A Chemical Glycoproteomics Platform To study the Human Prostate Secretome

Abstract

Accurate diagnosis and treatment of prostate cancer demands a highly specific biomarker of both the disease, progression and prognosis. As glycoproteins have proven to be altered in most cancers, we have focused on studying glycoproteins in human prostate tissue samples. We have developed an efficient method for labeling and enriching glycoproteins, followed by MS‐based analysis of normal and carcinoma tissue. We sought to selectively enrich secreted glycoproteins because of the potential of secreted glycoprotein biomarkers in non‐invasive prostate cancer detection. This is accomplished by culturing human prostate tissue derived from radical prostatectomies, at varying states of prostatic disease, and collecting the cultured media which samples secretome proteins. During culturing, tissue is chemically tagged with an azide functionalized monosaccharide, N‐azidoacetylmannosamine‐tetraacylated (ManNAz). Labeled proteins are subsequently reacted with a biotin‐functionalized probe for the capture of azido‐sialylated glycoproteins. After analysis of confirmed azide‐dependent labeling, glycoproteins were digested and analyzed on a linear ion trap mass spectrometer with collision‐induced dissociation (CID). Greater than 80% of the proteins identified by LC‐MS/MS were secreted proteins, indicating that this strategy achieves enrichment of low abundance proteins. Additionally, a comparative proteomics analysis between diseased and healthy tissue identified proteins unique to each tissue type, which are undergoing validation as potential biomarkers. Virtually all clinically approved cancer biomarkers are cell‐surface or secreted glycoproteins and this work extends the application of existing metabolic labeling techniques, while developing new methodologies for cell surface labeling to discover biomarkers associated with prostate cancer.Support or Funding InformationThis work was funded by grants from the NIH to C.R.B. (GM066047 and GM058867) and to S.L.M. (2T32DK007217‐37). D.R.S. is supported by NSF predoctoral fellowships.