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Journal articles on the topic 'Glycoproteome'

To see the other types of publications on this topic, follow the link: Glycoproteome.

Author: Grafiati

Published: 10 December 2022

Last updated: 28 January 2023

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1

Schulze, Stefan, Friedhelm Pfeiffer, Benjamin A. Garcia, and Mechthild Pohlschroder. "Comprehensive glycoproteomics shines new light on the complexity and extent of glycosylation in archaea." PLOS Biology 19, no. 6 (June 17, 2021): e3001277. http://dx.doi.org/10.1371/journal.pbio.3001277.

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Glycosylation is one of the most complex posttranslational protein modifications. Its importance has been established not only for eukaryotes but also for a variety of prokaryotic cellular processes, such as biofilm formation, motility, and mating. However, comprehensive glycoproteomic analyses are largely missing in prokaryotes. Here, we extend the phenotypic characterization of N-glycosylation pathway mutants in Haloferax volcanii and provide a detailed glycoproteome for this model archaeon through the mass spectrometric analysis of intact glycopeptides. Using in-depth glycoproteomic datasets generated for the wild-type (WT) and mutant strains as well as a reanalysis of datasets within the Archaeal Proteome Project (ArcPP), we identify the largest archaeal glycoproteome described so far. We further show that different N-glycosylation pathways can modify the same glycosites under the same culture conditions. The extent and complexity of the Hfx. volcanii N-glycoproteome revealed here provide new insights into the roles of N-glycosylation in archaeal cell biology.

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2

Yang, Lijun, Jie Liu, Hua Li, Yilian Liu, An He, Peiwu Huang, Weina Gao, Hua Cao, Ruilian Xu, and Ruijun Tian. "A fully integrated sample preparation strategy for highly sensitive intact glycoproteomics." Analyst 147, no. 5 (2022): 794–98. http://dx.doi.org/10.1039/d1an02166d.

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A fully integrated spintip-based glycoproteomic technology, termed Intact GlycoSISPROT, was developed for highly sensitive intact glycoproteome analysis with low microgram to nanogram level protein samples.

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3

Lu, Haojie, Ying Zhang, and Pengyuan Yang. "Advancements in mass spectrometry-based glycoproteomics and glycomics." National Science Review 3, no. 3 (April 21, 2016): 345–64. http://dx.doi.org/10.1093/nsr/nww019.

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Abstract Protein N-glycosylation plays a crucial role in a considerable number of important biological processes. Research studies on glycoproteomes and glycomes have already characterized many glycoproteins and glycans associated with cell development, life cycle, and disease progression. Mass spectrometry (MS) is the most powerful tool for identifying biomolecules including glycoproteins and glycans, however, utilizing MS-based approaches to identify glycoproteomes and glycomes is challenging due to the technical difficulties associated with glycosylation analysis. In this review, we summarize the most recent developments in MS-based glycoproteomics and glycomics, including a discussion on the development of analytical methodologies and strategies used to explore the glycoproteome and glycome, as well as noteworthy biological discoveries made in glycoproteome and glycome research. This review places special emphasis on China, where scientists have made sizeable contributions to the literature, as advancements in glycoproteomics and glycomincs are occurring quite rapidly.

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4

Mertz, Joseph L., Shisheng Sun, Bojiao Yin, Yingwei Hu, Rahul Bhattacharya, Michael J. Bettenbaugh, Kevin J. Yarema, and Hui Zhang. "Comparison of Three Glycoproteomic Methods for the Analysis of the Secretome of CHO Cells Treated with 1,3,4-O-Bu3ManNAc." Bioengineering 7, no. 4 (November 10, 2020): 144. http://dx.doi.org/10.3390/bioengineering7040144.

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Comprehensive analysis of the glycoproteome is critical due to the importance of glycosylation to many aspects of protein function. The tremendous complexity of this post-translational modification, however, makes it difficult to adequately characterize the glycoproteome using any single method. To overcome this pitfall, in this report we compared three glycoproteomic analysis methods; first the recently developed N-linked glycans and glycosite-containing peptides (NGAG) chemoenzymatic method, second, solid-phase extraction of N-linked glycoproteins (SPEG), and third, hydrophilic interaction liquid chromatography (HILIC) by characterizing N-linked glycosites in the secretome of Chinese hamster ovary (CHO) cells. Interestingly, the glycosites identified by SPEG and HILIC overlapped considerably whereas NGAG identified many glycosites not observed in the other two methods. Further, utilizing enhanced intact glycopeptide identification afforded by the NGAG workflow, we found that the sugar analog 1,3,4-O-Bu3ManNAc, a “high flux” metabolic precursor for sialic acid biosynthesis, increased sialylation of secreted proteins including recombinant human erythropoietin (rhEPO).

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5

Vivekanandan-Giri, Anuradha, Jessica L. Slocum, Carolyn L. Buller, Venkatesha Basrur, Wenjun Ju, Rodica Pop-Busui, David M. Lubman, Matthias Kretzler, and Subramaniam Pennathur. "Urine Glycoprotein Profile Reveals Novel Markers for Chronic Kidney Disease." International Journal of Proteomics 2011 (October 10, 2011): 1–18. http://dx.doi.org/10.1155/2011/214715.

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Chronic kidney disease (CKD) is a significant public health problem, and progression to end-stage renal disease leads to dramatic increases in morbidity and mortality. The mechanisms underlying progression of disease are poorly defined, and current noninvasive markers incompletely correlate with disease progression. Therefore, there is a great need for discovering novel markers for CKD. We utilized a glycoproteomic profiling approach to test the hypothesis that the urinary glycoproteome profile from subjects with CKD would be distinct from healthy controls. N-linked glycoproteins were isolated and enriched from the urine of healthy controls and subjects with CKD. This strategy identified several differentially expressed proteins in CKD, including a diverse array of proteins with endopeptidase inhibitor activity, protein binding functions, and acute-phase/immune-stress response activity supporting the proposal that inflammation may play a central role in CKD. Additionally, several of these proteins have been previously linked to kidney disease implicating a mechanistic role in disease pathogenesis. Collectively, our observations suggest that the human urinary glycoproteome may serve as a discovery source for novel mechanism-based biomarkers of CKD.

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6

Sharma, Ashok, James Cox, Joshua Glass, Tae Jin Lee, Sai Karthik Kodeboyina, Wenbo Zhi, Lane Ulrich, Zachary Lukowski, and Shruti Sharma. "Serum Glycoproteomic Alterations in Patients with Diabetic Retinopathy." Proteomes 8, no. 3 (September 13, 2020): 25. http://dx.doi.org/10.3390/proteomes8030025.

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The precise molecular mechanisms of diabetic retinopathy (DR) pathogenesis are unclear, and treatment options are limited. There is an urgent need to discover and develop novel therapeutic targets for the treatment of this disease. Glycosylation is a post-translational modification that plays a critical role in determining protein structure, function, and stability. Recent studies have found that serum glycoproteomic changes are associated with the presence or progression of several inflammatory diseases. However, very little is known about the glycoproteomic changes associated with DR. In this study, glycoproteomic profiling of the serum of diabetic patients with and without DR was performed. A total of 15 glycopeptides from 11 glycoproteins were found to be significantly altered (5 upregulated and 10 downregulated) within the serum glycoproteome of DR patients. These glycoproteins are known to be involved in the maintenance of the extracellular matrix and complement system through peptidolytic activity or regulation.

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7

Nilsson, Jonas, Adnan Halim, Ammi Grahn, and Göran Larson. "Targeting the glycoproteome." Glycoconjugate Journal 30, no. 2 (August 11, 2012): 119–36. http://dx.doi.org/10.1007/s10719-012-9438-6.

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8

Phung, Toan K., Cassandra L. Pegg, and Benjamin L. Schulz. "GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis." Beilstein Journal of Organic Chemistry 16 (September 1, 2020): 2127–35. http://dx.doi.org/10.3762/bjoc.16.180.

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Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an automated software pipeline which integrates the complementary outputs of Byonic and Proteome Discoverer to allow high-throughput automated quantitative glycoproteomic data analysis. The output of GlypNirO is clearly structured, allowing manual interrogation, and is also appropriate for input into diverse statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease.

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9

Chernykh, Anastasia, Rebeca Kawahara, and Morten Thaysen-Andersen. "Towards structure-focused glycoproteomics." Biochemical Society Transactions 49, no. 1 (January 13, 2021): 161–86. http://dx.doi.org/10.1042/bst20200222.

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Facilitated by advances in the separation sciences, mass spectrometry and informatics, glycoproteomics, the analysis of intact glycopeptides at scale, has recently matured enabling new insights into the complex glycoproteome. While diverse quantitative glycoproteomics strategies capable of mapping monosaccharide compositions of N- and O-linked glycans to discrete sites of proteins within complex biological mixtures with considerable sensitivity, quantitative accuracy and coverage have become available, developments supporting the advancement of structure-focused glycoproteomics, a recognised frontier in the field, have emerged. Technologies capable of providing site-specific information of the glycan fine structures in a glycoproteome-wide context are indeed necessary to address many pending questions in glycobiology. In this review, we firstly survey the latest glycoproteomics studies published in 2018–2020, their approaches and their findings, and then summarise important technological innovations in structure-focused glycoproteomics. Our review illustrates that while the O-glycoproteome remains comparably under-explored despite the emergence of new O-glycan-selective mucinases and other innovative tools aiding O-glycoproteome profiling, quantitative glycoproteomics is increasingly used to profile the N-glycoproteome to tackle diverse biological questions. Excitingly, new strategies compatible with structure-focused glycoproteomics including novel chemoenzymatic labelling, enrichment, separation, and mass spectrometry-based detection methods are rapidly emerging revealing glycan fine structural details including bisecting GlcNAcylation, core and antenna fucosylation, and sialyl-linkage information with protein site resolution. Glycoproteomics has clearly become a mainstay within the glycosciences that continues to reach a broader community. It transpires that structure-focused glycoproteomics holds a considerable potential to aid our understanding of systems glycobiology and unlock secrets of the glycoproteome in the immediate future.

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10

Liu, Jing, Fangjun Wang, Hui Lin, Jun Zhu, Yangyang Bian, Kai Cheng, and Hanfa Zou. "Monolithic Capillary Column Based Glycoproteomic Reactor for High-Sensitive Analysis of N-Glycoproteome." Analytical Chemistry 85, no. 5 (February 20, 2013): 2847–52. http://dx.doi.org/10.1021/ac400315n.

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11

Neubert, Patrick, Adnan Halim, Martin Zauser, Andreas Essig, Hiren J. Joshi, Ewa Zatorska, Ida Signe Bohse Larsen, et al. "Mapping theO-Mannose Glycoproteome inSaccharomyces cerevisiae." Molecular & Cellular Proteomics 15, no. 4 (January 13, 2016): 1323–37. http://dx.doi.org/10.1074/mcp.m115.057505.

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12

Zhang, Lijuang, Haojie Lu, and Pengyuan Yang. "Specific enrichment methods for glycoproteome research." Analytical and Bioanalytical Chemistry 396, no. 1 (September 4, 2009): 199–203. http://dx.doi.org/10.1007/s00216-009-3086-0.

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13

Blixt, Ola, and Ulrika Westerlind. "Arraying the post-translational glycoproteome (PTG)." Current Opinion in Chemical Biology 18 (February 2014): 62–69. http://dx.doi.org/10.1016/j.cbpa.2014.01.002.

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14

Kumar, Saravanan, Krishan Kumar, Pankaj Pandey, Vijayalakshmi Rajamani, Kethireddy Venkata Padmalatha, Gurusamy Dhandapani, Mogilicherla Kanakachari, Sadhu Leelavathi, Polumetla Ananda Kumar, and Vanga Siva Reddy. "Glycoproteome of Elongating Cotton Fiber Cells." Molecular & Cellular Proteomics 12, no. 12 (September 9, 2013): 3677–89. http://dx.doi.org/10.1074/mcp.m113.030726.

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15

Noborn, Fredrik, Alejandro Gomez Toledo, Waqas Nasir, Jonas Nilsson, Tabea Dierker, Lena Kjellén, and Göran Larson. "Expanding the chondroitin glycoproteome ofCaenorhabditis elegans." Journal of Biological Chemistry 293, no. 1 (November 14, 2017): 379–89. http://dx.doi.org/10.1074/jbc.m117.807800.

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16

Zhang, Ying, Meng Yu, Cheng Zhang, Yali Wang, Yi Di, Changchun Wang, and Haojie Lu. "Highly specific enrichment of N-glycoproteome through a nonreductive amination reaction using Fe3O4@SiO2-aniline nanoparticles." Chemical Communications 51, no. 27 (2015): 5982–85. http://dx.doi.org/10.1039/c4cc10285a.

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17

Cao, Xueyan, Shimo Kang, Mei Yang, Weixuan Li, Shangyi Wu, Hongjiao Han, Lingshuai Meng, Rina Wu, and Xiqing Yue. "Quantitative N-glycoproteomics of milk fat globule membrane in human colostrum and mature milk reveals changes in protein glycosylation during lactation." Food & Function 9, no. 2 (2018): 1163–72. http://dx.doi.org/10.1039/c7fo01796k.

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18

Miao, Weili, Cheng Zhang, Yan Cai, Ying Zhang, and Haojie Lu. "Fast solid-phase extraction of N-linked glycopeptides by amine-functionalized mesoporous silica nanoparticles." Analyst 141, no. 8 (2016): 2435–40. http://dx.doi.org/10.1039/c6an00285d.

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For the first time, mesoporous material has been introduced into N-glycoproteome extraction based on a reductive amination reaction, which greatly enhanced the enrichment efficiency and deglycosylation efficiency.

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19

Sleat, David E., Haiyan Zheng, and Peter Lobel. "The human urine mannose 6-phosphate glycoproteome." Biochimica et Biophysica Acta (BBA) - Proteins and Proteomics 1774, no. 3 (March 2007): 368–72. http://dx.doi.org/10.1016/j.bbapap.2006.12.004.

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20

Sun, Qiangling, Xiaonan Kang, Yang Zhang, Haijun Zhou, Zhi Dai, Wenjing Lu, Xinwen Zhou, Xiaohui Liu, Pengyuan Yang, and Yinkun Liu. "DSA affinity glycoproteome of human liver tissue." Archives of Biochemistry and Biophysics 484, no. 1 (April 2009): 24–29. http://dx.doi.org/10.1016/j.abb.2009.01.009.

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21

Halim, Adnan, Ida Signe Bohse Larsen, Patrick Neubert, Hiren Jitendra Joshi, Bent Larsen Petersen, Sergey Y. Vakhrushev, Sabine Strahl, and Henrik Clausen. "Discovery of a nucleocytoplasmic O-mannose glycoproteome in yeast." Proceedings of the National Academy of Sciences 112, no. 51 (December 7, 2015): 15648–53. http://dx.doi.org/10.1073/pnas.1511743112.

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Dynamic cycling of N-Acetylglucosamine (GlcNAc) on serine and threonine residues (O-GlcNAcylation) is an essential process in all eukaryotic cells except yeast, including Saccharomyces cerevisiae and Schizosaccharomyces pombe. O-GlcNAcylation modulates signaling and cellular processes in an intricate interplay with protein phosphorylation and serves as a key sensor of nutrients by linking the hexosamine biosynthetic pathway to cellular signaling. A longstanding conundrum has been how yeast survives without O-GlcNAcylation in light of its similar phosphorylation signaling system. We previously developed a sensitive lectin enrichment and mass spectrometry workflow for identification of the human O-linked mannose (O-Man) glycoproteome and used this to identify a pleothora of O-Man glycoproteins in human cell lines including the large family of cadherins and protocadherins. Here, we applied the workflow to yeast with the aim to characterize the yeast O-Man glycoproteome, and in doing so, we discovered hitherto unknown O-Man glycosites on nuclear, cytoplasmic, and mitochondrial proteins in S. cerevisiae and S. pombe. Such O-Man glycoproteins were not found in our analysis of human cell lines. However, the type of yeast O-Man nucleocytoplasmic proteins and the localization of identified O-Man residues mirror that of the O-GlcNAc glycoproteome found in other eukaryotic cells, indicating that the two different types of O-glycosylations serve the same important biological functions. The discovery opens for exploration of the enzymatic machinery that is predicted to regulate the nucleocytoplasmic O-Man glycosylations. It is likely that manipulation of this type of O-Man glycosylation will have wide applications for yeast bioprocessing.

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22

Xu, Yuanwei, and Hui Zhang. "Putting the pieces together: mapping the O-glycoproteome." Current Opinion in Biotechnology 71 (October 2021): 130–36. http://dx.doi.org/10.1016/j.copbio.2021.07.006.

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23

Gundry, Rebekah L., Kimberly Raginski, Yelena Tarasova, Irina Tchernyshyov, Damaris Bausch-Fluck, Steven T. Elliott, Kenneth R. Boheler, Jennifer E. Van Eyk, and Bernd Wollscheid. "The Mouse C2C12 Myoblast Cell SurfaceN-Linked Glycoproteome." Molecular & Cellular Proteomics 8, no. 11 (August 4, 2009): 2555–69. http://dx.doi.org/10.1074/mcp.m900195-mcp200.

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24

Baenziger, J. U. "Moving the O-glycoproteome from form to function." Proceedings of the National Academy of Sciences 109, no. 25 (June 1, 2012): 9672–73. http://dx.doi.org/10.1073/pnas.1206735109.

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25

Kumar, Saravanan, Pankaj Pandey, Krishan Kumar, Vijayalakshmi Rajamani, Kethireddy Venkata Padmalatha, Gurusamy Dhandapani, Mogilicherla Kanakachari, Sadhu Leelavathi, Polumetla Ananda Kumar, and Vanga Siva Reddy. "Delineating the glycoproteome of elongating cotton fiber cells." Data in Brief 5 (December 2015): 717–25. http://dx.doi.org/10.1016/j.dib.2015.10.015.

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26

Zhou, Hai-Jun, Yin-Kun Liu, Jie-Feng Chui, Qiang-Ling Sun, Wen-Jing Lu, Kun Guo, Hong Jin, Li-Ming Wei, and Peng-Yuan Yang. "A glycoproteome database of normal human liver tissue." Journal of Cancer Research and Clinical Oncology 133, no. 6 (January 12, 2007): 379–87. http://dx.doi.org/10.1007/s00432-006-0183-8.

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27

Fanayan, Susan, Marina Hincapie, and William S. Hancock. "Using lectins to harvest the plasma/serum glycoproteome." ELECTROPHORESIS 33, no. 12 (June 28, 2012): 1746–54. http://dx.doi.org/10.1002/elps.201100567.

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28

Zhang, Qi, Cheng Ma, Lih-Shen Chin, and Lian Li. "Integrative glycoproteomics reveals protein N-glycosylation aberrations and glycoproteomic network alterations in Alzheimer’s disease." Science Advances 6, no. 40 (October 2020): eabc5802. http://dx.doi.org/10.1126/sciadv.abc5802.

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Protein N-glycosylation plays critical roles in controlling brain function, but little is known about human brain N-glycoproteome and its alterations in Alzheimer’s disease (AD). Here, we report the first, large-scale, site-specific N-glycoproteome profiling study of human AD and control brains using mass spectrometry–based quantitative N-glycoproteomics. The study provided a system-level view of human brain N-glycoproteins and in vivo N-glycosylation sites and identified disease signatures of altered N-glycopeptides, N-glycoproteins, and N-glycosylation site occupancy in AD. Glycoproteomics-driven network analysis showed 13 modules of co-regulated N-glycopeptides/glycoproteins, 6 of which are associated with AD phenotypes. Our analyses revealed multiple dysregulated N-glycosylation–affected processes and pathways in AD brain, including extracellular matrix dysfunction, neuroinflammation, synaptic dysfunction, cell adhesion alteration, lysosomal dysfunction, endocytic trafficking dysregulation, endoplasmic reticulum dysfunction, and cell signaling dysregulation. Our findings highlight the involvement of N-glycosylation aberrations in AD pathogenesis and provide new molecular and system-level insights for understanding and treating AD.

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29

Xiao, Jing, Jinqiu Wang, Renyou Gan, Di Wu, Yisha Xu, Lianxin Peng, and Fang Geng. "Quantitative N-glycoproteome analysis of bovine milk and yogurt." Current Research in Food Science 5 (2022): 182–90. http://dx.doi.org/10.1016/j.crfs.2022.01.003.

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30

Wang, Gaigai, Yibo Wu, Tao Zhou, Yueshuai Guo, Bo Zheng, Jing Wang, Ye Bi, et al. "Mapping of the N-Linked Glycoproteome of Human Spermatozoa." Journal of Proteome Research 12, no. 12 (November 15, 2013): 5750–59. http://dx.doi.org/10.1021/pr400753f.

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31

Liu, Ze, Jing Cao, Yifeng He, Liang Qiao, Congjian Xu, Haojie Lu, and Pengyuan Yang. "Tandem18O Stable Isotope Labeling for Quantification of N-Glycoproteome." Journal of Proteome Research 9, no. 1 (January 4, 2010): 227–36. http://dx.doi.org/10.1021/pr900528j.

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32

Girsen, Anna, Juha Rasanen, Jorge Tolosa, Leonardo Pereira, Michael Gravett, and Srinivasa Nagalla. "First-trimester detection of preeclampsia by serum glycoproteome analysis." American Journal of Obstetrics and Gynecology 195, no. 6 (December 2006): S160. http://dx.doi.org/10.1016/j.ajog.2006.10.583.

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33

Sun, Bingyun, Li Ma, Xiaowei Yan, Denis Lee, Vinita Alexander, Laura J. Hohmann, Cynthia Lorang, Lalangi Chandrasena, Qiang Tian, and Leroy Hood. "N-Glycoproteome of E14.Tg2a Mouse Embryonic Stem Cells." PLoS ONE 8, no. 2 (February 6, 2013): e55722. http://dx.doi.org/10.1371/journal.pone.0055722.

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34

Oliveira, Tiago, Morten Thaysen-Andersen, Nicolle H. Packer, and Daniel Kolarich. "The Hitchhiker's guide to glycoproteomics." Biochemical Society Transactions 49, no. 4 (July 20, 2021): 1643–62. http://dx.doi.org/10.1042/bst20200879.

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Protein glycosylation is one of the most common post-translational modifications that are essential for cell function across all domains of life. Changes in glycosylation are considered a hallmark of many diseases, thus making glycoproteins important diagnostic and prognostic biomarker candidates and therapeutic targets. Glycoproteomics, the study of glycans and their carrier proteins in a system-wide context, is becoming a powerful tool in glycobiology that enables the functional analysis of protein glycosylation. This ‘Hitchhiker's guide to glycoproteomics’ is intended as a starting point for anyone who wants to explore the emerging world of glycoproteomics. The review moves from the techniques that have been developed for the characterisation of single glycoproteins to technologies that may be used for a successful complex glycoproteome characterisation. Examples of the variety of approaches, methodologies, and technologies currently used in the field are given. This review introduces the common strategies to capture glycoprotein-specific and system-wide glycoproteome data from tissues, body fluids, or cells, and a perspective on how integration into a multi-omics workflow enables a deep identification and characterisation of glycoproteins — a class of biomolecules essential in regulating cell function.

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35

Liang, Kung-Hao, Sung-Fang Chen, Yu-Hua Lin, Yu-De Chu, Yang-Hsiang Lin, Ming-Wei Lai, Chih-Lang Lin, and Chau-Ting Yeh. "Tenofovir Hampers the Efficacy of Sorafenib in Prolonging Overall Survival in Hepatocellular Carcinoma." Biomedicines 9, no. 11 (October 26, 2021): 1539. http://dx.doi.org/10.3390/biomedicines9111539.

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Sorafenib is a first-line treatment for patients with advanced hepatocellular carcinoma (HCC). These patients may simultaneously receive anti-hepatitis B treatment if they are viremic. The N-Acetylgalactosaminyltransferase 14 (GALNT14) gene can serve as a biomarker to guide HCC treatments. However, the enzyme substrates of its gene product, GalNAc-T14 (a glycosyltransferase), remained uncharacterized. Here, we conducted a glycoproteome-wide search for GalNAc-T14 substrates using lectin affinity chromatography followed by tandem mass spectrometry. Seventeen novel GalNAc-T14 substrates were identified. A connective map analysis showed that an antiviral drug, tenofovir, was the leading medicinal compound to down-regulate the expression of these substrates. In vitro assays showed that HCC cells were resistant to sorafenib if pretreated by tenofovir but not entecavir. Clinical analysis showed that the concomitant use of tenofovir and sorafenib was a previously unrecognized predictive factor for unfavorable overall survival (hazard ratio = 2.060, 95% confidence interval = [1.256, 3.381], p = 0.004) in a cohort of 181 hepatitis-B-related, sorafenib-treated HCC patients (concomitant tenofovir versus entecavir treatment; p = 0.003). In conclusion, by conducting a glycoproteome-wide search for GalNAc-T14 substrates, we unexpectedly found that tenofovir was a major negative regulator of GalNAc-T14 substrates and an unfavorable anti-hepatitis B drug in HCC patients receiving sorafenib.

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36

Parker, Benjamin L., Morten Thaysen-Andersen, Nestor Solis, Nichollas E. Scott, Martin R. Larsen, Mark E. Graham, Nicolle H. Packer, and Stuart J. Cordwell. "Site-Specific Glycan-Peptide Analysis for Determination ofN-Glycoproteome Heterogeneity." Journal of Proteome Research 12, no. 12 (November 2013): 5791–800. http://dx.doi.org/10.1021/pr400783j.

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37

Yang, Weiming, Angellina Song, Minghui Ao, Yuanwei Xu, and Hui Zhang. "Large-scale site-specific mapping of the O-GalNAc glycoproteome." Nature Protocols 15, no. 8 (July 17, 2020): 2589–610. http://dx.doi.org/10.1038/s41596-020-0345-1.

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38

Tyleckova, Jirina, Ivona Valekova, Martina Zizkova, Michaela Rakocyova, Silvia Marsala, Martin Marsala, Suresh Jivan Gadher, and Hana Kovarova. "Surface N-glycoproteome patterns reveal key proteins of neuronal differentiation." Journal of Proteomics 132 (January 2016): 13–20. http://dx.doi.org/10.1016/j.jprot.2015.11.008.

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39

Zhu, Feifei, Dong Li, Dandan Song, Shuhao Huo, Shangshang Ma, Peng Lü, Xiaoyong Liu, Qin Yao, and Keping Chen. "Glycoproteome in silkworm Bombyx mori and alteration by BmCPV infection." Journal of Proteomics 222 (June 2020): 103802. http://dx.doi.org/10.1016/j.jprot.2020.103802.

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40

Sun, Zhen, Rui Chen, Kai Cheng, Hongwei Liu, Hongqiang Qin, Mingliang Ye, and Hanfa Zou. "A new method for quantitative analysis of cell surface glycoproteome." PROTEOMICS 12, no. 22 (October 29, 2012): 3328–37. http://dx.doi.org/10.1002/pmic.201200150.

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41

Schürch, Patrick M., Liliana Malinovska, Mohammad Hleihil, Marco Losa, Mara C. Hofstetter, Mattheus H. E. Wildschut, Veronika Lysenko, et al. "Calreticulin mutations affect its chaperone function and perturb the glycoproteome." Cell Reports 41, no. 8 (November 2022): 111689. http://dx.doi.org/10.1016/j.celrep.2022.111689.

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Bouchard, Gina, Fernando Jose Garcia-Marques, Loukia Georgiou Karacosta, Weiruo Zhang, Abel Bermudez, Nicholas McIlvain Riley, Sushama Varma, et al. "Multiomics Analysis of Spatially Distinct Stromal Cells Reveals Tumor-Induced O-Glycosylation of the CDK4–pRB Axis in Fibroblasts at the Invasive Tumor Edge." Cancer Research 82, no. 4 (December 1, 2021): 648–64. http://dx.doi.org/10.1158/0008-5472.can-21-1705.

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Abstract The invasive leading edge represents a potential gateway for tumor metastasis. The role of fibroblasts from the tumor edge in promoting cancer invasion and metastasis has not been comprehensively elucidated. We hypothesize that cross-talk between tumor and stromal cells within the tumor microenvironment results in activation of key biological pathways depending on their position in the tumor (edge vs. core). Here we highlight phenotypic differences between tumor-adjacent-fibroblasts (TAF) from the invasive edge and tumor core fibroblasts from the tumor core, established from human lung adenocarcinomas. A multiomics approach that includes genomics, proteomics, and O-glycoproteomics was used to characterize cross-talk between TAFs and cancer cells. These analyses showed that O-glycosylation, an essential posttranslational modification resulting from sugar metabolism, alters key biological pathways including the cyclin-dependent kinase 4 (CDK4) and phosphorylated retinoblastoma protein axis in the stroma and indirectly modulates proinvasive features of cancer cells. In summary, the O-glycoproteome represents a new consideration for important biological processes involved in tumor–stroma cross-talk and a potential avenue to improve the anticancer efficacy of CDK4 inhibitors. Significance: A multiomics analysis of spatially distinct fibroblasts establishes the importance of the stromal O-glycoproteome in tumor–stroma interactions at the leading edge and provides potential strategies to improve cancer treatment. See related commentary by De Wever, p. 537

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Oldham, Robyn A. A., Mary L. Faber, Theodore R. Keppel, Amanda R. Buchberger, Matthew Waas, Parameswaran Hari, Rebekah L. Gundry, and Jeffrey A. Medin. "Discovery and validation of surface N-glycoproteins in MM cell lines and patient samples uncovers immunotherapy targets." Journal for ImmunoTherapy of Cancer 8, no. 2 (August 2020): e000915. http://dx.doi.org/10.1136/jitc-2020-000915.

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BackgroundMultiple myeloma (MM) is characterized by clonal expansion of malignant plasma cells in the bone marrow. While recent advances in treatment for MM have improved patient outcomes, the 5-year survival rate remains ~50%. A better understanding of the MM cell surface proteome could facilitate development of new directed therapies and assist in stratification and monitoring of patient outcomes.MethodsIn this study, we first used a mass spectrometry (MS)-based discovery-driven cell surface capture (CSC) approach to map the cell surface N-glycoproteome of MM cell lines. Next, we developed targeted MS assays, and applied these to cell lines and primary patient samples to refine the list of candidate tumor markers. Candidates of interest detected by MS on MM patient samples were further validated using flow cytometry (FCM).ResultsWe identified 696 MM cell surface N-glycoproteins by CSC, and developed 73 targeted MS detection assays. MS-based validation using primary specimens detected 30 proteins with significantly higher abundance in patient MM cells than controls. Nine of these proteins were identified as potential immunotherapeutic targets, including five that were validated by FCM, confirming their expression on the cell surface of primary MM patient cells.ConclusionsThis MM surface N-glycoproteome will be a valuable resource in the development of biomarkers and therapeutics. Further, we anticipate that our targeted MS assays will have clinical benefit for the diagnosis, stratification, and treatment of MM patients.

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Graham, Robert, and Sonja Hess. "Mass Spectrometry in the Elucidation of the Glycoproteome of Bacterial Pathogens." Current Proteomics 7, no. 1 (April 1, 2010): 57–81. http://dx.doi.org/10.2174/157016410790979662.

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Wang, Chunqun, Wenjie Gao, Shi Yan, Xing-Quan Zhu, Xun Suo, Xin Liu, Nishith Gupta, and Min Hu. "N-glycome and N-glycoproteome of a hematophagous parasitic nematode Haemonchus." Computational and Structural Biotechnology Journal 19 (2021): 2486–96. http://dx.doi.org/10.1016/j.csbj.2021.04.038.

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Oliveira, Tiago, Mingfeng Zhang, Eun Ji Joo, Hisham Abdel-Azim, Chun-Wei Chen, Lu Yang, Chih-Hsing Chou, et al. "Glycoproteome remodeling in MLL-rearranged B-cell precursor acute lymphoblastic leukemia." Theranostics 11, no. 19 (2021): 9519–37. http://dx.doi.org/10.7150/thno.65398.

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Zhang, Ming, Guan-Xing Chen, Dong-Wen Lv, Xiao-Hui Li, and Yue-Ming Yan. "N-Linked Glycoproteome Profiling of Seedling Leaf in Brachypodium distachyon L." Journal of Proteome Research 14, no. 4 (March 2, 2015): 1727–38. http://dx.doi.org/10.1021/pr501080r.

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Lee, Albert, Daniel Kolarich, Paul A. Haynes, Pia H. Jensen, Mark S. Baker, and Nicolle H. Packer. "Rat Liver Membrane Glycoproteome: Enrichment by Phase Partitioning and Glycoprotein Capture." Journal of Proteome Research 8, no. 2 (February 6, 2009): 770–81. http://dx.doi.org/10.1021/pr800910w.

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Steentoft, Catharina, Sergey Y. Vakhrushev, Malene B. Vester-Christensen, Katrine T.-B. G. Schjoldager, Yun Kong, Eric Paul Bennett, Ulla Mandel, Hans Wandall, Steven B. Levery, and Henrik Clausen. "Mining the O-glycoproteome using zinc-finger nuclease–glycoengineered SimpleCell lines." Nature Methods 8, no. 11 (October 9, 2011): 977–82. http://dx.doi.org/10.1038/nmeth.1731.

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Steentoft, Catharina, Sergey Y. Vakhrushev, Hiren J. Joshi, Yun Kong, Malene B. Vester-Christensen, Katrine T.-B. G. Schjoldager, Kirstine Lavrsen, et al. "Precision mapping of the human O-GalNAc glycoproteome through SimpleCell technology." EMBO Journal 32, no. 10 (April 12, 2013): 1478–88. http://dx.doi.org/10.1038/emboj.2013.79.

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