Defining subgroups of pediatric nephrotic patients with urine proteomics

Abstract

The molecular pathophysiology of nephrotic syndrome remains largely elusive in pediatric patients. While most children with minimal change disease (MCD) show favorable responses to immunosuppressive therapy, those with focal segmental glomerulosclerosis (FSGS) often exhibit poorer treatment responses, with many experiencing either partial remission or no remission of proteinuria. The need for reliable glomerular disease biomarkers to predict treatment response and understand molecular pathways governing responsiveness and resistance is a critical unmet need in pediatric nephrology. In this study, we sought to characterize urine proteomes in children with MCD and FSGS to identify biomarkers distinguishing disease activity and associated molecular pathways. Using quantitative proteomics, urine proteins from children with MCD and FSGS in the CureGN Study were identified and correlated with disease onset and activity. Unbiased cluster analyses of nephrotic urine proteomes demonstrated a cluster with relatively increased immune response and complement proteins, suggesting important distinctions in disease characteristics within the nephrotic subgroups. These analyses yielded patient subpopulations with proteinuria and distinct urine proteome differences associated with 116 proteins exerting cluster separation in the multivariate analyses. These findings highlight the potential of unsupervised clustering to identify disease subgroups and provide insights into the underlying molecular heterogeneity within nephrotic syndrome, paving the way for more tailored therapeutic strategies and improved patient management.

Keywords: FSGS; MCD; Nephrotic proteinuria; Pediatric glomerular disease; Proteomics; Urine biomarker.

Fig. 1

Proteomic data processing and bioinformatic analysis. Flowchart illustrating proteomic data handling and processing. Each enclosed box indicates specific data acquisition and processing approaches used to determine patient stratification and clustering as a function of the urine proteome. Box 1) shows data acquisition by mass spectrometry and initial data handling post proteome search by MaxQuant. High stringency identifications below 1% false discovery rate (FDR < 1%) are the initial list cut parameter followed by removal of single peptide identifications, thus increasing stringency. Box 2) shows data filtering and missing value imputations calculated in Metaboanalyst from the comma separated value (CSV) converted files from the initial proteome identification lists and removal of any patients deemed as CKD5 to diminish data skewing. Box 3) shows the re-clustering approach from initial groups of patients based on UPCR > 2 that would normally be grouped together based on clinical data (UPCR, GFR etc.) K-means clustering was used to identify patients with UPCR > 2 and determine if sub-groups existed based on urine proteome differences. Box 4) candidate selection by supervised multivariate analysis of patient clusters to determine proteins with the most influence and drivers of group separation. Box 5) shows pathways and ontological network analyses to define changes in patient groups.

Fig. 2

Unsupervised clustering analyses of the nephrotic urine proteome. (A) K-means clustering of nephrotic/subnephrotic patient (UPCR > 2) urine proteome. Dark boundary line is the median intensity of each cluster, score plot shows the PCA (principle component analysis) of the normalized data. (B) Partial least squares discriminant analysis (PLSDA) plot shows separation of patients into two distinct clusters, CL1-NP (n = 71) and CL2-NP (n = 17). Inset histogram showing PLSDA model statistics: R2 = 0.95, Q2 = 0.73 for 5 components. (C) Plot of VIP (variable of importance in projection) for the most influential proteins in each patient cluster from PLSDA analyses.

Fig. 3

Hierarchical heatmapping of the urine proteome in cluster 1 and 2 patients from PLSDA analysis. (A) Overall heatmap of total proteome post-filtering and normalization shows robust clustering and enrichment of cluster 2 proteins. (B) Heatmap of the top 40 enriched proteins.

Fig. 4

Enriched proteins (VIP > 0.9 in PLSDA) were analyzed for tissue network enrichment to determine biological processes and functions. (A) Glomerular networked proteins are separated into modules with lines between proteins and modules showing interconnectivity. Tables show functional characteristics mapped to each module and statistical evidence for ontology enrichment (q-val). (B) Podocyte networked proteins grouped into 6 modules, the table shows ontological enrichment for each module.

Fig. 5

STRING analysis of protein interactions for proteins from cluster 2 nephrotic patient urine with VIP > 0.9. Biological processes by gene ontology were sorted by false discovery rate, included are observed genes from the total and strength of association calculations for ontology and false-discovery rate.