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• W2098364431 abstract "The identification of biomarkers indicating the level of aggressiveness of prostate cancer (PCa) will address the urgent clinical need to minimize the general overtreatment of patients with non-aggressive PCa, who account for the majority of PCa cases. Here, we isolated formerly N-linked glycopeptides from normal prostate (n = 10) and from non-aggressive (n = 24), aggressive (n = 16), and metastatic (n = 25) PCa tumor tissues and analyzed the samples using SWATH mass spectrometry, an emerging data-independent acquisition method that generates a single file containing fragment ion spectra of all ionized species of a sample. The resulting datasets were searched using a targeted data analysis strategy in which an a priori spectral reference library representing known N-glycosites of the human proteome was used to identify groups of signals in the SWATH mass spectrometry data. On average we identified 1430 N-glycosites from each sample. Out of those, 220 glycoproteins showed significant quantitative changes associated with diverse biological processes involved in PCa aggressiveness and metastasis and indicated functional relationships. Two glycoproteins, N-acylethanolamine acid amidase and protein tyrosine kinase 7, that were significantly associated with aggressive PCa in the initial sample cohort were further validated in an independent set of patient tissues using tissue microarray analysis. The results suggest that N-acylethanolamine acid amidase and protein tyrosine kinase 7 may be used as potential tissue biomarkers to avoid overtreatment of non-aggressive PCa. The identification of biomarkers indicating the level of aggressiveness of prostate cancer (PCa) will address the urgent clinical need to minimize the general overtreatment of patients with non-aggressive PCa, who account for the majority of PCa cases. Here, we isolated formerly N-linked glycopeptides from normal prostate (n = 10) and from non-aggressive (n = 24), aggressive (n = 16), and metastatic (n = 25) PCa tumor tissues and analyzed the samples using SWATH mass spectrometry, an emerging data-independent acquisition method that generates a single file containing fragment ion spectra of all ionized species of a sample. The resulting datasets were searched using a targeted data analysis strategy in which an a priori spectral reference library representing known N-glycosites of the human proteome was used to identify groups of signals in the SWATH mass spectrometry data. On average we identified 1430 N-glycosites from each sample. Out of those, 220 glycoproteins showed significant quantitative changes associated with diverse biological processes involved in PCa aggressiveness and metastasis and indicated functional relationships. Two glycoproteins, N-acylethanolamine acid amidase and protein tyrosine kinase 7, that were significantly associated with aggressive PCa in the initial sample cohort were further validated in an independent set of patient tissues using tissue microarray analysis. The results suggest that N-acylethanolamine acid amidase and protein tyrosine kinase 7 may be used as potential tissue biomarkers to avoid overtreatment of non-aggressive PCa. Prostate cancer (PCa) 1The abbreviations used are: PCa, prostate cancer; AG, aggressive; NAG, non-aggressive; FDR, false discovery rate; GO, Gene Ontology; RFIN, Reactome Functional Interaction Network; IHC, immunohistochemistry; MS, mass spectrometry; MS/MS, tandem mass spectrometry; SWATH-MS, SWATH mass spectrometry; SRM, selected reaction monitoring; LC, liquid chromatography; TOF, time-of-flight; m/z, mass-to-charge; SPEG, solid phase extraction of formerly N-glycosylated peptides; de-N-glycopeptide, de-glycosylated N-glycopeptide treated by PNGase F; PSM, peptide spectrum match; PSA, prostate-specific antigen; TMA, tissue microarray; NAAA, N-acylethanolamine acid amidase; PTK7, protein tyrosine kinase 7; ROC, receiver operating characteristic; DDA, data-dependent acquisition. 1The abbreviations used are: PCa, prostate cancer; AG, aggressive; NAG, non-aggressive; FDR, false discovery rate; GO, Gene Ontology; RFIN, Reactome Functional Interaction Network; IHC, immunohistochemistry; MS, mass spectrometry; MS/MS, tandem mass spectrometry; SWATH-MS, SWATH mass spectrometry; SRM, selected reaction monitoring; LC, liquid chromatography; TOF, time-of-flight; m/z, mass-to-charge; SPEG, solid phase extraction of formerly N-glycosylated peptides; de-N-glycopeptide, de-glycosylated N-glycopeptide treated by PNGase F; PSM, peptide spectrum match; PSA, prostate-specific antigen; TMA, tissue microarray; NAAA, N-acylethanolamine acid amidase; PTK7, protein tyrosine kinase 7; ROC, receiver operating characteristic; DDA, data-dependent acquisition. is the most common noncutaneous cancer and the second leading cause of cancer-related death in men in the United States (1.Siegel R. Naishadham D. Jemal A. Cancer statistics, 2013.CA. 2013; 63: 11-30Google Scholar). Most diagnosed cases represent slow-growing, non-lethal forms of cancer. Unfortunately, neither the currently available diagnostic biomarkers, such as serum prostate-specific antigen (PSA), nor histological examination of (biopsied) tumor tissue can distinguish aggressive (AG) PCa from non-aggressive (NAG) PCa. This situation leads to the undertreatment of AG PCa and, more important, the overtreatment of NAG PCa (2.Andriole G.L. Crawford E.D. Grubb 3rd, R.L. Buys S.S. Chia D. Church T.R. Fouad M.N. Gelmann E.P. Kvale P.A. Reding D.J. Weissfeld J.L. Yokochi L.A. O'Brien B. Clapp J.D. Rathmell J.M. Riley T.L. Hayes R.B. Kramer B.S. Izmirlian G. Miller A.B. Pinsky P.F. Prorok P.C. Gohagan J.K. Berg C.D. Mortality results from a randomized prostate-cancer screening trial.N. Engl. J. Med. 2009; 360: 1310-1319Crossref PubMed Scopus (2376) Google Scholar). In fact, up to 90% of men with PCa harbor localized tumors that are unlikely to cause significant symptoms or mortality. Of these, many are overtreated because of a lack of clear (molecular) indicators that guide physicians to the appropriate treatment. All available treatment options, including surgery, radiation therapy, and hormonal therapy, carry a risk of complications and show a range of side effects that impact the patient's long-term quality of life. There is, therefore, a pressing clinical need to identify new PCa biomarkers in clinical tissue or blood that distinguish AG from NAG prostate tumors. PCa tissue samples (e.g. those obtained from biopsies) are routinely subjected to histopathological examination, and the results are reported by a Gleason score, a grading score ranging from 2 to 10 that is calculated by adding the score of the predominant grade pattern and that of the second most common grade pattern in a specific sample. The Gleason score helps guide patient treatment, but sometimes it fails to do so sufficiently because it cannot be used to distinguish significant molecular heterogeneities of PCa and a range of clinical trajectories (3.Lapointe J. Li C. Higgins J.P. van de Rijn M. Bair E. Montgomery K. Ferrari M. Egevad L. Rayford W. Bergerheim U. Ekman P. DeMarzo A.M. Tibshirani R. Botstein D. Brown P.O. Brooks J.D. Pollack J.R. Gene expression profiling identifies clinically relevant subtypes of prostate cancer.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 811-816Crossref PubMed Scopus (1057) Google Scholar). For example, the clinical outcome is unpredictable for most Gleason 7 PCas (4.Sakr W.A. Tefilli M.V. Grignon D.J. Banerjee M. Dey J. Gheiler E.L. Tiguert R. Powell I.J. Wood D.P. Gleason score 7 prostate cancer: a heterogeneous entity? Correlation with pathologic parameters and disease-free survival.Urology. 2000; 56: 730-734Abstract Full Text Full Text PDF PubMed Scopus (125) Google Scholar, 5.Pin E. Fredolini C. Petricoin 3rd, E.F. The role of proteomics in prostate cancer research: biomarker discovery and validation.Clin. Biochem. 2013; 46: 524-538Crossref PubMed Scopus (51) Google Scholar). Molecular-level phenotyping has been proposed as a means to develop a more highly resolving scoring system capable of correctly classifying clinically important PCa types. In principle, clinical samples can be phenotyped by different types of measurements (e.g. genomic (6.Berger M.F. Lawrence M.S. Demichelis F. Drier Y. Cibulskis K. Sivachenko A.Y. Sboner A. Esgueva R. Pflueger D. Sougnez C. Onofrio R. Carter S.L. Park K. Habegger L. Ambrogio L. Fennell T. Parkin M. Saksena G. Voet D. Ramos A.H. Pugh T.J. Wilkinson J. Fisher S. Winckler W. Mahan S. Ardlie K. Baldwin J. Simons J.W. Kitabayashi N. MacDonald T.Y. Kantoff P.W. Chin L. Gabriel S.B. Gerstein M.B. Golub T.R. Meyerson M. Tewari A. Lander E.S. Getz G. Rubin M.A. Garraway L.A. The genomic complexity of primary human prostate cancer.Nature. 2011; 470: 214-220Crossref PubMed Scopus (982) Google Scholar), epigenomic (7.Lin P.C. Giannopoulou E.G. Park K. Mosquera J.M. Sboner A. Tewari A.K. Garraway L.A. Beltran H. Rubin M.A. Elemento O. Epigenomic alterations in localized and advanced prostate cancer.Neoplasia. 2013; 15: 373-383Crossref PubMed Scopus (59) Google Scholar), transcriptomic (3.Lapointe J. Li C. Higgins J.P. van de Rijn M. Bair E. Montgomery K. Ferrari M. Egevad L. Rayford W. Bergerheim U. Ekman P. DeMarzo A.M. Tibshirani R. Botstein D. Brown P.O. Brooks J.D. Pollack J.R. Gene expression profiling identifies clinically relevant subtypes of prostate cancer.Proc. Natl. Acad. Sci. U.S.A. 2004; 101: 811-816Crossref PubMed Scopus (1057) Google Scholar), metabolomic (8.McDunn J.E. Li Z. Adam K.P. Neri B.P. Wolfert R.L. Milburn M.V. Lotan Y. Wheeler T.M. Metabolomic signatures of aggressive prostate cancer.Prostate. 2013; 73: 1547-1560Crossref PubMed Scopus (103) Google Scholar), and proteomic (5.Pin E. Fredolini C. Petricoin 3rd, E.F. The role of proteomics in prostate cancer research: biomarker discovery and validation.Clin. Biochem. 2013; 46: 524-538Crossref PubMed Scopus (51) Google Scholar)). To date, transcript profiling has been used most extensively, mainly because of the relatively advanced maturity and accessibility of the respective measurement techniques (9.Soon W.W. Hariharan M. Snyder M.P. High-throughput sequencing for biology and medicine.Mol. Syst. Biol. 2013; 9: 640Crossref PubMed Scopus (196) Google Scholar). However, proteomic measurements should be equally or more informative, because proteins are more dynamic and diverse and more directly reflective of cellular physiology than nucleic-acid-based markers (10.Aebersold R. Anderson L. Caprioli R. Druker B. Hartwell L. Smith R. Perspective: a program to improve protein biomarker discovery for cancer.J. Proteome Res. 2005; 4: 1104-1109Crossref PubMed Scopus (130) Google Scholar). Moreover, PSA and other approved protein markers (11.Gutman S. Kessler L.G. The US Food and Drug Administration perspective on cancer biomarker development.Nat. Rev. Cancer. 2006; 6: 565-571Crossref PubMed Scopus (162) Google Scholar) exemplify the potential information contents of proteins. The glycoproteome represents a subproteome that is particularly relevant for clinical research because glycoproteins are usually found on the cell surface or secreted by tissues and are more likely to be detected in the blood stream as non-invasive biomarkers (12.Zhang H. Yan W. Aebersold R. Chemical probes and tandem mass spectrometry: a strategy for the quantitative analysis of proteomes and subproteomes.Curr. Opin. Chem. Biol. 2004; 8: 66-75Crossref PubMed Scopus (132) Google Scholar, 13.Zhang H. Loriaux P. Eng J. Campbell D. Keller A. Moss P. Bonneau R. Zhang N. Zhou Y. Wollscheid B. Cooke K. Yi E.C. Lee H. Peskind E.R. Zhang J. Smith R.D. Aebersold R. UniPep, a database for human N-linked glycosites: a resource for biomarker discovery.Genome Biol. 2006; 7: R73Crossref PubMed Scopus (98) Google Scholar, 14.Zhang H. Chan D.W. Cancer biomarker discovery in plasma using a tissue-targeted proteomic approach.Cancer Epidemiol. Biomarkers Prev. 2007; 16: 1915-1917Crossref PubMed Scopus (42) Google Scholar, 15.Roth J. Protein N-glycosylation along the secretory pathway: relationship to organelle topography and function, protein quality control, and cell interactions.Chem. Rev. 2002; 102: 285-303Crossref PubMed Scopus (342) Google Scholar, 16.Schiess R. Wollscheid B. Aebersold R. Targeted proteomic strategy for clinical biomarker discovery.Mol. Oncol. 2009; 3: 33-44Crossref PubMed Scopus (291) Google Scholar, 17.Zhang H. Liu A.Y. Loriaux P. Wollscheid B. Zhou Y. Watts J.D. Aebersold R. Mass spectrometric detection of tissue proteins in plasma.Mol. Cell. Proteomics. 2007; 6: 64-71Abstract Full Text Full Text PDF PubMed Scopus (160) Google Scholar). In fact, all current blood tumor biomarkers, including PSA in the case of PCa, that are approved by the U.S. Food and Drug Administration are glycoproteins (14.Zhang H. Chan D.W. Cancer biomarker discovery in plasma using a tissue-targeted proteomic approach.Cancer Epidemiol. Biomarkers Prev. 2007; 16: 1915-1917Crossref PubMed Scopus (42) Google Scholar). We previously developed a protocol for the solid phase extraction of glycopeptides (SPEG) to robustly isolate the glycoproteome based on chemical immobilization and enzymatic release of N-linked glycopeptides with high specificity (18.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: 660-666Crossref PubMed Scopus (1275) Google Scholar, 19.Tian Y. Zhou Y. Elliott S. Aebersold R. Zhang H. Solid-phase extraction of N-linked glycopeptides.Nat. Protoc. 2007; 2: 334-339Crossref PubMed Scopus (271) Google Scholar) that was thereafter successfully applied in different cancer biomarker discovery studies (18.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: 660-666Crossref PubMed Scopus (1275) Google Scholar, 20.Chen J. Xi J. Tian Y. Bova G.S. Zhang H. Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody-based assays on tissue specimens.Proteomics. 2013; 13: 2268-2277Crossref PubMed Scopus (41) Google Scholar, 21.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: 1792-1802Crossref PubMed Scopus (198) Google Scholar, 22.Tian Y. Bova G.S. Zhang H. Quantitative glycoproteomic analysis of optimal cutting temperature-embedded frozen tissues identifying glycoproteins associated with aggressive prostate cancer.Anal. Chem. 2011; 83: 7013-7019Crossref PubMed Scopus (52) Google Scholar, 23.Cima I. Schiess R. Wild P. Kaelin M. Schuffler P. Lange V. Picotti P. Ossola R. Templeton A. Schubert O. Fuchs T. Leippold T. Wyler S. Zehetner J. Jochum W. Buhmann J. Cerny T. Moch H. Gillessen S. Aebersold R. Krek W. Cancer genetics—guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer.Proc. Natl. Acad. Sci. U.S.A. 2011; 108: 3342-3347Crossref PubMed Scopus (150) Google Scholar, 24.Chen R. Tan Y. Wang M. Wang F. Yao Z. Dong L. Ye M. Wang H. Zou H. Development of glycoprotein capture-based label-free method for the high-throughput screening of differential glycoproteins in hepatocellular carcinoma.Mol. Cell. Proteomics. 2011; 10Abstract Full Text Full Text PDF Scopus (53) Google Scholar, 25.Zeng X. Hood B.L. Sun M. Conrads T.P. Day R.S. Weissfeld J.L. Siegfried J.M. Bigbee W.L. Lung cancer serum biomarker discovery using glycoprotein capture and liquid chromatography mass spectrometry.J. Proteome Res. 2010; 9: 6440-6449Crossref PubMed Scopus (78) Google Scholar). In this study we used SPEG to profile the N-glycoproteome of PCa histotypes to identify glycoproteins associated with tumor aggressiveness. The isolated de-N-glycopeptide samples from prostate tissues were analyzed via the recently developed SWATH mass spectrometry (SWATH-MS) technology. SWATH-MS is a data-independent acquisition method (26.Gillet L.C. Navarro P. Tate S. Rost H. Selevsek N. Reiter L. Bonner R. Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Mol. Cell. Proteomics. 2012; 11Abstract Full Text Full Text PDF PubMed Scopus (1794) Google Scholar) that essentially allows one to convert all the peptides ionized from a clinical sample into a perpetually reusable digital map (27.Liu Y. Huttenhain R. Collins B. Aebersold R. Mass spectrometric protein maps for biomarker discovery and clinical research.Expert Rev. Mol. Diagn. 2013; 13: 811-825Crossref PubMed Scopus (100) Google Scholar). When combined with a targeted data analysis strategy, SWATH-MS was demonstrated to achieve the favorable accuracy, dynamic range, and reproducibility of selected reaction monitoring (SRM), the gold-standard quantitative proteomic technology, while greatly extending the degree of multiplexing to thousands of peptides (26.Gillet L.C. Navarro P. Tate S. Rost H. Selevsek N. Reiter L. Bonner R. Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Mol. Cell. Proteomics. 2012; 11Abstract Full Text Full Text PDF PubMed Scopus (1794) Google Scholar, 28.Liu Y. Huttenhain R. Surinova S. Gillet L.C. Mouritsen J. Brunner R. Navarro P. Aebersold R. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.Proteomics. 2013; 13: 1247-1256Crossref PubMed Scopus (180) Google Scholar, 29.Collins B.C. Gillet L.C. Rosenberger G. Rost H.L. Vichalkovski A. Gstaiger M. Aebersold R. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14–3-3 system.Nat. Methods. 2013; 10: 1246-1253Crossref PubMed Scopus (243) Google Scholar). We have recently demonstrated that the combination of SWATH-MS and de-N-glycopeptide isolation has promising quantitative performance for biomarker verification in human plasma (28.Liu Y. Huttenhain R. Surinova S. Gillet L.C. Mouritsen J. Brunner R. Navarro P. Aebersold R. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.Proteomics. 2013; 13: 1247-1256Crossref PubMed Scopus (180) Google Scholar). Here we establish that this integrated technology is also highly efficient for “molecular phenotyping” of tissue specimens because, once acquired, the quantitative data files representing control and disease-affected human tissues support iterative in silico biomarker discovery. To facilitate the targeted analysis of SWATH maps, we generated a spectral library covering a large part of the human N-glycoproteome, specifically optimized for SWATH-MS analysis. This library will also provide the community with a high-quality set of reference assays for future MS analyses of the global human N-glycoproteome and for related clinical applications. Furthermore, our SWATH dataset led to the identification of regulated proteins and pathways that might serve a predictive role in discriminating AG and NAG PCas. Hydrazide resin was from Bio-Rad (Hercules, CA); the BCA protein assay kit, Zeba spin desalting column (7k molecular weight cut off), urea, and tris (2-carboxyethyl) phosphine were from Thermo Fisher Scientific (Waltham, MA); sequencing-grade trypsin was from Promega (Madison, WI); PNGase F was from New England Biolabs (Ipswich, MA); and monoclonal mouse anti-NAAA and anti-PTK7 primary antibody was from R&D Systems (Minneapolis, MN). All other chemicals were from Sigma-Aldrich (St. Louis, MO). Samples and clinical information were obtained with informed consent, and procedures were performed with the approval of the Institutional Review Board of the Johns Hopkins University. NAG and AG primary prostate tumors were collected via radical prostatectomy or transurethral resection of the prostate at Johns Hopkins Hospital and Johns Hopkins Bayview Medical Center under the National Cancer Institute–funded Johns Hopkins prostate cancer SPORE project. The NAG PCa group included 22 tumor specimens with Gleason scores of 6 and 2 tumor specimens from tumors with Gleason scores of 7 with no evidence of recurrence in up to 15 years of follow-up. The AG PCa group included 11 tumor specimens with Gleason scores of 8 or 9 and 5 tumor specimens with Gleason scores of 7 from patients who either died of cancer metastasis within 6 years of surgery or were positive for metastatic tumor at the time of surgery (supplemental Table S1). The 25 metastatic tumors were from men who died of PCa and underwent autopsy as part of the Project to Eliminate Lethal Prostate Cancer rapid-autopsy program of the Johns Hopkins Autopsy Study of Lethal Prostate Cancer, initiated in 1994. All subjects underwent androgen deprivation during the course of their treatment. The 10 normal prostate tissues were from healthy transplant donors who died from accidents or suicide. The primary prostate tumor tissues were immediately frozen after resection from surgery. The normal prostate tissues were immediately frozen after resections from transplant donors. The metastatic tumor tissues were acquired via rapid autopsy (a few hours to a day after death). All specimens were snap-frozen, embedded in optimal cutting temperature compound, and stored at −80 °C until use. Frozen prostate tissues embedded in optimal cutting temperature compound were sectioned and stained with hematoxylin and eosin (H&E). The H&E staining was used to guide cryostat microdissection for enrichment of the tumor content of tissue. After cryostate microdissection, 6-μm-thick tissue sections for each specimen were collected in sterile screw-cap bullet tubes. Proteins were extracted using cell lysis buffer (50 mm Tris, pH 8.0, 150 mm NaCl, 0.1% SDS, 0.5% sodium deoxycholate, 1% Triton X-100). BCA assay was performed, and 100 μg of total protein mass per specimen was used to extract formerly N-linked glycopeptides via the SPEG procedure as described previously (20.Chen J. Xi J. Tian Y. Bova G.S. Zhang H. Identification, prioritization, and evaluation of glycoproteins for aggressive prostate cancer using quantitative glycoproteomics and antibody-based assays on tissue specimens.Proteomics. 2013; 13: 2268-2277Crossref PubMed Scopus (41) Google Scholar, 22.Tian Y. Bova G.S. Zhang H. Quantitative glycoproteomic analysis of optimal cutting temperature-embedded frozen tissues identifying glycoproteins associated with aggressive prostate cancer.Anal. Chem. 2011; 83: 7013-7019Crossref PubMed Scopus (52) Google Scholar). Briefly, the proteins were alkylated and digested into peptides, which were cleaned up by means of C18 chromatography prior to SPEG. The peptides were treated with sodium periodate to oxidize the glycan moieties of glycopeptides and purified by G-10 gel filtration cartridges (Nest Group Inc., Southborough, MA). The sample was then conjugated to Affi-gel Hydrazine resin (Bio-Rad) overnight. The unbound peptides were removed through an extensive washing procedure. N-linked glycopeptides were released by PNGase F. Finally, de-N-glycopeptides were used for downstream MS analysis. To generate a SWATH spectral library and to quantify the de-N-glycopeptides from each tissue group, equal amounts of peptide samples from each tissue group were pooled together and analyzed via LC-MS/MS. In addition, small-scale sample pools (from five individual samples each) were generated for non-aggressive, aggressive, and metastatic prostate tumors. Eleven retention time anchor peptides (iRT peptides, Biognosys AG, Zurich, Switzerland (30.Escher C. Reiter L. MacLean B. Ossola R. Herzog F. Chilton J. MacCoss M.J. Rinner O. Using iRT, a normalized retention time for more targeted measurement of peptides.Proteomics. 2012; 12: 1111-1121Crossref PubMed Scopus (386) Google Scholar)) were added into each sample at a ratio of 1:30 v/v. For each SWATH analysis, equal amounts of sample (estimated to be roughly 1 μg of total peptide mass) derived from pooled tissue were analyzed so that a meaningful comparison could be achieved across groups. SWATH-MS datasets (or SWATH maps) were acquired using an AB Sciex 5600 TripleTOF mass spectrometer (Concord, Ontario, Canada) interfaced to an Eksigent NanoLC Ultra 2D Plus HPLC system (Dublin, CA) as previously described (26.Gillet L.C. Navarro P. Tate S. Rost H. Selevsek N. Reiter L. Bonner R. Aebersold R. Targeted data extraction of the MS/MS spectra generated by data-independent acquisition: a new concept for consistent and accurate proteome analysis.Mol. Cell. Proteomics. 2012; 11Abstract Full Text Full Text PDF PubMed Scopus (1794) Google Scholar, 28.Liu Y. Huttenhain R. Surinova S. Gillet L.C. Mouritsen J. Brunner R. Navarro P. Aebersold R. Quantitative measurements of N-linked glycoproteins in human plasma by SWATH-MS.Proteomics. 2013; 13: 1247-1256Crossref PubMed Scopus (180) Google Scholar, 29.Collins B.C. Gillet L.C. Rosenberger G. Rost H.L. Vichalkovski A. Gstaiger M. Aebersold R. Quantifying protein interaction dynamics by SWATH mass spectrometry: application to the 14–3-3 system.Nat. Methods. 2013; 10: 1246-1253Crossref PubMed Scopus (243) Google Scholar). Peptides were directly injected onto a 20-cm PicoFrit emitter (New Objective, self-packed to 20 cm with Magic C18 AQ 3-μm, 200-Å material) and then separated using a 120-min gradient of 2% to 35% buffer (buffer A: 0.1% (v/v) formic acid, 2% (v/v) acetonitrile; buffer B: 0.1% (v/v) formic acid, 90% (v/v) acetonitrile) at a flow rate of 300 nl/min. In SWATH-MS mode, the instrument was specifically tuned to optimize the quadrupole settings for the selection of precursor ion selection windows 25 m/z wide. Using an isolation width of 26 m/z (containing 1 m/z for the window overlap), a set of 32 overlapping windows was constructed covering the precursor mass range of 400–1200 m/z. The effective isolation windows can be considered as 399.5–424.5, 424.5–449.5, etc. SWATH MS2 spectra were collected from 100 to 2000 m/z. The collision energy was optimized for each window according to the calculation for a charge 2+ ion centered upon the window with a spread of 15 eV. An accumulation time (dwell time) of 100 ms was used for all fragment-ion scans in high-sensitivity mode, and for each SWATH-MS cycle a survey scan in high-resolution mode was also acquired for 100 ms, resulting in a duty cycle of ∼3.4 s. For shotgun acquisition, peptides from subpools of each PCa group were firstly measured on an Oribtrap XL (Thermo Scientific) to check the sample quality in collision-induced dissociation mode, and then de-N-glycopeptides from all four tissue groups were pooled equally as a super-mixture and analyzed using classical shotgun data acquisition on a TripleTOF 5600 instrument via four injection replicates. For measurements on the Oribtrap XL, a 90-min gradient was used for each sample using the acquisition method published previously (31.Huttenhain R. Surinova S. Ossola R. Sun Z. Campbell D. Cerciello F. Schiess R. Bausch-Fluck D. Rosenberger G. Chen J. Rinner O. Kusebauch U. Hajduch M. Moritz R.L. Wollscheid B. Aebersold R. N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.Mol. Cell. Proteomics. 2013; 12: 1005-1016Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar). For shotgun MS/MS on the TripleTOF, the same chromatographic system and settings as described above for SWATH-MS were used. MS1 spectra were collected in the range of 360–1460 m/z for 250 ms. The 20 most intense precursors with charge states of 2 to 5 that exceeded 250 counts per second were selected for fragmentation, and MS2 spectra were collected in the range of 50–2000 m/z for 100 ms. The precursor ions were dynamically excluded from reselection for 20 s. We previously published an SRM assay library for 2007 human N-glycosylated proteins (N-glycoprotein SRMAtlas) for targeted proteomic analysis (31.Huttenhain R. Surinova S. Ossola R. Sun Z. Campbell D. Cerciello F. Schiess R. Bausch-Fluck D. Rosenberger G. Chen J. Rinner O. Kusebauch U. Hajduch M. Moritz R.L. Wollscheid B. Aebersold R. N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.Mol. Cell. Proteomics. 2013; 12: 1005-1016Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar). In that work the SRM assays were generated mainly by SRM-triggered MS2 acquisition on a QTrap instrument. For this study, all human synthetic peptides from the SRM assay library (31.Huttenhain R. Surinova S. Ossola R. Sun Z. Campbell D. Cerciello F. Schiess R. Bausch-Fluck D. Rosenberger G. Chen J. Rinner O. Kusebauch U. Hajduch M. Moritz R.L. Wollscheid B. Aebersold R. N-glycoprotein SRMAtlas: a resource of mass spectrometric assays for N-glycosites enabling consistent and multiplexed protein quantification for clinical applications.Mol. Cell. Proteomics. 2013; 12: 1005-1016Abstract Full Text Full Text PDF PubMed Scopus (48) Google Scholar) were re-acquired for spectral library generation, but this time using the shotgun mode on the 5600 mass spectrometer. Basically, the peptide selection sources were the N-glycosites identified in large discovery-driven MS-based experiments in diverse human tissues, cell lines, and plasma and the N-glycosites that were selected from the UniProt database (13.Zhang H. Loriaux P. Eng J. Campbell D. Keller A. Moss P. Bonneau R. Zhang N. Zhou Y. Wollscheid B. Cooke K. Yi E.C. Lee H. Peskind E.R. Zhang J. Smith R.D. Aebersold R. UniPep, a database for human" @default.
• W2098364431 created "2016-06-24" @default.
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• W2098364431 date "2014-07-01" @default.
• W2098364431 modified "2023-10-17" @default.
• W2098364431 title "Glycoproteomic Analysis of Prostate Cancer Tissues by SWATH Mass Spectrometry Discovers N-acylethanolamine Acid Amidase and Protein Tyrosine Kinase 7 as Signatures for Tumor Aggressiveness" @default.
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