Dataset Information

Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry.

ABSTRACT: Fucosylation (Fuc) of glycoproteins plays an important role in regulating protein function and has been associated with the development of several cancer types including prostate cancer (Pca). Therefore, the research of Fuc glycoproteins has attracted increasing attention recently in the analytical field. Herein, a strategy based on lectin affinity enrichments of intact glycopeptides followed by mass spectrometry has been established to evaluate the specificities of various Fuc-binding lectins for glycosite-specific Fuc analysis of nonaggressive (NAG) and aggressive (AG) Pca cell lines. The enrichment specificities of Fuc glycopeptides using lectins (LCA, PSA, AAL, LTL, UEA I, and AOL) and MAX extraction cartridges alone, or in tandem, were evaluated. Our results showed that the use of lectin enrichment significantly increased the ratio of fucosylated glycopeptides to total glycopeptides compared to MAX enrichment. Furthermore, tandem use of lectin followed by MAX increased the number of identifications of Fuc glycopeptides compared to using lectin enrichment alone. LCA, PSA, and AOL showed stronger binding capacity than AAL, LTL, and UEA I. Also, LCA and PSA bound specifically to core Fuc, whereas AOL, AAL, and UEA I showed binding to both core Fuc and branch Fuc. The optimized enrichment method with tandem enrichment of LCA followed by MAX (LCA-MAX) was then applied to examine the Fuc glycoproteomes in two NAG and two AG Pca cell lines. In total, 973 intact Fuc glycopeptides were identified and quantified from 252 Fuc proteins by using the tandem-mass-tags (TMT) labeling and nanoliquid chromatography-mass spectrometry (nanoLC-MS/MS) analysis. Further data analysis revealed that 51 Fuc glycopeptides were overexpressed more than 2-fold in AG cell lines compared to NAG cells. The analysis of protein core fucosylation has great potential for aiding our understanding of invasive activity of AG Pca and may lead to the development of diagnostic approaches for AG Pca.

SUBMITTER: Zhou J

PROVIDER: S-EPMC5599242 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry.

Zhou Jianliang J Yang Weiming W Hu Yingwei Y Höti Naseruddin N Liu Yang Y Shah Punit P Sun Shisheng S Clark David D Thomas Stefani S Zhang Hui H

Analytical chemistry 20170703 14

Fucosylation (Fuc) of glycoproteins plays an important role in regulating protein function and has been associated with the development of several cancer types including prostate cancer (Pca). Therefore, the research of Fuc glycoproteins has attracted increasing attention recently in the analytical field. Herein, a strategy based on lectin affinity enrichments of intact glycopeptides followed by mass spectrometry has been established to evaluate the specificities of various Fuc-binding lectins f ...[more]

PMID: 28627880

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Project description:Glycoprotein changes occur in not only protein abundance but also the occupancy of each glycosylation site by different glycoforms during biological or pathological processes. Recent advances in mass spectrometry instrumentation and techniques have facilitated analysis of intact glycopeptides in complex biological samples by allowing the users to generate spectra of intact glycopeptides with glycans attached to each specific glycosylation site. However, assigning these spectra, leading to identification of the glycopeptides, is challenging. Here, we report an algorithm, named GPQuest, for site-specific identification of intact glycopeptides using higher-energy collisional dissociation (HCD) fragmentation of complex samples. In this algorithm, a spectral library of glycosite-containing peptides in the sample was built by analyzing the isolated glycosite-containing peptides using HCD LC-MS/MS. Spectra of intact glycopeptides were selected by using glycan oxonium ions as signature ions for glycopeptide spectra. These oxonium-ion-containing spectra were then compared with the spectral library generated from glycosite-containing peptides, resulting in assignment of each intact glycopeptide MS/MS spectrum to a specific glycosite-containing peptide. The glycan occupying each glycosite was determined by matching the mass difference between the precursor ion of intact glycopeptide and the glycosite-containing peptide to a glycan database. Using GPQuest, we analyzed LC-MS/MS spectra of protein extracts from prostate tumor LNCaP cells. Without enrichment of glycopeptides from global tryptic peptides and at a false discovery rate of 1%, 1008 glycan-containing MS/MS spectra were assigned to 769 unique intact N-linked glycopeptides, representing 344 N-linked glycosites with 57 different N-glycans. Spectral library matching using GPQuest assigns the HCD LC-MS/MS generated spectra of intact glycopeptides in an automated and high-throughput manner. Additionally, spectral library matching gives the user the possibility of identifying novel or modified glycans on specific glycosites that might be missing from the predetermined glycan databases.

Project description:Background: There is an urgent need for the detection of aggressive prostate cancer. Glycoproteins play essential roles in cancer development, while urine is a noninvasive and easily obtainable biological fluid that contains secretory glycoproteins from the urogenital system. Therefore, here we aimed to identify urinary glycoproteins that are capable of differentiating aggressive from non-aggressive prostate cancer. Methods: Quantitative mass spectrometry data of glycopeptides from a discovery cohort comprised of 74 aggressive (Gleason score ≥8) and 68 non-aggressive (Gleason score = 6) prostate cancer urine specimens were acquired via a data independent acquisition approach. The glycopeptides showing distinct expression profiles in aggressive relative to non-aggressive prostate cancer were further evaluated for their performance in distinguishing the two groups either individually or in combination with others using repeated 5-fold cross validation with logistic regression to build predictive models. Predictive models showing good performance from the discovery cohort were further evaluated using a validation cohort. Results: Among the 20 candidate glycoproteins, urinary ACPP outperformed the other candidates. Urinary ACPP can also serve as an adjunct to serum PSA to further improve the discrimination power for aggressive prostate cancer (AUC= 0.82, 95% confidence interval 0.75 to 0.89). A three-signature panel including urinary ACPP, urinary CLU, and serum PSA displayed the ability to distinguish aggressive prostate cancer from non-aggressive prostate cancer with an AUC of 0.86 (95% confidence interval 0.8 to 0.92). Another three-signature panel containing urinary ACPP, urinary LOX, and serum PSA also demonstrated its ability in recognizing aggressive prostate cancer (AUC=0.82, 95% confidence interval 0.75 to 0.9). Moreover, consistent performance was observed from each panel when evaluated using a validation cohort. Conclusion: We have identified glycopeptides of urinary glycoproteins associated with aggressive prostate cancer using a quantitative mass spectrometry-based glycoproteomic approach and demonstrated their potential to serve as noninvasive urinary glycoprotein biomarkers worthy of further validation by a multi-center study.

Dataset Information

Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry.

Publications

Site-Specific Fucosylation Analysis Identifying Glycoproteins Associated with Aggressive Prostate Cancer Cell Lines Using Tandem Affinity Enrichments of Intact Glycopeptides Followed by Mass Spectrometry.

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