Proteomic analysis of podocyte exosome-enriched fraction from normal human urine

Prunotto, Marco; Farina, Annarita; Lane, Lydie; Pernin, Agnès; Schifferli, Jürg A.; Hochstrasser, Denis F.; Lescuyer, Pierre; Moll, Solange

doi:10.1016/j.jprot.2013.01.012

Cited by 131 publications

(98 citation statements)

References 73 publications

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“…Besides its primary function as enzyme in the glycolysis pathway, this protein moonlights as interferon-gamma-activated inhibitor of translation that silences ceruloplasmin mRNA translation (Sampath et al, 2004). In a proteomics study (Prunotto et al, 2013), this protein was identified as one of the urinary exosome proteins, and thus contains GO:0070062 extracellular exosome, which is a child term of GO:0005576 extracellular region, and hence falls in the latter GO category in Table 2. Both are these examples are correctly predicted MPs in human by the two omics-based combinations Phylo þ GE þ GI þ DOR þ NET and PPI þ Phylo þ GE.…”

Section: Analysis Of Genome-wide Mp Predictionmentioning

confidence: 99%

Genome-scale prediction of moonlighting proteins using diverse protein association information

Khan¹

2016

Bioinformatics

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Motivation: Moonlighting proteins (MPs) show multiple cellular functions within a single polypeptide chain. To understand the overall landscape of their functional diversity, it is important to establish a computational method that can identify MPs on a genome scale. Previously, we have systematically characterized MPs using functional and omics-scale information. In this work, we develop a computational prediction model for automatic identification of MPs using a diverse range of protein association information. Results: We incorporated a diverse range of protein association information to extract characteristic features of MPs, which range from gene ontology (GO), protein-protein interactions, gene expression, phylogenetic profiles, genetic interactions and network-based graph properties to protein structural properties, i.e. intrinsically disordered regions in the protein chain. Then, we used machine learning classifiers using the broad feature space for predicting MPs. Because many known MPs lack some proteomic features, we developed an imputation technique to fill such missing features. Results on the control dataset show that MPs can be predicted with over 98% accuracy when GO terms are available. Furthermore, using only the omics-based features the method can still identify MPs with over 75% accuracy. Last, we applied the method on three genomes: Saccharomyces cerevisiae, Caenorhabditis elegans and Homo sapiens, and found that about 2-10% of proteins in the genomes are potential MPs. Availability and Implementation: Code available at http://kiharalab.org/MPprediction

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Section: Analysis Of Genome-wide Mp Predictionmentioning

confidence: 99%

Genome-scale prediction of moonlighting proteins using diverse protein association information

Khan¹

2016

Bioinformatics

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“…Most studies on extracellular miRNA in plasma did not discriminate between miRNA in EVs and other forms of circulating miRNAs. Furthermore, it should be kept in mind that when EVs were isolated from blood either by ultracen- (68,(76)(77)(78)(79). In a recent study using mass spectrometry, the protein compositions of EVs isolated from 16 preoperative urine samples of PCa patients were compared with those from 15 healthy individuals, and as many as 246 proteins were found to be differentially expressed (56).…”

Section: R E V I E W S E R I E S : E X T R a C E L L U L A R V E S I mentioning

confidence: 99%

Prostasomes as a source of diagnostic biomarkers for prostate cancer

Zijlstra¹,

Stoorvogel²

2016

Journal of Clinical Investigation

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“…Exosomes are released by podocytes, pass through the renal tubule and they can be taken up by recipient epithelial cells of the collecting duct, thus influencing their function through the secretion of their content. Finally, they appear in the urine where they reflect the state of the urinary system, from podocytes to renal-tubular cells, thus making them an excellent source of samples for the study of kidney physiology and pathology [125,126]. In the setting of cardiovascular diseases, platelets have been found to secrete exosomes, which may be involved in the complex cross talk between diverse cell types in atherosclerosis plaques [127].…”

Section: The Role Of Evs In the Cardiorenal Cross Talkmentioning

confidence: 99%

Molecular and Genetic Mechanisms Involved in the Pathogenesis of Cardiorenal Cross Talk

Virzì

Clementi²,

Brocca³

et al. 2016

Pathobiology

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The term ‘cardiorenal syndrome' (CRS) encloses a scenario of clinical interactions in which cardiac and renal dysfunctions coexist. The cross talk between the heart and the kidney is clearly evidenced but not fully understood. Indeed, different factors have been shown to be involved in the pathogenesis of CRS, such as systemic inflammation, oxidative stress, apoptosis and immune dysregulation. Recently, considerable attention has been paid to the role of new alternative mechanisms which may be implicated in the pathogenesis of cardiorenal cross talk. In this review, we will focus on epigenetics, prenatal programming, small noncoding RNAs and extracellular vesicles, aiming to elucidate their possible role in heart and kidney diseases.

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Proteomic analysis of podocyte exosome-enriched fraction from normal human urine

Cited by 131 publications

References 73 publications

Genome-scale prediction of moonlighting proteins using diverse protein association information

Genome-scale prediction of moonlighting proteins using diverse protein association information

Prostasomes as a source of diagnostic biomarkers for prostate cancer

Molecular and Genetic Mechanisms Involved in the Pathogenesis of Cardiorenal Cross Talk

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