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A Strategy for Precise and Large Scale Identification of Core Fucosylated Glycoproteins*S

  • Wei Jia
    Footnotes
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China

    Institute of Biophysics, Chinese Academy of Sciences, No. 15 Datun Road, Chaoyang District, Beijing 100101, China
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  • Zhuang Lu
    Footnotes
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China

    Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China, and
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  • Yan Fu
    Footnotes
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Hai-Peng Wang
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Le-Heng Wang
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Hao Chi
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Zuo-Fei Yuan
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Zhao-Bin Zheng
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Li-Na Song
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Huan-Huan Han
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Yi-Min Liang
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Jing-Lan Wang
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Yun Cai
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Yu-Kui Zhang
    Affiliations
    Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China, and
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  • Yu-Lin Deng
    Affiliations
    Beijing Institute of Technology, No. 5 South Zhongguancun Street, Haidian District, Beijing 100081, China, and
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  • Wan-Tao Ying
    Correspondence
    To whom correspondence may be addressed
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Si-Min He
    Correspondence
    To whom correspondence may be addressed
    Affiliations
    Institute of Computing Technology, Chinese Academy of Sciences, No. 6 Kexueyuan South Road, Beijing 100190, China
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  • Xiao-Hong Qian
    Correspondence
    To whom correspondence may be addressed
    Affiliations
    State Key Laboratory of Proteomics-Beijing Proteome Research Center-Beijing Institute of Radiation Medicine, No. 33 Life Science Park Road, Changping District, Beijing 102206, China
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  • Author Footnotes
    * This study was supported by National Natural Science Foundation of China Grants 30621063 and 20735005; National Key Program for Basic Research Grants 2006CB910801, 2002CB713807, 2004CB518707, and 2007CB914104; Hi-Tech Research and Development Program of China Grants 2006AA02A308, 2007AA02Z315, and 2008AA02Z309; and Chinese Academy of Sciences Knowledge Innovation Program Grant KGGX1-YW-13.
    S The on-line version of this article (available at http://www.mcponline.org) contains supplemental material.
    ¶ These authors contributed equally to this work.
Open AccessPublished:January 12, 2009DOI:https://doi.org/10.1074/mcp.M800504-MCP200
      Core fucosylation (CF) patterns of some glycoproteins are more sensitive and specific than evaluation of their total respective protein levels for diagnosis of many diseases, such as cancers. Global profiling and quantitative characterization of CF glycoproteins may reveal potent biomarkers for clinical applications. However, current techniques are unable to reveal CF glycoproteins precisely on a large scale. Here we developed a robust strategy that integrates molecular weight cutoff, neutral loss-dependent MS3, database-independent candidate spectrum filtering, and optimization to effectively identify CF glycoproteins. The rationale for spectrum treatment was innovatively based on computation of the mass distribution in spectra of CF glycopeptides. The efficacy of this strategy was demonstrated by implementation for plasma from healthy subjects and subjects with hepatocellular carcinoma. Over 100 CF glycoproteins and CF sites were identified, and over 10,000 mass spectra of CF glycopeptide were found. The scale of identification results indicates great progress for finding biomarkers with a particular and attractive prospect, and the candidate spectra will be a useful resource for the improvement of database searching methods for glycopeptides.
      Glycoproteins are implicated in a wide range of biological processes such as fertilization, development, the immune response, cell signaling, and apoptosis. Altered glycosylation patterns can affect the conformations of glycoproteins and their functions and interactions with other molecules (
      • Parodi A.J.
      Protein glucosylation and its role in protein folding.
      ,
      • Walsh G.
      • Jefferis R.
      Post-translational modifications in the context of therapeutic proteins.
      ). Abnormal glycosylation has been demonstrated in many pathological processes. Targeted glycosylation research is considered increasingly important as a way to find novel therapeutic approaches (
      • Walsh G.
      • Jefferis R.
      Post-translational modifications in the context of therapeutic proteins.
      ,
      • Dwek R.A.
      • Butters T.D.
      • Platt F.M.
      • Zitzmann N.
      Targeting glycosylation as a therapeutic approach.
      ), and core fucosylation (CF)
      The abbreviations used are: CF, core fucosylation; HCC, hepatocellular carcinoma; rhEPO, recombinant human erythropoietin; RP, reversed phase; S2, symbol ion 2; S3, symbol ion 3; HS, Hereman-Schmid; SCX, strong cation exchange; LTQ, linear trap quadrupole.
      1The abbreviations used are: CF, core fucosylation; HCC, hepatocellular carcinoma; rhEPO, recombinant human erythropoietin; RP, reversed phase; S2, symbol ion 2; S3, symbol ion 3; HS, Hereman-Schmid; SCX, strong cation exchange; LTQ, linear trap quadrupole.
      glycoproteomics has attracted particularly great attention (
      • Kondo A.
      • Li W.
      • Nakagawa T.
      • Nakano M.
      • Koyama N.
      • Wang X.
      • Gu J.
      • Miyoshi E.
      • Taniguchi N.
      From glycomics to functional glycomics of sugar chains: identification of target proteins with functional changes using gene targeting mice and knock down cells of FUT8 as examples.
      ,
      • Ma B.
      • Simala-Grant J.L.
      • Taylor D.E.
      Fucosylation in prokaryotes and eukaryotes.
      ). Previous reports show that CF glycoproteins are involved in many important physiological processes, such as transforming growth factor-β1 (
      • Wang X.
      • Inoue S.
      • Gu J.
      • Miyoshi E.
      • Noda K.
      • Li W.
      • Mizuno-Horikawa Y.
      • Nakano M.
      • Asahi M.
      • Takahashi M.
      • Uozumi N.
      • Ihara S.
      • Lee S.H.
      • Ikeda Y.
      • Yamaguchi Y.
      • Aze Y.
      • Tomiyama Y.
      • Fujii J.
      • Suzuki K.
      • Kondo A.
      • Shapiro S.D.
      • Lopez-Otin C.
      • Kuwaki T.
      • Okabe M.
      • Honke K.
      • Taniguchi N.
      Dysregulation of TGF-β1 receptor activation leads to abnormal lung development and emphysema-like phenotype in core fucose deficient mice.
      ) and epidermal growth factor signaling pathways (
      • Wang X.
      • Gu J.
      • Ihara H.
      • Miyoshi E.
      • Honke K.
      • Taniguchi N.
      Core fucosylation regulates epidermal growth factor receptor-mediated intracellular signaling.
      ). They also play key roles in many pathological processes, such as hepatocellular carcinoma (HCC) (
      • Block T.M.
      • Comunale M.A.
      • Lowman M.
      • Steel L.F.
      • Romano P.R.
      • Fimmel C.
      • Tennant B.C.
      • London W.T.
      • Evans A.A.
      • Blumberg B.S.
      • Dwek R.A.
      • Mattu T.S.
      • Mehta A.S.
      Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans.
      ,
      • Comunale M.A.
      • Lowman M.
      • Long R.E.
      • Krakover J.
      • Philip R.
      • Seeholzer S.
      • Evans A.A.
      • Hann H.W.
      • Block T.M.
      • Mehta A.S.
      Proteomic analysis of serum associated fucosylated glycoproteins in the development of primary hepatocellular carcinoma.
      ), pancreatic cancer (
      • Okuyama N.
      • Ide Y.
      • Nakano M.
      • Nakagawa T.
      • Yamanaka K.
      • Moriwaki K.
      • Murata K.
      • Ohigashi H.
      • Yokoyama S.
      • Eguchi H.
      • Ishikawa O.
      • Ito T.
      • Kato M.
      • Kasahara A.
      • Kawano S.
      • Gu J.
      • Taniguchi N.
      • Miyoshi E.
      Fucosylated haptoglobin is a novel marker for pancreatic cancer: a detailed analysis of the oligosaccharide structure and a possible mechanism for fucosylation.
      ,
      • Barrabés S.
      • Pagès-Pons L.
      • Radcliffe C.M.
      • Tabarés G.
      • Fort E.
      • Royle L.
      • Harvey D.J.
      • Moenner M.
      • Dwek R.A.
      • Rudd P.M.
      • De Llorens R.
      • Peracaula R.
      Glycosylation of serum ribonuclease 1 indicates a major endothelial origin and reveals an increase in core fucosylation in pancreatic cancer.
      ), lung cancer (
      • Wang X.
      • Inoue S.
      • Gu J.
      • Miyoshi E.
      • Noda K.
      • Li W.
      • Mizuno-Horikawa Y.
      • Nakano M.
      • Asahi M.
      • Takahashi M.
      • Uozumi N.
      • Ihara S.
      • Lee S.H.
      • Ikeda Y.
      • Yamaguchi Y.
      • Aze Y.
      • Tomiyama Y.
      • Fujii J.
      • Suzuki K.
      • Kondo A.
      • Shapiro S.D.
      • Lopez-Otin C.
      • Kuwaki T.
      • Okabe M.
      • Honke K.
      • Taniguchi N.
      Dysregulation of TGF-β1 receptor activation leads to abnormal lung development and emphysema-like phenotype in core fucose deficient mice.
      ,
      • Geng F.
      • Shi B.Z.
      • Yuan Y.F.
      • Wu X.Z.
      The expression of core fucosylated E-cadherin in cancer cells and lung cancer patients: prognostic implications.
      ), ovarian cancer (
      • Saldova R.
      • Royle L.
      • Radcliffe C.M.
      • Abd Hamid U.M.
      • Evans R.
      • Arnold J.N.
      • Banks R.E.
      • Hutson R.
      • Harvey D.J.
      • Antrobus R.
      • Petrescu S.M.
      • Dwek R.A.
      • Rudd P.M.
      Ovarian cancer is associated with changes in glycosylation in both acute-phase proteins and IgG.
      ), and prostate cancer (
      • Tabarés G.
      • Radcliffe C.M.
      • Barrabés S.
      • Ramírez M.
      • Aleixandre R.N.
      • Hoesel W.
      • Dwek R.A.
      • Rudd P.M.
      • Peracaula R.
      • de Llorens R.
      Different glycan structures in prostate-specific antigen from prostate cancer sera in relation to seminal plasma PSA.
      ). Moreover the CF patterns of several glycoproteins have been reported to serve as more sensitive and specific biomarkers than their total respective protein levels (
      • Block T.M.
      • Comunale M.A.
      • Lowman M.
      • Steel L.F.
      • Romano P.R.
      • Fimmel C.
      • Tennant B.C.
      • London W.T.
      • Evans A.A.
      • Blumberg B.S.
      • Dwek R.A.
      • Mattu T.S.
      • Mehta A.S.
      Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans.
      ,
      • Comunale M.A.
      • Lowman M.
      • Long R.E.
      • Krakover J.
      • Philip R.
      • Seeholzer S.
      • Evans A.A.
      • Hann H.W.
      • Block T.M.
      • Mehta A.S.
      Proteomic analysis of serum associated fucosylated glycoproteins in the development of primary hepatocellular carcinoma.
      ,
      • Drake R.R.
      • Schwegler E.E.
      • Malik G.
      • Diaz J.
      • Block T.
      • Mehta A.
      • Semmes O.J.
      Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers.
      ,
      • Wright L.M.
      • Kreikemeier J.T.
      • Fimmel C.J.
      A concise review of serum markers for hepatocellular cancer.
      ). The combination of a biomarker panel of CF glycoproteins is expected to serve as a more reliable diagnostic standard (
      • Saldova R.
      • Royle L.
      • Radcliffe C.M.
      • Abd Hamid U.M.
      • Evans R.
      • Arnold J.N.
      • Banks R.E.
      • Hutson R.
      • Harvey D.J.
      • Antrobus R.
      • Petrescu S.M.
      • Dwek R.A.
      • Rudd P.M.
      Ovarian cancer is associated with changes in glycosylation in both acute-phase proteins and IgG.
      ).
      Glycoproteomics research has been conducted for several years and has led to the generation of many effective evaluation methods. Most of these methods use lectin or the chemical reagent hydrazide to enrich glycopeptides. The oligosaccharide chains are then completely released by treatment of the glycopeptides with peptide-N-glycosidase F. Finally the deglycosylated peptides and the deglycosylation sites are identified by tandem mass spectrometric analysis (
      • Zhang H.
      • Li X.J.
      • Martin D.B.
      • Aebersold R.
      Identification and quantification of N-linked glycoproteins using hydrazide chemistry stable isotope labeling and mass spectrometry.
      ,
      • Kaji H.
      • Saito H.
      • Yamauchi Y.
      • Shinkawa T.
      • Taoka M.
      • Hirabayashi J.
      • Kasai K.
      • Takahashi N.
      • Isobe T.
      Lectin affinity capture, isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins.
      ). Although impressive results have been attained, this commonly used strategy is not an ideal choice for CF glycoproteins research. First, the enrichment specificity of lectin is not satisfactory (
      • Zhao J.
      • Simeone D.M.
      • Heidt D.
      • Anderson M.A.
      • Lubman D.M.
      Comparative serum glycoproteomics using lectin selected sialic acid glycoproteins with mass spectrometric analysis: application to pancreatic cancer serum.
      ) as hydrazide chemical reactions irreversibly destroy glycan structures, particularly fucose tags. Second, the deglycosylation site is determined by the 0.9840-Da mass shift caused by the asparagine to aspartic acid transfer; its confidence can be compromised by deamination of the Asn. Besides that, the CF site can no longer be distinguished from other glycosylation sites in the same glycoprotein. Thus, the ideal way to precisely identify CF glycoproteins on a large scale is to provide direct evidence for the existence of CF modification. Traditional approaches, such as lectin blots, are not sufficiently powerful to meet this requirement. Instead recent advancements in high end MS-based techniques have ignited the hope to reach this challenging goal (
      • Hägglund P.
      • Bunkenborg J.
      • Elortza F.
      • Jensen O.N.
      • Roepstorff P.
      A new strategy for identification of N-glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation.
      ,
      • Hägglund P.
      • Matthiesen R.
      • Elortza F.
      • Højrup P.
      • Roepstorff P.
      • Jensen O.N.
      • Bunkenborg J.
      An enzymatic deglycosylation scheme enabling identification of core fucosylated N-glycans and O-glycosylation site mapping of human plasma proteins.
      ).
      Our group has developed an innovative and systematic strategy for the precise and large scale identification of CF glycoproteins. Several steps were taken leading up to the development of our strategy. 1) We established a novel enrichment step for CF glycopeptides, combining the use of lectin for CF glycoprotein enrichment with ultrafiltration for further enrichment of glycopeptide. Glycopeptide enrichment by ultrafiltration based on molecular weight cutoff technology has the added merit of integrating enrichment, desalting, and concentration into a one-step operation. 2) We established a neutral loss-dependent MS3 scan method that specifically captures partially deglycosylated CF glycopeptides (with fucosyl-N-acetylglucosamines residue retained). In MS3, the intensity distribution of the fragment peaks is much more homogeneous, and there are fewer theoretical fragment ions and interfering peaks than in MS2. 3) We established a novel database-independent candidate spectrum-filtering method for selecting partially deglycosylated CF glycopeptides and a spectrum optimization method. By introducing several strict and appropriate criteria into a scoring system, high quality candidate spectra can be selected before searching the database, which not only increases the database search efficiency but also improves the identification credibility. Furthermore by statistically analyzing candidate spectra, some important glycan-related fragmentation patterns were revealed. Based on these observations, many kinds of interfering peaks due to glycan fragmentation that are always very intensive and would decrease the accuracy of peptide scoring can be localized and removed from the spectra. This treatment can effectively increase the number of identifications through database searching or de novo analysis.
      The efficacy of this strategy was testified by implementing it on both healthy and HCC plasma. Respectively, 105 and 106 CF sites were identified from 72 and 79 glycoproteins, including 19 annotated potential glycosylation sites and 25 novel ones. This study holds promise for the large scale determination of core fucosylated biomarker panels from clinical samples, either body fluids or tissue biopsies.

      Acknowledgments

      We thank Ji-Yang Zhang, You Li, Chao Liu, Wen-Ping Wang, Li-yun Xiu, Xue-qun Zhang, and Lin-Juan Tian for contributions. We also thank the Digestive Department of the First Affiliated Hospital, College of Medicine, Zhejiang University for the offering of HCC plasma.

      REFERENCES

        • Parodi A.J.
        Protein glucosylation and its role in protein folding.
        Annu. Rev. Biochem. 2000; 69: 69-93
        • Walsh G.
        • Jefferis R.
        Post-translational modifications in the context of therapeutic proteins.
        Nat. Biotechnol. 2006; 24: 1241-1252
        • Dwek R.A.
        • Butters T.D.
        • Platt F.M.
        • Zitzmann N.
        Targeting glycosylation as a therapeutic approach.
        Nat. Rev. Drug Discov. 2002; 1: 65-75
        • Kondo A.
        • Li W.
        • Nakagawa T.
        • Nakano M.
        • Koyama N.
        • Wang X.
        • Gu J.
        • Miyoshi E.
        • Taniguchi N.
        From glycomics to functional glycomics of sugar chains: identification of target proteins with functional changes using gene targeting mice and knock down cells of FUT8 as examples.
        Biochim. Biophys. Acta. 2006; 1764: 1881-1889
        • Ma B.
        • Simala-Grant J.L.
        • Taylor D.E.
        Fucosylation in prokaryotes and eukaryotes.
        Glycobiology. 2006; 16: 158-184
        • Wang X.
        • Inoue S.
        • Gu J.
        • Miyoshi E.
        • Noda K.
        • Li W.
        • Mizuno-Horikawa Y.
        • Nakano M.
        • Asahi M.
        • Takahashi M.
        • Uozumi N.
        • Ihara S.
        • Lee S.H.
        • Ikeda Y.
        • Yamaguchi Y.
        • Aze Y.
        • Tomiyama Y.
        • Fujii J.
        • Suzuki K.
        • Kondo A.
        • Shapiro S.D.
        • Lopez-Otin C.
        • Kuwaki T.
        • Okabe M.
        • Honke K.
        • Taniguchi N.
        Dysregulation of TGF-β1 receptor activation leads to abnormal lung development and emphysema-like phenotype in core fucose deficient mice.
        Proc. Natl. Acad. Sci. U. S. A. 2005; 102: 15791-15796
        • Wang X.
        • Gu J.
        • Ihara H.
        • Miyoshi E.
        • Honke K.
        • Taniguchi N.
        Core fucosylation regulates epidermal growth factor receptor-mediated intracellular signaling.
        J. Biol. Chem. 2006; 281: 2572-2577
        • Block T.M.
        • Comunale M.A.
        • Lowman M.
        • Steel L.F.
        • Romano P.R.
        • Fimmel C.
        • Tennant B.C.
        • London W.T.
        • Evans A.A.
        • Blumberg B.S.
        • Dwek R.A.
        • Mattu T.S.
        • Mehta A.S.
        Use of targeted glycoproteomics to identify serum glycoproteins that correlate with liver cancer in woodchucks and humans.
        Proc. Natl. Acad. Sci. U. S. A. 2005; 102: 779-784
        • Comunale M.A.
        • Lowman M.
        • Long R.E.
        • Krakover J.
        • Philip R.
        • Seeholzer S.
        • Evans A.A.
        • Hann H.W.
        • Block T.M.
        • Mehta A.S.
        Proteomic analysis of serum associated fucosylated glycoproteins in the development of primary hepatocellular carcinoma.
        J. Proteome Res. 2006; 5: 308-315
        • Okuyama N.
        • Ide Y.
        • Nakano M.
        • Nakagawa T.
        • Yamanaka K.
        • Moriwaki K.
        • Murata K.
        • Ohigashi H.
        • Yokoyama S.
        • Eguchi H.
        • Ishikawa O.
        • Ito T.
        • Kato M.
        • Kasahara A.
        • Kawano S.
        • Gu J.
        • Taniguchi N.
        • Miyoshi E.
        Fucosylated haptoglobin is a novel marker for pancreatic cancer: a detailed analysis of the oligosaccharide structure and a possible mechanism for fucosylation.
        Int. J. Cancer. 2006; 118: 2803-2808
        • Barrabés S.
        • Pagès-Pons L.
        • Radcliffe C.M.
        • Tabarés G.
        • Fort E.
        • Royle L.
        • Harvey D.J.
        • Moenner M.
        • Dwek R.A.
        • Rudd P.M.
        • De Llorens R.
        • Peracaula R.
        Glycosylation of serum ribonuclease 1 indicates a major endothelial origin and reveals an increase in core fucosylation in pancreatic cancer.
        Glycobiology. 2007; 17: 388-400
        • Geng F.
        • Shi B.Z.
        • Yuan Y.F.
        • Wu X.Z.
        The expression of core fucosylated E-cadherin in cancer cells and lung cancer patients: prognostic implications.
        Cell Res. 2004; 14: 423-433
        • Saldova R.
        • Royle L.
        • Radcliffe C.M.
        • Abd Hamid U.M.
        • Evans R.
        • Arnold J.N.
        • Banks R.E.
        • Hutson R.
        • Harvey D.J.
        • Antrobus R.
        • Petrescu S.M.
        • Dwek R.A.
        • Rudd P.M.
        Ovarian cancer is associated with changes in glycosylation in both acute-phase proteins and IgG.
        Glycobiology. 2007; 17: 1344-1356
        • Tabarés G.
        • Radcliffe C.M.
        • Barrabés S.
        • Ramírez M.
        • Aleixandre R.N.
        • Hoesel W.
        • Dwek R.A.
        • Rudd P.M.
        • Peracaula R.
        • de Llorens R.
        Different glycan structures in prostate-specific antigen from prostate cancer sera in relation to seminal plasma PSA.
        Glycobiology. 2006; 16: 132-145
        • Drake R.R.
        • Schwegler E.E.
        • Malik G.
        • Diaz J.
        • Block T.
        • Mehta A.
        • Semmes O.J.
        Lectin capture strategies combined with mass spectrometry for the discovery of serum glycoprotein biomarkers.
        Mol. Cell. Proteomics. 2006; 5: 1957-1967
        • Wright L.M.
        • Kreikemeier J.T.
        • Fimmel C.J.
        A concise review of serum markers for hepatocellular cancer.
        Cancer Detect. Prev. 2007; 31: 35-44
        • Zhang H.
        • Li X.J.
        • Martin D.B.
        • Aebersold R.
        Identification and quantification of N-linked glycoproteins using hydrazide chemistry stable isotope labeling and mass spectrometry.
        Nat. Biotechnol. 2003; 21: 660-666
        • Kaji H.
        • Saito H.
        • Yamauchi Y.
        • Shinkawa T.
        • Taoka M.
        • Hirabayashi J.
        • Kasai K.
        • Takahashi N.
        • Isobe T.
        Lectin affinity capture, isotope-coded tagging and mass spectrometry to identify N-linked glycoproteins.
        Nat. Biotechnol. 2003; 21: 667-672
        • Zhao J.
        • Simeone D.M.
        • Heidt D.
        • Anderson M.A.
        • Lubman D.M.
        Comparative serum glycoproteomics using lectin selected sialic acid glycoproteins with mass spectrometric analysis: application to pancreatic cancer serum.
        J. Proteome Res. 2006; 5: 1792-1802
        • Hägglund P.
        • Bunkenborg J.
        • Elortza F.
        • Jensen O.N.
        • Roepstorff P.
        A new strategy for identification of N-glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation.
        J. Proteome Res. 2004; 3: 556-566
        • Hägglund P.
        • Matthiesen R.
        • Elortza F.
        • Højrup P.
        • Roepstorff P.
        • Jensen O.N.
        • Bunkenborg J.
        An enzymatic deglycosylation scheme enabling identification of core fucosylated N-glycans and O-glycosylation site mapping of human plasma proteins.
        J. Proteome Res. 2007; 6: 3021-3031
        • Peng J.
        • Elias J.E.
        • Thoreen C.C.
        • Licklider L.J.
        • Gygi S.P.
        Evaluation of multidimensional chromatography coupled with tandem mass spectrometry (LC/LC-MS/MS) for large-scale protein analysis: the yeast proteome.
        J. Proteome Res. 2003; 2: 43-50
        • Alvarez-Manilla G.
        • Atwood J.
        • II I
        • Guo Y.
        • Warren N.L.
        • Orlando R.
        • Pierce M.
        Tools for glycoproteomic analysis: size exclusion chromatography facilitates identification of tryptic glycopeptides with N-linked glycosylation sites.
        J. Proteome Res. 2006; 5: 701-708
        • Tarentino A.L.
        • Quinones G.
        • Changchien L.M.
        • Plummer T.H.
        Multiple endoglycosidase F activities expressed by Flavobacterium meningosepticum endoglycosidases F2 and F3.
        J. Biol. Chem. 1993; 268: 9702-9708
        • Wuhrer M.
        • Catalina M.I.
        • Deelder A.M.
        • Hokke C.H.
        Glycoproteomics based on tandem mass spectrometry of glycopeptides.
        J. Chromatogr. B Anal. Technol. Biomed. Life Sci. 2007; 849: 115-128
        • Fu Y.
        • Yang Q.
        • Sun R.
        • Li D.
        • Zeng R.
        • Ling C.X.
        • Gao W.
        Exploiting the kernel trick to correlate fragment ions for peptide identification via tandem mass spectrometry.
        Bioinformatics. 2004; 20: 1948-1954
        • Li D.
        • Fu Y.
        • Sun R.
        • Ling C.X.
        • Wei Y.
        • Zhou H.
        • Zeng R.
        • Yang Q.
        • He S.
        • Gao W.
        pFind: a novel database-searching software system for automated peptide and protein identification via tandem mass spectrometry.
        Bioinformatics. 2005; 21: 3049-3050
        • Wang L.H.
        • Li D.Q.
        • Fu Y.
        • Wang H.P.
        • Zhang J.F.
        • Yuan Z.F.
        • Sun R.X.
        • Zeng R.
        • He S.M.
        • Gao W.
        pFind 2.0: a software package for peptide and protein identification via tandem mass spectrometry.
        Rapid Commun. Mass Spectrom. 2007; 21: 2985-2991
        • Miyoshi E.
        • Nakano M.
        Fucosylated haptoglobin is a novel marker for pancreatic cancer: detailed analyses of oligosaccharide structures.
        Proteomics. 2008; 8: 3257-3262