Proteomic analysis of urine from proteinuric patients shows a proteolitic activity directed against albumin

Magistroni, Riccardo; Ligabue, Giulia; Lupo, Valentina; Furci, Luciana; Leonelli, Marco; Manganelli, Luca; Masellis, Mario; Gatti, Valentina; Fabrizio, Claudia; Tizzanini, Walter; Albertazzi, A

doi:10.1093/ndt/gfp020

Cited by 30 publications

(25 citation statements)

References 18 publications

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“…Similar to reports that observed A1AT protein or its fragments [4,15,17,18,19,21,22], we identified three peptides – potentially derived from A1AT cleavage from its C-terminus during protease inhibition and elevated in GKD patients. Although examining urinary peptides by immunoassay is difficult, e.g.…”

Section: Discussionsupporting

confidence: 83%

“…We speculate that three of the six differential peptides that we identified by HPLC-MS/MS belong to A1AT, consistent with the reported increase in A1AT levels in urine from patients with nephrotic syndrome [15,17]. A1AT is a protease inhibitor in plasma and urine [11].…”

Section: Discussionsupporting

confidence: 82%

“…Urine is one of the most amenable fluids in clinical proteomics, because it can be obtained noninvasively, allowing one to identify GKD-related markers [12,13,14,15,16,17,18,19,20,21,22]. Profiling methods are gaining popularity in the quest for new putative biomarkers for glomerular disorders [23,24,25,26,27,28,29,30,31].…”

Section: Introductionmentioning

confidence: 99%

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Uromodulin and α<sub>1</sub>-Antitrypsin Urinary Peptide Analysis to Differentiate Glomerular Kidney Diseases

Navarro-Muñoz

Ibernón

Bonet

et al. 2012

Kidney Blood Press Res

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Background/Aims: Glomerular kidney disease (GKD) is suspected in patients based on proteinuria, but its diagnosis relies primarily on renal biopsy. We used urine peptide profiling as a noninvasive means to link GKD-associated changes to each glomerular entity. Methods: Urinary peptide profiles of 60 biopsy-proven glomerular patients and 14 controls were analyzed by combining magnetic bead peptide enrichment, MALDI-TOF MS analysis, and ClinProTools v2.0 to select differential peptides. Tentative identification of the differential peptides was carried out by HPLC-MS/MS. Results: The HPLC-MS/MS results suggest that uromodulin (UMOD; m/z: 1682, 1898 and 1913) and α₁-antitrypsin (A1AT; m/z: 1945, 2392 and 2505) are differentially expressed urinary peptides that distinguish between GKD patients and healthy subjects. Low UMOD and high A1AT peptide abundance was observed in 80–92% of patients with GKD. Proliferative forms of GKD were distinguished from nonproliferative forms, based on a combination of UMOD and A1AT peptides. Nonproliferative forms correlated with higher A1AT peptide levels – focal segmental glomerulosclerosis was linked more closely to high levels of the m/z 1945 peptide than minimal change disease. Conclusion: We describe a workflow – urinary peptide profiling coupled with histological findings – that can be used to distinguish GKD accurately and noninvasively, particularly its nonproliferative forms.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionsupporting

confidence: 82%

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Uromodulin and α<sub>1</sub>-Antitrypsin Urinary Peptide Analysis to Differentiate Glomerular Kidney Diseases

Navarro-Muñoz

Ibernón

Bonet

et al. 2012

Kidney Blood Press Res

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“…The supernatant was frozen at −80°C in 1 mL aliquots. The method of trichloroacetic acid (TCA)/acetone precipitation was utilized to prepare urinary protein; the method of urinary protein precipitation was described elsewhere with little modification [19–21]. Briefly, 3 mL mixed urine specimen was mixed with 30 mL TCA 10%-acetone solution 90% (v/v), followed by overnight incubation at −20°C.…”

Section: Methodsmentioning

confidence: 99%

Glycopatterns of Urinary Protein as New Potential Diagnosis Indicators for Diabetic Nephropathy

Zhu

Liu

et al. 2017

Journal of Diabetes Research

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Diabetic nephropathy is a major cause of chronic kidney disease and end-stage kidney disease. However, so little is known about alterations of the glycopatterns in urine with the development of diabetic nephropathy. Presently, we interrogated glycopatterns in urine specimens using a lectin microarray. The results showed that expression levels of Siaα2-6Gal/GalNAc recognized by SNA exhibited significantly increased tendency with the development of diabetic nephropathy; moreover, SNA blotting indicated glycoproteins (90 kDa, 70 kDa, and 40 kDa) in urine may contribute to this alteration. Furthermore, the glycopatterns of (GlcNAc)2–4 recognized by STL exhibited difference between diabetic and nondiabetic nephropathy. The results of urinary protein microarray fabricated by another 48 urine specimens also indicated (GlcNAc)2–4 is a potential indictor to differentiate the patients with diabetic nephropathy from nondiabetic nephropathy. Furtherly, STL blotting showed that the 50 kDa glycoproteins were correlated with this alteration. In conclusion, our data provide pivotal information to monitor the development of diabetic nephropathy and distinguish between diabetic nephropathy and nondiabetic renal disease based on precise alterations of glycopatterns in urinary proteins, but further studies are needed in this regard.

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“…The quantification accuracy of 2D-DIGE is higher than 2DE method. This technique has been routinely used for the discovery of candidate urinary biomarkers of renal disease in patient and animal models [49–52]. 2D-DIGE-SELDI-TOF (surface-enhanced laser desorption ionization -time of flight) was used for the detection of early stage tubular injury in canine model of progressive glomerular disease [50].…”

Section: 2 Two-dimensional Difference Gel Electrophoresis (2d-dige)mentioning

confidence: 99%

Quantitative mass spectrometry of urinary biomarkers

Jerebtsova

Nekhai

2014

JIOMICS

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The effectiveness of treatment of renal diseases is limited because the lack of diagnostic, prognostic and therapeutic markers. Despite the more than a decade of intensive investigation of urinary biomarkers, no new clinical biomarkers were approved. This is in part because the early expectations toward proteomics in biomarkers discovery were significantly higher than the capability of technology at the time. However, during the last decade, proteomic technology has made dramatic progress in both the hardware and software methods. In this review we are discussing modern quantitative methods of mass-spectrometry and providing several examples of their applications for discovery and validation of renal disease biomarkers. We are optimistic about future prospects for the development of novel of specific clinical urinary biomarkers.

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Proteomic analysis of urine from proteinuric patients shows a proteolitic activity directed against albumin

Cited by 30 publications

References 18 publications

Uromodulin and α<sub>1</sub>-Antitrypsin Urinary Peptide Analysis to Differentiate Glomerular Kidney Diseases

Uromodulin and α<sub>1</sub>-Antitrypsin Urinary Peptide Analysis to Differentiate Glomerular Kidney Diseases

Glycopatterns of Urinary Protein as New Potential Diagnosis Indicators for Diabetic Nephropathy

Quantitative mass spectrometry of urinary biomarkers

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