Molecular Characterization of Membranous Nephropathy

Rachel Sealfon, Laura Mariani, Carmen Avila-Casado, Viji Nair, Rajasree Menon, Julien Funk, Aaron Wong, Gabriel Lerner, Norifumi Hayashi, Olga Troyanskaya, Matthias Kretzler, Laurence H. Beck

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Background Molecular characterization of nephropathies may facilitate pathophysiologic insight, development of targeted therapeutics, and transcriptome-based disease classification. Although membranous nephropathy (MN) is a common cause of adult-onset nephrotic syndrome, the molecular pathways of kidney damage in MN require further definition. Methods We applied a machine learning framework to predict diagnosis based on gene expression from microdissected kidney tissue of participants in the Nephrotic Syndrome Study Network (NEPTUNE) cohort. We sought to identify differentially expressed genes between participants with MN versus those of other glomerulonephropathies across the NEPTUNE and European Renal cDNA Bank (ERCB) cohorts, to find MN-specific gene modules in a kidney-specific functional network, and to identify cell-type specificity of MN-specific genes using single-cell sequencing data from reference nephrectomy tissue. Results Glomerular gene expression alone accurately separated participants with MN from those with other nephrotic syndrome etiologies. The top predictive classifier genes from NEPTUNE participants were also differentially expressed in the ERCB participants with MN. We identified a signature of 158 genes that are significantly differentially expressed in MN across both cohorts, finding 120 of these in a validation cohort. This signature is enriched in targets of transcription factor NF-кB. Clustering these MN-specific genes in a kidney-specific functional network uncovered modules with functional enrichments, including in ion transport, cell projection morphogenesis, regulation of adhesion, and wounding response. Expression data from reference nephrectomy tissue indicated that 43% of these genes are most highly expressed by podocytes. Conclusions These results suggest that relative to other glomerulonephropathies, MN has a distinctive molecular signature that includes upregulation of many podocyte-expressed genes, provide a molecular snapshot of MN, and facilitate insight into MN’s underlying pathophysiology.

Original languageEnglish (US)
Pages (from-to)1208-1221
Number of pages14
JournalJournal of the American Society of Nephrology
Volume33
Issue number6
DOIs
StatePublished - Jun 2022

All Science Journal Classification (ASJC) codes

  • Nephrology

Keywords

  • Glomerulonephritis
  • Membranous
  • machine learning
  • membranous nephropathy
  • podocyte
  • scRNA-seq
  • single-cell sequencing
  • transcriptional profiling

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