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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.
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      • Ikeda Y.
      • Yamaguchi Y.
      • Aze Y.
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      • 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.

      EXPERIMENTAL PROCEDURES

      Materials—

      The apotransferrin, fetuin, ribonuclease B, endoglycosidase F3, formic acid, TFA, α-cyano-4-hydroxycinnamic acid, and Lens culinaris lectin (agarose conjugate, saline suspension) were purchased from Sigma, methyl-α-d-mannopyranoside was purchased from Fluka (St. Louis, MO), and sodium-3-[(2-methyl-2-undecyl-1,3-dioxolan-4-yl)methoxy]-1-propanesulfonate (RapiGest™ SF) was purchased from Waters. Sequencing grade porcine trypsin was purchased from Promega (Madison, WI); IgG was purified by use of a HiTrap Protein G HP column from GE Healthcare. The PD-10 desalting column was also from GE Healthcare. Deionized water was produced by a Milli-Q A10 system from Millipore (Bedford, MA). HPLC-grade quality ACN was purchased from J. T. Baker Inc. Iodoacetamide and DTT were obtained from ACROS. The Handee mini spin column kit was purchased from Pierce. The C18 ZipTip and Microcon YM-3 were purchased from Millipore. Recombinant human erythropoietin (rhEPO) was a gift from the National Institute for the Control of Pharmaceutical and Biological Products. Healthy human plasma (0.8 ml for each experiment) was obtained from a healthy donor. Samples of hepatocellular carcinoma plasma were mixed from eight patients with 0.1 ml from each person.

      IgG Extraction—

      Plasma was supplemented with IgG binding buffer (20 mm sodium phosphate, pH 7.0), and then IgG was depleted by trapping on a column of HiTrap Protein G. The unbound samples were desalted by a PD-10 column.

      Lectin Affinity—

      Samples were supplemented with 1.6 ml of lectin binding buffer (20 mm Tris-buffered saline, 0.3 m NaCl, 1 mm MnCl2, 1 mm CaCl2, pH 7.4). The samples were incubated for 16 h at 4 °C with L. culinaris lectin in a spin column (about 300 μl of lectin-agarose and 400 μl of sample in each column). After unbound proteins were removed by washes with binding buffer, the CF glycoproteins were eluted with elution buffer (binding buffer supplemented with 200 mm α-d-methylmannoside), then desalted (by PD-10 column), and lyophilized.

      Reduction, Alkylation, and Trypsin Digestion—

      Samples were dissolved in 200 μl of solution that contained 8 m urea and 5 mm DTT and were reduced at 37 °C for 4 h. Then iodoacetamide was added to the solution (final concentration, 15 mm), which was then further incubated for 1 h in darkness at room temperature. Afterward 50 mm NH4HCO3 was added to reduce the concentration of urea below 1 m, and sequencing grade trypsin was added at a ratio of enzyme to protein of 1:50. The mixture was then vortexed and incubated at 37 °C overnight. 0.1%RapiGest SF was used instead of urea for protein denaturation in the repeat experiment of healthy and HCC plasma. TFA was added to the digested protein samples (final TFA concentration was 0.5%, pH < 2), and the samples were incubated at 37 °C for 45 min. Finally the acid-treated samples were centrifuged at 13,000 rpm for 10 min, and the supernatants were collected.

      Enrichment, Desalting, and Concentration of Glycopeptides—

      Tryptic digests were pipetted into Microcon YM-3 centrifugal filter devices. The absolute amount of glycoprotein in the digests was between 200 and 300 μg for each filter device, and the sample volume was diluted to 500 μl for each filter device. The samples were centrifuged at 8000 × g to reduce the sample volume from 500 μl to about 20 μl; this required about 3 h. Then 450 μl of deionized water were added to the reservoir and centrifuged at 8000 × g for 3 h; this was repeated twice. After that, the retentate fraction was transferred to a vial, and the reservoir was thrice washed with 20%ACN. All of the retentate fractions and wash solutions were pooled and lyophilized.

      Endoglycosidase F3 Digestion—

      Glycopeptides were resuspended in 100 μl of sodium acetate solution (50 mm, pH 4.5) and then incubated with endoglycosidase F3 overnight at 37 °C. Ammonium acetate (50 mm, pH 4.5) was used instead of the sodium acetate in the repeat experiments of healthy and HCC plasma.

      Strong Cation Exchange (SCX) Peptide Fractionation—

      10%enriched samples were directly analyzed with RP HPLC-MS two times. Other enriched CF glycopeptides were reconstituted with 300 μl of 5 mm ammonium chloride, pH 3.0, 25%acetonitrile and fractionated by SCX chromatography on a BioBasic SCX 250 × 4.6-mm column (Thermo Fisher). The particle size of the column was 5 μm and pore size was 300 Å. The separations were performed at a flow rate of 0.5 ml/min using the Elite HPLC system, and mobile phases consisted of 5 mm ammonium chloride, pH 3.0, 25%acetonitrile (A) and 500 mm ammonium chloride, pH 3.0, 25%acetonitrile (B). After loading 300 μl of sample onto the column, the gradient was maintained at 100%A for 10 min. Peptides were then separated using a gradient of 0–15%B over 1 min followed by a gradient of 15–50%B over 49 min. Then the gradient was changed to 50–100%over 5 min. The gradient was then held at 100%B for 5 min. A total of 15 fractions were collected, and each fraction was dried under vacuum.

      RP HPLC-MSn Analysis—

      RP HPLC-MSn experiments were performed on an LTQ-FT mass spectrometer (Thermo Fisher) equipped with a nanospray source and Agilent 1100 high performance liquid chromatography system (Agilent Technologies). Peptide mixes were separated on a fused silica microcapillary column with an internal diameter of 75 μm and an in-house prepared needle tip with an internal diameter of ∼15 μm. Columns were packed to a length of 10 cm with a C18 reversed phase resin (GEAgel C18 SP-300-ODS-AP; particle size, 5 μm; pore size, 300 Å; Jinouya, Beijing, China). Separation was achieved using a mobile phase from 1.95%ACN, 97.95%H2O, 0.1%FA (phase A) and 79.95%ACN, 19.95%H2O, 0.1%FA (phase B), and the linear gradient was from 5 to 50%buffer B for 80 min at a flow rate of 300 nl/min. The LTQ-FT mass spectrometer was operated in the data-dependent mode. A full-scan survey MS experiment (m/z range from 400 to 2000; automatic gain control target, 5e5 ions; resolution at 400 m/z, 100,000; maximum ion accumulation time, 750 ms) was acquired by the FT-ICR mass spectrometer, and the five most abundant ions detected in the full scan were analyzed by MS2 scan events (automatic gain control target, 1e4 ions; maximum ion accumulation time, 200 ms). The scan model of MS2 was set as the profile. An MS3 spectrum was automatically collected when one of the three most intense peaks from the MS2 spectrum corresponded to a neutral loss event of 73.0290 m/z, 48.6860 m/z, or 36.5145 m/z (charges of parent ions were not collected). The normalized collision energy was 35.

      On-line Two-dimensional LC-MSn

      The autosampler was used to inject samples onto the SCX column (BioX-SCX, 5 cm) after which they were eluted onto a trap column using a stepwise gradient of 0, 20, 30, 40, 50, 60, 70, 80, 90, and 100%SCX-B. Peptides on the trap column were desalted and then eluted onto the RP column and into the mass spectrometer (the same method as RP HPLC-MSn analysis, but the linear gradient was from 5 to 50%buffer B for 120 min). Mobile phase buffer for SCX-A was 10 mm citric ammonia buffer, pH 3.0, and mobile phase buffer for SCX-B was 50 mm citric ammonia buffer, pH 8.5. Experiments of HCC samples were analyzed by this system (Eksigent NanoLC-2D) and repeated one time.

      Database Search and Analysis—

      Dta files were generated by Bioworks 3.2 with default parameters and then treated by spectrum-filtering and spectrum optimization tools in pFind 2.1 Studio. The candidate spectra of MS3 were searched against UniProt Knowledgebase Release 12.6 (human, 76,137 entries; UniProt Knowledgebase Release 12.6 consists of UniProtKB/Swiss-Prot Release 54.6 of December 4, 2007 and UniProtKB/TrEMBL Release 37.6 of December 4, 2007) using the pFind 2.1 search engine. The database was modified by substituting the letter N in glycosylation sequence NX(S/T/C) with J, which was defined to have the same mass as Asn (
      • 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.
      ), and then the target and reversed decoy database were combined for the search. Carbamidomethylation was considered for all Cys residues. Variable modifications contained oxidation of Met residues, carbamidomethylation and carbamylation (carbamylation was only considered as a variable modification in experiments that used urea as the protein denature reagent) of peptide N-terminal and Lys residues, and a 203.0794-Da variable addition to J residues. At most, two missed tryptic cleavage sites were allowed. Tolerance of parent ions was ±20 ppm, and tolerance of fragment ions was ±0.5 m/z for the primary search. The final identified results had a 1%false-positive rate (
      • 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.
      ), and the tolerance for parent ions was ±10 ppm.

      MALDI-TOF MS Analysis—

      After desalting with the C18 ZipTip, all of the samples were mixed 1:9 with 5 mg/ml α-cyano-4-hydroxycinnamic acid in 50%acetonitrile supplemented with 0.1%TFA, and 0.5 μl of sample was applied to the MALDI target plate. The mass spectra were obtained using a 4800 Proteomics Analyzer MALDI-TOF/TOF instrument (Applied Biosystems). Prior to analysis, the mass spectrometer was externally calibrated with seven peptides obtained from tryptic digest of myoglobin. The m/z range of the MS scan was from 600 to 4000. Mass spectra were acquired in the positive reflector mode.

      RESULTS AND DISCUSSION

      Core-fucosylated Glycopeptide Enrichment from Plasma—

      Robust and convenient operation procedures were established to obtain partially deglycosylated CF glycopeptides. After IgG depletion, plasma proteins were mixed with L. culinaris lectin to enrich for the CF glycoproteins. Binding proteins were digested by trypsin, and the resulting glycopeptides were enriched through a molecular weight cutoff technique. N-Linked glycopeptides usually have larger molecular weights than non-glycopeptides (
      • 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.
      ,
      • 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.
      ); therefore, an ultrafiltration membrane with a molecular mass limit of 3000 Da was utilized to enrich for glycopeptides. This step integrates enrichment, desalting, and concentration into one operation. Glycopeptides were then treated with endoglycosidase F3, which specifically cleaves the glycosidic bond between the two proximal N-acetylglucosamines (GlcNAc) and leaves the fucosyl-GlcNAc residues on the peptides. Endoglycosidase F3 was chosen here for treating CF glycoprotein because a large number of the glycans of plasma glycoproteins have biantennary structure, which is a more efficient substrate for endoglycosidase F3 (
      • Tarentino A.L.
      • Quinones G.
      • Changchien L.M.
      • Plummer T.H.
      Multiple endoglycosidase F activities expressed by Flavobacterium meningosepticum endoglycosidases F2 and F3.
      ). For other structures, such as tetraantennary and other bulky glycans, the reactivity of endoglycosidase F3 is poor, so there may need to be additional evaluation to choose the proper glycosidase for other kinds of samples like tissue biopsies.
      A tryptic peptide mixture from four standard glycoproteins, apotransferrin, fetuin, rhEPO, and ribonuclease B, was used to illustrate the efficiency of the ultrafiltration method (Fig. 1). Half of this tryptic peptide mixture was directly treated with peptide-N-glycosidase F (untreated sample); the other half was separated by ultrafiltration into a retentate fraction (high molecular weight) and a filtrate fraction (low molecular weight), and then both fractions were treated with peptide-N-glycosidase F. The deglycosylated glycopeptides were detected by the +0.984-Da mass drift on Asn to Asp.
      Figure thumbnail gr1
      Fig. 1The efficiency of the ultrafiltration method for enriching glycopeptide. MS spectra from ultrafiltration experiments are shown with the retentate fraction (top), filtrate fraction (middle), and untreated fraction (bottom). Glycopeptide C#GLVPVLAENYN*K (A) from apotransferrin only appeared in the retentate fraction. LC#PDC#PLLAPLN*DSR (B), VVHAVEVALATFNAESN*GSYLQLVEISR (F), and RPTGEVYDIEIDTLETTC#HVLDPTPLAN*C#SVR (G) were from fetuin; GQALLVN*SSQPWEPLQLHVDK (C) and EAEN*ITTGC#AEHC#SLNEN*ITVPDTK (E) were from rhEPO; QQQHLFGSN*VTDC#SGNFC#LFR (D) was from apotransferrin. *, annotated glycosite; #, carbamidomethylation.
      In total, eight N-glycopeptides were reported for four glycoproteins. Six of these glycopeptides were directly found in untreated samples by MALDI-TOF MS. However, in addition to these six glycopeptides, one more glycopeptide (CGLVPVLAENYN*K from apotransferrin; N* represents the annotated glycosite) was detected in the retentate fraction. The relative intensities of all deglycosylated glycopeptides were heightened compared with the untreated sample. In the untreated sample, the failure to detect CGLVPVLAENYN*K is ascribed to suppression by a non-glycopeptide with similar mass. In the filtrate fraction, the relative intensity of deglycosylated glycopeptides decreased to a very low level, illustrating that few glycopeptides were lost. One reported glycopeptide was not detected in the three fractions (N*LTK from ribonuclease B). One possible reason is that its sequence is too short to detect.

      Development of Neutral Loss-dependent MS3 Scan Method—

      A neutral loss-dependent MS3 method specifically designed for partially deglycosylated CF glycopeptides was developed. During CID, the glycosidic bond that links the two remaining sugars is prone to breakage compared with the other bonds (
      • Wuhrer M.
      • Catalina M.I.
      • Deelder A.M.
      • Hokke C.H.
      Glycoproteomics based on tandem mass spectrometry of glycopeptides.
      ). In our experiments on three partially deglycosylated CF glycopeptides, the highest peaks in the MS2 spectra all resulted from subtraction of 146 Da (mass of the fucose residue) from the parent ions that had the same charge state as the corresponding parent ions (Fig. 2). Based on this trait, a neutral loss-dependent MS3 scan method was utilized as an automatic event in the LTQ-FT mass spectrometer: MS3 spectra were automatically collected when one of the three most intense peaks from the MS2 spectrum corresponded to a neutral loss event of the fucose residue mass. MS3 spectra were generated from fragmentation of the GlcNAc-attached peptides. Compared with the MS2 spectra, which were generated from fragmentation of the fucosyl-GlcNAc-attached peptides, the MS3 spectra have three remarkable advantages. 1) They have better spectrum quality: the peak intensity distribution of the MS3 spectrum is much more homogeneous. This is beneficial because there are more fragment ion signals with good signal to noise ratios. 2) They have simpler spectrum information: the number of theoretical fragment ions in the MS3 spectrum is fewer. This makes the algorithm for peak matching simpler and easier. 3) They have clearer spectrum signals: two parent ion selections (from MS to MS2 and from MS2 to MS3) reduce the probability of collecting interference signals adjacent to parent ions in the full scan (Fig. 3). In addition, direct assignment of CF glycosites can be deduced from the b-type and y-type ions series attached with a GlcNAc residue, providing much higher confidence levels of glycosite assignment compared with the 0.984-Da mass shift method. It should be noted that the retained intact GlcNAc residues were found to be lost from the b and y ions (Fig. 3); therefore, these kinds of special product ions must be considered in addition to GlcNAc attached b and y ions when searching the database. This observation was taken into account for peptide scoring in the pFind 2.1 search engine (
      • 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.
      ,
      • 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.
      ,
      • 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.
      ). Compared with other popular software tools, pFind discovered more results (supplemental Data 1).
      Figure thumbnail gr2
      Fig. 2The neutral loss peaks in MS2 spectra of partially deglycosylated CF glycopeptides. The intensities of the highest peaks are several times higher than that of the second most intense peak in all of these MS2 spectra in the ion trap, resulting from loss of the fucose residue in CID. a, b, and c are MS2 spectra from the same partially deglycosylated CF glycopeptide, EEQYJSTYR (from human IgG). Intensities of the base peaks were 1.86e5, 2.10e4, and 2.53e3, respectively. d and e are MS2 spectra of simplified CF glycopeptides GQALLVJSSQPWEPLQLHVDK (intensity, 3.21e4; from rhEPO) and QQQHLFGSJVTDC#SGNFC#LFR (intensity, 7.59e4; from apotransferrin). The MS2 spectra in FT-ICR were collected to check the identities of the strongest peaks: f for IgG, g for d, and h for e. J, CF site; #, carbamidomethylation.
      Figure thumbnail gr3
      Fig. 3MS2 and MS3 spectra of fucosyl-GlcNAc-attached peptides. The peak intensity distribution of the MS3 spectrum is much more homogeneous than that of MS2, so better peptide sequence information can be obtained; the direct assignment of CF glycosites can be deduced from the b-type and y-type ion series attached with a GlcNAc residue in MS3. a and b are MS2 and MS3 spectra of GLC#VJASAVSR from insulin-like growth factor-binding protein 3, respectively. The peaks of b-type and y-type ions with or without GlcNAc residues appear synchronously and frequently, such as y7+ and b6+. c and d are MS2 and MS3 spectra of a candidate that was analyzed de novo, respectively. The resulting de novo sequence GVEIJR (because the m/z of ion b1 is too low to detect, the sequence of the first two residues can also be “VG,” and “I” can also be “L” because of their same mass) was not found in the peptide database of tryptic digests (J located in the sequon NX(S/T/C) where X is any amino acid except proline). D 1, C8H14NO5 (GlcNAc); D 2, C8H12NO4; D 3, C8H10NO3; D 4, C6H10NO3; D 5, C7H8NO2. The y 7G+ identifies the GlcNAc residue with the same sequence as y7+. J, CF site; #, carbamidomethylation.

      Development of Candidate Spectrum-filtering and Spectrum Optimization Methods—

      Due to the complexity of real samples and the massive spectra generated in these large scale glycopeptide analyses, more professional and specialized processing methods are absolutely necessary. Here a database-independent method for discovery of spectra of partially deglycosylated CF glycopeptides was developed. Two kinds of ions in MS2 were scrutinized and used to judge whether the precursor was a CF glycopeptide: ions of a peptide attached to a GlcNAc residue (symbol ion 2, logogram: S2, attained from the breakage of the glycosidic bond between the remaining two monosaccharide residues) and ions of a pure peptide (symbol ion 3, logogram: S3, obtained from fragmentation between the GlcNAc and the Asn residue of the peptide). By introduction of the highly accurate parent ion mass from a full scan (recorded in FT-ICR), we can calculate the m/z of symbol ions. Next according to the quality of the symbol ions in MS2, several criteria were established to sort out the spectra. First of all the strongest peak in MS2 must be S2 (±0.5 m/z errors) with the same charge state as the parent ion. Additional information of symbol ions is then used to further evaluate their confidence into five ranks (Fig. 4). The spectra in the top two ranks are retained, and their relevant MS3 spectra are regarded as candidates. This strict spectrum-filtering method greatly improved the credibility of identification. Furthermore by statistically analyzing candidate spectra, many important neutral loss signals, which result from GlcNAc-related fragmentation, were revealed. These fragmentation patterns are always accompanied by very strong signals and had not been reported previously (Fig. 5). In addition, diagnostic ions of GlcNAc residues were observed in MS3 spectra (Fig. 3). Based upon these observations, these interfering peaks from GlcNAc fragmentation that are very intense and would decrease the accuracy of peptide scoring were localized and subtracted from the spectra. This novel optimization method can effectively increase the identification efficacy. Both the spectrum-filtering and the spectrum-optimizing processes have been performed automatically in pFind Studio. In addition, the unidentified candidates can be analyzed de novo. This can supply novel information, which is not in the database (Fig. 3).
      Figure thumbnail gr4
      Fig. 4The process of the strategy for CF glycoprotein identification. CF glycoprotein identification was achieved through enrichment of CF glycopeptides, partial deglycosylation of CF glycopeptides, HPLC neutral loss-dependent MS3, candidate spectrum filtering, spectrum optimization, and database searching. F 1 identifies the intensity ratio of the second strongest peaks (logogram: second strong peak (SSP), which does not contain different states for S2, such as a different charge state or states of H2O and NH3 loss) to S2; F 2 identifies the difference between the calculated and experimental m/z of S2; F 3 identifies the intensity ratio of the second strongest peak (logogram: SSP′) to S3 within the range of the S3 monoisotopic peak ±3 m/z. Information on different charge state ions of S3 is considered, and the better result is recorded. Additionally the absolute intensities of S2 and S3 are required to be higher than 500 and 50, respectively. As shown, different scores correspond to different signal qualities. The confidence of the spectrum is sorted into five ranks by total score. ▴, fucose residue; ▪, GlcNAc residue. 2D, two-dimensional; Endo, endoglycosidase; LCH, L. culinaris lectin.
      Figure thumbnail gr5
      Fig. 5Frequency histogram of intact and partial GlcNAc loss peaks in candidate MS3 spectra of charge 2. The m/z values of S2 were set as 0 m/z. Offsets with high peak frequencies reveal potential masses of neutral losses that frequently occur on peptide-attached GlcNAc residues. The possible loss groups are shown in the table.

      Identified Results and Their Illumination for Further Clinical Research—

      The efficacy of our strategy was first demonstrated by implementation on healthy human plasma (IgG-extracted); 115 different CF glycopeptides (105 CF sites) from 72 glycoproteins were identified. To further demonstrate its feasibility for clinical samples, we applied this strategy to plasma from HCC patients; 108 different CF glycopeptides (106 CF sites) from 79 glycoproteins were identified. Altogether 25 novel glycosylation sites and 19 annotated potential sites were identified from these two experiments (Table I). The scale of our results shows that these innovative methods provide a breakthrough in CF glycoproteomics research and may meet the needs of clinical medicine. Although the comparison between two types of samples was not a designated outcome of this study, it still gave us illuminations in several aspects. First, the CF sites of many glycoproteins whose CF levels have been reported as altered in patients with HCC were confirmed in our research, such as α1-antitrypsin (one site), α2-HS-glycoprotein (one site), α2-macroglobulin (two sites), apolipoprotein D (one site), β2-glycoprotein 1 (one site), ceruloplasmin (four sites), fibrinogen γ chain (one site), haptoglobin (three sites), histidine-rich glycoprotein (one site), Ig α-2 chain C region (one site), Ig γ-1 chain C region (one site), and serotransferrin (one site) (
      • 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.
      ). Direct evidence of a CF site by MS would not only help to enhance the reliability of the CF modification as a biomarker but may also lead to further clinical research at a deeper modification site level instead of the protein level. As shown previously, the CF patterns of some glycoproteins may be used as biomarkers because they are more sensitive and specific than evaluation of the respective total protein levels (
      • 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.
      ). The question of whether the specific CF site would be the more effective “marker” is interesting. This question could not be answered previously because of the limitations of the traditional techniques, but it can be tackled by application of this strategy. Second, a specific marker, CF GP-73, was reported to be more sensitive and specific for HCC diagnosis than α-fetoprotein (
      • 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.
      ). This marker was specifically identified in the HCC samples in our research, whereas hemopexin (two CF sites identified), IgM (two sites), and kininogen (three sites) were identified in both of our two experiments. These glycoproteins have not previously been reported in healthy plasma (
      • 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.
      ). These results remind us that although CF glycoproteomics research has significantly advanced during recent years and impressive results have been obtained in clinical research more extensive research is needed. This further research inevitably depends on the acquisition of massive qualitative and quantitative data on CF glycoproteins and CF sites. Recently fucosylated haptoglobin was reported as a novel marker for pancreatic cancer, and site-specific increases in fucosylation were observed (
      • Miyoshi E.
      • Nakano M.
      Fucosylated haptoglobin is a novel marker for pancreatic cancer: detailed analyses of oligosaccharide structures.
      ). However, the specificity of this marker is still not ideal for diagnosis; evaluation of the CF levels of a combination of glycoproteins would permit more reliable discrimination among different disease stages. In our research, all three tryptic CF glycopeptides of haptoglobin were identified. Moreover our strategy possesses the merit that stable isotope labeling techniques can be embedded for quantitative research. The relative abundance of CF glycoproteins in some diseases, such as pancreatic cancer, could be quantified with the strategy. It should be mentioned that because lectin enrichment strategy was used in the early step the quantitation information obtained would only represent the relative difference in CF glycoprotein abundance, whereas the ratios between glycans with and without core fucose could not be reached as reported in other researches (
      • 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.
      ).
      Table IBold “J” indicates the CF site. Bold “j” indicates the possible CF site. ADAM, a disintegrin and metalloprotease; ADAMTS, a disintegrin and metalloprotease with thrombospondin type 1 motifs.
      Protein nameUniProtCore fucosylated glycopeptideSite
      ADAMTS-13Q76LX8WVJYSCLDQAR707
      Potential glycosite is in the database.
      AfaminP43652JCCNTENPPGCYR383
      The glycosite is not annotated in the database.
      YAEDKFJETTEK
      The CF site was identified in both the healthy and HCC samples.
      402
      α1-AntitrypsinP01009YLGJATAIFFLPDEGK
      The CF site was identified in both the healthy and HCC samples.
      271
      α2-HS-glycoproteinP02765VCQDCPLLAPLJDTR
      The CF site was identified in both the healthy and HCC samples.
      156
      α2-MacroglobulinP01023GCVLLSYLJETVTVSASLESVR55
      VSJQTLSLFFTVLQDVPVR
      The CF site was identified in both the healthy and HCC samples.
      1424
      VSJQTLSLFFTVLQDVPVRDLKPAIVK1424
      AN1-type zinc finger and ubiquitin domain-containing protein 1Q86XD8MKNMJLSKK235
      The glycosite is not annotated in the database.
      Angiopoietin-related protein 6Q8NI99VLJASAEAQR145
      Apolipoprotein DP05090ADGTVNQIEGEATPVJLTEPAK
      The CF site was identified in both the healthy and HCC samples.
      98
      ADGTVNQIEGEATPVJLTEPAKLEVK98
      AttractinO75882GICJSSDVR300
      EWLPLJR
      The CF site was identified in both the healthy and HCC samples.
      383
      JHSCSEGQISIFR731
      β2-Glycoprotein 1P02749PSAGJNSLYR
      The CF site was identified in both the healthy and HCC samples.
      162
      VYKPSAGJNSLYR
      The CF site was identified in both the healthy and HCC samples.
      162
      BiotinidaseP43251FJDTEVLQR
      The CF site was identified in both the healthy and HCC samples.
      130
      C10orf111 proteinQ49AL1AFCVPTAJVSVVGLNCHLEK9
      The glycosite is not annotated in the database.
      C1q and tumor necrosis factor-related protein 3Q0VAN4TGTVDJNTSTDLK
      The CF site was identified in both the healthy and HCC samples.
      70
      The glycosite is not annotated in the database.
      Cadherin-5P33151EVYPWYJLTVEAK
      The CF site was identified in both the healthy and HCC samples.
      442
      CalumeninO43852JATYGYVLDDPDPDDGFNYK131
      Potential glycosite is in the database.
      CeruloplasminP00450EHEGAIYPDJTTDFQR
      The CF site was identified in both the healthy and HCC samples.
      138
      AGLQAFFQVQECJK358
      EJLTAPGSDSAVFFEQGTTR
      The CF site was identified in both the healthy and HCC samples.
      397
      ELHHLQEQJVSNAFLDK
      The CF site was identified in both the healthy and HCC samples.
      762
      ELHHLQEQJVSNAFLDKGEFYIGSK762
      CholinesteraseP06276EJETEIIK
      The CF site was identified in both the healthy and HCC samples.
      284
      ClusterinP10909EDALJETR
      The CF site was identified in both the healthy and HCC samples.
      86
      KKEDALJETR86
      LAJLTQGEDQYYLR
      The CF site was identified in both the healthy and HCC samples.
      374
      Coiled coil domain-containing protein 146Q8IYE0IKJATEKMMALVAELSMK815
      The glycosite is not annotated in the database.
      Complement C1r subcomponent-like proteinQ9NZP8PVTPIAQJQTTLGSSR
      The CF site was identified in both the healthy and HCC samples.
      242
      Complement C2P06681TMFPJLTDVR
      The CF site was identified in both the healthy and HCC samples.
      651
      Complement C4-AP0C0L4GLJVTLSSTGR
      The CF site was identified in both the healthy and HCC samples.
      1328
      Complement component C7P10643JYTLTGR
      The CF site was identified in both the healthy and HCC samples.
      754
      Complement factor HP08603IPCSQPPQIEHGTIJSSR
      The CF site was identified in both the healthy and HCC samples.
      882
      ISEEJETTCYMGK
      The CF site was identified in both the healthy and HCC samples.
      911
      MDGASJVTCINSR1029
      Potential glycosite is in the database.
      Complement-activating component of Ra-reactive factorP48740FGYILHTDJR178
      Potential glycosite is in the database.
      NJLTTYK
      The CF site was identified in both the healthy and HCC samples.
      385
      Potential glycosite is in the database.
      Contactin-1Q12860AJSTGTLVITDPTR494
      Dopamine β-hydroxylaseP09172SLEAIJGSGLQMGLQR
      The CF site was identified in both the healthy and HCC samples.
      184
      E3 ubiquitin-protein ligase Mdm2Q00987LEJSTQAEEGFDVPDCKK349
      The glycosite is not annotated in the database.
      Exportin 5Q5JTE9TRSJYTKVSR138
      The glycosite is not annotated in the database.
      Extracellular matrix protein 1Q16610HIPGLIHJMTAR444
      Fibrinogen γ chainP02679DLQSLEDILHQVEJK78
      VDKDLQSLEDILHQVEJK78
      FibronectinP02751DQCIVDDITYNVJDTFHK528
      HEEGHMLJCTCFGQGR542
      LDAPTNLQFVJETDSTVLVR
      The CF site was identified in both the healthy and HCC samples.
      1007
      Fibulin-1P23142CATPHGDJASLEATFVK
      The CF site was identified in both the healthy and HCC samples.
      98
      Potential glycosite is in the database.
      Ficolin-3O75636VELEDFNGJR
      The CF site was identified in both the healthy and HCC samples.
      189
      Galectin-3-binding proteinQ08380ALGFEJATQALGR
      The CF site was identified in both the healthy and HCC samples.
      69
      DAGVVCTJETR125
      GLJLTEDTYKPR
      The CF site was identified in both the healthy and HCC samples.
      398
      AAIPSALDTJSSK
      The CF site was identified in both the healthy and HCC samples.
      551
      TVIRPFYLTJSSGVD
      The CF site was identified in both the healthy and HCC samples.
      580
      HaptoglobinP00738MVSHHJLTTGATLINEQWLLTTAK
      The CF site was identified in both the healthy and HCC samples.
      184
      NLFLjHSEjATAK
      The CF site was identified in both the healthy and HCC samples.
      ,
      Both sites were identified as glycosylation sites, and one must be core-fucosylated.
      207, 211
      VVLHPJYSQVDIGLIK
      The CF site was identified in both the healthy and HCC samples.
      241
      HemopexinP02790SWPAVGJCSSALR
      The CF site was identified in both the healthy and HCC samples.
      187
      ALPQPQJVTSLLGCTH
      The CF site was identified in both the healthy and HCC samples.
      453
      Hyaluronidase-4Q2M3T9LISDMGKJVSATDIEYLAK177
      The glycosite is not annotated in the database.
      Ig α-1 chain C regionP01876PALEDLLLGSEAJLTCTLTGLR
      The CF site was identified in both the healthy and HCC samples.
      144
      LSLHRPALEDLLLGSEAJLTCTLTGLR144
      PTHVJVSVVMAEVDGTCY
      The CF site was identified in both the healthy and HCC samples.
      340
      LAGKPTHVJVSVVMAEVDGTCY
      The CF site was identified in both the healthy and HCC samples.
      340
      Ig α-2 chain C regionP01877TPLTAJITK
      The CF site was identified in both the healthy and HCC samples.
      205
      Ig γ-1 chain C regionP01857EEQYJSTYR180
      Ig γ-2 chain C regionP01859EEQFJSTFR176
      Ig γ-4 chain C regionP01861EEQFJSTYR177
      The glycosite is not annotated in the database.
      Ig μ chain C regionP01871YKJNSDISSTR
      The CF site was identified in both the healthy and HCC samples.
      46
      GLTFQQJASSMCVPDQDTAIR
      The CF site was identified in both the healthy and HCC samples.
      210
      Immunoglobulin J chainP01591EJISDPTSPLR
      The CF site was identified in both the healthy and HCC samples.
      49
      IIVPLNNREJISDPTSPLR49
      Insulin-like growth factor-binding protein 3P17936GLCVJASAVSR
      The CF site was identified in both the healthy and HCC samples.
      116
      AYLLPAPPAPGJASESEEDR
      The CF site was identified in both the healthy and HCC samples.
      136
      VDYESQSTDTQJFSSESK199
      Inter-α-trypsin inhibitor heavy chain H1P19827ICDLLVANNHFAHFFAPQJLTNMNK285
      Interleukin-6 receptor subunit βP40189LTVJLTNDR390
      The glycosite is not annotated in the database.
      KallistatinP29622DFYVDEJTTVR238
      Kininogen-1P01042YNSQJQSNNQFVLYR48
      ITYSIVQTJCSK205
      LNAENJATFYFK
      The CF site was identified in both the healthy and HCC samples.
      294
      Ligand-dependent nuclear receptor corepressor-like proteinQ8N3X6JGTVDGTSENTEDGLDRKDSK493
      The glycosite is not annotated in the database.
      Metalloproteinase inhibitor 1P01033FVGTPEVJQTTLYQR
      The CF site was identified in both the healthy and HCC samples.
      53
      Multimerin-1Q13201FNPGAESVVLSJSTLK
      The CF site was identified in both the healthy and HCC samples.
      136
      OtoancorinQ7RTW8JLSAVFKDLYDK211
      Potential glycosite is in the database.
      Phospholipid transfer proteinP55058EGHFYYJISEVK
      The CF site was identified in both the healthy and HCC samples.
      64
      VSJVSCQASVSR
      The CF site was identified in both the healthy and HCC samples.
      143
      JWSLPNR
      The CF site was identified in both the healthy and HCC samples.
      245
      Plasma protease C1 inhibitorP05155DTFVJASR
      The CF site was identified in both the healthy and HCC samples.
      238
      GVTSVSQIFHSPDLAIRDTFVJASR238
      VLSJNSDANLELINTWVAK
      The CF site was identified in both the healthy and HCC samples.
      253
      VGQLQLSHJLSLVILVPQNLK352
      Polymeric immunoglobulin receptorP01833VPGJVTAVLGETLK
      The CF site was identified in both the healthy and HCC samples.
      469
      Potassium voltage-gated channel subfamily H member 6Q9H252YJGSDPASGPSVQDK449
      The glycosite is not annotated in the database.
      Pro-low density lipoprotein receptor-related protein 1Q07954IETILLJGTDR729
      Prostaglandin-H2 d-isomeraseP41222SVVAPATDGGLJLTSTFLR
      The CF site was identified in both the healthy and HCC samples.
      78
      Protein phosphatase 1 regulatory subunit 1CQ8WVI7HLKGQJESAFPEEEEGTNER89
      The glycosite is not annotated in the database.
      Putative uncharacterized protein DKFZp686O16217Q6N041HYTJSSQDVTVPCR
      The CF site was identified in both the healthy and HCC samples.
      250
      The glycosite is not annotated in the database.
      MAGKPTHIJVSVVMAEADGTCY485
      The glycosite is not annotated in the database.
      Putative uncharacterized protein DKFZp566E164Q9NTU4JISKTRGWHSPGR
      The CF site was identified in both the healthy and HCC samples.
      58
      The glycosite is not annotated in the database.
      Selenoprotein PP49908EGYSJISYIVVNHQGISSR83
      SerotransferrinP02787QQQHLFGSJVTDCSGNFCLFR
      The CF site was identified in both the healthy and HCC samples.
      630
      Signal peptide peptidase-like 2AQ8TCT8DMNQTLGDJITVK155
      The glycosite is not annotated in the database.
      Sulfhydryl oxidase 1O00391JGSGAVFPVAGADVQTLR
      The CF site was identified in both the healthy and HCC samples.
      130
      Tomoregulin-2Q9UIK5SYDJACQIKEASCQKQEK204
      The glycosite is not annotated in the database.
      Uncharacterized protein ENSP00000375008A6NH92JTSISTAYMELSSLR92
      The glycosite is not annotated in the database.
      VitronectinP04004JISDGFDGIPDNVDAALALPAHSYSGR242
      von Willebrand factorP04275IGEADFJR
      The CF site was identified in both the healthy and HCC samples.
      1515
      Zinc-α2-glycoproteinP25311DIVEYYNDSJGSHVLQGR
      The CF site was identified in both the healthy and HCC samples.
      109
      FGCEIENJR125
      4F2 cell surface antigen heavy chainP08195SLVTQYLJATGNR323
      ADAMTS-like protein 2Q86TH1DRJVTGTPLTGDK
      The CF site was identified only in the HCC sample.
      428
      Potential glycosite is in the database.
      AfaminP43652DIENFJSTQK
      The CF site was identified only in the HCC sample.
      33
      ADAM DEC1O15204EHAVFTSNQEEQDPAJHTCGVK
      The CF site was identified only in the HCC sample.
      184
      Potential glycosite is in the database.
      ADAMTS-13Q76LX8YGEEYGJLTR
      The CF site was identified only in the HCC sample.
      667
      ADAMTS-like protein 4Q6UY14LVSGJLTDR
      The CF site was identified only in the HCC sample.
      490
      α1-AntitrypsinP01009ADTHDEILEGLNFJLTEIPEAQIHEGFQELLR
      The CF site was identified only in the HCC sample.
      107
      AttractinO75882CIJQSICEK
      The CF site was identified only in the HCC sample.
      1073
      Potential glycosite is in the database.
      Cadherin-5P33151JTSLPHHVGK
      The CF site was identified only in the HCC sample.
      61
      Potential glycosite is in the database.
      LDREJISEYHLTAVIVDK
      The CF site was identified only in the HCC sample.
      112
      Cholesteryl ester transfer proteinP11597SIDVSIQJVSVVFK
      The CF site was identified only in the HCC sample.
      105
      Potential glycosite is in the database.
      Complement factor IP05156FLNJGTCTAEGK
      The CF site was identified only in the HCC sample.
      103
      Complement component C9P02748AVJITSENLIDDVVSLIR
      The CF site was identified only in the HCC sample.
      415
      Complement factor HP08603SPDVIJGSPISQK
      The CF site was identified only in the HCC sample.
      217
      The glycosite is not annotated in the database.
      Desmoglein-2Q14126YVQJGTYTVK
      The CF site was identified only in the HCC sample.
      462
      Desmocollin-2Q02487AJYTILK
      The CF site was identified only in the HCC sample.
      392
      Glutaminase kidney isoformO94925WJNTPMDEALHFGHHDVFK
      The CF site was identified only in the HCC sample.
      620
      The glycosite is not annotated in the database.
      Golgi membrane protein GP73Q8NBJ4AVLVNJITTGER
      The CF site was identified only in the HCC sample.
      109
      Heparin cofactor 2P05546JLSMPLLPADFHK
      The CF site was identified only in the HCC sample.
      49
      Histidine-rich glycoproteinP04196HSHNjjSSDLHPHK
      The CF site was identified only in the HCC sample.
      344 or 345
      Potential glycosite is in the database.
      IgGFc-binding proteinQ9Y6R7VITVQVAJFTLR
      The CF site was identified only in the HCC sample.
      1317
      The glycosite is not annotated in the database.
      Ig μ chain C regionP01871JNSDISSTR
      The CF site was identified in both the healthy and HCC samples.
      46
      Intercellular adhesion molecule 1P05362AJLTVVLLR
      The CF site was identified only in the HCC sample.
      145
      FER proteinQ6PEJ9GSTVQMNYVSJVSKSWLLMIQQTEQLSRIMK
      The CF site was identified only in the HCC sample.
      66
      The glycosite is not annotated in the database.
      Leucine-rich α2-glycoproteinP02750MFSQJDTR
      The CF site was identified only in the HCC sample.
      325
      LPPGLLAJFTLLR
      The CF site was identified only in the HCC sample.
      186
      Macrophage mannose receptor 2Q9UBG0VTPACJTSLPAQR
      The CF site was identified only in the HCC sample.
      69
      Potential glycosite is in the database.
      Membrane copper-amine oxidaseQ16853YLYLASJHSNK
      The CF site was identified only in the HCC sample.
      592
      Neuronal cell adhesion moleculeQ92823VNVVJSTLAEVHWDPVPLK
      The CF site was identified only in the HCC sample.
      858
      Potential glycosite is in the database.
      Pigment epithelium-derived factorP36955VTQJLTLIEESLTSEFIHDIDR
      The CF site was identified only in the HCC sample.
      285
      ProperdinP27918JVTFWGR
      The CF site was identified only in the HCC sample.
      428
      ProthrombinP00734JFTENDLLVR
      The CF site was identified only in the HCC sample.
      416
      Platelet glycoprotein VP40197LLDLSGNJLTHLPK181
      JLSSLESVQLDHNQLETLPGDVFGALPR
      The CF site was identified only in the HCC sample.
      243
      Potential glycosite is in the database.
      Protein HEG homolog 1Q9ULI3LjjSTGLQSSSVSQTK
      The CF site was identified only in the HCC sample.
      313
      The glycosite is not annotated in the database.
      or 314
      Potential glycosite is in the database.
      Polycystin-2Q13563VRJGSCSIPQDLRDEIK
      The CF site was identified only in the HCC sample.
      328
      Potential glycosite is in the database.
      Protein PARM-1Q6UWI2JISIESR
      The CF site was identified only in the HCC sample.
      80
      Potential glycosite is in the database.
      Poliovirus receptor-related protein 1Q15223NPJGTVTVISR
      The CF site was identified only in the HCC sample.
      202
      Pro-low density lipoprotein receptor-related protein 1Q07954WTGHJVTVVQR
      The CF site was identified only in the HCC sample.
      1511
      Potential glycosite is in the database.
      Proteasome subunit β type-4P28070FRJISR
      The CF site was identified only in the HCC sample.
      83
      The glycosite is not annotated in the database.
      Protein phosphatase 1HQ9ULR3DFJMTGWAYKTIEDEDLKFPLIYGEGK
      The CF site was identified only in the HCC sample.
      354
      The glycosite is not annotated in the database.
      Roundabout homolog 4Q8WZ75IQLEJVTLLNPDPAEGPKPR
      The CF site was identified only in the HCC sample.
      246
      Scavenger receptor cysteine-rich type 1 protein M130Q86VB7APGWAJSSAGSGR
      The CF site was identified only in the HCC sample.
      105
      Tetratricopeptide repeat protein 6P02790jGTGHGjSTHHGPEYMR
      Both sites were identified as glycosylation sites, and one must be core-fucosylated.
      ,
      The CF site was identified only in the HCC sample.
      240, 246
      Type-1 angiotensin II receptorP30556MILJSSTEDGIKRIQDDCPK
      The CF site was identified only in the HCC sample.
      4
      Potential glycosite is in the database.
      VitronectinP04004NJATVHEQVGGPSLTSDLQAQSK
      The CF site was identified only in the HCC sample.
      86
      VasorinQ6EMK4LHEITJETFR
      The CF site was identified only in the HCC sample.
      117
      a Potential glycosite is in the database.
      b The glycosite is not annotated in the database.
      c The CF site was identified in both the healthy and HCC samples.
      d Both sites were identified as glycosylation sites, and one must be core-fucosylated.
      e The CF site was identified only in the HCC sample.
      In conclusion, this study holds promise for the large scale identification of CF glycoproteins, which can serve as a tool for the discovery of novel biomarker panels from clinical samples, such as body fluids or tissue biopsies. In addition, it is our hope that both identified and unidentified candidate spectra (over 10,000) will be a useful resource for the improvement of database searching methods for glycopeptides. Spectra data sets of this sort are rare and should arouse the interest of scientists in both glycoproteomics and bioinformatics research fields.

      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.

      Supplementary Material

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