Precision nephrology identified tumor necrosis factor activation variability in minimal change disease and focal segmental glomerulosclerosis

Abstract

The diagnosis of nephrotic syndrome relies on clinical presentation and descriptive patterns of injury on kidney biopsies, but not specific to underlying pathobiology. Consequently, there are variable rates of progression and response to therapy within diagnoses. Here, an unbiased transcriptomic-driven approach was used to identify molecular pathways which are shared by subgroups of patients with either minimal change disease (MCD) or focal segmental glomerulosclerosis (FSGS). Kidney tissue transcriptomic profile-based clustering identified three patient subgroups with shared molecular signatures across independent, North American, European, and African cohorts. One subgroup had significantly greater disease progression (Hazard Ratio 5.2) which persisted after adjusting for diagnosis and clinical measures (Hazard Ratio 3.8). Inclusion in this subgroup was retained even when clustering was limited to those with less than 25% interstitial fibrosis. The molecular profile of this subgroup was largely consistent with tumor necrosis factor (TNF) pathway activation. Two TNF pathway urine markers were identified, tissue inhibitor of metalloproteinases-1 (TIMP-1) and monocyte chemoattractant protein-1 (MCP-1), that could be used to predict an individual's TNF pathway activation score. Kidney organoids and single-nucleus RNA-sequencing of participant kidney biopsies, validated TNF-dependent increases in pathway activation score, transcript and protein levels of TIMP-1 and MCP-1, in resident kidney cells. Thus, molecular profiling identified a subgroup of patients with either MCD or FSGS who shared kidney TNF pathway activation and poor outcomes. A clinical trial testing targeted therapies in patients selected using urinary markers of TNF pathway activation is ongoing.

Keywords: TNF; data integration; nephrotic syndrome; transcriptomics.

Figure 1:. Analysis strategy.

Flowchart of tubulointerstitial compartment gene expression to identify molecular subgroups and associated non-invasive urinary markers.

Figure 2:. Kidney transcriptomic cluster membership and unadjusted Kaplan Meier curves.

Consensus clustering using kmeans identified optimal cluster membership from tubulointerstitial transcriptomic profiles with 3 clusters (Clusters were designated by tissue compartment and cluster number, T1, T2, T3 for tubulointerstitial clusters) in a cluster matrix from (A) NEPTUNE, (B) ERCB, and (C) H3 Africa cohorts. The values ranged from 0 (pale yellow, samples do not cluster together) to 1 (brown, samples demonstrate high affinity and cluster together). Scatter plots showed strong correlation of significant fold change differences of genes differentially expressed in (D) tubulointerstitial and glomerular compartments in NEPTUNE; (C) Tubulointerstitium Cluster 3 (T3) compared to T2 and T1 from NEPTUNE (y-axis) and cluster T3 compared to T1 and T2 from ERCB (x-axis); and, similarly, for (F) H3 Africa (x-axis). (G) Alluvial plot of correspondence between diagnosis and cluster membership of participants in the NEPTUNE cohort. Unadjusted Kaplan Meier survival curves by NEPTUNE tubulointerstitial cluster for (H) composite endpoint of 40% loss of eGFR or ESKD and (I) complete remission.

Figure 3.. Molecular and functional context of cluster T3 expression profiles.

Differential expression profiles from T3 compared to T1 and T2 in the NEPTUNE cohort were generated, and enrichment analysis was performed using Ingenuity Pathways Analysis. (A) Granulocyte adhesion and diapedesis was the top enriched canonical pathway; a subset of the pathway is shown highlighting TNF as an input to the pathway. Genes highlighted in red were up-regulated in the differential expression profile. (B) A mechanistic network of predicted upstream regulators from the differential expression profile indicating TNF as an input. (C) TNF was identified in a gene interaction network (red indicates the gene was up-regulated in the differential expression profile, while green indicates down-regulation). (D) Cell selective gene expression markers were previously identified and were intersected with voom-transformed gene (row) normalized expression data (yellow indicates higher expression, blue indicates lower expression) to elucidate probable cell contribution to differential expression profiles.

Figure 4:

TNF activity scores across all profiled participants (A) from the indicated cohorts colored by cluster membership in tubular transcriptomes (B) Pearson’s correlation of TNF activity scores from the glomeruli (y-axis) and tubular (x-axis) transcriptomes in the same NEPTUNE participants. (C) Correlation of the TNF activity score from the tubular transcriptome with interstitial fibrosis. The horizontal bar (red) indicates 25% interstitial fibrosis.

Figure 5:. Non-invasive surrogate selection for TNF activation.

(A) Fourteen genes up-regulated in cluster T3, were downstream of TNF activation, as characterized by curated cause and effect relationships, and were present on the Luminex panel used to profile urine profile from NEPTUNE participants. TNF activation score plotted against (B) Log2 uMCP1/Cr and (C) Log2 uTIMP1/Cr.

Figure 6:. TNF effects on kidney organoids and surrogate markers.

TNF directly stimulates TNF activation and expression of the selected surrogate markers in human pluripotent stem cell derived-kidney organoids. (A) TNF activation scores were calculated from bulk RNAseq data obtained from kidney organoids treated with vehicle control (VC) or 1, 5, 25ng/ml TNF for 3 or 20h. Quantification of (B) CCL2 (left) and TIMP1 (right) transcript levels in kidney organoid cell lysates by qRT-PCR relative to control, and of (C) MCP-1 (left) and TIMP-1 (right) protein levels in kidney organoid culture supernatant by ELISA normalized to total protein, generated from the same samples following treatment with 5 ng/ml TNF or vehicle control for 24h. Each data point was generated from a unique sample and represents the average of analysis in triplicate. Long bar, mean; short bar, 1 S.D. ; *p-value < 0.05 by Student’s t-test. Representative experiment (1 of 4 independent) shown.

Figure 7:. TNF effects on kidney and immune cell types by single nucleus RNAseq from glomerular-depleted biopsies.

(A) UMAP plot of snRNAseq profiles from TI of selected NEPTUNE participants found to have elevated TNF activation scores (TNF high) and low to moderate TNF activation scores (TNF low) and (B) Single nuclear cluster expression of CCL2 and TIMP1 by TNF activation status. Cell type specific markers were based on Lake et al, 2021. Abbreviations: ATL = ascending thin limb cells; DCT = distal convoluted tubule cells; DTL = descending thin limb cells; EC-GC = glomerular endothelial cells; FIB = fibroblast cells; Immune = several types of immune cells; PC/IC = principal cells / intercalated cells; POD = podocytes; PT = proximal tubule cells; TAL = thick ascending limb cells.

Figure 8:. Correlation of observed TNF activation score with a predicted score based on urinary biomarkers and clinical features.

Linear regression models were used to generate predicted tissue TNF activation scores based on eGFR, UPCR, urinary TIMP1 and urinary MCP1. Correlation was 0.61, p-value <0.001.