Urinary Glycopeptide Analysis for the Investigation of Novel Biomarkers

Abstract

Purpose: Urine is a rich source of potential biomarkers, including glycoproteins. Glycoproteomic analysis remains difficult due to the high heterogeneity of glycans. Nevertheless, recent advances in glycoproteomics software solutions facilitate glycopeptide identification and characterization. The aim is to investigate intact glycopeptides in the urinary peptide profiles of normal subjects using a novel PTM-centric software-Byonic. Experimental design: The urinary peptide profiles of 238 normal subjects, previously analyzed using CE-MS and CE-MS/MS and/or LC-MS/MS, are subjected to glycopeptide analysis. Additionally, glycopeptide distribution is assessed in a set of 969 patients with five different cancer types: bladder, prostate and pancreatic cancer, cholangiocarcinoma, and renal cell carcinoma. Results: A total of 37 intact O-glycopeptides and 23 intact N-glycopeptides are identified in the urinary profiles of 238 normal subjects. Among the most commonly identified O-glycoproteins are Apolipoprotein C-III and insulin-like growth factor II, while titin among the N-glycoproteins. Further statistical analysis reveals that three O-glycopeptides and five N-glycopeptides differed significantly in their abundance among the different cancer types, comparing to normal subjects. Conclusions and clinical relevance: Through the established glycoproteomics workflow, intact O-and N-glycopeptides in human urine are identified and characterized, providing novel insights for further exploration of the glycoproteome with respect to specific diseases.

Discovery, validation and sequencing of urinary peptides for diagnosis of liver fibrosis—A multicentre study

“…Reasons for not retrieving the amino acid sequence for all peptides include the inability to identify all possible post-translational modifications, e.g. due to high glycan heterogeneity [34] , and resistance of the peptide to its total fragmentation even at high collision energies in tandem mass spectrometry. This is of no relevance for the diagnostic use of the peptide marker model, as indicated for example by clinical application of a combined bile and urine proteome test for cholangiocarcinoma diagnosis based on similar peptide marker models [35] , [36] , [37] , and is based on the fact that the peptide markers are clearly defined in the CE-MS as the device for operating the diagnostic test by their molecular mass and by their capillary electrophoresis migration time.…”

Peptides in Plasma, Urine, and Dialysate: Toward Unravelling Renal Peptide Handling

“…Among others, fragment signals typically for glycosylation are present in many of the spectra where no sequence could be assigned. [3,37] As a result, we are not aiming at improving peptide sequencing by investigating potential posttranslational modifications in more detail.…”

“…Based on this theorem, diseases can be studied via the investigation of proteomics/peptidomics changes, [1] with the help of recent advances in proteomics and peptidomics. [2,3] Biofluids such as urine, plasma, and spent hemodialysate (a mixture of technical HD fluid and patient's ultrafiltrate) are rich sources for investigation, [4,5] and highly relevant in kidney disease. [6][7][8] In particular, peptides in spent hemodialysate may be valuable biomarkers to guide the treatment of dialysis patients: for example, deficiency of vitamin D-binding proteins in dialysis was reported to be associated with poor survival.…”

Proteomic approaches for characterizing renal cell carcinoma

“…From these results, we can begin to use rationalized, precision-based therapies currently approved for one cancer type as viable therapeutic options in other cancer types. Several reports have already attempted this undertaking using publicly available datasets, including exploration of TCGA somatic, transcriptomics, and RPPA data [113], CPTAC gene expression data [114], and peptidome profiles [115], however, the results of these pan-cancer efforts are still preliminary. One approach that may find more utility as larger and larger sample sets are being investigated is data-independent data acquisition (DIA), which achieves similar, if not more, depth of proteomic characterization, with the added benefits of robust sample-to-sample quantitation and reduced instrument time needed for analysis [116].…”