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Accelerating ScienceAccelerating Proteomics / Methods / Understanding Glycoprotein Synthesis: A New Strategy Using Genome-Editing Techniques

Understanding Glycoprotein Synthesis: A New Strategy Using Genome-Editing Techniques

Written by Melanie Woodward | Published: 11.12.2012

Mucins are a highly important and abundant glycoprotein in the human body, with function ranging from cell adhesion in mucous to roles in fertilization and the immune response. Moreover, abnormal mucin genes or structures are associated with a variety of diseases, including cancer. Therefore, understanding how they are synthesized is of vital importance, with the roles of enzymes involved in this synthesis a key component. Methods to identify the function of such enzymes are largely unavailable, but new methods are being designed which could pave the way for such research.

Glycoproteins are proteins with a carbohydrate group (a glycan) attached to the polypeptide chain. There are various types, but O-glycoproteins, specifically those known as mucins, are some of the most abundant in the human body. Mucins are defined by an abundance of O-glycan linkages to either the serine or threonine of the polypeptide chain via N-acetylgalactosamine (GalNAc).1

Mucins are found in mucous secretions of the throat and lungs, of the stomach and intestines, and of the genital and urinary tracts. They help to both hydrate and protect the epithelial cells beneath but have also been shown to have roles in fertilization and the immune response.1 Additionally, mucins are found as cell-surface glycoproteins, with the O-glycans externally exposed to aid in cell adhesion.1

Various diseases are associated with the expression of abnormal mucin genes, as well as with abnormal O-glycan structures. These include cancer, lung disease, inflammatory bowel disease and cystic fibrosis.1 Therefore, understanding O-glycosylation (the process of glycan attachment to proteins to form mucins) is of vital importance in the study of such diseases.

O-glycosylation is regulated by differential expression of a subset of 20 different polypeptide GalNAc-transferases (GalNAcTs), enzymes that aid in the attachment of O-glycans to the mucin protein chain. If the functioning of these individual GalNAcTs could be determined, much more could be learned about the O-glycosylation process. However a recurrent obstruction to this has been the huge variability in glycan structures and their attachment sites along the polypeptide chain.2 This variability means creating methods to identify O-glycosylation sites, as well as determine functioning of individual GalNAcTs, is very difficult.

Yet, recently, Steentoft and colleagues2 have suggested a method to reduce the variability in O-glycosylation. Their study used zinc finger nuclease (ZFN) technology, which allows for specific genes to be identified and altered or cut from the genome, i.e., a genome-editing tool.3 They used this technology to target O-glycan elongation, generating stable “SimpleCell” lines with homogenous O-glycosylation. Having this cell line with reduced variability allowed for uncomplicated isolation of GalNAc O-glycoproteins from these cells, defining areas of O-glycosylation.

Schjoldager and colleagues4 have further developed this strategy for the discovery of individual GalNAcT functions. In their study, they focused on GalNAcT2, which has been potentially implicated in lipid metabolism and dyslipidemia (high blood cholesterol). An epithelial liver carcinoma cell line (HepG2) was used to generate the SimpleCell line as lipid metabolism is predominantly carried out in liver cells. Again, O-glycan elongation was removed to produce homogenous O-glycosylation. However, in half of their cell lines, they also knocked out the gene encoding GalNAcT2: GALNT2. Therefore, two models of the HepG2 SimpleCell line were generated; one with GALNT2 (HepG2+T2) and one without GALNT2 (HepG2-T2).

Both total cell lysates (broken down cells) and cell secretions from these two cell lines were then analyzed. GalNAc O-glycoproteins were isolated and identified using nanoflow liquid chromatography tandem mass spectrometry (nLC/MS/MS). This was done using an EASY-NLC II (Thermo Scientific) interfaced via a nanoSpray Flex ion source to an LTQ-Orbitrap XL Hybrid Spectrometer (Thermo Scientific).

Based on the glycoproteins identified in the two different cell lines, 219 O-glycosylation sites were determined, of which 73 were found only in HepG2+T2 and not in HepG2-T2. Therefore, these areas of the mucin polypeptide chain are clearly potential GalNAcT2-specific glycan binding sites in HepG2. Of those identified only in HepG2+T2 and not in HepG2-T2, 8 were glycosylated exclusively by GalNAcT2 and 5 were glycosylated by T2 and several other isoforms. This demonstrates there are important isoform-specific functions of GalNAcT2. Moreover, several of the glycoproteins identified are linked to high-density lipoprotein cholesterol (HDL) and triglycerides, both of which are associated with dyslipidemia,5 giving more support to the link between lipid metabolism and GalNAcT2.

Using this method has uncovered O-glycosylation sites and site-specific biological functions of just 1 of 20 GalNAcTs. There is increasing evidence for important biological functions of the large GalNAcT gene family.4 The identification of the O-glycosylation sites of other GalNacTs, as well as the biological pathways affected, is urgent, and the method described here paves the way for such studies.

References

1. Brockhausen, I., Schachter, H., and Stanley, P. (2009). O-GalNAc Glycans. In Essentials of Glycobiology. Varki, A., Cummings, R.D., Esko, J. D., et al., eds. New York: Cold Spring Harbor Laboratory Press, Chapter 9.

2. Steentoft, C., et al. (2011) ‘Mining the O-glycoproteome using zinc-finger nuclease – glycoengineered SimpleCell lines‘, Nature Methods, 8 (11), (pp. 977-982)

3. Baker, M. (2012) ‘Gene-editing nucleases‘, Nature Methods, 9 (1), (pp. 23-26)

4. Schjoldager, K.T.-B.G., et al. (2012) ‘Probing isoform-specific functions of polypeptide GalNAc-transferases using zinc finger nuclease glycoengineered SimpleCells‘, Proceedings of the National Academy of Sciences of the United States of America, 109 (25), (pp. 9893-9898)

5. Wägner, A.M., et al. (2005) ‘Triglyceride-to-HDL cholesterol ratio in the dyslipidemic classification of type 2 diabetes‘, Diabetes Care, 28 (7), (pp. 1798-1800)

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