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Lih TM, Cao L, Minoo P, Omenn GS, Hruban RH, Chan DW, Bathe OF, Zhang H. Detection of Pancreatic Ductal Adenocarcinoma-Associated Proteins in Serum. Mol Cell Proteomics 2024; 23:100687. [PMID: 38029961 PMCID: PMC10792492 DOI: 10.1016/j.mcpro.2023.100687] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2023] [Revised: 11/14/2023] [Accepted: 11/24/2023] [Indexed: 12/01/2023] Open
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal cancer types, partly because it is frequently identified at an advanced stage, when surgery is no longer feasible. Therefore, early detection using minimally invasive methods such as blood tests may improve outcomes. However, studies to discover molecular signatures for the early detection of PDAC using blood tests have only been marginally successful. In the current study, a quantitative glycoproteomic approach via data-independent acquisition mass spectrometry was utilized to detect glycoproteins in 29 patient-matched PDAC tissues and sera. A total of 892 N-linked glycopeptides originating from 141 glycoproteins had PDAC-associated changes beyond normal variation. We further evaluated the specificity of these serum-detectable glycoproteins by comparing their abundance in 53 independent PDAC patient sera and 65 cancer-free controls. The PDAC tissue-associated glycoproteins we have identified represent an inventory of serum-detectable PDAC-associated glycoproteins as candidate biomarkers that can be potentially used for the detection of PDAC using blood tests.
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Affiliation(s)
- T Mamie Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
| | - Liwei Cao
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Parham Minoo
- Department of Pathology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Gilbert S Omenn
- Departments of Computational Medicine & Bioinformatics, Internal Medicine, Human Genetics, and School of Public Health, University of Michigan, Ann Arbor, Michigan, USA
| | - Ralph H Hruban
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, Maryland, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Oliver F Bathe
- Departments of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, Maryland, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
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2
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Lih TM, Cho KC, Schnaubelt M, Hu Y, Zhang H. Integrated glycoproteomic characterization of clear cell renal cell carcinoma. Cell Rep 2023; 42:112409. [PMID: 37074911 DOI: 10.1016/j.celrep.2023.112409] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 03/03/2023] [Accepted: 04/04/2023] [Indexed: 04/20/2023] Open
Abstract
Clear cell renal cell carcinoma (ccRCC), a common form of RCC, is responsible for the high mortality rate of kidney cancer. Dysregulations of glycoproteins have been shown to associate with ccRCC. However, the molecular mechanism has not been well characterized. Here, a comprehensive glycoproteomic analysis is conducted using 103 tumors and 80 paired normal adjacent tissues. Altered glycosylation enzymes and corresponding protein glycosylation are observed, while two of the major ccRCC mutations, BAP1 and PBRM1, show distinct glycosylation profiles. Additionally, inter-tumor heterogeneity and cross-correlation between glycosylation and phosphorylation are observed. The relation of glycoproteomic features to genomic, transcriptomic, proteomic, and phosphoproteomic changes shows the role of glycosylation in ccRCC development with potential for therapeutic interventions. This study reports a large-scale tandem mass tag (TMT)-based quantitative glycoproteomic analysis of ccRCC that can serve as a valuable resource for the community.
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Affiliation(s)
- T Mamie Lih
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
| | - Kyung-Cho Cho
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Oncology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA; Department of Urology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA.
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3
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Cao L, Huang C, Cui Zhou D, Hu Y, Lih TM, Savage SR, Krug K, Clark DJ, Schnaubelt M, Chen L, da Veiga Leprevost F, Eguez RV, Yang W, Pan J, Wen B, Dou Y, Jiang W, Liao Y, Shi Z, Terekhanova NV, Cao S, Lu RJH, Li Y, Liu R, Zhu H, Ronning P, Wu Y, Wyczalkowski MA, Easwaran H, Danilova L, Mer AS, Yoo S, Wang JM, Liu W, Haibe-Kains B, Thiagarajan M, Jewell SD, Hostetter G, Newton CJ, Li QK, Roehrl MH, Fenyö D, Wang P, Nesvizhskii AI, Mani DR, Omenn GS, Boja ES, Mesri M, Robles AI, Rodriguez H, Bathe OF, Chan DW, Hruban RH, Ding L, Zhang B, Zhang H. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 2021; 184:5031-5052.e26. [PMID: 34534465 PMCID: PMC8654574 DOI: 10.1016/j.cell.2021.08.023] [Citation(s) in RCA: 205] [Impact Index Per Article: 68.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 03/19/2021] [Accepted: 08/18/2021] [Indexed: 02/07/2023]
Abstract
Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer with poor patient survival. Toward understanding the underlying molecular alterations that drive PDAC oncogenesis, we conducted comprehensive proteogenomic analysis of 140 pancreatic cancers, 67 normal adjacent tissues, and 9 normal pancreatic ductal tissues. Proteomic, phosphoproteomic, and glycoproteomic analyses were used to characterize proteins and their modifications. In addition, whole-genome sequencing, whole-exome sequencing, methylation, RNA sequencing (RNA-seq), and microRNA sequencing (miRNA-seq) were performed on the same tissues to facilitate an integrated proteogenomic analysis and determine the impact of genomic alterations on protein expression, signaling pathways, and post-translational modifications. To ensure robust downstream analyses, tumor neoplastic cellularity was assessed via multiple orthogonal strategies using molecular features and verified via pathological estimation of tumor cellularity based on histological review. This integrated proteogenomic characterization of PDAC will serve as a valuable resource for the community, paving the way for early detection and identification of novel therapeutic targets.
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Affiliation(s)
- Liwei Cao
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Chen Huang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel Cui Zhou
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yingwei Hu
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - T Mamie Lih
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Sara R Savage
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Karsten Krug
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - David J Clark
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Lijun Chen
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | | | | | - Weiming Yang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Jianbo Pan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Bo Wen
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yongchao Dou
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Wen Jiang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Yuxing Liao
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Zhiao Shi
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA
| | - Nadezhda V Terekhanova
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Song Cao
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Rita Jui-Hsien Lu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yize Li
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Ruiyang Liu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Houxiang Zhu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Peter Ronning
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Yige Wu
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Matthew A Wyczalkowski
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA
| | - Hariharan Easwaran
- The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ludmila Danilova
- Department of Oncology, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Arvind Singh Mer
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada
| | - Seungyeul Yoo
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | - Joshua M Wang
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Wenke Liu
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Benjamin Haibe-Kains
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 1L7, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON M5G 1L7, Canada
| | - Mathangi Thiagarajan
- Leidos Biomedical Research Inc., Frederick National Laboratory for Cancer Research, Frederick, MD 21702, USA
| | - Scott D Jewell
- Van Andel Research Institute, Grand Rapids, MI 49503, USA
| | | | | | - Qing Kay Li
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Michael H Roehrl
- Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA; Human Oncology and Pathogenesis Program, Memorial Sloan Kettering Cancer Center, New York, NY 10065, USA
| | - David Fenyö
- Institute for Systems Genetics, NYU Grossman School of Medicine, New York, NY 10016, USA; Department of Biochemistry and Molecular Pharmacology, NYU Grossman School of Medicine, New York, NY 10016, USA
| | - Pei Wang
- Sema4, a Mount Sinai venture, Stamford, CT 06902, USA
| | | | - D R Mani
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA 02142, USA
| | - Gilbert S Omenn
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Emily S Boja
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Mehdi Mesri
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Ana I Robles
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Henry Rodriguez
- Office of Cancer Clinical Proteomics Research, National Cancer Institute, Bethesda, MD 20892, USA
| | - Oliver F Bathe
- Departments of Surgery and Oncology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Daniel W Chan
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Ralph H Hruban
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA; The Sol Goldman Pancreatic Cancer Research Center, Johns Hopkins University, Baltimore, MD 21231, USA
| | - Li Ding
- Department of Medicine, Washington University in St. Louis, St. Louis, MO 631110, USA; McDonnell Genome Institute, Washington University in St. Louis, St. Louis, MO 63108, USA.
| | - Bing Zhang
- Lester and Sue Smith Breast Center, Baylor College of Medicine, Houston, TX 77030, USA; Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030, USA.
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University, Baltimore, MD 21231, USA; The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University, Baltimore, MD 21287, USA.
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4
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Dong M, Lih TM, Chen SY, Cho KC, Eguez RV, Höti N, Zhou Y, Yang W, Mangold L, Chan DW, Zhang Z, Sokoll LJ, Partin A, Zhang H. Urinary glycoproteins associated with aggressive prostate cancer. Am J Cancer Res 2020; 10:11892-11907. [PMID: 33204318 PMCID: PMC7667684 DOI: 10.7150/thno.47066] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 08/17/2020] [Indexed: 12/12/2022] Open
Abstract
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.
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5
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Chen SY, Dong M, Yang G, Zhou Y, Clark DJ, Lih TM, Schnaubelt M, Liu Z, Zhang H. Glycans, Glycosite, and Intact Glycopeptide Analysis of N-Linked Glycoproteins Using Liquid Handling Systems. Anal Chem 2020; 92:1680-1686. [PMID: 31859482 PMCID: PMC7331092 DOI: 10.1021/acs.analchem.9b03761] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023]
Abstract
Aberrant glycosylation has been shown to associate with disease progression, and with glycoproteins representing the major protein component of biological fluids this makes them attractive targets for disease monitoring. Leveraging glycoproteomic analysis via mass spectrometry (MS) could provide the insight into the altered glycosylation patterns that relate to disease progression. However, investigation of large sample cohorts requires rapid, efficient, and highly reproducible sample preparation. To address the limitation, we developed a high-throughput method for characterizing glycans, glycosites, and intact glycopeptides (IGPs) derived from N-linked glycoproteins. We combined disparate peptide enrichment strategies (i.e., hydrophilic and hydrophobic) and a liquid handling platform allowing for a high throughput and rapid enrichment of IGP in a 96-well plate format. The C18/MAX-Tip workflow reduced sample processing time and facilitated the selective enrichment of IGPs from complex samples. Furthermore, our approach enabled the analysis of deglycosylated peptides and glycans from enriched IGPs following PNGase F digest. Following development and optimization of the C18/MAX-Tip methodology using the standard glycoprotein, fetuin, we investigated normal urine samples to obtain N-linked glycoprotein information. Together, our method enables a high-throughput enrichment of glycan, glycosites, and IGPs from biological samples.
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Affiliation(s)
- Shao-Yung Chen
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering,
Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Mingming Dong
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - Ganglong Yang
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - Yangying Zhou
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - David J. Clark
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - T. Mamie Lih
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - Michael Schnaubelt
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
| | - Zichen Liu
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering,
Johns Hopkins University, Baltimore, Maryland 21218, United States
| | - Hui Zhang
- Department of Pathology, Johns Hopkins University
School of Medicine, Baltimore, Maryland 21218, United States
- Department of Chemical and Biomolecular Engineering,
Johns Hopkins University, Baltimore, Maryland 21218, United States
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6
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Lih TM, Choong WK, Chen CC, Cheng CW, Lin HN, Chen CT, Chang HY, Hsu WL, Sung TY. MAGIC-web: a platform for untargeted and targeted N-linked glycoprotein identification. Nucleic Acids Res 2016; 44:W575-80. [PMID: 27084943 PMCID: PMC4987873 DOI: 10.1093/nar/gkw254] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2016] [Accepted: 04/02/2016] [Indexed: 01/25/2023] Open
Abstract
MAGIC-web is the first web server, to the best of our knowledge, that performs both untargeted and targeted analyses of mass spectrometry-based glycoproteomics data for site-specific N-linked glycoprotein identification. The first two modules, MAGIC and MAGIC+, are designed for untargeted and targeted analysis, respectively. MAGIC is implemented with our previously proposed novel Y1-ion pattern matching method, which adequately detects Y1- and Y0-ion without prior information of proteins and glycans, and then generates in silico MS2 spectra that serve as input to a database search engine (e.g. Mascot) to search against a large-scale protein sequence database. On top of that, the newly implemented MAGIC+ allows users to determine glycopeptide sequences using their own protein sequence file. The third module, Reports Integrator, provides the service of combining protein identification results from Mascot and glycan-related information from MAGIC-web to generate a complete site-specific protein-glycan summary report. The last module, Glycan Search, is designed for the users who are interested in finding possible glycan structures with specific numbers and types of monosaccharides. The results from MAGIC, MAGIC+ and Reports Integrator can be downloaded via provided links whereas the annotated spectra and glycan structures can be visualized in the browser. MAGIC-web is accessible from http://ms.iis.sinica.edu.tw/MAGIC-web/index.html.
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Affiliation(s)
- T Mamie Lih
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wai-Kok Choong
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chen-Chun Chen
- Genomics Research Center, Academia Sinica, Taipei 11529, Taiwan Department of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Cheng-Wei Cheng
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Hsin-Nan Lin
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Hui-Yin Chang
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
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7
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Chang HY, Chen CT, Lih TM, Lynn KS, Juo CG, Hsu WL, Sung TY. iMet-Q: A User-Friendly Tool for Label-Free Metabolomics Quantitation Using Dynamic Peak-Width Determination. PLoS One 2016; 11:e0146112. [PMID: 26784691 PMCID: PMC4718670 DOI: 10.1371/journal.pone.0146112] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2015] [Accepted: 12/14/2015] [Indexed: 11/25/2022] Open
Abstract
Efficient and accurate quantitation of metabolites from LC-MS data has become an important topic. Here we present an automated tool, called iMet-Q (intelligent Metabolomic Quantitation), for label-free metabolomics quantitation from high-throughput MS1 data. By performing peak detection and peak alignment, iMet-Q provides a summary of quantitation results and reports ion abundance at both replicate level and sample level. Furthermore, it gives the charge states and isotope ratios of detected metabolite peaks to facilitate metabolite identification. An in-house standard mixture and a public Arabidopsis metabolome data set were analyzed by iMet-Q. Three public quantitation tools, including XCMS, MetAlign, and MZmine 2, were used for performance comparison. From the mixture data set, seven standard metabolites were detected by the four quantitation tools, for which iMet-Q had a smaller quantitation error of 12% in both profile and centroid data sets. Our tool also correctly determined the charge states of seven standard metabolites. By searching the mass values for those standard metabolites against Human Metabolome Database, we obtained a total of 183 metabolite candidates. With the isotope ratios calculated by iMet-Q, 49% (89 out of 183) metabolite candidates were filtered out. From the public Arabidopsis data set reported with two internal standards and 167 elucidated metabolites, iMet-Q detected all of the peaks corresponding to the internal standards and 167 metabolites. Meanwhile, our tool had small abundance variation (≤ 0.19) when quantifying the two internal standards and had higher abundance correlation (≥ 0.92) when quantifying the 167 metabolites. iMet-Q provides user-friendly interfaces and is publicly available for download at http://ms.iis.sinica.edu.tw/comics/Software_iMet-Q.html.
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Affiliation(s)
- Hui-Yin Chang
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ching-Tai Chen
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - T. Mamie Lih
- Bioinformatics Program, Taiwan International Graduate Program, Academia Sinica, Taipei 11529, Taiwan
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ke-Shiuan Lynn
- Department of Mathematics, Fu Jen Catholic University, New Taipei City 24205, Taiwan
| | - Chiun-Gung Juo
- Molecular Medicine Research Center, Chang Gung University, Taoyuan 33302, Taiwan
| | - Wen-Lian Hsu
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Ting-Yi Sung
- Institute of Information Science, Academia Sinica, Taipei 11529, Taiwan
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8
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Lynn KS, Cheng ML, Chen YR, Hsu C, Chen A, Lih TM, Chang HY, Huang CJ, Shiao MS, Pan WH, Sung TY, Hsu WL. Metabolite Identification for Mass Spectrometry-Based Metabolomics Using Multiple Types of Correlated Ion Information. Anal Chem 2015; 87:2143-51. [DOI: 10.1021/ac503325c] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Affiliation(s)
- Ke-Shiuan Lynn
- Institute of Information
Science, Academia Sinica, Taipei, Taiwan
| | - Mei-Ling Cheng
- Department
of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Yet-Ran Chen
- Agricultural Biotechnology
Research Center, Academia Sinica, Taipei, Taiwan
| | - Chin Hsu
- Department
of Exercise Health Science, National Taiwan University of Physical Education and Sport, Taichung, Taiwan
| | - Ann Chen
- Department
of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - T. Mamie Lih
- Bioinformatics
Program, TIGP, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Hui-Yin Chang
- Bioinformatics
Program, TIGP, Institute of Information Science, Academia Sinica, Taipei, Taiwan
| | - Ching-jang Huang
- Department
of Biochemical Science and Technology, National Taiwan University, Taipei, Taiwan
| | - Ming-Shi Shiao
- Department
of Biomedical Sciences, Chang Gung University, Taoyuan, Taiwan
| | - Wen-Harn Pan
- Institute of Biomedical
Sciences, Academia Sinica, Taipei, Taiwan
| | - Ting-Yi Sung
- Institute of Information
Science, Academia Sinica, Taipei, Taiwan
| | - Wen-Lian Hsu
- Institute of Information
Science, Academia Sinica, Taipei, Taiwan
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9
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Lynn KS, Chen CC, Lih TM, Cheng CW, Su WC, Chang CH, Cheng CY, Hsu WL, Chen YJ, Sung TY. MAGIC: An Automated N-Linked Glycoprotein Identification Tool Using a Y1-Ion Pattern Matching Algorithm and in Silico MS2 Approach. Anal Chem 2015; 87:2466-73. [DOI: 10.1021/ac5044829] [Citation(s) in RCA: 58] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2023]
Affiliation(s)
- Ke-Shiuan Lynn
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chen-Chun Chen
- Genomics
Research Center, Academia Sinica, Taipei 11529, Taiwan
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - T. Mamie Lih
- Bioinformatics
Program, Taiwan International Graduate Program, Institute of Information
Science, Academia Sinica, Taipei 11529, Taiwan
- Institute
of Biomedical Informatics, National Yang-Ming University, Taipei 11221, Taiwan
| | - Cheng-Wei Cheng
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Wan-Chih Su
- Institute
of Chemistry, Academia Sinica, Taipei 11529, Taiwan
| | - Chun-Hao Chang
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Chia-Ying Cheng
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Wen-Lian Hsu
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
| | - Yu-Ju Chen
- Institute
of Chemistry, Academia Sinica, Taipei 11529, Taiwan
- Department
of Chemistry, National Taiwan University, Taipei 10617, Taiwan
| | - Ting-Yi Sung
- Institute
of Information Science, Academia Sinica, Taipei 11529, Taiwan
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