Sean Eddy

Local TNF causes NFATc1-dependent cholesterol-mediated podocyte injury

High levels of circulating TNF and its receptors, TNFR1 and TNFR2, predict the progression of diabetic kidney disease (DKD), but their contribution to organ damage in DKD remains largely unknown. Here, we investigated the function of local and systemic TNF in podocyte injury. We cultured human podocytes with sera collected from DKD patients, who displayed elevated TNF levels, and focal segmental glomerulosclerosis (FSGS) patients, whose TNF levels resembled those of healthy patients. Exogenous TNF administration or local TNF expression was equally sufficient to cause free cholesterol-dependent apoptosis in podocytes by acting through a dual mechanism that required a reduction in ATP-binding cassette transporter A1-mediated (ABCA1-mediated) cholesterol efflux and reduced cholesterol esterification by sterol-O-acyltransferase 1 (SOAT1). TNF-induced albuminuria was aggravated in mice with podocyte-specific ABCA1 deficiency and was partially prevented by cholesterol depletion with cyclodextrin. TNF-stimulated free cholesterol-dependent apoptosis in podocytes was mediated by nuclear factor of activated T cells 1 (NFATc1). ABCA1 overexpression or cholesterol depletion was sufficient to reduce albuminuria in mice with podocyte-specific NFATc1 activation. Our data implicate an NFATc1/ABCA1-dependent mechanism in which local TNF is sufficient to cause free cholesterol-dependent podocyte injury irrespective of TNF, TNFR1, or TNFR2 serum levels.

Urinary epidermal growth factor predicts renal prognosis in antineutrophil cytoplasmic antibody-associated vasculitis

Introduction The current study aimed to investigate the association between urinary epidermal growth factor (uEGF) and renal disease severity and outcomes in patients with antineutrophil cytoplasmic antibody-associated vasculitis (AAV). Methods Intrarenal EGFmRNA expression was extracted from transcriptomic data of microdissected tubulointerstitial compartments of kidney biopsies of patients with AAV. uEGF was measured in 173 patients with AAV in active stage and 143 in remission, and normalised to urine creatinine excretion (uEGF/Cr). The association between uEGF/Cr (or EGFmRNA) and clinical–pathological parameters was tested using linear regression analysis. The ability of uEGF/Cr to predict renal outcomes was analysed using Cox’s regression analysis. Results In patients with AAV, intrarenal EGFmRNA expression was significantly associated with estimated glomerular filtration rate (eGFR)(log2) at time of biopsy (β=0.63, p<0.001). The level of uEGF/Cr was significantly higher in patients in remission than in patients with active disease, both when looking at patients with sequential measurements (2.75±1.03vs 2.08±0.98, p<0.001) and in cross-sectional comparison. uEGF/Cr level was positively associated with eGFR(log2) at time of sampling in both active and remission stage (β=0.60, p<0.001; β=0.74, p<0.001, respectively). Patients with resistant renal disease had significantly lower uEGF/Cr levels than responders (1.65±1.22vs 2.16±1.26, p=0.04). Moreover, after adjusting for other potential predictors, uEGF/Cr was independently associated with composite endpoint of end-stage renal disease or 30% reduction of eGFR (HR 0.61, 95% CI 0.45 to 0.83, p=0.001). Conclusion Lower uEGF/Cr levels are associated with more severe renal disease, renal resistance to treatment and higher risk of progression to composite outcome in patients with AAV.

MultiPLIER: a transfer learning framework reveals systemic features of rare autoimmune disease

SUMMARY Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most gene expression datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We trained a Pathway Level Information ExtractoR (PLIER) model using on a large public data compendium comprised of multiple experiments, tissues, and biological conditions. We then transferred the model to small rare disease datasets in an approach we term MultiPLIER. Models constructed from large, diverse public data i) included features that aligned well to important biological factors; ii) were more comprehensive than those constructed from individual datasets or conditions; iii) transferred to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained on specifically those datasets.Unsupervised machine learning methods provide a promising means to analyze and interpret large datasets. However, most datasets generated by individual researchers remain too small to fully benefit from these methods. In the case of rare diseases, there may be too few cases available, even when multiple studies are combined. We sought to determine whether or not machine learning models could be constructed from large public data compendia and then transferred to small datasets for subsequent analysis. We trained models using Pathway Level Information ExtractoR (PLIER) over datasets of different types and scales. Models constructed from large public datasets were i) more detailed than those constructed from individual datasets; ii) included features that aligned well to important biological factors; iii) transferrable to rare disease datasets where the models describe biological processes related to disease severity more effectively than models trained within those datasets.

Inflammatory and JAK-STAT Pathways as Shared Molecular Targets for ANCA-Associated Vasculitis and Nephrotic Syndrome

Background Glomerular diseases of the kidney are presently differentiated, diagnosed and treated according to conventional clinical or structural features. While etiologically diverse, these diseases share common clinical features including but not limited to reduced glomerular filtration rate, increased serum creatinine and proteinuria suggesting shared pathogenic mechanisms across diseases. Renal biopsies from patients with nephrotic syndrome (NS) or ANCA-associated vasculitis (AAV) were evaluated for molecular signals cutting across conventional disease categories as candidates for therapeutic targets. Methods Renal biopsies were obtained from patients with NS (minimal change disease, focal segmental glomerulosclerosis, or membranous nephropathy) (n=187) or AAV (granulomatosis with polyangiitis or microscopic polyangiitis) (n=80) from the Nephrotic Syndrome Study Network (NEPTUNE) and the European Renal cDNA Bank. Transcriptional profiles were assessed for shared disease mechanisms. Results In the discovery cohort, 10–25% transcripts were differentially regulated versus healthy controls in both NS and AAV, >500 transcripts were shared across diseases. The majority of shared transcripts (60–77%) were validated in independent samples. Therapeutically targetable networks were identified, including inflammatory JAK-STAT signaling. STAT1 eQTLs were identified and STAT1 expression associated with GFR-based outcome. A transcriptional STAT1 activity score was generated from STAT1-regulated target genes which correlated with CXCL10 (p<0.001), a JAK-STAT biomarker, predictors of CKD progression, interstitial fibrosis (r=0.41, p<0.001), and urinary EGF (r=-0.51, p<0.001). Conclusion AAV and NS caused from histopathologically distinct disease categories share common intra-renal molecular pathways cutting across conventional disease classifications. This approach provides a starting point for de novo drug development, and repurposing efforts in rare kidney diseases.

Redefining Nephrotic Syndrome in Molecular Terms: Outcome-associated molecular clusters and patient stratification with noninvasive surrogate biomarkers

A tissue transcriptome driven classification of nephrotic syndrome patients identified a high risk group of patients with TNF activation and established a non-invasive marker panel for pathway activity assessment paving the way towards precision medicine trials in NS. Abstract Nephrotic syndrome from primary glomerular diseases can lead to chronic kidney disease (CKD) and/or end-stage renal disease (ESRD). Conventional diagnoses using a combination of clinical presentation and descriptive biopsy information do not accurately predict risk for progression in patients with nephrotic syndrome, which complicates disease management. To address this challenge, a transcriptome-driven approach was used to classify patients with minimal change disease and focal segmental glomerulosclerosis in the Nephrotic Syndrome Study Network (NEPTUNE). Transcriptome-based classification revealed a group of patients at risk for disease progression. High risk patients had a transcriptome profile consistent with TNF activation. Non-invasive urine biomarkers TIMP1 and CCL2 (MCP1), which are causally downstream of TNF, accurately predicted TNF activation in the NEPTUNE cohort setting the stage for patient stratification approaches and precision medicine in kidney disease.

FAR2 is associated with kidney disease in mice and humans

Mesangial matrix expansion is an important process in the initiation of chronic kidney disease, yet the genetic factors driving its development are unknown. Our previous studies have implicated Far2 as a candidate gene associated with differences in mesangial matrix expansion between mouse inbred strains. Consistent with the hypothesis that increased expression of Far2 leads to mesangial matrix expansion through increased production of platelet-activating factor precursors, we show that FAR2 is capable of mediating de novo platelet-activating factor synthesis in vitro and driven by the transcription factor NKX3.2. We demonstrate that knockdown of Far2 in mice delays the progression of mesangial matrix expansion with at least six months (equivalent to ~15 yr in human). Furthermore, we show that increased FAR2 expression in human patients is associated with diabetic nephropathy, lupus nephritis, and IgA nephropathy. Taken together, these results highlight FAR2s role in the development of mesangial matrix expansion and chronic kidney disease.

Hydroxypropyl-β-cyclodextrin protects from kidney disease in experimental Alport syndrome and focal segmental glomerulosclerosis

Studies suggest that altered renal lipid metabolism plays a role in the pathogenesis of diabetic kidney disease and that genetic or pharmacological induction of cholesterol efflux protects from the development of diabetic kidney disease and focal segmental glomerulosclerosis (FSGS). Here we tested whether altered lipid metabolism contributes to renal failure in the Col4a3 knockout mouse model for Alport Syndrome. There was an eight-fold increase in the cholesterol content in renal cortexes of mice with Alport Syndrome. This was associated with increased glomerular lipid droplets and cholesterol crystals. Treatment of mice with Alport Syndrome with hydroxypropyl-β-cyclodextrin (HPβCD) reduced cholesterol content in the kidneys of mice with Alport Syndrome and protected from the development of albuminuria, renal failure, inflammation and tubulointerstitial fibrosis. Cholesterol efflux and trafficking-related genes were primarily affected in mice with Alport Syndrome and were differentially regulated in the kidney cortex and isolated glomeruli. HPβCD also protected from proteinuria and mesangial expansion in a second model of non-metabolic kidney disease, adriamycin-induced nephropathy. Consistent with our experimental findings, microarray analysis confirmed dysregulation of several lipid-related genes in glomeruli isolated from kidney biopsies of patients with primary FSGS enrolled in the NEPTUNE study. Thus, lipid dysmetabolism occurs in non-metabolic glomerular disorders such as Alport Syndrome and FSGS, and HPβCD improves renal function in experimental Alport Syndrome and FSGS.

Metabolic pathways and immunometabolism in rare kidney diseases

Objectives To characterise renal tissue metabolic pathway gene expression in different forms of glomerulonephritis. Methods Patients with nephrotic syndrome (NS), antineutrophil cytoplasmic antibody-associated vasculitis (AAV), systemic lupus erythematosus (SLE) and healthy living donors (LD) were studied. Clinically indicated renal biopsies were obtained at time of diagnosis and microdissected into glomerular and tubulointerstitial compartments. Microarray-derived differential gene expression of 88 genes representing critical enzymes of metabolic pathways and 25 genes related to immune cell markers was compared between disease groups. Correlation analyses measured relationships between metabolic pathways, kidney function and cytokine production. Results Reduced steady state levels of mRNA species were enriched in pathways of oxidative phosphorylation and increased in the pentose phosphate pathway (PPP) with maximal perturbation in AAV and SLE followed by NS, and least in LD. Transcript regulation was isozymes specific with robust regulation in hexokinases, enolases and glucose transporters. Intercorrelation networks were observed between enzymes of the PPP (eg, transketolase) and macrophage markers (eg, CD68) (r=0.49, p<0.01). Increased PPP transcript levels were associated with reduced glomerular filtration rate in the glomerular (r=−0.49, p<0.01) and tubulointerstitial (r=−0.41, p<0.01) compartments. PPP expression and tumour necrosis factor activation were tightly co-expressed (r=0.70, p<0.01). Conclusion This study demonstrated concordant alterations of the renal transcriptome consistent with metabolic reprogramming across different forms of glomerulonephritis. Activation of the PPP was tightly linked with intrarenal macrophage marker expression, reduced kidney function and increased production of cytokines. Modulation of glucose metabolism may offer novel immune-modulatory therapeutic approaches in rare kidney diseases.