Serum Protein Signatures Using Aptamer-Based Proteomics for Minimal Change Disease and Membranous Nephropathy

Authors

Daniel A. Muruve, University of Calgary
Hanna Debiec, Sorbonne Université and Institut National de la Santé et de la Recherche Médicale
Simon T. Dillon, Beth Israel Deaconess Medical Center and Harvard Medical School
Xuesong Gu, Beth Israel Deaconess Medical Center and Harvard Medical School,
Emmanuelle Plaisier, Sorbonne Université and Institut National de la Santé et de la Recherche Médicale, Association pour l’Utilisation du Rein Artificiel Paris Plaisance
Handan Can, University of Nebraska-LincolnFollow
Hasan H. Otu, University of Nebraska-LincolnFollow
Towia A. Libermann, Beth Israel Deaconess Medical Center and Harvard Medical School
Pierre Ronco, Sorbonne Université and Institut National de la Santé et de la Recherche Médicale, Center Hospitalier du Mans

Document Type

Article

Date of this Version

2022

Citation

Kidney Int Rep (2022) 7, 1539–1556; https://doi.org/10.1016/j.ekir.2022.04.006

Comments

Open access.

Abstract

Introduction: Minimal change disease (MCD) and membranous nephropathy (MN) are glomerular diseases (glomerulonephritis [GN]) that present with the nephrotic syndrome. Although circulating PLA2R antibodies have been validated as a biomarker for MN, the diagnosis of MCD and PLA2R-negative MN still relies on the results of kidney biopsy or empirical corticosteroids in children. We aimed to identify serum protein biomarker signatures associated with MCD and MN pathogenesis using aptamer-based proteomics.

Methods: Quantitative SOMAscan proteomics was applied to the serum of adult patients with MCD (n = 15) and MN(n = 37) and healthy controls (n = 20). Associations between the 1305proteins detected with SOMAscan were assessed using multiple statistical tests, expression pattern analysis, and systems biology analysis.

Results: A total of 208 and 244 proteins were identified that differentiated MCD and MN, respectively, with high statistical significance from the healthy controls (Benjamin-Hochberg [BH] P < 0.0001). There were 157 proteins that discriminated MN from MCD (BH P < 0.05). In MCD, 65 proteins were differentially expressed as compared with MN and healthy controls. When compared with MCD and healthy controls, 44 discriminatory proteins were specifically linked to MN. Systems biology analysis of these signatures identified cell death and inflammation as key pathways differentiating MN from MCD and healthy controls. Dysregulation of fatty acid metabolism pathways was confirmed in both MN and MCD as compared with the healthy subjects.

Conclusion: SOMAscan represents a promising proteomic platform for biomarker development in GN. Validation of a greater number of discovery biomarkers in larger patient cohorts is needed before these data can be translated for clinical care.