Dataset Information

Large-scale proteomics and phosphoproteomics of urinary exosomes.

ABSTRACT: Normal human urine contains large numbers of exosomes, which are 40- to 100-nm vesicles that originate as the internal vesicles in multivesicular bodies from every renal epithelial cell type facing the urinary space. Here, we used LC-MS/MS to profile the proteome of human urinary exosomes. Overall, the analysis identified 1132 proteins unambiguously, including 177 that are represented on the Online Mendelian Inheritance in Man database of disease-related genes, suggesting that exosome analysis is a potential approach to discover urinary biomarkers. We extended the proteomic analysis to phosphoproteomic profiling using neutral loss scanning, and this yielded multiple novel phosphorylation sites, including serine-811 in the thiazide-sensitive Na-Cl co-transporter, NCC. To demonstrate the potential use of exosome analysis to identify a genetic renal disease, we carried out immunoblotting of exosomes from urine samples of patients with a clinical diagnosis of Bartter syndrome type I, showing an absence of the sodium-potassium-chloride co-transporter 2, NKCC2. The proteomic data are publicly accessible at http://dir.nhlbi.nih.gov/papers/lkem/exosome/.

SUBMITTER: Gonzales PA

PROVIDER: S-EPMC2637050 | biostudies-literature | 2009 Feb

REPOSITORIES: biostudies-literature

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Large-scale proteomics and phosphoproteomics of urinary exosomes.

Gonzales Patricia A PA Pisitkun Trairak T Hoffert Jason D JD Tchapyjnikov Dmitry D Star Robert A RA Kleta Robert R Wang Nam Sun NS Knepper Mark A MA

Journal of the American Society of Nephrology : JASN 20081203 2

Normal human urine contains large numbers of exosomes, which are 40- to 100-nm vesicles that originate as the internal vesicles in multivesicular bodies from every renal epithelial cell type facing the urinary space. Here, we used LC-MS/MS to profile the proteome of human urinary exosomes. Overall, the analysis identified 1132 proteins unambiguously, including 177 that are represented on the Online Mendelian Inheritance in Man database of disease-related genes, suggesting that exosome analysis i ...[more]

PMID: 19056867

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Project description:In response to stimuli, biological processes are tightly controlled by dynamic cellular signaling mechanisms. Reversible protein phosphorylation occurs on rapid time-scales (milliseconds to seconds), making it an ideal carrier of these signals. Advances in mass spectrometry-based proteomics have led to the identification of many tens of thousands of phosphorylation sites, yet for the majority of these the kinase is unknown and the underlying network topology of signaling networks therefore remains obscured. Identifying kinase substrate relationships (KSRs) is therefore an important goal in cell signaling research. Existing consensus sequence motif based prediction algorithms do not consider the biological context of KSRs, and are therefore insensitive to many other mechanisms guiding kinase-substrate recognition in cellular contexts. Here, we use temporal information to identify biologically relevant KSRs from Large-scale In Vivo Experiments (KSR-LIVE) in a data-dependent and automated fashion. First, we used available phosphorylation databases to construct a repository of existing experimentally-predicted KSRs. For each kinase in this database, we used time-resolved phosphoproteomics data to examine how its substrates changed in phosphorylation over time. Although substrates for a particular kinase clustered together, they often exhibited a different temporal pattern to the phosphorylation of the kinase. Therefore, although phosphorylation regulates kinase activity, our findings imply that substrate phosphorylation likely serve as a better proxy for kinase activity than kinase phosphorylation. KSR-LIVE can thereby infer which kinases are regulated within a biological context. Moreover, KSR-LIVE can also be used to automatically generate positive training sets for the subsequent prediction of novel KSRs using machine learning approaches. We demonstrate that this approach can distinguish between Akt and Rps6kb1, two kinases that share the same linear consensus motif, and provide evidence suggesting IRS-1 S265 as a novel Akt site. KSR-LIVE is an open-access algorithm that allows users to dissect phosphorylation signaling within a specific biological context, with the potential to be included in the standard analysis workflow for studying temporal high-throughput signal transduction data.

Dataset Information

Large-scale proteomics and phosphoproteomics of urinary exosomes.

Publications

Large-scale proteomics and phosphoproteomics of urinary exosomes.

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