Abstract
Mucin domains are densely O-glycosylated modular protein domains found in a wide variety of cell surface and secreted proteins. When strung together in “tandem repeats”, mucin domains can form the large structures characteristic of mucin family proteins, such as MUC1 and MUC16. These canonical mucins are known to be key players in a host of human diseases, especially cancer. However, several other glycoproteins are known to contain mucin domains, such as: CD43, PSGL-1, and CD45, and their mucin domains are key to their molecular functions. However, a comprehensive list of mucin-domain glycoproteins does not exist, and this knowledge is critical in unraveling the role of mucin domains in health and disease. Recently, we characterized a bacterial mucinase, StcE, and used its unique properties to improve analysis of mucin-domain glycoproteins by mass spectrometry. Given StcE's selectivity for mucins, we reasoned that it could be employed to purify mucins from complex mixtures. Thus, the inactivated point mutant of StcE was conjugated to beads and we demonstrated that we could selectively enrich canonical mucins from HeLa cell lysate. However, we were faced with the issue of how to define a mucin domain. Thus, we developed a “mucin candidate algorithm” in order to calculate which human proteins have a high probability of bearing a mucin domain. With this program in-hand, we expanded our “mucinomics” platform to several other cell lines and crude ovarian cancer patient ascites fluid. We demonstrated high mucin overlap between ovarian cancer patients, and the enrichment strategy allowed us to detect hundreds of glycopeptides from the mucin proteins. Finally, we used a KRAS dox-inducible system to show which mucins contribute to molecular bulk at the cell surface. Ultimately, we demonstrate this mucinomics platform can be used in a wide variety of systems to define key molecular signatures of cancer.
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