Urine Proteomics for the Purpose of Biomarker Discovery

Abstract

Body fluids have gained widespread importance for proteomics based biomarker discovery as they have proved to reveal many candidate biomarkers for a variety of physiological diseases. Urine is a particularly favourable body fluid when profiling diseases associated with proximal tissues (kidney, urinary tract, etc.). The collection of urine is also non-invasive compared to other fluids such as blood, plasma and amniotic or cerebral spinal fluids. The main objective of this work was to determine an optimal protocol for profiling of the urine proteome via peptide mass sequencing. Subsequently, the urine proteome was characterized as a potential source for protein biomarker(s) related to kidney obstruction. Of particular relevance to a quantitative investigation, it was found that the conventional urinary proteome workflow inadvertently introduces a sampling bias. Demonstrated here is the fact that the sediment proteins of urine, which are typically discarded prior to analysis, contain important protein constituents which were previously reported in the literature as candidate biomarkers. A solution-based intact protein separation workflow was demonstrated for the analysis of urinary proteins. The mass-based separation platform, namely gel-eluted liquid fractionation entrapment electrophoresis (GELFrEE), incorporates the use of sodium dodecyl sulphate (SDS). As a known signal suppressant in mass spectrometry (MS), the MS tolerance of SDS in a proteome workflow was determined. Simple and effective protocols for the isolation of proteins from SDS-containing solutions are presented. Also presented is a comparison between GELFrEE and the conventional „GeLC? protocol for SDS-based protein separation, with similar numbers of proteins identified by the two platforms. Lastly, a novel strategy for isotope labelling of proteins at the intact level is presented. This intact labelling of proteins is therefore compatible with intact proteome prefractionation, as seen with GELFrEE-MS2 separation. This quantitative workflow was applied to biomarker profiling from urine samples obtained for a model (rodent) kidney obstruction.