Glycomic complexity reflects cellular complexity

All organisms are composed of four basic substances: nucleic acids (DNA and RNA), proteins, lipids, and glycans. New research areas such as genomics, proteomics, lipidomics, and metabolomics have appeared that correspond to each of the substances. In the post-genomic era, people have found that the number of proteins encoded in the human genome is between 20,000 and 25,000, but the entire proteome contains more than a million proteins. One of the reasons for this difference is the post-translational modifications of protein that increase the complexity of the proteome. Among them, glycosylation is particularly important.

Glycobiology is defined as the study of the structure, biosynthesis, biological functions of glycans and saccharides conjugates (including glycoproteins, glycolipids, proteoglycans, etc.), as well as protein-glycan interactions. Glycogenomics, glycoproteomics, glycomics, and glycoinformatics can help us better understand the various cellular events involved in saccharides and the role of glycans in diseases. In the production of biological agents, glycans also affect the therapeutic properties of protein drugs and their immune responses.

At present, a large number of advanced analysis techniques and data analysis tools have been developed for glycobiology research. Glycogen microarrays, lectin chips and other tools are widely used to analyze the entire glycogenome and glycosylase in pathological conditions. In addition, the development of mass spectrometry (MS) has allowed us to analyze glycans, glycosyls, glycopeptides and intact glycoproteins at the qualitative and quantitative levels. The glycomics results can be integrated with other "omics data sets" to achieve a deeper understanding and insight into the nature of glycosylation in complex cellular processes.

Glycomic complexity reflects cellular complexityFig 1. Glycomic complexity reflects cellular complexity (Hart, G.W.; Copeland, R.J. 2010)

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  1. Hart, G.W.; Copeland, R.J. Glycomics Hits the Big Time. Cell. 2010, 143(5): 672-676. 
  2. Bennun, S.V.; et al. Systems glycobiology: Integrating glycogenomics, glycoproteomics, glycomics, and other 'omics data sets to characterize cellular glycosylation processes. Journal of Molecular Biology. 2016, 428(16): 3337-3352.
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