Systems Glycobiology: Integrating Glycogenomics, Glycoproteomics, Glycomics, and Other 'Omics Data Sets to Characterize Cellular Glycosylation Processes

被引:40
作者
Bennun, Sandra V. [1 ]
Hizal, Deniz Baycin [1 ]
Heffner, Kelley [1 ]
Can, Ozge [2 ]
Zhang, Hui [3 ]
Betenbaugh, Michael J. [1 ]
机构
[1] Johns Hopkins Univ, Dept Chem & Biomol Engn, Baltimore, MD 21218 USA
[2] Acibadem Univ, Dept Med Engn, Istanbul, Turkey
[3] Johns Hopkins Univ, Sch Med, Baltimore, MD 21287 USA
关键词
glycoinformatics; systems biology; N-glycosylation; glycans; Chinese hamster ovary; MASS-SPECTROMETRIC DATA; N-LINKED GLYCOPROTEINS; GLYCAN-RELATED-GENES; PROTEIN GLYCOSYLATION; BIOMARKER DISCOVERY; HUMAN-DISEASE; MATHEMATICAL-MODEL; CANCER BIOMARKERS; ENZYME DATABASE; PROSTATE-CANCER;
D O I
10.1016/j.jmb.2016.07.005
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
The number of proteins encoded in the human genome has been estimated at between 20,000 and 25,000, despite estimates that the entire proteome contains more than a million proteins. One reason for this difference is due to many post-translational modifications of protein that contribute to proteome complexity. Among these, glycosylation is of particular relevance because it serves to modify a large number of cellular proteins. Glycogenomics, glycoproteomics, glycomics, and glycoinformatics are helping to accelerate our understanding of the cellular events involved in generating the glycoproteome, the variety of glycan structures possible, and the importance of roles that glycans play in therapeutics and disease. Indeed, interest in glycosylation has expanded rapidly over the past decade, as large amounts of experimental 'omics data relevant to glycosylation processing have accumulated. Furthermore, new and more sophisticated glycoinformatics tools and databases are now available for glycan and glycosylation pathway analysis. Here, we summarize some of the recent advances in both experimental profiling and analytical methods involving N- and O-linked glycosylation processing for biotechnological and medically relevant cells together with the unique opportunities and challenges associated with interrogating and assimilating multiple, disparate high-throughput glycosylation data sets. This emerging era of advanced glycomics will lead to the discovery of key glycan biomarkers linked to diseases and help establish a better understanding of physiology and improved control of glycosylation processing in diverse cells and tissues important to disease and production of recombinant therapeutics. Furthermore, methodologies that facilitate the integration of glycomics measurements together with other 'omics data sets will lead to a deeper understanding and greater insights into the nature of glycosylation as a complex cellular process. (C) 2016 Elsevier Ltd. All rights reserved.
引用
收藏
页码:3337 / 3352
页数:16
相关论文
共 110 条
[91]   Glycosylation changes as markers for the diagnosis and treatment of human disease [J].
Tong, L ;
Baskaran, G ;
Jones, MB ;
Rhee, JK ;
Yarema, KJ .
BIOTECHNOLOGY & GENETIC ENGINEERING REVIEWS, VOL 20, 2003, 20 :199-244
[92]  
Tousi F., 2011, ANAL METHODS, V3, P195
[93]   A H-1-NMR DATABASE COMPUTER-PROGRAM FOR THE ANALYSIS OF THE PRIMARY STRUCTURE OF COMPLEX CARBOHYDRATES [J].
VANKUIK, JA ;
HARD, K ;
VLIEGENTHART, JFG .
CARBOHYDRATE RESEARCH, 1992, 235 :53-68
[94]   EUROCarbDB: An open-access platform for glycoinformatics [J].
von der Lieth, Claus-Wilhelm ;
Freire, Ana Arda ;
Blank, Dennis ;
Campbell, Matthew P. ;
Ceroni, Alessio ;
Damerell, David R. ;
Dell, Anne ;
Dwek, Raymond A. ;
Ernst, Beat ;
Fogh, Rasmus ;
Frank, Martin ;
Geyer, Hildegard ;
Geyer, Rudolf ;
Harrison, Mathew J. ;
Henrick, Kim ;
Herget, Stefan ;
Hull, William E. ;
Ionides, John ;
Joshi, Hiren J. ;
Kamerling, Johannis P. ;
Leeflang, Bas R. ;
Lutteke, Thomas ;
Lundborg, Magnus ;
Maass, Kai ;
Merry, Anthony ;
Ranzinger, Rene ;
Rosen, Jimmy ;
Royle, Louise ;
Rudd, Pauline M. ;
Schloissnig, Siegfried ;
Stenutz, Roland ;
Vranken, Wim F. ;
Widmalm, Goran ;
Haslam, Stuart M. .
GLYCOBIOLOGY, 2011, 21 (04) :493-502
[95]   Bioinformatics for glycomics: Status, methods, requirements and perspectives [J].
von der Lieth, CW ;
Bohne-Lang, A ;
Lohmann, KK ;
Frank, M .
BRIEFINGS IN BIOINFORMATICS, 2004, 5 (02) :164-178
[96]   The genomic sequence of the Chinese hamster ovary (CHO)-K1 cell line [J].
Xu, Xun ;
Nagarajan, Harish ;
Lewis, Nathan E. ;
Pan, Shengkai ;
Cai, Zhiming ;
Liu, Xin ;
Chen, Wenbin ;
Xie, Min ;
Wang, Wenliang ;
Hammond, Stephanie ;
Andersen, Mikael R. ;
Neff, Norma ;
Passarelli, Benedetto ;
Koh, Winston ;
Fan, H. Christina ;
Wang, Jianbin ;
Gui, Yaoting ;
Lee, Kelvin H. ;
Betenbaugh, Michael J. ;
Quake, Stephen R. ;
Famili, Iman ;
Palsson, Bernhard O. ;
Wang, Jun .
NATURE BIOTECHNOLOGY, 2011, 29 (08) :735-U131
[97]   Glycan classification with tree kernels [J].
Yamanishi, Yoshihiro ;
Bach, Francis ;
Vert, Jean-Philippe .
BIOINFORMATICS, 2007, 23 (10) :1211-1216
[98]   Chemoenzymatic method for glycomics: Isolation, identification, and quantitation [J].
Yang, Shuang ;
Rubin, Abigail ;
Eshghi, Shadi Toghi ;
Zhang, Hui .
PROTEOMICS, 2016, 16 (02) :241-256
[99]   QUANTITY: An Isobaric Tag for Quantitative Glycomics [J].
Yang, Shuang ;
Wang, Meiyao ;
Chen, Lijun ;
Yin, Bojiao ;
Song, Guoqiang ;
Turko, Illarion V. ;
Phinney, Karen W. ;
Betenbaugh, Michael J. ;
Zhang, Hui ;
Li, Shuwei .
SCIENTIFIC REPORTS, 2015, 5
[100]   Glycoproteins identified from heart failure and treatment models [J].
Yang, Shuang ;
Chen, Lijun ;
Sun, Shisheng ;
Shah, Punit ;
Yang, Weiming ;
Zhang, Bai ;
Zhang, Zhen ;
Chan, Daniel W. ;
Kass, David A. ;
Van Eyk, Jennifer E. ;
Zhang, Hui .
PROTEOMICS, 2015, 15 (2-3) :567-579