Molecular Characterization of Membranous Nephropathy

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 on the basis of gene expression from the 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-κB. 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 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, provides a molecular snapshot of MN, and facilitates insight into MN's underlying pathophysiology.

Keywords: machine learning; membranous nephropathy; podocyte; scRNA-seq; single-cell sequencing; transcriptional profiling.

Graphical abstract

Figure 1.

A glomerular gene expression signature can classify MN relative to other causes of nephrotic syndrome. (A) MN participants cluster by expression of top glomerular compartment genes that are predictive of diagnosis. (B) MN has the highest prediction accuracy (AUC) across diseases in both the NEPTUNE (solid blue line) and ERCB (dotted blue line) glomerular expression datasets, on the basis of a random forest classifier with five-fold crossvalidation. (C) Top genes are highly informative for classifying MN subjects. Gene importances are the Gini importances of the feature, averaged across all estimators.

Figure 2.

Top genes implicated in MN on the basis of analysis of NEPTUNE cohort are also differentially expressed in MN participants in the independent ERCB cohort and in an independent validation cohort of 177 additional NEPTUNE participants. (A) FAM114A1 is upregulated in the MN participants in the NEPTUNE (P=2.2e-13 by t test), ERCB (P=2.3e-5), and validation (P=1.5e-8) cohorts relative to subjects with other diseases. (B) TRPC6 (P=2.9e-12 in NEPTUNE, P=0.0002 in ERCB, P=8.1e-09 in validation cohort). (C) ATP10A (P=2.4e-10 in NEPTUNE, P=9.9e-7 in ERCB, P=2.4e-10 in validation cohort). (D) SPACA9 (P=1.3e-7 in NEPTUNE, P=0.0003 in ERCB, P=0.003 in validation cohort).

Figure 3.

Schematic of approach and modules identified by community clustering. (A) Schematic representing the approach used for identification of MN-related glomerular compartment genes. (B) Community clustering of MN-related genes in the kidney functional network identifies multiple modules enriched for functional processes. Each node in the graph represents a gene, and the node size corresponds to the degree of the node (number of adjacent genes).

Figure 4.

Podocyte-expressed genes are enriched in the set of MN classifier genes. (A) Podocytes represent the cell type expressing the greatest number of MN classifier genes in a mouse kidney single-cell dataset (Park et al., 2018). (B) A human kidney single-cell dataset (Gillies et al., 2018) also identifies podocytes as the cell type with the greatest overlap with the MN classifier gene set. (C) Many top MN markers are expressed predominantly in the podocyte cluster in single-cell data from normal tissue from three human tumor nephrectomies. aloh/dct, ascending loop of Henle/distal convoluted tubule; b_lymph, B lymphocyte; cd8, CD8⁺ T cell; cd_trans, transitional collecting duct cell; dct, distal convoluted tubule; endo, endothelial cell; fib, fibroblast; ic, collecting duct intercalated cell; imm, immune cell; loh, loop of Henle; macro, macrophage; mes, mesangial cell; neutro, neutrophil; nk, natural killer cell; pc, collecting duct principal cell; pod, podocyte; pt, proximal tubule; tcell, T cell.

Figure 5.

Immunohistochemistry images from the Human Protein Atlas (HPA) showing localization to podocytes for nine of the top 25 MN classifier genes. Representative images of single glomeruli are selected from larger HPA images. See Supplemental Figure 12 for further HPA images.