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Open Access Research article

Predictive urinary biomarkers for steroid-resistant and steroid-sensitive focal segmental glomerulosclerosis using high resolution mass spectrometry and multivariate statistical analysis

Shiva Kalantari12, Mohsen Nafar234, Dorothea Rutishauser56, Shiva Samavat3, Mostafa Rezaei-Tavirani7, Hongqian Yang5 and Roman A Zubarev56*

Author Affiliations

1 Department of Basic Sciences, Faculty of Paramedical Sciences, Shahid Beheshti University of Medical Science, Tehran, Iran

2 Chronic Kidney Disease Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

3 Department of Nephrology, Shahid Labbafinejad Medical Center, Shahid Beheshti University of Medical Science, Tehran, Iran

4 Urology and Nephrology Research Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5 Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden

6 SciLifeLab, Stockholm, Sweden

7 Proteomics Research Center, Shahid Beheshti University of Medical Science, Tehran, Iran

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BMC Nephrology 2014, 15:141  doi:10.1186/1471-2369-15-141

Published: 2 September 2014

Abstract

Background

Focal segmental glomerulosclerosis (FSGS) is a glomerular scarring disease diagnosed mostly by kidney biopsy. Since there is currently no diagnostic test that can accurately predict steroid responsiveness in FSGS, prediction of the responsiveness of patients to steroid therapy with noninvasive means has become a critical issue. In the present study urinary proteomics was used as a noninvasive tool to discover potential predictive biomarkers.

Methods

Urinary proteome of 10 patients (nā€‰=ā€‰6 steroid-sensitive, nā€‰=ā€‰4 steroid-resistant) with biopsy proven FSGS was analyzed using nano-LC-MS/MS and supervised multivariate statistical analysis was performed.

Results

Twenty one proteins were identified as discriminating species among which apolipoprotein A-1 and Matrix-remodeling protein 8 had the most drastic fold changes being over- and underrepresented, respectively, in steroid sensitive compared to steroid resistant urine samples. Gene ontology enrichment analysis revealed acute inflammatory response as the dominant biological process.

Conclusion

The obtained results suggest a panel of predictive biomarkers for FSGS. Proteins involved in the inflammatory response are shown to be implicated in the responsiveness. As a tool for biomarker discovery, urinary proteomics is especially fruitful in the area of prediction of responsiveness to drugs. Further validation of these biomarkers is however needed.

Keywords:
Urine proteomics; Inflammatory response; Responsiveness