Increasing attention has been paid to the urinary proteome because it holds the promise of discovering various disease biomarkers. However, most of the urine proteomics studies routinely relied on protein pre-fractionation and so far did not present characterization on phosphorylation status. Two robust approaches, integrated multidimensional liquid chromatography (IMDL) and Yin-yang multidimensional liquid chromatography (MDLC) tandem mass spectrometry, were recently developed in our laboratory, with high-coverage identification of peptide mixtures. In this study, we adopted a strategy without pre-fractionation on the protein level for urinary proteome identification, using both the IMDL and the Yin-yang MDLC methods for peptide fractionation followed by identification using a linear ion trap-orbitrap (LTQ-Orbitrap) mass spectrometer with high resolution and mass accuracy. A total of 1310 non-redundant proteins were highly confidently identified from two experiments, significantly including 59 phosphorylation sites. More than half the annotated identifications were membrane-related proteins. In addition, the lysosomal as well as kidney-associated proteins were detected. Compared with the six largest datasets of urinary proteins published previously, we found our data included most of the reported proteins. Our study developed a robust approach for exploring the human urinary proteome, which would provide a catalogue of urine proteins on a global scale. It is the first report, to our best knowledge, to profile the urinary phosphoproteome. This work significantly extends current comprehension of urinary protein modification and its potential biological significance. Moreover, the strategy could further serve as a reference for biomarker discovery.

BackgroundRecent advances in proteomics have shed light to discover serum proteins or peptides as biomarkers for tracking the progression of diabetes as well as understanding molecular mechanisms of the disease.ResultsIn this work, human serum of non-diabetic and diabetic cohorts was analyzed by proteomic approach. To analyze total 1377 high-confident serum-proteins, we developed a computing strategy called localized statistics of protein abundance distribution (LSPAD) to calculate a significant bias of a particular protein-abundance between these two cohorts. As a result, 68 proteins were found significantly over-represented in the diabetic serum (p<0.01). In addition, a pathway-associated analysis was developed to obtain the overall pathway bias associated with type 2 diabetes, from which the significant over-representation of complement system associated with type 2 diabetes was uncovered. Moreover, an up-stream activator of complement pathway, ficolin-3, was observed over-represented in the serum of type 2 diabetic patients, which was further validated with statistic significance (p = 0.012) with more clinical samples.ConclusionsThe developed LSPAD approach is well fit for analyzing proteomic data derived from biological complex systems such as plasma proteome. With LSPAD, we disclosed the comprehensive distribution of the proteins associated with diabetes in different abundance levels and the involvement of ficolin-related complement activation in diabetes.