Immune checkpoint inhibitor therapy in patients with cancer and pre-existing systemic sclerosis.

OBJECTIVE: Immune checkpoint inhibitor (ICI) therapies have dramatically improved outcomes in multiple cancers. ICI's mechanism of action involves immune system activation to augment anti-tumor immunity. Patients with pre-existing autoimmune diseases, such as systemic sclerosis (SSc), were excluded from initial ICI clinical trials due to concern that such immune system activation could precipitate an autoimmune disease flare or new, severe immune related adverse events (irAE). In the present study, we report our experience with ICIs in patients with pre-existing SSc. METHODS: Patients with SSc who received ICI therapy for cancer were identified from the Johns Hopkins Scleroderma Center Research Registry. Through chart review and prespecified definitions, we identified whether patients experienced worsening SSc activity or new irAEs. SSc disease activity worsening was pre-defined as an increase in modified Rodnan skin score (mRSS), new scleroderma renal crisis, progression of interstitial lung disease (ILD) on CT scan, increased Raynaud's phenomenon frequency or severity, new pulmonary hypertension, or myositis flare. IrAEs also included active inflammatory arthritis and dermatitis. RESULTS: Eight patients with SSc who received ICI therapy for cancer were included. Overall, SSc symptoms remained stable during and after ICI therapy. None of the patients with long-standing sine or limited cutaneous SSc (lcSSc) had progressive skin thickening after ICI therapy. One patient, who was early in his diffuse cutaneous SSc (dcSSc) disease course, experienced worsening skin thickening and renal crisis. Three patients (38 %) experienced a total of five irAEs (grade 2: diarrhea, mucositis and dermatitis; grade 3: pneumonitis, and grade 4: nephritis). The patient with grade 4 nephritis developed scleroderma renal crisis and immune checkpoint related nephritis simultaneously. There were no deaths due to irAEs. CONCLUSION: In this study, ICI therapy was well tolerated in patients with longstanding, sine or lcSSc. IrAE were common but generally manageable. Patients with early, active SSc may be at greater risk from ICI therapy, but more research is needed.

Predicting proximal tubule failed repair drivers through regularized regression analysis of single cell multiomic sequencing.

Renal proximal tubule epithelial cells have considerable intrinsic repair capacity following injury. However, a fraction of injured proximal tubule cells fails to undergo normal repair and assumes a proinflammatory and profibrotic phenotype that may promote fibrosis and chronic kidney disease. The healthy to failed repair change is marked by cell state-specific transcriptomic and epigenomic changes. Single nucleus joint RNA- and ATAC-seq sequencing offers an opportunity to study the gene regulatory networks underpinning these changes in order to identify key regulatory drivers. We develop a regularized regression approach to construct genome-wide parametric gene regulatory networks using multiomic datasets. We generate a single nucleus multiomic dataset from seven adult human kidney samples and apply our method to study drivers of a failed injury response associated with kidney disease. We demonstrate that our approach is a highly effective tool for predicting key cis- and trans-regulatory elements underpinning the healthy to failed repair transition and use it to identify NFAT5 as a driver of the maladaptive proximal tubule state.

Mosaic loss of Y chromosome is associated with aging and epithelial injury in chronic kidney disease.

BACKGROUND: Mosaic loss of Y chromosome (LOY) is the most common chromosomal alteration in aging men. Here, we use single-cell RNA and ATAC sequencing to show that LOY is present in the kidney and increases with age and chronic kidney disease. RESULTS: The likelihood of a cell having LOY varies depending on its location in the nephron. Cortical epithelial cell types have a greater proportion of LOY than medullary or glomerular cell types, which may reflect their proliferative history. Proximal tubule cells are the most abundant cell type in the cortex and are susceptible to hypoxic injury. A subset of these cells acquires a pro-inflammatory transcription and chromatin accessibility profile associated with expression of HAVCR1, VCAM1, and PROM1. These injured epithelial cells have the greatest proportion of LOY and their presence predicts future kidney function decline. Moreover, proximal tubule cells with LOY are more likely to harbor additional large chromosomal gains and express pro-survival pathways. Spatial transcriptomics localizes injured proximal tubule cells to a pro-fibrotic microenvironment where they adopt a secretory phenotype and likely communicate with infiltrating immune cells. CONCLUSIONS: We hypothesize that LOY is an indicator of increased DNA damage and potential marker of cellular senescence that can be applied to single-cell datasets in other tissues.

Mapping genomic regulation of kidney disease and traits through high-resolution and interpretable eQTLs.

Expression quantitative trait locus (eQTL) studies illuminate genomic variants that regulate specific genes and contribute to fine-mapped loci discovered via genome-wide association studies (GWAS). Efforts to maximize their accuracy are ongoing. Using 240 glomerular (GLOM) and 311 tubulointerstitial (TUBE) micro-dissected samples from human kidney biopsies, we discovered 5371 GLOM and 9787 TUBE genes with at least one variant significantly associated with expression (eGene) by incorporating kidney single-nucleus open chromatin data and transcription start site distance as an "integrative prior" for Bayesian statistical fine-mapping. The use of an integrative prior resulted in higher resolution eQTLs illustrated by (1) smaller numbers of variants in credible sets with greater confidence, (2) increased enrichment of partitioned heritability for GWAS of two kidney traits, (3) an increased number of variants colocalized with the GWAS loci, and (4) enrichment of computationally predicted functional regulatory variants. A subset of variants and genes were validated experimentally in vitro and using a Drosophila nephrocyte model. More broadly, this study demonstrates that tissue-specific eQTL maps informed by single-nucleus open chromatin data have enhanced utility for diverse downstream analyses.

Quality assessment and refinement of chromatin accessibility data using a sequence-based predictive model.

Chromatin accessibility assays are central to the genome-wide identification of gene regulatory elements associated with transcriptional regulation. However, the data have highly variable quality arising from several biological and technical factors. To surmount this problem, we developed a sequence-based machine learning method to evaluate and refine chromatin accessibility data. Our framework, gapped k-mer SVM quality check (gkmQC), provides the quality metrics for a sample based on the prediction accuracy of the trained models. We tested 886 DNase-seq samples from the ENCODE/Roadmap projects to demonstrate that gkmQC can effectively identify "high-quality" (HQ) samples with low conventional quality scores owing to marginal read depths. Peaks identified in HQ samples are more accurately aligned at functional regulatory elements, show greater enrichment of regulatory elements harboring functional variants, and explain greater heritability of phenotypes from their relevant tissues. Moreover, gkmQC can optimize the peak-calling threshold to identify additional peaks, especially for rare cell types in single-cell chromatin accessibility data.

Defining cellular complexity in human autosomal dominant polycystic kidney disease by multimodal single cell analysis.

Autosomal dominant polycystic kidney disease (ADPKD) is the leading genetic cause of end stage renal disease characterized by progressive expansion of kidney cysts. To better understand the cell types and states driving ADPKD progression, we analyze eight ADPKD and five healthy human kidney samples, generating single cell multiomic atlas consisting of ~100,000 single nucleus transcriptomes and ~50,000 single nucleus epigenomes. Activation of proinflammatory, profibrotic signaling pathways are driven by proximal tubular cells with a failed repair transcriptomic signature, proinflammatory fibroblasts and collecting duct cells. We identify GPRC5A as a marker for cyst-lining collecting duct cells that exhibits increased transcription factor binding motif availability for NF-κB, TEAD, CREB and retinoic acid receptors. We identify and validate a distal enhancer regulating GPRC5A expression containing these motifs. This single cell multiomic analysis of human ADPKD reveals previously unrecognized cellular heterogeneity and provides a foundation to develop better diagnostic and therapeutic approaches.

Multimodal single cell sequencing implicates chromatin accessibility and genetic background in diabetic kidney disease progression.

The proximal tubule is a key regulator of kidney function and glucose metabolism. Diabetic kidney disease leads to proximal tubule injury and changes in chromatin accessibility that modify the activity of transcription factors involved in glucose metabolism and inflammation. Here we use single nucleus RNA and ATAC sequencing to show that diabetic kidney disease leads to reduced accessibility of glucocorticoid receptor binding sites and an injury-associated expression signature in the proximal tubule. We hypothesize that chromatin accessibility is regulated by genetic background and closely-intertwined with metabolic memory, which pre-programs the proximal tubule to respond differently to external stimuli. Glucocorticoid excess has long been known to increase risk for type 2 diabetes, which raises the possibility that glucocorticoid receptor inhibition may mitigate the adverse metabolic effects of diabetic kidney disease.

Neuroblastoma suppressor of tumorigenicity 1 is a circulating protein associated with progression to end-stage kidney disease in diabetes.

Circulating proteins associated with transforming growth factor-ß (TGF-ß) signaling are implicated in the development of diabetic kidney disease (DKD). It remains to be comprehensively examined which of these proteins are involved in the pathogenesis of DKD and its progression to end-stage kidney disease (ESKD) in humans. Using the SOMAscan proteomic platform, we measured concentrations of 25 TGF-ß signaling family proteins in four different cohorts composed in total of 754 Caucasian or Pima Indian individuals with type 1 or type 2 diabetes. Of these 25 circulating proteins, we identified neuroblastoma suppressor of tumorigenicity 1 (NBL1, aliases DAN and DAND1), a small secreted protein known to inhibit members of the bone morphogenic protein family, to be most strongly and independently associated with progression to ESKD during 10-year follow-up in all cohorts. The extent of damage to podocytes and other glomerular structures measured morphometrically in 105 research kidney biopsies correlated strongly with circulating NBL1 concentrations. Also, in vitro exposure to NBL1 induced apoptosis in podocytes. In conclusion, circulating NBL1 may be involved in the disease process underlying progression to ESKD, and its concentration in circulation may identify subjects with diabetes at increased risk of progression to ESKD.

Circulating Plasma Biomarkers in Biopsy-Confirmed Kidney Disease.

BACKGROUND AND OBJECTIVES: Biomarkers for noninvasive assessment of histopathology and prognosis are needed in patients with kidney disease. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: Using a proteomics assay, we measured a multimarker panel of 225 circulating plasma proteins in a prospective cohort study of 549 individuals with biopsy-confirmed kidney diseases and semiquantitative assessment of histopathology. We tested the associations of each biomarker with histopathologic lesions and the risks of kidney disease progression (defined as ≥40% decline in eGFR or initiation of KRT) and death. RESULTS: After multivariable adjustment and correction for multiple testing, 46 different proteins were associated with histopathologic lesions. The top-performing markers positively associated with acute tubular injury and interstitial fibrosis/tubular atrophy were kidney injury molecule-1 (KIM-1) and V-set and Ig domain-containing protein 2 (VSIG2), respectively. Thirty proteins were significantly associated with kidney disease progression, and 35 were significantly associated with death. The top-performing markers for kidney disease progression were placental growth factor (hazard ratio per doubling, 5.4; 95% confidence interval, 3.4 to 8.7) and BMP and activin membrane-bound inhibitor (hazard ratio, 3.0; 95% confidence interval, 2.1 to 4.2); the top-performing markers for death were TNF-related apoptosis-inducing ligand receptor-2 (hazard ratio, 2.9; 95% confidence interval, 2.0 to 4.0) and CUB domain-containing protein-1 (hazard ratio, 2.4; 95% confidence interval, 1.8 to 3.3). CONCLUSION: We identified several plasma protein biomarkers associated with kidney disease histopathology and adverse clinical outcomes in individuals with a diverse set of kidney diseases. PODCAST: This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2021_12_28_CJN09380721.mp3.

Spatially Resolved Transcriptomic Analysis of Acute Kidney Injury in a Female Murine Model.

BACKGROUND: Single-cell sequencing technologies have advanced our understanding of kidney biology and disease, but the loss of spatial information in these datasets hinders our interpretation of intercellular communication networks and regional gene expression patterns. New spatial transcriptomic sequencing platforms make it possible to measure the topography of gene expression at genome depth. METHODS: We optimized and validated a female bilateral ischemia-reperfusion injury model. Using the 10× Genomics Visium Spatial Gene Expression solution, we generated spatial maps of gene expression across the injury and repair time course, and applied two open-source computational tools, Giotto and SPOTlight, to increase resolution and measure cell-cell interaction dynamics. RESULTS: An ischemia time of 34 minutes in a female murine model resulted in comparable injury to 22 minutes for males. We report a total of 16,856 unique genes mapped across our injury and repair time course. Giotto, a computational toolbox for spatial data analysis, enabled increased resolution mapping of genes and cell types. Using a seeded nonnegative matrix regression (SPOTlight) to deconvolute the dynamic landscape of cell-cell interactions, we found that injured proximal tubule cells were characterized by increasing macrophage and lymphocyte interactions even 6 weeks after injury, potentially reflecting the AKI to CKD transition. CONCLUSIONS: In this transcriptomic atlas, we defined region-specific and injury-induced loss of differentiation markers and their re-expression during repair, as well as region-specific injury and repair transcriptional responses. Lastly, we created an interactive data visualization application for the scientific community to explore these results (http://humphreyslab.com/SingleCell/).

Single cell transcriptional and chromatin accessibility profiling redefine cellular heterogeneity in the adult human kidney.

The integration of single cell transcriptome and chromatin accessibility datasets enables a deeper understanding of cell heterogeneity. We performed single nucleus ATAC (snATAC-seq) and RNA (snRNA-seq) sequencing to generate paired, cell-type-specific chromatin accessibility and transcriptional profiles of the adult human kidney. We demonstrate that snATAC-seq is comparable to snRNA-seq in the assignment of cell identity and can further refine our understanding of functional heterogeneity in the nephron. The majority of differentially accessible chromatin regions are localized to promoters and a significant proportion are closely associated with differentially expressed genes. Cell-type-specific enrichment of transcription factor binding motifs implicates the activation of NF-κB that promotes VCAM1 expression and drives transition between a subpopulation of proximal tubule epithelial cells. Our multi-omics approach improves the ability to detect unique cell states within the kidney and redefines cellular heterogeneity in the proximal tubule and thick ascending limb.

Development and Validation of a Deep Learning Model to Quantify Glomerulosclerosis in Kidney Biopsy Specimens.

Importance: A chronic shortage of donor kidneys is compounded by a high discard rate, and this rate is directly associated with biopsy specimen evaluation, which shows poor reproducibility among pathologists. A deep learning algorithm for measuring percent global glomerulosclerosis (an important predictor of outcome) on images of kidney biopsy specimens could enable pathologists to more reproducibly and accurately quantify percent global glomerulosclerosis, potentially saving organs that would have been discarded. Objective: To compare the performances of pathologists with a deep learning model on quantification of percent global glomerulosclerosis in whole-slide images of donor kidney biopsy specimens, and to determine the potential benefit of a deep learning model on organ discard rates. Design, Setting, and Participants: This prognostic study used whole-slide images acquired from 98 hematoxylin-eosin-stained frozen and 51 permanent donor biopsy specimen sections retrieved from 83 kidneys. Serial annotation by 3 board-certified pathologists served as ground truth for model training and for evaluation. Images of kidney biopsy specimens were obtained from the Washington University database (retrieved between June 2015 and June 2017). Cases were selected randomly from a database of more than 1000 cases to include biopsy specimens representing an equitable distribution within 0% to 5%, 6% to 10%, 11% to 15%, 16% to 20%, and more than 20% global glomerulosclerosis. Main Outcomes and Measures: Correlation coefficient (r) and root-mean-square error (RMSE) with respect to annotations were computed for cross-validated model predictions and on-call pathologists' estimates of percent global glomerulosclerosis when using individual and pooled slide results. Data were analyzed from March 2018 to August 2020. Results: The cross-validated model results of section images retrieved from 83 donor kidneys showed higher correlation with annotations (r = 0.916; 95% CI, 0.886-0.939) than on-call pathologists (r = 0.884; 95% CI, 0.825-0.923) that was enhanced when pooling glomeruli counts from multiple levels (r = 0.933; 95% CI, 0.898-0.956). Model prediction error for single levels (RMSE, 5.631; 95% CI, 4.735-6.517) was 14% lower than on-call pathologists (RMSE, 6.523; 95% CI, 5.191-7.783), improving to 22% with multiple levels (RMSE, 5.094; 95% CI, 3.972-6.301). The model decreased the likelihood of unnecessary organ discard by 37% compared with pathologists. Conclusions and Relevance: The findings of this prognostic study suggest that this deep learning model provided a scalable and robust method to quantify percent global glomerulosclerosis in whole-slide images of donor kidneys. The model performance improved by analyzing multiple levels of a section, surpassing the capacity of pathologists in the time-sensitive setting of examining donor biopsy specimens. The results indicate the potential of a deep learning model to prevent erroneous donor organ discard.

Cell profiling of mouse acute kidney injury reveals conserved cellular responses to injury.

After acute kidney injury (AKI), patients either recover or alternatively develop fibrosis and chronic kidney disease. Interactions between injured epithelia, stroma, and inflammatory cells determine whether kidneys repair or undergo fibrosis, but the molecular events that drive these processes are poorly understood. Here, we use single nucleus RNA sequencing of a mouse model of AKI to characterize cell states during repair from acute injury. We identify a distinct proinflammatory and profibrotic proximal tubule cell state that fails to repair. Deconvolution of bulk RNA-seq datasets indicates that this failed-repair proximal tubule cell (FR-PTC) state can be detected in other models of kidney injury, increasing during aging in rat kidney and over time in human kidney allografts. We also describe dynamic intercellular communication networks and discern transcriptional pathways driving successful vs. failed repair. Our study provides a detailed description of cellular responses after injury and suggests that the FR-PTC state may represent a therapeutic target to improve repair.

Kidney tissue hypoxia dictates T cell-mediated injury in murine lupus nephritis.

The kidney is a frequent target of autoimmune injury, including in systemic lupus erythematosus; however, how immune cells adapt to kidney's unique environment and contribute to tissue damage is unknown. We found that renal tissue, which normally has low oxygen tension, becomes more hypoxic in lupus nephritis. In the injured mouse tissue, renal-infiltrating CD4+ and CD8+ T cells express hypoxia-inducible factor-1 (HIF-1), which alters their cellular metabolism and prevents their apoptosis in hypoxia. HIF-1-dependent gene-regulated pathways were also up-regulated in renal-infiltrating T cells in human lupus nephritis. Perturbation of these environmental adaptations by selective HIF-1 blockade inhibited infiltrating T cells and reversed tissue hypoxia and injury in murine models of lupus. The results suggest that targeting HIF-1 might be effective for treating renal injury in autoimmune diseases.

Epigenomics and the kidney.

PURPOSE OF REVIEW: Epigenetic modifications are reversible changes to a cell's DNA or histones that alter gene expression but not DNA sequence. The present review will explore epigenomic profiling and bioinformatics techniques for the study of kidney development and disease. RECENT FINDINGS: Reversible DNA and histone modifications influence chromatin accessibility and can be measured by a variety of recent techniques including DNase-seq, ATAC-seq, and single cell ATAC-seq. These approaches have been used to demonstrate that DNA methylation is critical for nephron progenitor maturation, for example. New bioinformatics techniques allow the prediction of chromatin loops that connect regulatory elements to target genes. Recent studies have demonstrated that DNA elements regulate transcription in the kidney via long-range physical interactions and create a new framework for understanding how genome wide association studies risk loci contribute to kidney disease. Increasingly, epigenomic approaches are being combined with transcriptomic analyses to generate multimodal datasets. SUMMARY: Epigenomics has expanded our knowledge of gene architecture and regulation. Novel tools and techniques have led to the emergence of 'multiomics' in which epigenomic profiling, transcriptomics, and additional methods complement each other to improve our understanding of kidney disease and development.

Polycystin 2 is increased in disease to protect against stress-induced cell death.

Polycystin 2 (PC2 or TRPP1, formerly TRPP2) is a calcium-permeant Transient Receptor Potential (TRP) cation channel expressed primarily on the endoplasmic reticulum (ER) membrane and primary cilia of all cell and tissue types. Despite its ubiquitous expression throughout the body, studies of PC2 have focused primarily on its role in the kidney, as mutations in PC2 lead to the development of autosomal dominant polycystic kidney disease (ADPKD), a debilitating condition for which there is no cure. However, the endogenous role that PC2 plays in the regulation of general cellular homeostasis remains unclear. In this study, we measure how PC2 expression changes in different pathological states, determine that its abundance is increased under conditions of cellular stress in multiple tissues including human disease, and conclude that PC2-deficient cells have increased susceptibility to cell death induced by stress. Our results offer new insight into the normal function of PC2 as a ubiquitous stress-sensitive protein whose expression is up-regulated in response to cell stress to protect against pathological cell death in multiple diseases.

Beyond Panel-Based Testing: Exome Analysis Increases Sensitivity for Diagnosis of Genetic Kidney Disease.

Background: Next-generation sequencing (NGS) is a useful tool for evaluating patients with suspected genetic kidney disease. Clinical practice relies on the use of targeted gene panels that are ordered based on patient presentation. We compare the diagnostic yield of clinical panel-based testing to exome analysis. Methods: In total, 324 consecutive patients underwent physician-ordered, panel-based NGS testing between December 2014 and October 2018. Gene panels were available for four clinical phenotypes, including atypical hemolytic uremic syndrome (n=224), nephrotic syndrome (n=56), cystic kidney disease (n=26), and Alport syndrome (n=13). Variants were analyzed and clinical reports were signed out by a pathologist or clinical geneticist at the time of testing. Subsequently, all patients underwent retrospective exome analysis to detect additional clinically significant variants in kidney disease genes that were not analyzed as part of the initial clinical gene panel. Resulting variants were classified according to the American College of Medical Genetics and Genomics 2015 guidelines. Results: In the initial physician-ordered gene panels, we identified clinically significant pathogenic or likely pathogenic variants in 13% of patients (n=42/324). CFHR3-CFHR1 homozygous deletion was detected in an additional 13 patients with aHUS without a pathogenic or likely pathogenic variant. Diagnostic yield of the initial physician-ordered gene panel was 20% and varied between groups. Retrospective exome analysis identified 18 patients with a previously unknown pathogenic or likely pathogenic variant in a kidney disease gene and eight patients with a high-risk APOL1 genotype. Overall, retrospective exome analysis increased the diagnostic yield of panel-based testing from 20% to 30%. Conclusions: These results highlight the importance of a broad and collaborative approach between the clinical laboratory and their physician clients that employs additional analysis when a targeted panel of kidney disease-causing genes does not return a clinically meaningful result.