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Abstract: PO1561

CureGN Pediatric Glomerulopathy: Identifying Pathologic Molecular Signatures in Glomerulopathy with Quantitative Proteomics

Session Information

Category: Glomerular Diseases

  • 1203 Glomerular Diseases: Clinical, Outcomes, and Trials

Authors

  • Cummins, Timothy, Kidney Disease Program and Clinical Proteomics Center, University of Louisville School of Medicine, Louisville, Kentucky, United States
  • Merchant, Michael, Kidney Disease Program and Clinical Proteomics Center, University of Louisville School of Medicine, Louisville, Kentucky, United States
  • Mariani, Laura H., University of Michigan School of Medicine, Division of Nephrology, Ann Arbor, Michigan, United States
  • Pathmasiri, Wimal, University of North Carolina at Chapel Hill Nutrition Research Institute, Kannapolis, North Carolina, United States
  • Sumner, Susan Jenkins, University of North Carolina at Chapel Hill Nutrition Research Institute, Kannapolis, North Carolina, United States
  • Smoyer, William E., Nationwide Children's Hospital, Columbus, Ohio, United States
  • Klein, Jon B., Robley Rex VA Medical Center, Louisville, Kentucky, United States
Background

Glomerular disease is a leading cause of end stage kidney disease with related treatment costs placing a significant burden on healthcare systems worldwide. Immunosuppressive glucocorticoids are a primary therapeutic approach yet can be ineffective in nearly 50% of patients. In this study we tested the hypothesis that the pediatric urinary proteome would contain unique protein signature that predict treatment response vs. resistance in glomerular diseases.

Methods

We performed proteomic analyses of serial clinically, histologically phenotyped and biopsy-confirmed pediatric urine samples from children with 3 glomerulopathies (MCD, FSGS and IgAN) to identify putative mechanistic differences between glomerular disease pathology and treatment responsiveness. 434 patient urine samples were analyzed by LC-MS/MS, and subsequently 1119 unique proteins were identified using MaxQuant and Scaffold-5 Q+ applying strict FDR (1%) with 2 peptide identification required. Protein lists were analyzed by clinical category (urine protein) using MetaboAnalyst 5.0 to perform univariate and multivariate statistical analysis, volcano plot, and heatmap visualization of protein clusters.

Results

Our analysis indicates specific and unique protein/molecular signatures separating MCD, FSGS and IgAN with respect to disease status and pathological diagnosis. Univariate analysis indicated that at least 62 proteins were significantly different in complete remission vs. proteinuric patients across all pathologies. Multivariate analysis showed significant contributions by several proteins to identify disease state and pathology. Heatmap analysis identified protein signatures that are differentially abundant across the 3 distinct glomerulopathies.

Conclusion

Pediatric glomerular disease and disease activity are associated with unique urine proteomes and if confirmed will lead to further identification of important biomarkers. We are currently performing validation of a subset of proteins as biomarkers to determine if they are diagnostic and predictive of treatment response.

Funding

  • NIDDK Support