Literature DB >> 25205225

Label-free quantitative urinary proteomics identifies the arginase pathway as a new player in congenital obstructive nephropathy.

Chrystelle Lacroix1, Cécile Caubet2, Anne Gonzalez-de-Peredo1, Benjamin Breuil2, David Bouyssié1, Alexandre Stella1, Luc Garrigues1, Caroline Le Gall3, Anthony Raevel2, Angelique Massoubre2, Julie Klein2, Stéphane Decramer4, Frédérique Sabourdy5, Flavio Bandin4, Odile Burlet-Schiltz1, Bernard Monsarrat1, Joost-Peter Schanstra6, Jean-Loup Bascands6.   

Abstract

Obstructive nephropathy is a frequently encountered situation in newborns. In previous studies, the urinary peptidome has been analyzed for the identification of clinically useful biomarkers of obstructive nephropathy. However, the urinary proteome has not been explored yet and should allow additional insight into the pathophysiology of the disease. We have analyzed the urinary proteome of newborns (n = 5/group) with obstructive nephropathy using label free quantitative nanoLC-MS/MS allowing the identification and quantification of 970 urinary proteins. We next focused on proteins exclusively regulated in severe obstructive nephropathy and identified Arginase 1 as a potential candidate molecule involved in the development of obstructive nephropathy, located at the crossroad of pro- and antifibrotic pathways. The reduced urinary abundance of Arginase 1 in obstructive nephropathy was verified in independent clinical samples using both Western blot and MRM analysis. These data were confirmed in situ in kidneys obtained from a mouse obstructive nephropathy model. In addition, we also observed increased expression of Arginase 2 and increased total arginase activity in obstructed mouse kidneys. mRNA expression analysis of the related arginase pathways indicated that the pro-fibrotic arginase-related pathway is activated during obstructive nephropathy. Taken together we have identified a new actor in the development of obstructive nephropathy in newborns using quantitative urinary proteomics and shown its involvement in an in vivo model of disease. The present study demonstrates the relevance of such a quantitative urinary proteomics approach with clinical samples for a better understanding of the pathophysiology and for the discovery of potential therapeutic targets.
© 2014 by The American Society for Biochemistry and Molecular Biology, Inc.

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Year:  2014        PMID: 25205225      PMCID: PMC4256494          DOI: 10.1074/mcp.M114.040121

Source DB:  PubMed          Journal:  Mol Cell Proteomics        ISSN: 1535-9476            Impact factor:   5.911


  70 in total

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Journal:  J Proteome Res       Date:  2005 Nov-Dec       Impact factor: 4.466

2.  Automated detection of inaccurate and imprecise transitions in peptide quantification by multiple reaction monitoring mass spectrometry.

Authors:  Susan E Abbatiello; D R Mani; Hasmik Keshishian; Steven A Carr
Journal:  Clin Chem       Date:  2009-12-18       Impact factor: 8.327

Review 3.  Hyperargininemia due to liver arginase deficiency.

Authors:  Eric A Crombez; Stephen D Cederbaum
Journal:  Mol Genet Metab       Date:  2004-12-19       Impact factor: 4.797

Review 4.  Arginine metabolism: nitric oxide and beyond.

Authors:  G Wu; S M Morris
Journal:  Biochem J       Date:  1998-11-15       Impact factor: 3.857

Review 5.  Arginine metabolism: boundaries of our knowledge.

Authors:  Sidney M Morris
Journal:  J Nutr       Date:  2007-06       Impact factor: 4.798

Review 6.  Regulation of nitric oxide synthesis and apoptosis by arginase and arginine recycling.

Authors:  Masataka Mori
Journal:  J Nutr       Date:  2007-06       Impact factor: 4.798

7.  Predicting the clinical outcome of congenital unilateral ureteropelvic junction obstruction in newborn by urinary proteome analysis.

Authors:  Stephane Decramer; Stefan Wittke; Harald Mischak; Petra Zürbig; Michael Walden; François Bouissou; Jean-Loup Bascands; Joost P Schanstra
Journal:  Nat Med       Date:  2006-03-19       Impact factor: 53.440

8.  Label-free quantitative proteomics reveals differentially regulated proteins influencing urolithiasis.

Authors:  C A Wright; S Howles; D C Trudgian; B M Kessler; J M Reynard; J G Noble; F C Hamdy; B W Turney
Journal:  Mol Cell Proteomics       Date:  2011-04-07       Impact factor: 5.911

9.  Arginase II Promotes Macrophage Inflammatory Responses Through Mitochondrial Reactive Oxygen Species, Contributing to Insulin Resistance and Atherogenesis.

Authors:  Xiu-Fen Ming; Angana G Rajapakse; Gautham Yepuri; Yuyan Xiong; João M Carvas; Jean Ruffieux; Isabelle Scerri; Zongsong Wu; Katja Popp; Jianhui Li; Claudio Sartori; Urs Scherrer; Brenda R Kwak; Jean-Pierre Montani; Zhihong Yang
Journal:  J Am Heart Assoc       Date:  2012-08-24       Impact factor: 5.501

10.  Arginase inhibition mediates renal tissue protection in diabetic nephropathy by a nitric oxide synthase 3-dependent mechanism.

Authors:  Hanning You; Ting Gao; Timothy K Cooper; Sidney M Morris; Alaa S Awad
Journal:  Kidney Int       Date:  2013-06-12       Impact factor: 10.612

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  4 in total

Review 1.  Urinary biomarkers for renal tract malformations.

Authors:  Pedro Magalhães; Joost P Schanstra; Emma Carrick; Harald Mischak; Petra Zürbig
Journal:  Expert Rev Proteomics       Date:  2016-11-15       Impact factor: 3.940

2.  Comprehensive Metaproteomic Analyses of Urine in the Presence and Absence of Neutrophil-Associated Inflammation in the Urinary Tract.

Authors:  Yanbao Yu; Patricia Sikorski; Madeline Smith; Cynthia Bowman-Gholston; Nicolas Cacciabeve; Karen E Nelson; Rembert Pieper
Journal:  Theranostics       Date:  2017-01-01       Impact factor: 11.556

3.  Systems biology combining human- and animal-data miRNA and mRNA data identifies new targets in ureteropelvic junction obstruction.

Authors:  Theofilos Papadopoulos; Audrey Casemayou; Eric Neau; Benjamin Breuil; Cécile Caubet; Denis Calise; Barbara A Thornhill; Magdalena Bachvarova; Julie Belliere; Robert L Chevalier; Panagiotis Moulos; Dimcho Bachvarov; Benedicte Buffin-Meyer; Stéphane Decramer; Françoise Conte Auriol; Jean-Loup Bascands; Joost P Schanstra; Julie Klein
Journal:  BMC Syst Biol       Date:  2017-03-01

4.  PRYNT: a tool for prioritization of disease candidates from proteomics data using a combination of shortest-path and random walk algorithms.

Authors:  Franck Boizard; Bénédicte Buffin-Meyer; Joost P Schanstra; Julie Klein; Julien Aligon; Olivier Teste
Journal:  Sci Rep       Date:  2021-03-11       Impact factor: 4.379

  4 in total

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