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Urinary Prognostic Biomarkers and Classification of IgA Nephropathy by High Resolution Mass Spectrometry Coupled with Liquid Chromatography

Author

Listed:
  • Shiva Kalantari
  • Dorothea Rutishauser
  • Shiva Samavat
  • Mohsen Nafar
  • Leyla Mahmudieh
  • Mostafa Rezaei-Tavirani
  • Roman A Zubarev

Abstract

IgA nephropathy is the most common cause of primary glomerulonephritis. There are different pathologic biopsy-based scoring systems in use, but there is no consensus among nephrologists yet regarding the best classification method. Our aim was to test urine proteomics as a non-invasive method for classification of IgA nephropathy. This aim was pursued by discovering novel prognostic protein biomarkers in urine, and linking them to pathogenesis of the disease through known signaling and metabolic pathways. 13 urine samples of the patients with biopsy-proven IgA nephropathy were analyzed via two proteomics approaches: nanoflow LC-MS/MS and GeLC-MS/MS. The results of label-free quantification were subjected to multivariate statistical analysis, which could classify patients into two groups, broadly corresponding to the primary and advance stages. The proteome classification correlated well with biopsy-based scoring systems, especially endocapillary hypercellularity score of the Oxford’s classification. Differentially excreted candidate proteins were found as potential prognostic biomarkers: afamin, leucine-rich alpha-2-glycoprotein, ceruloplasmin, alpha-1-microgolbulin, hemopexin, apolipoprotein A-I, complement C3, vitamin D-binding protein, beta-2-microglobulin, and retinol-binding protein 4. Pathway analysis suggested impairment of Extra Cellular Matrix (ECM)-Receptor Interaction pathways as well as activation of complement and coagulation pathway in progression of IgA nephropathy.

Suggested Citation

  • Shiva Kalantari & Dorothea Rutishauser & Shiva Samavat & Mohsen Nafar & Leyla Mahmudieh & Mostafa Rezaei-Tavirani & Roman A Zubarev, 2013. "Urinary Prognostic Biomarkers and Classification of IgA Nephropathy by High Resolution Mass Spectrometry Coupled with Liquid Chromatography," PLOS ONE, Public Library of Science, vol. 8(12), pages 1-1, December.
    • Handle: RePEc:plo:pone00:0080830
    DOI: 10.1371/journal.pone.0080830
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    References listed on IDEAS

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    1. Dayle L Sampson & Tony J Parker & Zee Upton & Cameron P Hurst, 2011. "A Comparison of Methods for Classifying Clinical Samples Based on Proteomics Data: A Case Study for Statistical and Machine Learning Approaches," PLOS ONE, Public Library of Science, vol. 6(9), pages 1-11, September.
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