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系统糖生物学:整合糖原组学、糖蛋白质组学、糖组学及其他“组学”数据集以表征细胞糖基化过程。

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

作者信息

Bennun Sandra V, Hizal Deniz Baycin, Heffner Kelley, Can Ozge, Zhang Hui, Betenbaugh Michael J

机构信息

Department of Chemical and Biomolecular Engineering, Johns Hopkins University, Baltimore, MD, 21218, USA.

Department of Medical Engineering, Acibadem University, Istanbul, Turkey.

出版信息

J Mol Biol. 2016 Aug 14;428(16):3337-3352. doi: 10.1016/j.jmb.2016.07.005. Epub 2016 Jul 15.

Abstract

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.

摘要

据估计,人类基因组中编码的蛋白质数量在20000至25000之间,尽管据估计整个蛋白质组包含超过一百万个蛋白质。造成这种差异的一个原因是蛋白质的许多翻译后修饰增加了蛋白质组的复杂性。其中,糖基化尤为重要,因为它修饰大量细胞蛋白。糖原组学、糖蛋白质组学、糖组学和糖生物信息学有助于加速我们对生成糖蛋白组所涉及的细胞事件、可能的聚糖结构种类以及聚糖在治疗和疾病中所起作用的重要性的理解。事实上,在过去十年中,随着大量与糖基化加工相关的实验性“组学”数据的积累,对糖基化的兴趣迅速扩大。此外,现在有了新的、更复杂的糖生物信息学工具和数据库用于聚糖和糖基化途径分析。在这里,我们总结了生物技术和医学相关细胞中涉及N-和O-连接糖基化加工的实验分析和分析方法的一些最新进展,以及与查询和整合多个不同的高通量糖基化数据集相关的独特机遇和挑战。这个先进糖组学的新兴时代将导致发现与疾病相关的关键聚糖生物标志物,并有助于更好地理解生理学,以及改善对疾病和重组治疗药物生产至关重要的不同细胞和组织中糖基化加工的控制。此外,促进糖组学测量与其他“组学”数据集整合的方法将导致对糖基化作为一个复杂细胞过程的本质有更深入的理解和更深刻的认识。

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