, 177 from 3, 83 from 2 and 27 from 1. To reconstruct the overview of the total plasma N-glycome, the low- and high-mass averages were normalized on the value of their overlapping composition H5N4S1, and subsequently combined. The resulting combined pattern was normalized by dividing each glycan value by the sum of all glycan values (total area normalization). Hereof, we calculated derived glycosylation traits on basis of enzymatic steps and protein groupings (supplemental Table S2). Glycan and derived trait variation within the replicate low-mass and high-mass measurements was assessed on the basis of mean, standard deviation (S.D.) and coefficient of variation (CV) (supplemental Fig. S2; supplemental Fig. S3). Mass spectrometric figures were exported from DataAnalysis 4.2 (Bruker Daltonics) and annotated with glycan depictions following the symbol nomenclature proposed by the Consortium for Functional Glycomics (CFG), created in GlycoWorkbench 2.1 build 146 (
      • Varki A.
      • Cummings R.D.
      • Aebi M.
      • Packer N.H.
      • Seeberger P.H.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Darvill A.
      • Kinoshita T.
      • Prestegard J.J.
      • Schnaar R.L.
      • Freeze H.H.
      • Marth J.D.
      • Bertozzi C.R.
      • Etzler M.E.
      • Frank M.
      • Vliegenthart J.F.
      • Lutteke T.
      • Perez S.
      • Bolton E.
      • Rudd P.
      • Paulson J.
      • Kanehisa M.
      • Toukach P.
      • Aoki-Kinoshita K.F.
      • Dell A.
      • Narimatsu H.
      • York W.
      • Taniguchi N.
      • Kornfeld S.
      Symbol nomenclature for graphical representations of glycans.
      ,
      • Ceroni A.
      • Maass K.
      • Geyer H.
      • Geyer R.
      • Dell A.
      • Haslam S.M.
      GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans.
      ).

       Data Analysis

      Throughout data analysis we employed R 3.1.2 in an environment of RStudio 0.98.1091 (RStudio Team, Boston, MA) (
      • RCore Team
      ). In case of significance testing, a study-wide significance threshold was maintained of α = 1.00·10−5, values below or equal being considered statistically significant. The value arises from being the lower bound of the order of magnitude of an α = 0.05 significance threshold Bonferroni corrected for the total number of regression tests throughout the study (number of tests = 27 phenotypes·100 glycan features + 26 sex comparisons + 25 age comparisons = 2751; α = 0.05/2751 = 1.82 × 10−5 ≈ 1.00 ×10−5).
      To limit the experimental component within the sample variability, batch correction was performed on the glycan and derived trait variables using the R package ComBat, using sample plate as batch (
      • Johnson W.E.
      • Li C.
      • Rabinovic A.
      Adjusting batch effects in microarray expression data using empirical Bayes methods.
      ). To limit outlier influence, individual glycosylation values exceeding a 5 times S.D. value from the mean of that variable were excluded from statistical analysis. Insulin, hsCRP, IL-6, TG, adiponectin, leptin, ALT, AST, GGT, and DHEA-S levels were transformed to the natural logarithm because of nonnormal distribution of the data. In addition, to obtain interpretable estimates, the glycosylation variables were scaled before analysis (i.e. mean subtraction and division by S.D.).

       Association of Variables with Age and Sex

      Linear and logistic regression analyses were performed to establish the association between age and sex (female = 0; male = 1) as outcome variables, and nonglycan clinical variables (Table I), glycans (Table II) and derived glycosylation traits (Table III) as predictor variables. As no glycosylation differences were found between LLS offspring and partners (a grouping to test predisposition for longevity), these individuals were grouped for all analyses. Furthermore, because the LLS contains multiple offspring from the same family, within-family (between-siblings) dependence was taken into account by using a sandwich estimator for the standard errors (
      • Liang K.Y.
      • Zeger S.L.
      Longitudinal data-analysis using generalized linear-models.
      ).
      Table INonglycan descriptives for the study population and association thereof with age and sex. Displayed are mean values with standard deviation (S.D.) for continuous variables, and the percentage of positive cases for binary variables. To assess if the variables differ by sex (female = 0; male = 1) and with age, respective logistic and linear regression was performed. Within-family dependence was taken into account by using a sandwich estimator for the standard errors. Effect sizes for the traits are displayed as coefficient for the trait (β) with standard error (S.E.). Displayed in bold are the p values considered significant at or below the study-wide significance threshold of α = 1.0·10−5
      PhenotypeTotal mean (S.D.) or %Female mean (S.D.) or %Male mean (S.D.) or %Effect of trait with sex (F = 0; M = 1)Effect of trait with age
      n = 2144n = 1170n = 974β (S.E.)p valueβ (S.E.)p value
      Calendar age59.2 (6.76)58.6 (6.64)59.8 (6.84)0.19 (0.05)6.1E-05--
      Alanine transamidase (IU/L)24.2 (12.2)21.8 (11.0)27.1 (13.0)0.53 (0.05)<2.2E-16−0.06 (0.14)6.9E-01
      Aspartate transamidase (IU/L)27.0 (7.92)25.9 (7.71)28.3 (7.99)0.36 (0.05)2.6E-130.54 (0.14)1.8E-04
      AST ALT ratio1.27 (0.53)1.33 (0.52)1.19 (0.54)−0.33 (0.08)5.7E-050.31 (0.15)3.9E-02
      Gamma-glutamyl transferase (IU/L)31.4 (37.0)25.7 (33.2)38.3 (40.0)0.74 (0.06)<2.2E-160.47 (0.15)2.2E-03
      Non-fasted glucose (mmol/L)5.87 (1.58)5.75 (1.37)6.01 (1.78)0.17 (0.05)2.4E-040.58 (0.15)6.6E-05
      Insulin (mU/L)23.1 (22.0)21.0 (17.8)25.6 (25.9)0.21 (0.04)1.3E-060.54 (0.14)1.8E-04
      Glucose insulin ratio0.46 (0.39)0.49 (0.41)0.43 (0.37)−0.18 (0.04)3.8E-05−0.38 (0.14)8.0E-03
      Total cholesterol (mmol/L)5.59 (1.17)5.68 (1.21)5.47 (1.12)−0.19 (0.05)3.4E-050.02 (0.16)8.9E-01
      Low-density lipoprotein cholesterol (mmol/L)3.34 (0.96)3.37 (0.98)3.30 (0.94)−0.07 (0.04)1.1E-01−0.10 (0.15)5.0E-01
      High-density lipoprotein cholesterol (mmol/L)1.44 (0.46)1.59 (0.47)1.26 (0.36)-0.89 (0.06)<2.2E-16−0.38 (0.15)9.1E-03
      Total cholesterol HDL ratio4.20 (1.41)3.82 (1.22)4.65 (1.49)0.69 (0.05)<2.2E-160.40 (0.15)7.0E-03
      Triglycerides (mmol/L)1.82 (1.16)1.60 (0.93)2.07 (1.34)0.48 (0.05)<2.2E-160.74 (0.15)5.3E-07
      Lipid lowering medication (%)10.8%8.87%13.1%0.14 (0.04)1.6E-031.13 (0.12)<2.2E-16
      Leptin (ng/ml)20.5 (21.6)29.3 (24.7)9.92 (9.83)-1.61 (0.09)<2.2E-160.17 (0.15)2.6E-01
      Adiponectin (mg/L)6.27 (3.30)7.49 (3.55)4.81 (2.23)-1.04 (0.06)<2.2E-160.08 (0.15)5.7E-01
      Body mass index25.4 (3.58)25.1 (4.00)25.7 (2.97)0.18 (0.05)7.1E-040.41 (0.15)4.4E-03
      Hypertension (%)24.2%24.3%24.0%−0.01 (0.05)8.8E-011.12 (0.15)5.0E-14
      Antihypertensive medication (%)20.5%20.3%20.7%0.01 (0.05)7.5E-011.38 (0.15)<2.2E-16
      Dehydroepiandrosterone sulfate (μmol/L)4.45 (2.80)3.59 (2.21)5.49 (3.07)0.85 (0.06)<2.2E-16-1.87 (0.16)<2.2E-16
      High sensitivity C-reactive protein (mg/L)3.15 (11.0)3.11 (10.4)3.21 (11.7)−0.05 (0.04)2.9E-010.63 (0.15)3.5E-05
      Interleukin 6 (pg/ml)0.68 (1.46)0.62 (1.17)0.75 (1.74)0.11 (0.04)9.8E-030.77 (0.15)5.6E-07
      Smoking (%)13.6%13.2%14.1%0.02 (0.05)6.1E-01-0.90 (0.15)2.4E-09
      Free triiodothyronine (pmol/L)4.11 (0.72)3.94 (0.71)4.31 (0.67)0.62 (0.07)<2.2E-16−0.55 (0.14)9.4E-05
      Cytomegalovirus infection (%)46.9%50.6%42.3%−0.17 (0.05)5.3E-040.56 (0.16)4.9E-04
      Member of long-lived family (%)69.2%67.4%71.4%0.09 (0.04)5.3E-020.33 (0.17)5.0E-02
      Table IIN-glycan descriptives and their association with age and sex. Displayed are mean values with S.D. To assess if the variables differ by sex (female = 0; male = 1) and with age, respective logistic and linear regression was performed, adjusted for within-family dependence. Effect sizes for the traits are displayed as coefficient for the trait (β) with S.E., all of which are representative of a 1 S.D. increase in the glycosylation value. Displayed in bold are the p values considered significant at or below the study-wide significance threshold of α = 1.0·10−5
      Figure thumbnail fx2
      Table IIIDerived glycosylation trait descriptives and their association with age and sex. Displayed are mean values with S.D. To assess if the variables differ by sex (female = 0; male = 1) and with age, respective logistic and linear regression was performed, adjusted for within-family dependence. Effect sizes for the traits are displayed as coefficient for the trait (β) with S.E., and are representative of a 1 S.D. increase in the glycosylation value. Displayed in bold are the p values considered significant at or below the study-wide significance threshold of α = 1.0·10−5
      Derived traitDescriptionTotal mean % (S.D.)Female mean % (S.D.)Male mean % (S.D.)Effect of trait with sex (F = 0; M = 1)Effect of trait with age
      β (S.E.)p valueβ (S.E.)p value
      Complexity
      MOverall high mannose type0.78 (0.17)0.78 (0.17)0.78 (0.17)−0.03 (0.04)4.5E-01−0.51 (0.14)3.9E-04
      MMHigh mannose occupancy690 (11.0)689 (11.0)691 (11.0)0.19 (0.04)2.1E-05−0.12 (0.14)4.0E-01
      HyOverall hybrid type0.44 (0.09)0.45 (0.09)0.44 (0.09)−0.07 (0.04)1.0E-010.06 (0.14)6.6E-01
      COverall complex type97.3 (0.46)97.3 (0.47)97.3 (0.45)−0.02 (0.04)7.0E-010.36 (0.14)1.0E-02
      A1Overall monoantennary1.16 (0.22)1.15 (0.23)1.17 (0.22)0.10 (0.04)1.6E-02−0.21 (0.14)1.3E-01
      A2Overall diantennary49.2 (5.32)48.8 (5.30)49.7 (5.30)0.18 (0.04)3.5E-05−0.09 (0.15)5.5E-01
      A3Overall triantennary37.1 (4.50)37.5 (4.46)36.7 (4.51)−0.18 (0.04)2.2E-05−0.10 (0.15)5.0E-01
      A4Overall tetraantennary10.2 (1.95)10.3 (2.01)10.1 (1.86)−0.13 (0.04)3.4E-030.52 (0.14)2.6E-04
      Fucosylation
      FOverall33.0 (5.58)31.4 (5.21)35.0 (5.37)0.72 (0.05)<2.2E-160.78 (0.15)3.9E-07
      A2FWithin A232.4 (4.47)31.9 (4.48)32.9 (4.41)0.22 (0.04)3.3E-070.19 (0.15)1.9E-01
      A3FWithin A334.7 (9.87)31.7 (9.04)38.2 (9.67)0.73 (0.05)<2.2E-160.82 (0.15)7.4E-08
      A4FWithin A435.6 (8.41)33.1 (7.85)38.7 (8.05)0.74 (0.05)<2.2E-160.77 (0.15)2.0E-07
      Bisection
      BOverall6.12 (1.52)6.09 (1.54)6.17 (1.51)0.05 (0.04)2.2E-010.18 (0.15)2.2E-01
      A2BWithin A212.5 (2.79)12.5 (2.86)12.4 (2.71)−0.05 (0.04)2.1E-010.28 (0.15)5.8E-02
      A2F0BWithin nonfucosylated A21.77 (0.41)1.78 (0.41)1.75 (0.41)−0.09 (0.04)3.7E-020.16 (0.15)3.1E-01
      A2FBWithin fucosylated A234.7 (4.87)35.3 (4.84)34.0 (4.81)-0.27 (0.04)3.8E-100.24 (0.14)8.7E-02
      A2FS0BWithin nonsialylated fucosylated A218.9 (2.99)19.2 (2.94)18.5 (3.01)-0.25 (0.04)1.1E-080.77 (0.14)6.7E-08
      A2FSBWithin sialylated fucosylated A240.9 (5.91)41.6 (5.88)40.1 (5.85)-0.25 (0.04)4.5E-090.24 (0.15)9.4E-02
      Galactosylation per antenna
      AGOverall96.5 (1.11)96.6 (1.10)96.4 (1.11)−0.15 (0.04)3.9E-04-0.94 (0.15)1.3E-10
      A2GWithin A293.3 (1.68)93.4 (1.71)93.2 (1.65)−0.10 (0.04)1.5E-02-1.25 (0.14)<2.2E-16
      A2F0GWithin nonfucosylated A298.5 (0.30)98.5 (0.30)98.5 (0.29)0.05 (0.04)2.6E-01-0.99 (0.14)5.4E-13
      A2F0S0GWithin nonsialylated nonfucosylated A281.1 (3.84)81.3 (3.94)80.8 (3.69)−0.14 (0.05)2.4E-03-1.32 (0.15)<2.2E-16
      A2FGWithin fucosylated A282.6 (3.97)82.6 (4.04)82.6 (3.89)−0.03 (0.04)4.8E-01-1.36 (0.14)<2.2E-16
      A2FS0GWithin nonsialylated fucosylated A241.4 (5.40)41.7 (5.80)41.2 (4.86)−0.10 (0.04)2.1E-02-2.82 (0.13)<2.2E-16
      A2FSGWithin sialylated fucosylated A298.5 (0.26)98.5 (0.26)98.5 (0.26)0.10 (0.04)1.7E-02−0.50 (0.14)3.5E-04
      Sialylation per antenna
      ASOverall79.1 (1.71)79.1 (1.69)79.1 (1.72)0.02 (0.04)5.5E-01−0.41 (0.15)5.2E-03
      A2SWithin A276.6 (2.47)76.6 (2.45)76.6 (2.50)0.00 (0.04)9.8E-01−0.35 (0.15)1.6E-02
      A2F0SWithin nonfucosylated A284.5 (1.31)84.4 (1.30)84.6 (1.32)0.18 (0.04)1.0E-05−0.56 (0.13)3.4E-05
      A2FSWithin fucosylated A260.1 (4.79)60.0 (4.71)60.3 (4.89)0.05 (0.04)2.3E-01−0.20 (0.15)1.7E-01
      Sialylation per galactose
      GSOverall81.9 (1.18)81.8 (1.18)82.0 (1.17)0.17 (0.04)6.0E-050.23 (0.15)1.2E-01
      A2GSWithin A282.1 (1.53)82.0 (1.53)82.2 (1.52)0.11 (0.04)7.4E-030.63 (0.15)2.4E-05
      A2F0GSWithin nonfucosylated A285.8 (1.17)85.7 (1.16)85.8 (1.18)0.19 (0.04)1.2E-05−0.34 (0.14)1.4E-02
      A2FGSWithin fucosylated A272.7 (3.37)72.5 (3.38)73.0 (3.33)0.14 (0.04)1.2E-031.14 (0.15)9.8E-15
      A3GSWithin A384.8 (1.01)84.7 (0.98)84.8 (1.05)0.12 (0.04)3.5E-03−0.15 (0.14)2.8E-01
      A3F0GSWithin nonfucosylated A384.9 (1.09)84.9 (1.05)84.9 (1.14)0.01 (0.04)7.8E-01−0.30 (0.14)3.7E-02
      A3FGSWithin fucosylated A384.2 (1.32)84.0 (1.32)84.5 (1.28)0.34 (0.04)2.7E-140.22 (0.14)1.2E-01
      A4GSWithin A471.4 (1.93)71.3 (1.88)71.6 (1.96)0.16 (0.04)9.0E-05−0.11 (0.14)4.3E-01
      A4F0GSWithin nonfucosylated A471.9 (2.16)71.6 (2.05)72.3 (2.22)0.34 (0.04)2.5E-140.09 (0.14)5.1E-01
      A4FGSWithin fucosylated A470.8 (1.96)70.8 (1.95)70.7 (1.97)−0.08 (0.04)5.2E-02−0.24 (0.14)8.7E-02

       Association of Glycosylation with Clinical Variables

      To eliminate possible confounding effects, age, sex and the interaction thereof were included as covariates in further models. For these analyses, the remaining nonglycan variables were used as outcome (using linear and logistic regression for respectively continuous and dichotomous variables), whereas glycans and derived traits were used as predictor (model: nonglycan ∼ β1·age + β2·sex + β3·age*sex + β4·glycan).
      To visualize the association between nonglycans and (derived) glycan traits, the t-statistics (or Wald statistics in case of logistic regression, both β4/S.E.4) from the models were expressed in heatmap format. Sorting of the heatmap variables was performed using hierarchical clustering (Euclidean distance, complete linkage).

      RESULTS

      To investigate the association of plasma protein N-glycosylation with clinical markers of metabolic health and inflammation, we analyzed the total plasma N-glycomes of 2144 middle-aged individuals of the LLS. N-glycans were enzymatically released from their protein backbones, labeled at the reducing end with 2-AA, purified by HILIC- and carbon-SPE, and analyzed by intermediate pressure MALDI-FTICR-MS. The acidic tag 2-AA allowed the joint negative mode mass spectrometric detection and relative quantification of neutral and sialylated glycan species, whereas the intermediate pressure of the measurement limited the decay commonly observed for sialylated glycans with MALDI (Fig. 1) (
      • Asakawa D.
      • Calligaris D.
      • Zimmerman T.A.
      • De Pauw E.
      In-source decay during matrix-assisted laser desorption/ionization combined with the collisional process in an FTICR mass spectrometer.
      ,
      • Powell A.K.
      • Harvey D.J.
      Stabilization of sialic acids in N-linked oligosaccharides and gangliosides for analysis by positive ion matrix-assisted laser desorption/ionization mass spectrometry.
      ,
      • Selman M.H.
      • McDonnell L.A.
      • Palmblad M.
      • Ruhaak L.R.
      • Deelder A.M.
      • Wuhrer M.
      Immunoglobulin G glycopeptide profiling by matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry.
      ,
      • Lee H.
      • An H.J.
      • Lerno Jr, L.A.
      • German J.B.
      • Lebrilla C.B.
      Rapid profiling of bovine and human milk gangliosides by matrix-assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry.
      ,
      • Park Y.
      • Lebrilla C.B.
      Application of Fourier transform ion cyclotron resonance mass spectrometry to oligosaccharides.
      ,
      • O'Connor P.B.
      • Mirgorodskaya E.
      • Costello C.E.
      High pressure matrix-assisted laser desorption/ionization Fourier transform mass spectrometry for minimization of ganglioside fragmentation.
      ).
      Figure thumbnail gr1
      Fig. 1A typical total plasma N-glycome as analyzed by negative mode MALDI-FTICR-MS after enzymatic N-glycan release, 2-AA labeling, and purification. A, Combination of the low mass (red) and high mass (blue) mass spectra originating from a single case measurement. The relative abundances were normalized to the signal at m/z 2051.733, reflecting the N-glycan composition H5N4S1 [M-H]. Whereas the glycan compositions could be established with high confidence, the displayed linkages are presumed based on literature knowledge (
      • Varki A.
      • Cummings R.D.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Aebi M.
      • Darvill A.
      • Kinoshita T.
      • Packer N.H.
      • Prestegard J.J.
      • Schnaar R.L.
      • Seeberger P.H.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ,
      • Nairn A.V.
      • York W.S.
      • Harris K.
      • Hall E.M.
      • Pierce J.M.
      • Moremen K.W.
      Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes.
      ,
      • Freeze H.H.
      Genetic defects in the human glycome.
      ). B, Area integration of the detectable N-glycan compositions, summing the isotopes within 95% of the isotopic envelope for each species. Each composition is represented as a fraction of the total spectrum area.

       Measurement Variability

      Following spectrum curation, we retained a total of 16,346 mass spectra originating from low- and high-mass measurements of MALDI spotting quadruplicates for each individual. In the measurement optimized for lower masses (m/z 1000 to 2500) 37 N-glycans could be detected, ranging from H4N2 to H5N4S1, with an average absolute ppm error of 2.24 (S.D. ± 3.18). In the measurement optimized for higher masses (m/z 2200 to 4000) 25 additional N-glycans were detected, from the overlapping composition H5N4S1 to H7N6F1S4, the average absolute ppm error being 3.65 (S.D. ± 3.98) (supplemental Table S1). Based on literature, the glycan compositions within the TPNG were presumed to have certain structural features (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ,
      • Nairn A.V.
      • York W.S.
      • Harris K.
      • Hall E.M.
      • Pierce J.M.
      • Moremen K.W.
      Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes.
      ,
      • Freeze H.H.
      Genetic defects in the human glycome.
      ). Examples of this are the antennarity, judged as the number of N-acetylhexosamines minus two unless bisected, and bisection, judged to be the case if the number of N-acetylhexosamines equaled five and the number of hexoses five or less. Although these structural assignments are expected to represent the majority of structures contributing to an MS signal, additional structural isomers are likely to be present in the signals. For example, a composition assigned as tetraantennary may instead contain diantennary structures with two N-acetyllactosamine repeats, and the bisected species could be triantennary with incomplete galactosylation.
      Assessing repeatability, an example quadruplicate measurement from a single individual yielded an average CV of 6.52% (S.D. ± 3.42%) for the 10 most abundant signals in the low-mass measurement (total area normalized for the mass range), and an average CV of 5.97% (S.D. ± 2.53%) for the 10 most abundant signals in the high-mass measurement (supplemental Fig. S2A; supplemental Fig. S2B). Combining the measurements by the overlapping composition H5N4S1 yielded for the 20 must abundant species an average CV of 9.29% (S.D. ± 5.88%) (supplemental Fig. S2C). Derived glycosylation traits, constructed to provide mathematical expressions of monosaccharide differences and groupings with structural similarity, showed a lower CV, a phenomenon previously observed for mass spectrometric plasma glycomics (
      • Bladergroen M.R.
      • Reiding K.R.
      • Hipgrave Ederveen A.L.
      • Vreeker G.C.
      • Clerc F.
      • Holst S.
      • Bondt A.
      • Wuhrer M.
      • van der Burgt Y.E.
      Automation of high-throughput mass spectrometry-based plasma n-glycome analysis with linkage-specific sialic acid esterification.
      ), i.e. on average 1.20% (S.D. ± 0.84%) for the 20 most abundant members (supplemental Fig. S2D). For a listing of derived traits and their calculations see supplemental Table S2.
      In total, the glycosylation analysis workflow allowed for 2144 individuals the relative quantification of 61 N-glycan compositions and 39 derived traits. The LLS provided an additional 27 clinical variables to facilitate association analysis. Next to age and sex, measures were included on liver function (GGT, ALT, AST, AST/ALT), glucose metabolism (glucose, insulin, glucose/insulin), lipid metabolism (cholesterol, LDL-C, HDL-C, cholesterol/HDL-C, TG, lipid lowering medication, leptin, adiponectin), inflammation (hsCRP, IL-6), blood pressure (hypertension, antihypertensive medication), adrenal function (DHEA-S), thyroid function (free T3), as well as information on BMI, smoking and CMV infection, and familial propensity for longevity (Table I).

       Association of Glycosylation with Age and Sex

      Glycosylation was found to highly associate with age and sex by respectively linear and logistic regression analysis (Fig. 2; Table II; Table III). A GEE approach was used for all statistical analyses to adjust the standard errors (S.E.) for between-sibling dependence, and a study-wide significance threshold was maintained of α = 1.0·10−5. Changes with aging included a decrease of galactosylation of diantennary glycans, visible most specifically for the galactosylation of nonsialylated diantennaries with fucose (βA2FS0G = −2.82 S.E. ± 0.13; pA2FS0G < 2.2 × 10−16) and without fucose (βA2F0S0G = −1.34 ± 0.15; pA2F0S0G < 2.2 × 10−16). These changes were mainly driven by the increases in glycan compositions H3N4 (βH3N4 = 1.21 ± 0.14; pH3N4 < 2.2 × 10−16), H3N5 (βH3N5 = 1.68 ± 0.14; pH3N5 < 2.2 × 10−16), H3N4F1 (βH3N4F1 = 1.50 ± 0.14; pH3N4F1 < 2.2 × 10−16), H3N5F1 (βH3N5F1 = 1.67 ± 0.14; pH3N5F1 < 2.2 × 10−16) and the decreases in H5N4F1 (βH5N4F1 = −1.60 ± 0.15; pH5N4F1 < 2.2 × 10−16) and H5N4F1S1 (βH5N4F1S1 = −0.81 ± 0.15; pH5N4F1S1 = 4.5 × 10−8). Further increasing with age were the bisection of nonsialylated fucosylated diantennaries (βA2FS0B = 0.77 ± 0.14; pA2FS0B = 6.7 × 10−8), sialylation per galactose of fucosylated diantennaries (βA2FGS = 1.14 ± 0.15; pA2FGS = 9.8 × 10−15), and the fucosylation of both tri- and tetraantennary compositions (βA3F = 0.82 ± 0.15; pA3F = 7.4 × 10−8 and βA4F = 0.77 ± 0.15; pA4F = 2.0 × 10−7).
      Figure thumbnail gr2
      Fig. 2Scatterplots with local regression providing an overview of the three main derived glycosylation traits changing with age and differing by sex (males in blue, females in red). A, Galactosylation of nonsialylated fucosylated diantennary compositions (A2FS0G), to a large degree representative of IgG-Fc galactosylation (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ). B, Fucosylation of triantennary compositions (A3F). C, Bisection of diantennary fucosylated compositions (A2FB).
      Fucosylation of triantennary and tetraantennary structures (A3F and A4F) proved also to be a major glycosylation difference between females and males (female = 0; male = 1) (βA3F =0.73 ± 0.05; pA3F < 2.2 × 10−16 and βA4F = 0.74 ± 0.05;pA4F < 2.2 × 10−16), driven by higher male values in all fucosylated tri- and tetraantennary compositions, such as H6N5F1S3 (βH6N5F1S3 = 0.61 ± 0.05; pH6N5F1S3 < 2.2 × 10−16) and H7N6F1S4 (βH7N6F1S4 = 0.45 ± 0.05; pH7N6F1S4 < 2.2 × 10−16), and significantly lower levels of all nonfucosylated tri- and tetraantennary compositions, such as H6N5S3 (βH6N5S3 =−0.61 ± 0.05; pH6N5S3 < 2.2 × 10−16) and H7N6S4 (βH7N6S4 = −0.26 ± 0.05; pH7N6S4 = 5.8 × 10−8). Furthermore, males proved to have lower bisection of diantennary fucosylated species (βA2FB = −0.27 ± 0.04; p = 3.8 × 10−10) when compared with females, but did have a higher sialylation per galactose of tetraantennary nonfucosylated compositions (βA4F0GS = 0.34 ± 0.04; p = 2.5 × 10−14).

       Association Glycosylation With Inflammation and Metabolic Health

      Regression analysis was used to establish the relationship between the glycosylation traits and clinical markers, adding age, sex and the interaction thereof as covariates to limit their confounding influence (supplemental Table S3). The t-statistics (or Wald-statistics; both β4/S.E.4) arising from the models were expressed in clustered heatmap format (Fig. 3; for a heatmap visualization of the results without age and sex adjustment see supplemental Fig. S4). Significantly associating with a selection of derived glycosylation traits were hsCRP (23 statistically significant associations out of a possible 39), GGT (
      • Ruhaak L.R.
      • Miyamoto S.
      • Lebrilla C.B.
      Developments in the identification of glycan biomarkers for the detection of cancer.
      ), BMI (
      • Maverakis E.
      • Kim K.
      • Shimoda M.
      • Gershwin M.E.
      • Patel F.
      • Wilken R.
      • Raychaudhuri S.
      • Ruhaak L.R.
      • Lebrilla C.B.
      Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: a critical review.
      ), leptin (
      • Maverakis E.
      • Kim K.
      • Shimoda M.
      • Gershwin M.E.
      • Patel F.
      • Wilken R.
      • Raychaudhuri S.
      • Ruhaak L.R.
      • Lebrilla C.B.
      Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: a critical review.
      ), smoking (
      • Axford J.S.
      Glycosylation and rheumatic disease.
      ), TG (
      • Arnold J.N.
      • Saldova R.
      • Hamid U.M.
      • Rudd P.M.
      Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation.
      ), insulin (
      • Varki A.
      • Gagneux P.
      Multifarious roles of sialic acids in immunity.
      ), the ratio of total cholesterol and HDL (
      • Varki A.
      • Gagneux P.
      Multifarious roles of sialic acids in immunity.
      ), the ratio of glucose and insulin (
      • Yang W.H.
      • Aziz P.V.
      • Heithoff D.M.
      • Mahan M.J.
      • Smith J.W.
      • Marth J.D.
      An intrinsic mechanism of secreted protein aging and turnover.
      ), adiponectin (
      • Yang W.H.
      • Aziz P.V.
      • Heithoff D.M.
      • Mahan M.J.
      • Smith J.W.
      • Marth J.D.
      An intrinsic mechanism of secreted protein aging and turnover.
      ), HDL (
      • Kontermann R.E.
      Strategies for extended serum half-life of protein therapeutics.
      ), IL-6 (
      • Xu C.
      • Ng D.T.
      Glycosylation-directed quality control of protein folding.
      ), ALT (
      • Varki A.
      • Cummings R.D.
      • Esko J.D.
      • Stanley P.
      • Hart G.
      • Aebi M.
      • Darvill A.
      • Kinoshita T.
      • Packer N.H.
      • Prestegard J.J.
      • Schnaar R.L.
      • Seeberger P.H.
      ), the ratio of ASL and ALT (
      • Varki A.
      Biological roles of oligosaccharides: all of the theories are correct.
      ) and lipid medication (
      • Varki A.
      Biological roles of oligosaccharides: all of the theories are correct.
      ) (supplemental Table S4; supplemental Table S5). Only individual N-glycan associations could be proven for AST, glucose, cholesterol, LDL and free T3, whereas no associations were found for hypertension, the usage of antihypertensive medication, DHEA-S and CMV infection.
      Figure thumbnail gr3
      Fig. 3Heatmaps showing the t- (or Wald) statistics (β/S.E.) of the associations between clinical markers of metabolic health and inflammation, and glycosylation. A, Single total plasma N-glycans after total area normalization. B, Derived glycosylation traits. All models were adjusted for age, sex, the interaction thereof, and within-family dependence. Crosses (x) indicate a statistical significance of p < = 1.0 × 10−5 thereby surpassing the study-wide significance threshold, whereas periods (.) indicate associations with a significance of p < = 0.05.
      Inflammatory marker hsCRP showed the most associations with the total plasma N-glycome, including a positive association with tri- and tetraantennary glycans (βA3 = 0.24 ± 0.03; pA3 < 2.2 × 10−16 and βA4 = 0.20 ± 0.03; pA4 = 2.9 × 10−13) at the expense of high-mannose (βM = −0.18 ± 0.02; pM = 1.2 × 10−13), hybrid (βHy = −0.24 ± 0.03; pHy < 2.2 × 10−16), monoantennary (βA1 = −0.17 ± 0.02; pA1 = 1.8 × 10−12) and diantennary species (βA2 = −0.25 ± 0.03; pA2 < 2.2 × 10−16). Although fucosylation of diantennary species proved to decrease with higher hsCRP levels (βA2F = −0.13 ± 0.02; pA2F = 1.5 × 10−7), an increase was seen in the fucosylation of triantennary species (βA3F = 0.13 ± 0.03; pA3F = 1.0 × 10−6). Additional increases were found for sialylation of (most specifically) fucosylated diantennary (βA2FGS = 0.25 ± 0.02; pA2FGS < 2.2 × 10−16) and triantennary (βA3FGS = 0.15 ± 0.02; pA3FGS = 1.0 × 10−9) species, as well as an increase in average high-mannose size (βMM = 0.13 ± 0.02; pMM = 7.9 × 10−8) and a decrease in bisection of the nonfucosylated diantennaries in particular (βA2F0B = −0.14 ± 0.02; pA2F0B = 1.4 × 10−8). Notably, although galactosylation of nonsialylated diantennaries without fucose increased (βA2F0S0G = 0.12 ± 0.02; pA2F0S0G = 2.6 × 10−7), galactosylation of the same species, but with fucose, decreased instead (βA2FS0G = −0.20 ± 0.03; pA2FS0G = 1.7 × 10−12). Interestingly, the associations observed with hsCRP could only in part be translated to the upstream cytokine IL-6 (
      • Heinrich P.C.
      • Castell J.V.
      • Andus T.
      Interleukin-6 and the acute phase response.
      ,
      • Vigushin D.M.
      • Pepys M.B.
      • Hawkins P.N.
      Metabolic and scintigraphic studies of radioiodinated human C-reactive protein in health and disease.
      ), which only showed reproduction of the decreased A2F galactosylation (βA2FS0G = −0.19 ± 0.03; pA2FS0G = 4.6 × 10−12) and increased sialylation per galactose thereof (βA2FGS = 0.25 ± 0.02; pA2FGS < 2.2 × 10−16).
      BMI and leptin proved highly similar with respect to total plasma N-glycosylation associations, and showed considerable overlap with the aforementioned hsCRP as well. Taking an increase in BMI as example, galactosylation was decreased for fucosylated diantennary species (βA2FS0G = −0.55 ± 0.10; pA2FS0G = 9.6 × 10−9) and increased for nonfucosylated variants (βA2F0G = 0.58 ± 0.09; pA2F0G = 9.9 × 10−12). Changes were also seen with average high-mannose size (βMM = 0.89 ± 0.08; pMM < 2.2 × 10−16), bisection of nonfucosylated diantennary species (βA2F0B = −0.39 ± 0.08; pA2F0B = 2.0 × 10−6), and sialylation of diantennary glycans (βA2GS = 0.60 ± 0.08; pA2GS = 3.1 × 10−13). All of these effects were replicable within both leptin and hsCRP. However, shared with leptin but not seen with hsCRP, was the negative association of BMI with the sialylation of tetraantennary species, and specifically the nonfucosylated variants thereof (βA4F0GS = −0.74 ± 0.08; pA4F0GS < 2.2 × 10−16). When including hsCRP and leptin as variables in the model between BMI and glycosylation, most associations between the latter two are lost with the exception of high-mannose size (βMM = 0.32 ± 0.07; pMM = 4.0 × 10−7) and a strong remaining trend with glycan composition H4N4S1 (βH4N4S1 = −0.24 ± 0.07; pH4N4S1 = 1.8 × 10−4) (supplemental Table S6).
      The liver marker GGT showed major associations with the total plasma N-glycome, whereas AST, ALT, and the ratio thereof were of only minor influence. GGT appeared similar to hsCRP in changes of increased galactosylation of nonfucosylated diantennary glycans (e.g. βA2F0G = 0.08 ± 0.02; pA2F0G = 2.5 × 10−7), sialylation of fucosylated diantennaries (e.g. βA2FGS = 0.09 ± 0.01; pA2FGS = 9.9 × 10−12), as well as an overall increased in antennarity (e.g. βA3 = 0.07 ± 0.01; pA3 = 3.7 × 10−8). However, the marker showed a decrease in tetraantennary sialylation similar to BMI and not hsCRP (βA4F0GS = −0.11 ± 0.01; pA4F0GS < 2.2 × 10−16), while lacking the decreasing galactosylation of nonsialylated fucosylated diantennaries (A2FS0G) prominently seen in both BMI and hsCRP.
      Clinical markers considered of beneficial metabolic nature, in our study represented by glucose/insulin, HDL-C and adiponectin (
      • O'Neill S.
      • Bohl M.
      • Gregersen S.
      • Hermansen K.
      • O'Driscoll L.
      Blood-based biomarkers for metabolic syndrome.
      ,
      • Renaldi O.
      • Pramono B.
      • Sinorita H.
      • Purnomo L.B.
      • Asdie R.H.
      • Asdie A.H.
      Hypoadiponectinemia: a risk factor for metabolic syndrome.
      ), showed associations largely opposite to those established for hsCRP, BMI and GGT. Examples of this include, in case of adiponectin, the decreased galactosylation of nonfucosylated diantennaries (βA2F0G = −0.06 ± 0.01; pA2F0G =3.1 × 10−8), a decreased sialylation of diantennaries (βA2GS = −0.05 ± 0.01; pA2GS = 2.1 × 10−6), a decreased average high-mannose size (βMM = −0.08 ± 0.01; pMM = 1.7 × 10−15), and an increased sialylation of tetraantennary nonfucosylated species (βA4F0GS = 0.06 ± 0.01; pA4F0GS = 3.1 × 10−9).
      Interestingly, the only clinical markers affecting tri- and tetraantennary fucosylation (after correction for sex) proved to be smoking with a positive association (βA3F = 0.50 ± 0.08; pA3F = 2.6 × 10−10 and βA4F = 0.46 ± 0.08; p = 2.3 × 10−8), and TG levels with a negative association (βA3F = −0.09 ± 0.01; pA3F = 8.4 × 10−14 and βA4F = −0.09 ± 0.01; pA4F = 1.6 × 10−13). In addition, smoking was also the only clinical variable to positively associate with the bisection of fucosylated nonsialylated diantennary species (βA2FS0B = 0.56 ± 0.07; pA2FS0B = 4.7 × 10−15). Of particular interest is the high-mannose size trait (MM), which shows several of the strongest correlations, positively associating with cholesterol/HDL-C (βMM = 0.08 ± 0.01; pMM < 2.2 × 10−16), TG levels (βMM = 0.11 ± 0.01; pMM < 2.2 × 10−16), leptin (βMM = 0.20 ± 0.02; pMM < 2.2 × 10−16) and BMI (βMM = 0.89 ± 0.08; pMM < 2.2 × 10−16), whereas negatively associating with glucose/insulin (βMM = −0.10 ± 0.02; pMM = 3.0 × 10−10), HDL (βMM = −0.09 ± 0.01; pMM < 2.2 × 10−16), and adiponectin (βMM = −0.08 ± 0.01; pMM = 1.7 × 10−15). Considering the individual glycans contributing to the MM trait, the significant compositions proved to be H5N2 and H9N2.

      DISCUSSION

      Here we report the MALDI-FTICR-MS analysis of the total plasma N-glycomes of 2144 principally healthy individuals from the LLS, and the association thereof with clinical markers of inflammation and metabolic health. In doing so, we have confirmed expectations from literature by showing a decrease of galactosylation and increase in bisection of diantennary fucosylated glycans with increasing age (
      • Dall'Olio F.
      • Vanhooren V.
      • Chen C.C.
      • Slagboom P.E.
      • Wuhrer M.
      • Franceschi C.
      N-glycomic biomarkers of biological aging and longevity: a link with inflammaging.
      ,
      • Shikata K.
      • Yasuda T.
      • Takeuchi F.
      • Konishi T.
      • Nakata M.
      • Mizuochi T.
      Structural changes in the oligosaccharide moiety of human IgG with aging.
      ,
      • Yamada E.
      • Tsukamoto Y.
      • Sasaki R.
      • Yagyu K.
      • Takahashi N.
      Structural changes of immunoglobulin G oligosaccharides with age in healthy human serum.
      ), and by men having a higher tri- and tetraantennary fucosylation and lower bisection than women (
      • Ding N.
      • Nie H.
      • Sun X.
      • Sun W.
      • Qu Y.
      • Liu X.
      • Yao Y.
      • Liang X.
      • Chen C.C.
      • Li Y.
      Human serum N-glycan profiles are age and sex dependent.
      ,
      • Ruhaak L.R.
      • Koeleman C.A.
      • Uh H.W.
      • Stam J.C.
      • van Heemst D.
      • Maier A.B.
      • Houwing-Duistermaat J.J.
      • Hensbergen P.J.
      • Slagboom P.E.
      • Deelder A.M.
      • Wuhrer M.
      Targeted biomarker discovery by high throughput glycosylation profiling of human plasma alpha1-antitrypsin and immunoglobulin A.
      ). Adjusting for the age and sex effects as well as the literature-reported interaction of age and sex (
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Hokke C.H.
      • Westendorp R.G.
      • Houwing-Duistermaat J.
      • Wuhrer M.
      • Deelder A.M.
      • Slagboom P.E.
      Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health.
      ,
      • Kristic J.
      • Vuckovic F.
      • Menni C.
      • Klaric L.
      • Keser T.
      • Beceheli I.
      • Pucic-Bakovic M.
      • Novokmet M.
      • Mangino M.
      • Thaqi K.
      • Rudan P.
      • Novokmet N.
      • Sarac J.
      • Missoni S.
      • Kolcic I.
      • Polasek O.
      • Rudan I.
      • Campbell H.
      • Hayward C.
      • Aulchenko Y.
      • Valdes A.
      • Wilson J.F.
      • Gornik O.
      • Primorac D.
      • Zoldos V.
      • Spector T.
      • Lauc G.
      Glycans are a novel biomarker of chronological and biological ages.
      ), we proved additional associations between clinical markers for metabolic health/inflammation and plasma N-glycosylation characteristics including antennarity, sialylation, bisection, and galactosylation of various diantennary subgroups, and the size of high-mannose species.

       Methodology

      MS analysis of plasma N-glycosylation is not without challenges, particularly when employing MALDI ionization. A downside of this technique is the in-source and metastable loss of sialic acid residues, which is problematic given that plasma N-glycans are often highly sialylated (a notable exception being those from the fragment crystallizable (Fc) region of IgG) (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Powell A.K.
      • Harvey D.J.
      Stabilization of sialic acids in N-linked oligosaccharides and gangliosides for analysis by positive ion matrix-assisted laser desorption/ionization mass spectrometry.
      ). Although chemical derivatization prior to MALDI-MS has been found to solve these stabilization issues (e.g. by permethylation, methyl/ethyl esterification or amidation), this adds extra steps to the sample preparation workflow and often leads to byproducts (
      • Reiding K.R.
      • Blank D.
      • Kuijper D.M.
      • Deelder A.M.
      • Wuhrer M.
      High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification.
      ,
      • Johnson S.B.
      • Brown R.E.
      Simplified derivatization for determining sphingolipid fatty acyl composition by gas chromatography-mass spectrometry.
      ,
      • Morelle W.
      • Michalski J.C.
      Analysis of protein glycosylation by mass spectrometry.
      ,
      • Wheeler S.F.
      • Domann P.
      • Harvey D.J.
      Derivatization of sialic acids for stabilization in matrix-assisted laser desorption/ionization mass spectrometry and concomitant differentiation of alpha(2 –> 3)- and alpha (2 –> 6)-isomers.
      ,
      • Alley Jr., W.R.
      • Novotny M.V.
      Glycomic analysis of sialic acid linkages in glycans derived from blood serum glycoproteins.
      ,
      • de Haan N.
      • Reiding K.R.
      • Haberger M.
      • Reusch D.
      • Falck D.
      • Wuhrer M.
      Linkage-specific sialic acid derivatization for MALDI-TOF-MS profiling of IgG glycopeptides.
      ,
      • Reiding K.R.
      • Lonardi E.
      • Hipgrave Ederveen A.L.
      • Wuhrer M.
      Ethyl esterification for MALDI-MS analysis of protein glycosylation.
      ). Instead, here we have used MALDI-FTICR-MS equipped with an intermediate pressure source to decrease sialic acid decay (
      • Asakawa D.
      • Calligaris D.
      • Zimmerman T.A.
      • De Pauw E.
      In-source decay during matrix-assisted laser desorption/ionization combined with the collisional process in an FTICR mass spectrometer.
      ,
      • Selman M.H.
      • McDonnell L.A.
      • Palmblad M.
      • Ruhaak L.R.
      • Deelder A.M.
      • Wuhrer M.
      Immunoglobulin G glycopeptide profiling by matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry.
      ). The intermediate pressure source is known to promote ion integrity by cooling of the MALDI-generated ions (
      • Asakawa D.
      • Calligaris D.
      • Zimmerman T.A.
      • De Pauw E.
      In-source decay during matrix-assisted laser desorption/ionization combined with the collisional process in an FTICR mass spectrometer.
      ,
      • Lee H.
      • An H.J.
      • Lerno Jr, L.A.
      • German J.B.
      • Lebrilla C.B.
      Rapid profiling of bovine and human milk gangliosides by matrix-assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry.
      ,
      • Park Y.
      • Lebrilla C.B.
      Application of Fourier transform ion cyclotron resonance mass spectrometry to oligosaccharides.
      ,
      • O'Connor P.B.
      • Mirgorodskaya E.
      • Costello C.E.
      High pressure matrix-assisted laser desorption/ionization Fourier transform mass spectrometry for minimization of ganglioside fragmentation.
      ). In our study this has facilitated the relative quantification of N-glycan species containing up to four N-acetylneuraminic acids.
      Although similarly sized investigations have analyzed plasma N-glycosylation by separation techniques like (U) HPLC or CGE-LIF (
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Hokke C.H.
      • Westendorp R.G.
      • Houwing-Duistermaat J.
      • Wuhrer M.
      • Deelder A.M.
      • Slagboom P.E.
      Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health.
      ,
      • Lu J.P.
      • Knezevic A.
      • Wang Y.X.
      • Rudan I.
      • Campbell H.
      • Zou Z.K.
      • Lan J.
      • Lai Q.X.
      • Wu J.J.
      • He Y.
      • Song M.S.
      • Zhang L.
      • Lauc G.
      • Wang W.
      Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations.
      ,
      • Knezevic A.
      • Gornik O.
      • Polasek O.
      • Pucic M.
      • Redzic I.
      • Novokmet M.
      • Rudd P.M.
      • Wright A.F.
      • Campbell H.
      • Rudan I.
      • Lauc G.
      Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma N-glycans.
      ,
      • Igl W.
      • Polasek O.
      • Gornik O.
      • Knezevic A.
      • Pucic M.
      • Novokmet M.
      • Huffman J.
      • Gnewuch C.
      • Liebisch G.
      • Rudd P.M.
      • Campbell H.
      • Wilson J.F.
      • Rudan I.
      • Gyllensten U.
      • Schmitz G.
      • Lauc G.
      Glycomics meets lipidomics–associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations.
      ,
      • Ruhaak L.R.
      • Hennig R.
      • Huhn C.
      • Borowiak M.
      • Dolhain R.J.
      • Deelder A.M.
      • Rapp E.
      • Wuhrer M.
      Optimized workflow for preparation of APTS-labeled N-glycans allowing high-throughput analysis of human plasma glycomes using 48-channel multiplexed CGE-LIF.
      ), to our knowledge this is the largest study of its kind performed by MS (
      • Kang P.
      • Madera M.
      • Alley Jr, W.R.
      • Goldman R.
      • Mechref Y.
      • Novotny M.V.
      Glycomic alterations in the highly-abundant and lesser-abundant blood serum protein fractions for patients diagnosed with hepatocellular carcinoma.
      ,
      • Borelli V.
      • Vanhooren V.
      • Lonardi E.
      • Reiding K.R.
      • Capri M.
      • Libert C.
      • Garagnani P.
      • Salvioli S.
      • Franceschi C.
      • Wuhrer M.
      Plasma N-glycome signature of Down Syndrome.
      ,
      • Jansen B.C.
      • Bondt A.
      • Reiding K.R.
      • Lonardi E.
      • de Jong C.J.
      • Falck D.
      • Kammeijer G.S.
      • Dolhain R.J.
      • Rombouts Y.
      • Wuhrer M.
      Pregnancy-associated serum N-glycome changes studied by high-throughput MALDI-TOF-MS.
      ). Glycan compositions as obtained by MS provide orthogonal information to chromatographic peaks. UHPLC, for example, can separate diantennary N-glycan isomers with α1,3- versus α1,6-arm galactosylation and can likewise distinguish an antennary from a bisecting GlcNAc, whereas MS can provide more precise groups of di-, tri-, and tetraantennary compositions, number of sialic acids, and clear separation of high-mannose type glycans (
      • Harvey D.J.
      Matrix-assisted laser desorption/ionization mass spectrometry of carbohydrates.
      ,
      • Huffman J.E.
      • Pucic-Bakovic M.
      • Klaric L.
      • Hennig R.
      • Selman M.H.
      • Vuckovic F.
      • Novokmet M.
      • Kristic J.
      • Borowiak M.
      • Muth T.
      • Polasek O.
      • Razdorov G.
      • Gornik O.
      • Plomp R.
      • Theodoratou E.
      • Wright A.F.
      • Rudan I.
      • Hayward C.
      • Campbell H.
      • Deelder A.M.
      • Reichl U.
      • Aulchenko Y.S.
      • Rapp E.
      • Wuhrer M.
      • Lauc G.
      Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research.
      ). As such, the construction of derived glycosylation traits making use of these features, while still biased on a compositional level, is simple to perform and provides additional insight into the complexity of glycan changes.
      One example of the added information of derived traits can be found in the association of glycosylation with hsCRP. On an individual glycan level, we can only observe a relative increase in tri- and tetraantennary compositions together with the inflammation marker, and a corresponding relative decrease in all other compositions (which may be because of the total area normalization). However, when we mathematically take several individual glycans with shared biological features out of the total plasma N-glycome and compare them relative to each other in the form of a derived trait, we now additionally reveal, for example, a decrease in galactosylation within the subset of glycan compositions predominantly occurring on IgG-Fc (nonsialylated fucosylated diantennary species; A2FS0), a finding expected from literature (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Collins E.S.
      • Galligan M.C.
      • Saldova R.
      • Adamczyk B.
      • Abrahams J.L.
      • Campbell M.P.
      • Ng C.T.
      • Veale D.J.
      • Murphy T.B.
      • Rudd P.M.
      • Fitzgerald O.
      Glycosylation status of serum in inflammatory arthritis in response to anti-TNF treatment.
      ,
      • Saldova R.
      • Wormald M.R.
      • Dwek R.A.
      • Rudd P.M.
      Glycosylation changes on serum glycoproteins in ovarian cancer may contribute to disease pathogenesis.
      ).
      Additional aspects of the analytical methodology need to be considered to allow valid interpretation of the presented findings. First, N-glycans are released from their protein backbones, and thus information is lost whether an observed glycan change originates from protein glycosylation or from glycoprotein abundance. Second, mass spectrometry does not distinguish isomers, meaning that a given monosaccharide mass, e.g. a hexose, is assigned differently based on literature knowledge of its compositional context (e.g. as mannose for H5N2 and as galactose for H4N4F1) (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ,
      • Nairn A.V.
      • York W.S.
      • Harris K.
      • Hall E.M.
      • Pierce J.M.
      • Moremen K.W.
      Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes.
      ,
      • Freeze H.H.
      Genetic defects in the human glycome.
      ). Similarly, literature is the main source of information on the linkages between monosaccharides, for example presuming bisection for compositions having five N-acetylhexosamines but less than three galactoses (e.g. H5N5F1). Although this group indeed encompasses bisection, the information within will also be confounded by triantennary structures with incomplete galactosylation, even if these are not abundant in human plasma (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Saldova R.
      • Asadi Shehni A.
      • Haakensen V.D.
      • Steinfeld I.
      • Hilliard M.
      • Kifer I.
      • Helland A.
      • Yakhini Z.
      • Borresen-Dale A.L.
      • Rudd P.M.
      Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
      ). Third, the methodology presented here will not provide biologically true relative ratios of glycan compositions, as the profiles will, for example, be skewed by the ionization advantage of sialylated species in negative ion mode MS. Nonetheless, the relative signal differences will still be representative for the biological directions of change, as well as providing an estimate of the magnitude.

       Clinical Findings

      With the help of literature, we can speculate on the biological background of the observed associations. Acute inflammation, represented in our study by hsCRP and IL-6, shows to confirm previous glycomics studies with regard to the increase in antennarity, fucosylation of triantennary species, and a decrease in galactosylation of IgG glycans which is particularly well-established (
      • Arnold J.N.
      • Saldova R.
      • Hamid U.M.
      • Rudd P.M.
      Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation.
      ,
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Koeleman C.A.
      • Hokke C.H.
      • Westendorp R.G.
      • Wuhrer M.
      • Houwing-Duistermaat J.J.
      • Slagboom P.E.
      • Deelder A.M.
      Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity.
      ,
      • Kristic J.
      • Vuckovic F.
      • Menni C.
      • Klaric L.
      • Keser T.
      • Beceheli I.
      • Pucic-Bakovic M.
      • Novokmet M.
      • Mangino M.
      • Thaqi K.
      • Rudan P.
      • Novokmet N.
      • Sarac J.
      • Missoni S.
      • Kolcic I.
      • Polasek O.
      • Rudan I.
      • Campbell H.
      • Hayward C.
      • Aulchenko Y.
      • Valdes A.
      • Wilson J.F.
      • Gornik O.
      • Primorac D.
      • Zoldos V.
      • Spector T.
      • Lauc G.
      Glycans are a novel biomarker of chronological and biological ages.
      ,
      • Knezevic A.
      • Polasek O.
      • Gornik O.
      • Rudan I.
      • Campbell H.
      • Hayward C.
      • Wright A.
      • Kolcic I.
      • O'Donoghue N.
      • Bones J.
      • Rudd P.M.
      • Lauc G.
      Variability, heritability and environmental determinants of human plasma N-glycome.
      ,
      • Dall'Olio F.
      • Vanhooren V.
      • Chen C.C.
      • Slagboom P.E.
      • Wuhrer M.
      • Franceschi C.
      N-glycomic biomarkers of biological aging and longevity: a link with inflammaging.
      ,
      • Gornik O.
      • Royle L.
      • Harvey D.J.
      • Radcliffe C.M.
      • Saldova R.
      • Dwek R.A.
      • Rudd P.
      • Lauc G.
      Changes of serum glycans during sepsis and acute pancreatitis.
      ). The first two may be explained by an inflammation-induced increase in plasma levels and glycosylation changes of acute phase proteins such as alpha-1-antitrypsin and alpha-1-acid glycoprotein (orosomucoid-1) (
      • Arnold J.N.
      • Saldova R.
      • Hamid U.M.
      • Rudd P.M.
      Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation.
      ,
      • Peracaula R.
      • Sarrats A.
      • Rudd P.M.
      Liver proteins as sensor of human malignancies and inflammation.
      ). Not only are these carriers of N-glycans with two or more antennae at baseline conditions, they furthermore display increased antennarity and sialyl-Lewis X upon acute inflammation (
      • McCarthy C.
      • Saldova R.
      • Wormald M.R.
      • Rudd P.M.
      • McElvaney N.G.
      • Reeves E.P.
      The role and importance of glycosylation of acute phase proteins with focus on alpha-1 antitrypsin in acute and chronic inflammatory conditions.
      ,
      • Higai K.
      • Azuma Y.
      • Aoki Y.
      • Matsumoto K.
      Altered glycosylation of alpha1-acid glycoprotein in patients with inflammation and diabetes mellitus.
      ,
      • De Graaf T.W.
      • Van der Stelt M.E.
      • Anbergen M.G.
      • van Dijk W.
      Inflammation-induced expression of sialyl Lewis X-containing glycan structures on alpha 1-acid glycoprotein (orosomucoid) in human sera.
      ). Additional observations include the increase of galactosylation and sialylation per galactose of diantennary fucosylated (A2F) species in general, which is notably different from the behavior of IgG. A contributor to this observation could be the level of IgM, an abundant immunoglobulin shown to increase with many autoimmune and inflammatory conditions and which carries the required highly galactosylated and sialylated A2F species (
      • Duarte-Rey C.
      • Bogdanos D.P.
      • Leung P.S.
      • Anaya J.M.
      • Gershwin M.E.
      IgM predominance in autoimmune disease: genetics and gender.
      ,
      • Pabst M.
      • Kuster S.K.
      • Wahl F.
      • Krismer J.
      • Dittrich P.S.
      • Zenobi R.
      A microarray-matrix-assisted laser desorption/ionization-mass spectrometry approach for site-specific protein N-glycosylation analysis, as demonstrated for human serum immunoglobulin M (IgM).
      ).
      Of particular interest is the highly significantly increased size of high-mannose glycans (MM) which is not only observed with increased hsCRP, but as well with increasing BMI, non-HDL cholesterol and TG. This glycosylation change appears to largely stem from the increase in the single glycan composition H9N2, an analyte difficult to assess by commonly used liquid chromatography with fluorescence detection (
      • Igl W.
      • Polasek O.
      • Gornik O.
      • Knezevic A.
      • Pucic M.
      • Novokmet M.
      • Huffman J.
      • Gnewuch C.
      • Liebisch G.
      • Rudd P.M.
      • Campbell H.
      • Wilson J.F.
      • Rudan I.
      • Gyllensten U.
      • Schmitz G.
      • Lauc G.
      Glycomics meets lipidomics–associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations.
      ,
      • Bai L.
      • Li Q.
      • Li L.
      • Lin Y.
      • Zhao S.
      • Wang W.
      • Wang R.
      • Li Y.
      • Yuan J.
      • Wang C.
      • Wang Z.
      • Fan J.
      • Liu E.
      Plasma high-mannose and complex/hybrid N-glycans e.
      ). Within the total plasma N-glycome, this large high-mannose glycan may predominantly originate from apolipoprotein B, the main protein constituent of most non-HDL lipoproteins (e.g. VLDL, LDL) (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • Garner B.
      • Harvey D.J.
      • Royle L.
      • Frischmann M.
      • Nigon F.
      • Chapman M.J.
      • Rudd P.M.
      Characterization of human apolipoprotein B100 oligosaccharides in LDL subfractions derived from normal and hyperlipidemic plasma: deficiency of alpha-N-acetylneuraminyllactosyl-ceramide in light and small dense LDL particles.
      ,
      • Olofsson S.O.
      • Bjursell G.
      • Bostrom K.
      • Carlsson P.
      • Elovson J.
      • Protter A.A.
      • Reuben M.A.
      • Bondjers G.
      Apolipoprotein B: structure, biosynthesis and role in the lipoprotein assembly process.
      ). The observed correlations with H9N2 and MM can represent differing apolipoprotein B levels or glycosylation thereof, and may be indicative of an unhealthy glycosylation profile with regard to lipid transport and metabolism. The association of MM with inflammatory marker hsCRP is likely a consequence of the connection between inflammation and obesity (
      • Ellulu M.S.
      • Khaza'ai H.
      • Rahmat A.
      • Patimah I.
      • Abed Y.
      Obesity can predict and promote systemic inflammation in healthy adults.
      ), as a model corrected for BMI no longer shows significant association between high mannose size and inflammation (data not shown). The effect size being less pronounced with hsCRP than with BMI is explainable by the aforementioned increase in IgM, which contains the smaller high-mannose compositions H5N2 and H6N2 at its Asn279 site (
      • Pabst M.
      • Kuster S.K.
      • Wahl F.
      • Krismer J.
      • Dittrich P.S.
      • Zenobi R.
      A microarray-matrix-assisted laser desorption/ionization-mass spectrometry approach for site-specific protein N-glycosylation analysis, as demonstrated for human serum immunoglobulin M (IgM).
      ). On the other hand, the association between MM and BMI remains true in a model adjusted for CRP and leptin, indicating the glycosylation trait may have potential to discriminate healthy from unhealthy obese.
      Although largely following an inflammatory glycosylation profile, BMI, leptin, non-HDL cholesterol and TG do show a unique negative association with the sialylation of nonfucosylated tetraantennary compositions (A4F0GS), a change mainly occurring because of the relative increase of the lowly sialylated H7N6S1 and H7N6S2. Many proteins may contribute to this decrease in sialylation, a notable one being alpha-1-acid glycoprotein (
      • Zhang S.
      • Jiang K.
      • Sun C.
      • Lu H.
      • Liu Y.
      Quantitative analysis of site-specific N-glycans on sera haptoglobin beta chain in liver diseases.
      ,
      • Pompach P.
      • Brnakova Z.
      • Sanda M.
      • Wu J.
      • Edwards N.
      • Goldman R.
      Site-specific glycoforms of haptoglobin in liver cirrhosis and hepatocellular carcinoma.
      ,
      • Dage J.L.
      • Ackermann B.L.
      • Halsall H.B.
      Site localization of sialyl Lewis (x) antigen on alpha1-acid glycoprotein by high performance liquid chromatography-electrospray mass spectrometry.
      ), which is known to bind lipophilic compounds with affinity modulated by its degree of sialylation (
      • Fournier T.
      • Medjoubi N.N.
      • Porquet D.
      Alpha-1-acid glycoprotein.
      ,
      • Wong A.K.
      • Hsia J.C.
      In vitro binding of propranolol and progesterone to native and desialylated human orosomucoid.
      ,
      • Ponganis K.V.
      • Stanski D.R.
      Factors affecting the measurement of lidocaine protein binding by equilibrium dialysis in human serum.
      ). This effect also applies to binding of hydrophobic drugs, possibly explaining why we similarly observed trends of decreased A4F0GS with the usage of lipid- and antihypertensive medication (
      • Wong A.K.
      • Hsia J.C.
      In vitro binding of propranolol and progesterone to native and desialylated human orosomucoid.
      ,
      • Ponganis K.V.
      • Stanski D.R.
      Factors affecting the measurement of lidocaine protein binding by equilibrium dialysis in human serum.
      ). Additionally, increased levels of glycoproteins with exposed galactose residues can reflect a modulation of protein turnover, e.g. recycling by the liver-based asialoglycoprotein receptors (
      • Yang W.H.
      • Aziz P.V.
      • Heithoff D.M.
      • Mahan M.J.
      • Smith J.W.
      • Marth J.D.
      An intrinsic mechanism of secreted protein aging and turnover.
      ,
      • Morell A.G.
      • Gregoriadis G.
      • Scheinberg I.H.
      • Hickman J.
      • Ashwell G.
      The role of sialic acid in determining the survival of glycoproteins in the circulation.
      ,
      • Ashwell G.
      • Morell A.G.
      The role of surface carbohydrates in the hepatic recognition and transport of circulating glycoproteins.
      ,
      • Ellies L.G.
      • Ditto D.
      • Levy G.G.
      • Wahrenbrock M.
      • Ginsburg D.
      • Varki A.
      • Le D.T.
      • Marth J.D.
      Sialyltransferase ST3Gal-IV operates as a dominant modifier of hemostasis by concealing asialoglycoprotein receptor ligands.
      ). Low sialylation would induce rapid turnover of lipid scavengers like alpha-1-acid glycoprotein, a situation which is beneficial when the blood needs to be cleared of high levels of lipophilic compounds.
      Smoking proved the only phenotype to positively associate with the fucosylation of tri- and tetraantennary species (A3F and A4F), as well as with the bisection of IgG-Fc type glycans (A2FS0B) (
      • Knezevic A.
      • Gornik O.
      • Polasek O.
      • Pucic M.
      • Redzic I.
      • Novokmet M.
      • Rudd P.M.
      • Wright A.F.
      • Campbell H.
      • Rudan I.
      • Lauc G.
      Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma N-glycans.
      ,
      • Vasseur J.A.
      • Goetz J.A.
      • Alley Jr, W.R.
      • Novotny M.V.
      Smoking and lung cancer-induced changes in N-glycosylation of blood serum proteins.
      ). Likely these reflect the chronic response to vascular injury obtained from smoking-induced shear stress and oxidative damage (
      • Powell J.T.
      Vascular damage from smoking: disease mechanisms at the arterial wall.
      ). Increased fucosylation of acute phase proteins, when antennary-linked in the form of sialyl-Lewis X or A, facilitates recruitment to sites of injury, e.g. by interactions with selectins presented on inflammation-activated endothelial cells (
      • Clerc F.
      • Reiding K.R.
      • Jansen B.C.
      • Kammeijer G.S.
      • Bondt A.
      • Wuhrer M.
      Human plasma protein N-glycosylation.
      ,
      • McCarthy C.
      • Saldova R.
      • Wormald M.R.
      • Rudd P.M.
      • McElvaney N.G.
      • Reeves E.P.
      The role and importance of glycosylation of acute phase proteins with focus on alpha-1 antitrypsin in acute and chronic inflammatory conditions.
      ,
      • Vestweber D.
      • Blanks J.E.
      Mechanisms that regulate the function of the selectins and their ligands.
      ).
      Although it is invalid to interpret absence of statistical significance as an absence of association, we have nevertheless failed to identify derived traits or single glycans to be predictive of DHEA-S, CMV infection or the propensity for longevity. The latter is a peculiar absence, as plasma N-glycosylation analysis of the same cohort by HPLC had revealed two chromatographic peaks to be predictors of the phenotype (
      • Ruhaak L.R.
      • Uh H.W.
      • Beekman M.
      • Hokke C.H.
      • Westendorp R.G.
      • Houwing-Duistermaat J.
      • Wuhrer M.
      • Deelder A.M.
      • Slagboom P.E.
      Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health.
      ). Reasons for this lack of biological reproduction may include the measurement error (HPLC tends to provide more robust measurements than mass spectrometry) (
      • Huffman J.E.
      • Pucic-Bakovic M.
      • Klaric L.
      • Hennig R.
      • Selman M.H.
      • Vuckovic F.
      • Novokmet M.
      • Kristic J.
      • Borowiak M.
      • Muth T.
      • Polasek O.
      • Razdorov G.
      • Gornik O.
      • Plomp R.
      • Theodoratou E.
      • Wright A.F.
      • Rudan I.
      • Hayward C.
      • Campbell H.
      • Deelder A.M.
      • Reichl U.
      • Aulchenko Y.S.
      • Rapp E.
      • Wuhrer M.
      • Lauc G.
      Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research.
      ), the chromatographic peaks representing a culmination of multiple mass spectrometric compositions that are not individually significant, the previous findings being incidental, or the inability of mass spectrometry to separate or detect the responsible analytes.
      Glycosylation is of high interest for the development or improvement of biomarkers for disease and patient stratification (
      • Ruhaak L.R.
      • Miyamoto S.
      • Lebrilla C.B.
      Developments in the identification of glycan biomarkers for the detection of cancer.
      ,
      • Krishnan S.
      • Huang J.
      • Lee H.
      • Guerrero A.
      • Berglund L.
      • Anuurad E.
      • Lebrilla C.B.
      • Zivkovic A.M.
      Combined high-density lipoprotein proteomic and glycomic profiles in patients at risk for coronary artery disease.
      ). In particular, the findings within this study may be of benefit to the detection and discrimination of metabolic syndrome or inflammatory disorders, and may bolster the predictability of existing biomarkers such as the Framingham Risk Score (
      • Wilson P.W.
      • D'Agostino R.B.
      • Levy D.
      • Belanger A.M.
      • Silbershatz H.
      • Kannel W.B.
      Prediction of coronary heart disease using risk factor categories.
      ). Important glycosylation phenotypes in this regard would then be the derived traits MM, A3F, A4F, A2F0G, A2F0B, A2FS0G, A2FGS, and A4F0GS, as well as the various individual glycans comprising these groups, e.g. most tri- and tetraantennary compositions, high-mannose compositions H5N2, H6N2, and H9N2, and the truncated N-glycans suggested by compositions H3N3, H4N4, and H4N4S1. Interestingly, although this last example, H4N4S1, is a glycan composition difficult to characterize in a derived trait, it does show to be the major single glycan to positively associate with most beneficial markers of metabolic health (e.g. glucose/insulin, HDL-C, adiponectin) and negatively with most detrimental ones (e.g. hsCRP, BMI, GGT, hypertension, TG). The protein source of the N-glycan is as of yet unclear, but is of interest to study in more detail.
      To summarize, we have reported a large number of associations between total plasma N-glycosylation as measured by MALDI-FTICR-MS and clinical markers of metabolic health and inflammation. By this, we have identified glycan compositions and derived traits indicative of overall metabolic health and inflammation, as well as finding glycosylation traits uniquely associating with single marker variables. With this knowledge, we hope to contribute to the interpretation of the plasma N-glycome as biomarker for health and disease, and to assist clinical translation of mass spectrometric glycosylation analysis.

      Supplementary Material

      REFERENCES

        • Varki A.
        Biological roles of oligosaccharides: all of the theories are correct.
        Glycobiology. 1993; 3: 97-130
        • Varki A.
        • Cummings R.D.
        • Esko J.D.
        • Stanley P.
        • Hart G.
        • Aebi M.
        • Darvill A.
        • Kinoshita T.
        • Packer N.H.
        • Prestegard J.J.
        • Schnaar R.L.
        • Seeberger P.H.
        Essentials of Glycobiology. 3rd Ed. Cold Spring Harbor (NY), 2015
        • Xu C.
        • Ng D.T.
        Glycosylation-directed quality control of protein folding.
        Nat. Rev. Mol. Cell Biol. 2015; 16: 742-752
        • Kontermann R.E.
        Strategies for extended serum half-life of protein therapeutics.
        Curr. Opin. Biotechnol. 2011; 22: 868-876
        • Yang W.H.
        • Aziz P.V.
        • Heithoff D.M.
        • Mahan M.J.
        • Smith J.W.
        • Marth J.D.
        An intrinsic mechanism of secreted protein aging and turnover.
        Proc. Natl. Acad. Sci. U.S.A. 2015; 112: 13657-13662
        • Ferrara C.
        • Grau S.
        • Jager C.
        • Sondermann P.
        • Brunker P.
        • Waldhauer I.
        • Hennig M.
        • Ruf A.
        • Rufer A.C.
        • Stihle M.
        • Umana P.
        • Benz J.
        Unique carbohydrate-carbohydrate interactions are required for high affinity binding between FcgammaRIII and antibodies lacking core fucose.
        Proc. Natl. Acad. Sci. U.S.A. 2011; 108: 12669-12674
        • Varki A.
        • Gagneux P.
        Multifarious roles of sialic acids in immunity.
        Ann. N.Y. Acad. Sci. 2012; 1253: 16-36
        • Pinho S.S.
        • Reis C.A.
        Glycosylation in cancer: mechanisms and clinical implications.
        Nat. Rev. Cancer. 2015; 15: 540-555
        • Arnold J.N.
        • Saldova R.
        • Hamid U.M.
        • Rudd P.M.
        Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation.
        Proteomics. 2008; 8: 3284-3293
        • Axford J.S.
        Glycosylation and rheumatic disease.
        Biochim. Biophys. Acta. 1999; 1455: 219-229
        • Bondt A.
        • Selman M.H.
        • Deelder A.M.
        • Hazes J.M.
        • Willemsen S.P.
        • Wuhrer M.
        • Dolhain R.J.
        Association between galactosylation of immunoglobulin G and improvement of rheumatoid arthritis during pregnancy is independent of sialylation.
        J. Proteome Res. 2013; 12: 4522-4531
        • Moremen K.W.
        • Tiemeyer M.
        • Nairn A.V.
        Vertebrate protein glycosylation: diversity, synthesis and function.
        Nat. Rev. Mol. Cell Biol. 2012; 13: 448-462
        • Maverakis E.
        • Kim K.
        • Shimoda M.
        • Gershwin M.E.
        • Patel F.
        • Wilken R.
        • Raychaudhuri S.
        • Ruhaak L.R.
        • Lebrilla C.B.
        Glycans in the immune system and The Altered Glycan Theory of Autoimmunity: a critical review.
        J. Autoimmun. 2015; 57: 1-13
        • Ruhaak L.R.
        • Miyamoto S.
        • Lebrilla C.B.
        Developments in the identification of glycan biomarkers for the detection of cancer.
        Mol. Cell. Proteomics. 2013; 12: 846-855
        • Klein A.
        Human total serum N-glycome.
        Adv. Clin. Chem. 2008; 46: 51-85
        • Clerc F.
        • Reiding K.R.
        • Jansen B.C.
        • Kammeijer G.S.
        • Bondt A.
        • Wuhrer M.
        Human plasma protein N-glycosylation.
        Glycoconj. J. 2016; 33: 309-343
        • Ruhaak L.R.
        • Uh H.W.
        • Beekman M.
        • Koeleman C.A.
        • Hokke C.H.
        • Westendorp R.G.
        • Wuhrer M.
        • Houwing-Duistermaat J.J.
        • Slagboom P.E.
        • Deelder A.M.
        Decreased levels of bisecting GlcNAc glycoforms of IgG are associated with human longevity.
        PLoS ONE. 2010; 5: e12566
        • Ruhaak L.R.
        • Uh H.W.
        • Beekman M.
        • Hokke C.H.
        • Westendorp R.G.
        • Houwing-Duistermaat J.
        • Wuhrer M.
        • Deelder A.M.
        • Slagboom P.E.
        Plasma protein N-glycan profiles are associated with calendar age, familial longevity and health.
        J. Proteome Res. 2011; 10: 1667-1674
        • Lu J.P.
        • Knezevic A.
        • Wang Y.X.
        • Rudan I.
        • Campbell H.
        • Zou Z.K.
        • Lan J.
        • Lai Q.X.
        • Wu J.J.
        • He Y.
        • Song M.S.
        • Zhang L.
        • Lauc G.
        • Wang W.
        Screening novel biomarkers for metabolic syndrome by profiling human plasma N-glycans in Chinese Han and Croatian populations.
        J. Proteome Res. 2011; 10: 4959-4969
        • Knezevic A.
        • Gornik O.
        • Polasek O.
        • Pucic M.
        • Redzic I.
        • Novokmet M.
        • Rudd P.M.
        • Wright A.F.
        • Campbell H.
        • Rudan I.
        • Lauc G.
        Effects of aging, body mass index, plasma lipid profiles, and smoking on human plasma N-glycans.
        Glycobiology. 2010; 20: 959-969
        • Kristic J.
        • Vuckovic F.
        • Menni C.
        • Klaric L.
        • Keser T.
        • Beceheli I.
        • Pucic-Bakovic M.
        • Novokmet M.
        • Mangino M.
        • Thaqi K.
        • Rudan P.
        • Novokmet N.
        • Sarac J.
        • Missoni S.
        • Kolcic I.
        • Polasek O.
        • Rudan I.
        • Campbell H.
        • Hayward C.
        • Aulchenko Y.
        • Valdes A.
        • Wilson J.F.
        • Gornik O.
        • Primorac D.
        • Zoldos V.
        • Spector T.
        • Lauc G.
        Glycans are a novel biomarker of chronological and biological ages.
        J. Gerontol. A Biol. Sci. Med. Sci. 2014; 69: 779-789
        • Vanhooren V.
        • Desmyter L.
        • Liu X.E.
        • Cardelli M.
        • Franceschi C.
        • Federico A.
        • Libert C.
        • Laroy W.
        • Dewaele S.
        • Contreras R.
        • Chen C.
        N-glycomic changes in serum proteins during human aging.
        Rejuvenation Res. 2007; 10: 521-531a
        • Igl W.
        • Polasek O.
        • Gornik O.
        • Knezevic A.
        • Pucic M.
        • Novokmet M.
        • Huffman J.
        • Gnewuch C.
        • Liebisch G.
        • Rudd P.M.
        • Campbell H.
        • Wilson J.F.
        • Rudan I.
        • Gyllensten U.
        • Schmitz G.
        • Lauc G.
        Glycomics meets lipidomics–associations of N-glycans with classical lipids, glycerophospholipids, and sphingolipids in three European populations.
        Mol. Biosyst. 2011; 7: 1852-1862
        • Knezevic A.
        • Polasek O.
        • Gornik O.
        • Rudan I.
        • Campbell H.
        • Hayward C.
        • Wright A.
        • Kolcic I.
        • O'Donoghue N.
        • Bones J.
        • Rudd P.M.
        • Lauc G.
        Variability, heritability and environmental determinants of human plasma N-glycome.
        J. Proteome Res. 2009; 8: 694-701
        • Trbojevic Akmacic I.
        • Ventham N.T.
        • Theodoratou E.
        • Vuckovic F.
        • Kennedy N.A.
        • Kristic J.
        • Nimmo E.R.
        • Kalla R.
        • Drummond H.
        • Stambuk J.
        • Dunlop M.G.
        • Novokmet M.
        • Aulchenko Y.
        • Gornik O.
        • Campbell H.
        • Pucic Bakovic M.
        • Satsangi J.
        • Lauc G.
        • IBD-BIOM Consortium
        Inflammatory bowel disease associates with proinflammatory potential of the immunoglobulin G glycome.
        Inflamm. Bowel Dis. 2015; 21: 1237-1247
        • Novokmet M.
        • Lukic E.
        • Vuckovic F.
        • Ethuric Z.
        • Keser T.
        • Rajsl K.
        • Remondini D.
        • Castellani G.
        • Gasparovic H.
        • Gornik O.
        • Lauc G.
        Changes in IgG and total plasma protein glycomes in acute systemic inflammation.
        Sci. Rep. 2014; 4: 4347
        • Saldova R.
        • Asadi Shehni A.
        • Haakensen V.D.
        • Steinfeld I.
        • Hilliard M.
        • Kifer I.
        • Helland A.
        • Yakhini Z.
        • Borresen-Dale A.L.
        • Rudd P.M.
        Association of N-glycosylation with breast carcinoma and systemic features using high-resolution quantitative UPLC.
        J. Proteome Res. 2014; 13: 2314-2327
        • Ruhaak L.R.
        • Uh H.W.
        • Deelder A.M.
        • Dolhain R.E.
        • Wuhrer M.
        Total plasma N-glycome changes during pregnancy.
        J. Proteome Res. 2014; 13: 1657-1668
        • Harvey D.J.
        Matrix-assisted laser desorption/ionization mass spectrometry of carbohydrates.
        Mass Spectrom. Rev. 1999; 18: 349-450
        • Canis K.
        • McKinnon T.A.
        • Nowak A.
        • Haslam S.M.
        • Panico M.
        • Morris H.R.
        • Laffan M.A.
        • Dell A.
        Mapping the N-glycome of human von Willebrand factor.
        Biochem. J. 2012; 447: 217-228
        • Reiding K.R.
        • Blank D.
        • Kuijper D.M.
        • Deelder A.M.
        • Wuhrer M.
        High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification.
        Anal. Chem. 2014; 86: 5784-5793
        • Kang P.
        • Madera M.
        • Alley Jr, W.R.
        • Goldman R.
        • Mechref Y.
        • Novotny M.V.
        Glycomic alterations in the highly-abundant and lesser-abundant blood serum protein fractions for patients diagnosed with hepatocellular carcinoma.
        Int. J. Mass Spectrom. 2011; 305: 185-198
        • Borelli V.
        • Vanhooren V.
        • Lonardi E.
        • Reiding K.R.
        • Capri M.
        • Libert C.
        • Garagnani P.
        • Salvioli S.
        • Franceschi C.
        • Wuhrer M.
        Plasma N-glycome signature of Down Syndrome.
        J. Proteome Res. 2015; 14: 4232-4245
        • Jansen B.C.
        • Bondt A.
        • Reiding K.R.
        • Lonardi E.
        • de Jong C.J.
        • Falck D.
        • Kammeijer G.S.
        • Dolhain R.J.
        • Rombouts Y.
        • Wuhrer M.
        Pregnancy-associated serum N-glycome changes studied by high-throughput MALDI-TOF-MS.
        Sci. Rep. 2016; 6: 23296
        • Schoenmaker M.
        • de Craen A.J.
        • de Meijer P.H.
        • Beekman M.
        • Blauw G.J.
        • Slagboom P.E.
        • Westendorp R.G.
        Evidence of genetic enrichment for exceptional survival using a family approach: the Leiden Longevity Study.
        Eur. J. Hum. Genet. 2006; 14: 79-84
        • Asakawa D.
        • Calligaris D.
        • Zimmerman T.A.
        • De Pauw E.
        In-source decay during matrix-assisted laser desorption/ionization combined with the collisional process in an FTICR mass spectrometer.
        Anal. Chem. 2013; 85: 7809-7817
        • Powell A.K.
        • Harvey D.J.
        Stabilization of sialic acids in N-linked oligosaccharides and gangliosides for analysis by positive ion matrix-assisted laser desorption/ionization mass spectrometry.
        Rapid Commun. Mass Spectrom. 1996; 10: 1027-1032
        • Selman M.H.
        • McDonnell L.A.
        • Palmblad M.
        • Ruhaak L.R.
        • Deelder A.M.
        • Wuhrer M.
        Immunoglobulin G glycopeptide profiling by matrix-assisted laser desorption ionization Fourier transform ion cyclotron resonance mass spectrometry.
        Anal. Chem. 2010; 82: 1073-1081
        • Lee H.
        • An H.J.
        • Lerno Jr, L.A.
        • German J.B.
        • Lebrilla C.B.
        Rapid profiling of bovine and human milk gangliosides by matrix-assisted laser desorption/ionization fourier transform ion cyclotron resonance mass spectrometry.
        Int. J. Mass Spectrom. 2011; 305: 138-150
        • Park Y.
        • Lebrilla C.B.
        Application of Fourier transform ion cyclotron resonance mass spectrometry to oligosaccharides.
        Mass Spectrom. Rev. 2005; 24: 232-264
        • O'Connor P.B.
        • Mirgorodskaya E.
        • Costello C.E.
        High pressure matrix-assisted laser desorption/ionization Fourier transform mass spectrometry for minimization of ganglioside fragmentation.
        J. Am. Soc. Mass Spectrom. 2002; 13: 402-407
        • Westendorp R.G.
        • van Heemst D.
        • Rozing M.P.
        • Frolich M.
        • Mooijaart S.P.
        • Blauw G.J.
        • Beekman M.
        • Heijmans B.T.
        • de Craen A.J.
        • Slagboom P.E.
        • Leiden Longevity Study Group
        Nonagenarian siblings and their offspring display lower risk of mortality and morbidity than sporadic nonagenarians: The Leiden Longevity Study.
        J. Am. Geriatr. Soc. 2009; 57: 1634-1637
        • Friedewald W.T.
        • Levy R.I.
        • Fredrickson D.S.
        Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge.
        Clin. Chem. 1972; 18: 499-502
        • Ruhaak L.R.
        • Huhn C.
        • Waterreus W.J.
        • de Boer A.R.
        • Neususs C.
        • Hokke C.H.
        • Deelder A.M.
        • Wuhrer M.
        Hydrophilic interaction chromatography-based high-throughput sample preparation method for N-glycan analysis from total human plasma glycoproteins.
        Anal. Chem. 2008; 80: 6119-6126
        • Nairn A.V.
        • York W.S.
        • Harris K.
        • Hall E.M.
        • Pierce J.M.
        • Moremen K.W.
        Regulation of glycan structures in animal tissues: transcript profiling of glycan-related genes.
        J. Biol. Chem. 2008; 283: 17298-17313
        • Freeze H.H.
        Genetic defects in the human glycome.
        Nat. Rev. Genet. 2006; 7: 537-551
        • Chambers M.C.
        • Maclean B.
        • Burke R.
        • Amodei D.
        • Ruderman D.L.
        • Neumann S.
        • Gatto L.
        • Fischer B.
        • Pratt B.
        • Egertson J.
        • Hoff K.
        • Kessner D.
        • Tasman N.
        • Shulman N.
        • Frewen B.
        • Baker T.A.
        • Brusniak M.Y.
        • Paulse C.
        • Creasy D.
        • Flashner L.
        • Kani K.
        • Moulding C.
        • Seymour S.L.
        • Nuwaysir L.M.
        • Lefebvre B.
        • Kuhlmann F.
        • Roark J.
        • Rainer P.
        • Detlev S.
        • Hemenway T.
        • Huhmer A.
        • Langridge J.
        • Connolly B.
        • Chadick T.
        • Holly K.
        • Eckels J.
        • Deutsch E.W.
        • Moritz R.L.
        • Katz J.E.
        • Agus D.B.
        • MacCoss M.
        • Tabb D.L.
        • Mallick P.
        A cross-platform toolkit for mass spectrometry and proteomics.
        Nat. Biotechnol. 2012; 30: 918-920
        • Jansen B.C.
        • Reiding K.R.
        • Bondt A.
        • Hipgrave Ederveen A.L.
        • Palmblad M.
        • Falck D.
        • Wuhrer M.
        MassyTools: A high-throughput targeted data processing tool for relative quantitation and quality control developed for glycomic and glycoproteomic MALDI-MS.
        J. Proteome Res. 2015; 14: 5088-5098
        • Varki A.
        • Cummings R.D.
        • Aebi M.
        • Packer N.H.
        • Seeberger P.H.
        • Esko J.D.
        • Stanley P.
        • Hart G.
        • Darvill A.
        • Kinoshita T.
        • Prestegard J.J.
        • Schnaar R.L.
        • Freeze H.H.
        • Marth J.D.
        • Bertozzi C.R.
        • Etzler M.E.
        • Frank M.
        • Vliegenthart J.F.
        • Lutteke T.
        • Perez S.
        • Bolton E.
        • Rudd P.
        • Paulson J.
        • Kanehisa M.
        • Toukach P.
        • Aoki-Kinoshita K.F.
        • Dell A.
        • Narimatsu H.
        • York W.
        • Taniguchi N.
        • Kornfeld S.
        Symbol nomenclature for graphical representations of glycans.
        Glycobiology. 2015; 25: 1323-1324
        • Ceroni A.
        • Maass K.
        • Geyer H.
        • Geyer R.
        • Dell A.
        • Haslam S.M.
        GlycoWorkbench: a tool for the computer-assisted annotation of mass spectra of glycans.
        J. Proteome Res. 2008; 7: 1650-1659
        • RCore Team
        R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria2014 (http://www.R-project.org/)
        • Johnson W.E.
        • Li C.
        • Rabinovic A.
        Adjusting batch effects in microarray expression data using empirical Bayes methods.
        Biostatistics. 2007; 8: 118-127
        • Liang K.Y.
        • Zeger S.L.
        Longitudinal data-analysis using generalized linear-models.
        Biometrika. 1986; 73: 13-22
        • Bladergroen M.R.
        • Reiding K.R.
        • Hipgrave Ederveen A.L.
        • Vreeker G.C.
        • Clerc F.
        • Holst S.
        • Bondt A.
        • Wuhrer M.
        • van der Burgt Y.E.
        Automation of high-throughput mass spectrometry-based plasma n-glycome analysis with linkage-specific sialic acid esterification.
        J. Proteome Res. 2015; 14: 4080-4086
        • Heinrich P.C.
        • Castell J.V.
        • Andus T.
        Interleukin-6 and the acute phase response.
        Biochem. J. 1990; 265: 621-636
        • Vigushin D.M.
        • Pepys M.B.
        • Hawkins P.N.
        Metabolic and scintigraphic studies of radioiodinated human C-reactive protein in health and disease.
        J. Clin. Invest. 1993; 91: 1351-1357
        • O'Neill S.
        • Bohl M.
        • Gregersen S.
        • Hermansen K.
        • O'Driscoll L.
        Blood-based biomarkers for metabolic syndrome.
        Trends Endocrinol. Metab. 2016; 27: 363-374
        • Renaldi O.
        • Pramono B.
        • Sinorita H.
        • Purnomo L.B.
        • Asdie R.H.
        • Asdie A.H.
        Hypoadiponectinemia: a risk factor for metabolic syndrome.
        Acta Med. Indones. 2009; 41: 20-24
        • Dall'Olio F.
        • Vanhooren V.
        • Chen C.C.
        • Slagboom P.E.
        • Wuhrer M.
        • Franceschi C.
        N-glycomic biomarkers of biological aging and longevity: a link with inflammaging.
        Ageing Res. Rev. 2013; 12: 685-698
        • Shikata K.
        • Yasuda T.
        • Takeuchi F.
        • Konishi T.
        • Nakata M.
        • Mizuochi T.
        Structural changes in the oligosaccharide moiety of human IgG with aging.
        Glycoconj. J. 1998; 15: 683-689
        • Yamada E.
        • Tsukamoto Y.
        • Sasaki R.
        • Yagyu K.
        • Takahashi N.
        Structural changes of immunoglobulin G oligosaccharides with age in healthy human serum.
        Glycoconj. J. 1997; 14: 401-405
        • Ding N.
        • Nie H.
        • Sun X.
        • Sun W.
        • Qu Y.
        • Liu X.
        • Yao Y.
        • Liang X.
        • Chen C.C.
        • Li Y.
        Human serum N-glycan profiles are age and sex dependent.
        Age Ageing. 2011; 40: 568-575
        • Ruhaak L.R.
        • Koeleman C.A.
        • Uh H.W.
        • Stam J.C.
        • van Heemst D.
        • Maier A.B.
        • Houwing-Duistermaat J.J.
        • Hensbergen P.J.
        • Slagboom P.E.
        • Deelder A.M.
        • Wuhrer M.
        Targeted biomarker discovery by high throughput glycosylation profiling of human plasma alpha1-antitrypsin and immunoglobulin A.
        PLoS ONE. 2013; 8: e73082
        • Johnson S.B.
        • Brown R.E.
        Simplified derivatization for determining sphingolipid fatty acyl composition by gas chromatography-mass spectrometry.
        J. Chromatogr. 1992; 605: 281-286
        • Morelle W.
        • Michalski J.C.
        Analysis of protein glycosylation by mass spectrometry.
        Nat. Protoc. 2007; 2: 1585-1602
        • Wheeler S.F.
        • Domann P.
        • Harvey D.J.
        Derivatization of sialic acids for stabilization in matrix-assisted laser desorption/ionization mass spectrometry and concomitant differentiation of alpha(2 –> 3)- and alpha (2 –> 6)-isomers.
        Rapid Commun. Mass Spectrom. 2009; 23: 303-312
        • Alley Jr., W.R.
        • Novotny M.V.
        Glycomic analysis of sialic acid linkages in glycans derived from blood serum glycoproteins.
        J. Proteome Res. 2010; 9: 3062-3072
        • de Haan N.
        • Reiding K.R.
        • Haberger M.
        • Reusch D.
        • Falck D.
        • Wuhrer M.
        Linkage-specific sialic acid derivatization for MALDI-TOF-MS profiling of IgG glycopeptides.
        Anal. Chem. 2015; 87: 8284-8291
        • Reiding K.R.
        • Lonardi E.
        • Hipgrave Ederveen A.L.
        • Wuhrer M.
        Ethyl esterification for MALDI-MS analysis of protein glycosylation.
        Methods Mol. Bio.l. 2016; 1394: 151-162
        • Ruhaak L.R.
        • Hennig R.
        • Huhn C.
        • Borowiak M.
        • Dolhain R.J.
        • Deelder A.M.
        • Rapp E.
        • Wuhrer M.
        Optimized workflow for preparation of APTS-labeled N-glycans allowing high-throughput analysis of human plasma glycomes using 48-channel multiplexed CGE-LIF.
        J. Proteome Res. 2010; 9: 6655-6664
        • Huffman J.E.
        • Pucic-Bakovic M.
        • Klaric L.
        • Hennig R.
        • Selman M.H.
        • Vuckovic F.
        • Novokmet M.
        • Kristic J.
        • Borowiak M.
        • Muth T.
        • Polasek O.
        • Razdorov G.
        • Gornik O.
        • Plomp R.
        • Theodoratou E.
        • Wright A.F.
        • Rudan I.
        • Hayward C.
        • Campbell H.
        • Deelder A.M.
        • Reichl U.
        • Aulchenko Y.S.
        • Rapp E.
        • Wuhrer M.
        • Lauc G.
        Comparative performance of four methods for high-throughput glycosylation analysis of immunoglobulin G in genetic and epidemiological research.
        Mol. Cell. Proteomics. 2014; 13: 1598-1610
        • Collins E.S.
        • Galligan M.C.
        • Saldova R.
        • Adamczyk B.
        • Abrahams J.L.
        • Campbell M.P.
        • Ng C.T.
        • Veale D.J.
        • Murphy T.B.
        • Rudd P.M.
        • Fitzgerald O.
        Glycosylation status of serum in inflammatory arthritis in response to anti-TNF treatment.
        Rheumatology. 2013; 52: 1572-1582
        • Saldova R.
        • Wormald M.R.
        • Dwek R.A.
        • Rudd P.M.
        Glycosylation changes on serum glycoproteins in ovarian cancer may contribute to disease pathogenesis.
        Dis. Markers. 2008; 25: 219-232
        • Gornik O.
        • Royle L.
        • Harvey D.J.
        • Radcliffe C.M.
        • Saldova R.
        • Dwek R.A.
        • Rudd P.
        • Lauc G.
        Changes of serum glycans during sepsis and acute pancreatitis.
        Glycobiology. 2007; 17: 1321-1332
        • Peracaula R.
        • Sarrats A.
        • Rudd P.M.
        Liver proteins as sensor of human malignancies and inflammation.
        Proteomics Clin. Appl. 2010; 4: 426-431
        • McCarthy C.
        • Saldova R.
        • Wormald M.R.
        • Rudd P.M.
        • McElvaney N.G.
        • Reeves E.P.
        The role and importance of glycosylation of acute phase proteins with focus on alpha-1 antitrypsin in acute and chronic inflammatory conditions.
        J. Proteome Res. 2014; 13: 3131-3143
        • Higai K.
        • Azuma Y.
        • Aoki Y.
        • Matsumoto K.
        Altered glycosylation of alpha1-acid glycoprotein in patients with inflammation and diabetes mellitus.
        Clin. Chim. Acta. 2003; 329: 117-125
        • De Graaf T.W.
        • Van der Stelt M.E.
        • Anbergen M.G.
        • van Dijk W.
        Inflammation-induced expression of sialyl Lewis X-containing glycan structures on alpha 1-acid glycoprotein (orosomucoid) in human sera.
        J. Exp. Med. 1993; 177: 657-666
        • Duarte-Rey C.
        • Bogdanos D.P.
        • Leung P.S.
        • Anaya J.M.
        • Gershwin M.E.
        IgM predominance in autoimmune disease: genetics and gender.
        Autoimmun. Rev. 2012; 11: A404-A412
        • Pabst M.
        • Kuster S.K.
        • Wahl F.
        • Krismer J.
        • Dittrich P.S.
        • Zenobi R.
        A microarray-matrix-assisted laser desorption/ionization-mass spectrometry approach for site-specific protein N-glycosylation analysis, as demonstrated for human serum immunoglobulin M (IgM).
        Mol. Cell. Proteomics. 2015; 14: 1645-1656
        • Bai L.
        • Li Q.
        • Li L.
        • Lin Y.
        • Zhao S.
        • Wang W.
        • Wang R.
        • Li Y.
        • Yuan J.
        • Wang C.
        • Wang Z.
        • Fan J.
        • Liu E.
        Plasma high-mannose and complex/hybrid N-glycans e.
        PLoS ONE. 2016; 11: e0146982
        • Garner B.
        • Harvey D.J.
        • Royle L.
        • Frischmann M.
        • Nigon F.
        • Chapman M.J.
        • Rudd P.M.
        Characterization of human apolipoprotein B100 oligosaccharides in LDL subfractions derived from normal and hyperlipidemic plasma: deficiency of alpha-N-acetylneuraminyllactosyl-ceramide in light and small dense LDL particles.
        Glycobiology. 2001; 11: 791-802
        • Olofsson S.O.
        • Bjursell G.
        • Bostrom K.
        • Carlsson P.
        • Elovson J.
        • Protter A.A.
        • Reuben M.A.
        • Bondjers G.
        Apolipoprotein B: structure, biosynthesis and role in the lipoprotein assembly process.
        Atherosclerosis. 1987; 68: 1-17
        • Ellulu M.S.
        • Khaza'ai H.
        • Rahmat A.
        • Patimah I.
        • Abed Y.
        Obesity can predict and promote systemic inflammation in healthy adults.
        Int. J. Cardiol. 2016; 215: 318-324
        • Zhang S.
        • Jiang K.
        • Sun C.
        • Lu H.
        • Liu Y.
        Quantitative analysis of site-specific N-glycans on sera haptoglobin beta chain in liver diseases.
        Acta Biochim. Biophys. Sin. 2013; 45: 1021-1029
        • Pompach P.
        • Brnakova Z.
        • Sanda M.
        • Wu J.
        • Edwards N.
        • Goldman R.
        Site-specific glycoforms of haptoglobin in liver cirrhosis and hepatocellular carcinoma.
        Mol. Cell. Proteomics. 2013; 12: 1281-1293
        • Dage J.L.
        • Ackermann B.L.
        • Halsall H.B.
        Site localization of sialyl Lewis (x) antigen on alpha1-acid glycoprotein by high performance liquid chromatography-electrospray mass spectrometry.
        Glycobiology. 1998; 8: 755-760
        • Fournier T.
        • Medjoubi N.N.
        • Porquet D.
        Alpha-1-acid glycoprotein.
        Biochim. Biophys. Acta. 2000; 1482: 157-171
        • Wong A.K.
        • Hsia J.C.
        In vitro binding of propranolol and progesterone to native and desialylated human orosomucoid.
        Can. J. Biochem. Cell Biol. 1983; 61: 1114-1116
        • Ponganis K.V.
        • Stanski D.R.
        Factors affecting the measurement of lidocaine protein binding by equilibrium dialysis in human serum.
        J. Pharm. Sci. 1985; 74: 57-60
        • Morell A.G.
        • Gregoriadis G.
        • Scheinberg I.H.
        • Hickman J.
        • Ashwell G.
        The role of sialic acid in determining the survival of glycoproteins in the circulation.
        J. Biol. Chem. 1971; 246: 1461-1467
        • Ashwell G.
        • Morell A.G.
        The role of surface carbohydrates in the hepatic recognition and transport of circulating glycoproteins.
        Adv. Enzymol. Relat. Areas Mol. Biol. 1974; 41: 99-128
        • Ellies L.G.
        • Ditto D.
        • Levy G.G.
        • Wahrenbrock M.
        • Ginsburg D.
        • Varki A.
        • Le D.T.
        • Marth J.D.
        Sialyltransferase ST3Gal-IV operates as a dominant modifier of hemostasis by concealing asialoglycoprotein receptor ligands.
        Proc. Natl. Acad. Sci. U.S.A. 2002; 99: 10042-10047
        • Vasseur J.A.
        • Goetz J.A.
        • Alley Jr, W.R.
        • Novotny M.V.
        Smoking and lung cancer-induced changes in N-glycosylation of blood serum proteins.
        Glycobiology. 2012; 22: 1684-1708
        • Powell J.T.
        Vascular damage from smoking: disease mechanisms at the arterial wall.
        Vasc. Med. 1998; 3: 21-28
        • Vestweber D.
        • Blanks J.E.
        Mechanisms that regulate the function of the selectins and their ligands.
        Physiol. Rev. 1999; 79: 181-213
        • Krishnan S.
        • Huang J.
        • Lee H.
        • Guerrero A.
        • Berglund L.
        • Anuurad E.
        • Lebrilla C.B.
        • Zivkovic A.M.
        Combined high-density lipoprotein proteomic and glycomic profiles in patients at risk for coronary artery disease.
        J. Proteome Res. 2015; 14: 5109-5118
        • Wilson P.W.
        • D'Agostino R.B.
        • Levy D.
        • Belanger A.M.
        • Silbershatz H.
        • Kannel W.B.
        Prediction of coronary heart disease using risk factor categories.
        Circulation. 1998; 97: 1837-1847