Chen-Chun Chen (Presenter)
Academia Sinica
Authorship: Chen-Chun Chen(1,2), Han-Wen Huang(2), T. Mamie Lih(3), Yu-Ju Chen(4), Ting-Yi Sung(3), and Chi-Huey Wong(1,2)*
(1) Department of Chemistry, National Taiwan University, Taipei, Taiwan; (2) Genomics Research Center, Academia Sinica, Taipei, Taiwan; (3) Institute of Information Science, Academia Sinica, Taipei, Taiwan; (4) Institute of Chemistry, Academia Sinica, Taipei, Taiwan
| Short Abstract Alpha-fetoprotein (AFP) has long been used as a diagnostic and prognostic marker for HCC. However, high false negative rate of diagnosis necessitates the more specific biomarkers and the high sensitive method for detection. Here we developed an analytical pipeline including an automated tool for N-glycopeptide identification and the enzyme-assisted targeted quantitation method for understanding the altered core-fucosylation of AFP. We anticipate applying the proposed protocol to understand the correlation between glycosylation and liver diseases for early detection purpose. |
Long Abstract
Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third most common cause of cancer death worldwide. Precisely diagnosis of HCC at its early stage provides up to 50% enhancement of survival rate. However, insufficient detection sensitivity of current approaches necessitates the development of a robust analytical strategy with high throughput multiplexed capability for the quantification and verification of protein biomarker candidates. Moreover, considering the altered glycosylation patterns in cancer cells may also provide better specificity and sensitivity for disease diagnosis. In this work, we established an analytical workflow integrating a newly-designed automated tool for glycopeptide profiling followed by MS-based targeted glycopeptide quantitation for further verification of biomarker. Alpha-fetoprotein (AFP) has been used as a diagnostic and prognostic marker for HCC, thus, it was selected as a model target analyte in this study. Sample preparation was conducted as commonly-used proteomic protocol, including antibody-based protein purification, tryptic digestion, ZIC-HILIC glycopeptide enrichment, and LC-MS/MS data acquisition. After mass spectrometric analysis, the in-house automated computational tool was used to accurately profile the intact glycopeptides. The algorithm was designed to handle the beam-type CID tandem mass spectra. The identification was carried out through a novel strategy called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0−NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. Subsequent Y1 assignment, peptide sequence identification by common database search engines, the software generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. With the aforementioned techniques, 19 glycoforms from tandem mass glycopeptide spectra were confidently identified in purchased standards purified from human cord blood. A closer look on the different forms present in the sample reveals that the major glycoform of AFP is sialylated biantennary with or without core-fucosylation. We further established the targeted quantitation method of AFP glycopeptide with an aid of endoglycosidase, which specifically cleaves the glycosidic bond between two GlcNAc, to reduce the microheterogeniety of glycan on peptide backbone. The truncated glycopeptides were directly analyzed by SRM and quantified by the peak area of target m/z. The ratio of core-fucosylated to non-core-fucosylated glycopeptide in different patient cohort is easy to obtain after comparison between peak areas. We evaluated the analytical figures of merit of the proposed method, followed by the guideline from US Food and Drugs Administration (US FDA) for bioanalytical method validation. The targeted glycopeptide quantitation had good linearity (r^2=0.9906 for interday experiments) and a limit of detection of 15.6 ng/mL. We anticipate applying the proposed protocol to understand the correlation between glycosylation and liver diseases for early detection purpose.
References & Acknowledgements:
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