Biology subjects

Eddy, S.

Publications and source records attributed to Eddy, S..

8 recordsLinked to original sources

bioRxiv2024

Glomerular Elasticity and Gene Expression Patterns Define Two Phases of Alport Nephropathy

Alport syndrome (AS), caused by COL4A3,4,5 mutations, leads to progressive glomerular disease and eventual kidney failure. In Col43-/- mice (C57BL/6 background), we found that increased glomerular capillary deformability (reduced Youngs modulus, E) appears 2-3 months before detectable proteinuria or elevated serum creatinine. This early change indicates that podocyte injury precedes traditional clinical markers of disease and corresponds to reduced podocyte adhesion and loss. Bulk and podocyte-enriched RNA-seq data, obtained from deconvoluting bulk RNA sequencing data, showed that starting at 4 months, endoplasmic reticulum (ER) stress and the unfolded protein response (UPR) steadily rise while extracellular matrix remodeling, inflammation, epithelial-mesenchymal transition, and maladaptive repair begin. By 7 months, pathology shifts toward widespread parenchymal fibrosis, interleukin, cytokine, and chemokine signaling, cytoskeleton disruption, metabolic failure, and podocyte dedifferentiation. Notably, administration of the chemical chaperone Tauro-Urso-Deoxcycholic Acid (TUDCA) from weaning reduced ER stress, preserved glomerular stiffness, minimized podocyte detachment and loss, normalized inflammatory, injury, and fibrotic gene expression, and halved proteinuria and serum creatinine at 7 months, preserving kidney structure. Differentially expressed podocyte enriched genes from 4-month Col43-/- (vs WT) mice were mapped to human orthologs in the NEPTUNE cohort. Four genes (CRB2, GPC6, NKD1, STX11) were associated with End Stage Renal Disease or 40% loss of eGFR (ESRD40). Collectively, our results identify these four genes and ER stress/UPR as key and treatable drivers of podocyte injury and disease progression in Alport syndrome, well before overt proteinuria, and highlight UPR activation and several genes as promising targets for disease-modifying therapies.

cell biology↗

bioRxiv2021

An atlas of healthy and injured cell states and niches in the human kidney

Understanding kidney disease relies upon defining the complexity of cell types and states, their associated molecular profiles, and interactions within tissue neighborhoods. We have applied multiple single-cell or -nucleus assays (>400,000 nuclei/cells) and spatial imaging technologies to a broad spectrum of healthy reference (n = 42) and disease (n = 42) kidneys. This has provided a high resolution cellular atlas of 100 cell types that include rare and novel cell populations. The multi-omic approach provides detailed transcriptomic profiles, epigenomic regulatory factors, and spatial localizations for major cell types spanning the entire kidney. We further identify and define cellular states altered in kidney injury, encompassing cycling, adaptive or maladaptive repair, transitioning and degenerative states affecting several segments. Molecular signatures of these states permitted their localization within injury neighborhoods using spatial transcriptomics, and large-scale 3D imaging analysis of [~]1.2 million neighborhoods provided linkages to active immune responses. These analyses further defined biological pathways relevant to injury niches, including signatures underlying the transition from reference to predicted maladaptive states that were associated with a decline in kidney function during chronic kidney disease. This human kidney cell atlas, including injury cell states and neighborhoods, will be a valuable resource for future studies.

genomics↗

bioRxiv2020

Urine single cell RNA-sequencing in focal segmental glomerulosclerosis reveals inflammatory signatures in immune cells and podocytes

The diagnosis of focal segmental glomerulosclerosis (FSGS) requires a renal biopsy, which is invasive and can be problematic in children and in some adults. We used single cell RNA-sequencing to explore disease-related cellular signatures in 23 urine samples from 12 FSGS subjects. We identified immune cells, predominantly monocytes, and renal epithelial cells, including podocytes. Analysis revealed M1 and M2 monocyte subsets, and podocytes showing high expression of genes for epithelial-to-mesenchymal transition (EMT). We confirmed M1 and M2 gene signatures using published monocyte/macrophage data from lupus nephritis and cancer. Using renal transcriptomic data from the Nephrotic Syndrome Study Network (NEPTUNE), we found that urine cell immune and EMT signature genes showed higher expression in FSGS biopsies compared to minimal change disease biopsies. These results suggest that urine cell profiling may serve as a diagnostic and prognostic tool in nephrotic syndrome and aid in identifying novel biomarkers and developing personalized therapeutic strategies.

immunology↗

bioRxiv2018

Organoid single-cell profiling identifies a transcriptional signature of glomerular disease

Podocyte injury is central to many forms of kidney disease, but transcriptional signatures reflecting podocyte injury and compensation mechanisms are challenging to analyze in vivo. Human kidney organoids derived from pluripotent stem cells (PSCs), a new model for disease and regeneration, present an opportunity to explore the transcriptional plasticity of podocytes. Here, transcriptional profiling of over 12,000 single cells from human PSC-derived kidney organoid cultures was used to identify robust and reproducible cell-lineage gene expression signatures shared with developing human kidneys based on trajectory analysis. Surprisingly, the gene expression signature characteristic of developing glomerular epithelial cells was also observed in glomerular tissue from a kidney disease cohort. This signature correlated with proteinuria and inverse eGFR, and was confirmed in an independent podocytopathy cohort. Three genes in particular were further identified as critical components of the glomerular disease signature. We conclude that cells in human PSC-derived kidney organoids reliably recapitulate the developmental transcriptional program of podocytes and other cell lineages in the human kidney, and that the early transcriptional profile seen in developing podocytes is reactivated in glomerular disease. Our findings demonstrate an innovative approach to identifying novel molecular programs involved in the pathogenesis of glomerulopathies.

systems biology↗

bioRxiv2018

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.\n\nAbstractNephrotic 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.

systems biology↗

bioRxiv2018

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

BackgroundGlomerular 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.\n\nMethodsRenal 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.\n\nResultsIn 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).\n\nConclusionAAV 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.

systems biology↗

bioRxiv2018

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

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.

bioinformatics↗

bioRxiv2018

An eQTL landscape of kidney tissue in human nephrotic syndrome

Expression quantitative trait loci (eQTL) studies illuminate the genetics of gene expression and, in disease research, can be particularly illuminating when using the tissues directly impacted by the condition. In nephrology, there is a paucity of eQTLs studies of human kidney. Here, we used whole genome sequencing (WGS) and microdissected glomerular (GLOM) & tubulointerstitial (TI) transcriptomes from 187 patients with nephrotic syndrome (NS) to describe the eQTL landscape in these functionally distinct kidney structures.\n\nUsing MatrixEQTL, we performed cis-eQTL analysis on GLOM (n=136) and TI (n=166). We used the Bayesian \"Deterministic Approximation of Posteriors\" (DAP) to fine-map these signals, eQtlBma to discover GLOM-or TI-specific eQTLs, and single cell RNA-Seq data of control kidney tissue to identify cell-type specificity of significant eQTLs. We integrated eQTL data with an IgA Nephropathy (IGAN) GWAS to perform a transcriptome-wide association study (TWAS).\n\nWe discovered 894 GLOM eQTLs and 1767 TI eQTLs at FDR <0.05. 14% and 19% of GLOM & TI eQTLs, respectively, had > 1 independent signal associated with its expression. 12% and 26% of eQTLs were GLOM-specific and TI-specific, respectively. GLOM eQTLs were most significantly enriched in podocyte transcripts and TI eQTLs in proximal tubules. The IGAN TWAS identified significant GLOM & TI genes, primarily at the HLA region.\n\nIn this study of NS patients, we discovered GLOM & TI eQTLs, identified those that were tissue-specific, deconvoluted them into cell-specific signals, and used them to characterize known GWAS alleles. These data are publicly available for browsing and download at http://nephqtl.org.

genomics↗