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The signaling molecule stimulator of IFN genes (STING) was identified as a crucial regulator of the DNA-sensing cyclic GMP-AMP synthase (cGAS)-STING pathway, and this signaling pathway regulates inflammation and energy homeostasis under conditions of obesity, kidney fibrosis, and AKI. However, the role of STING in causing CKD, including diabetic kidney disease (DKD) and Alport syndrome, is unknown.To investigate whether STING activation contributes to the development and progression of glomerular diseases such as DKD and Alport syndrome, immortalized human and murine podocytes were differentiated for 14 days and treated with a STING-specific agonist. We used diabeticThe activation of the STING pathway acts as a mediator of disease progression in DKD and Alport syndrome. Targeting STING may offer a therapeutic option to treat glomerular diseases of metabolic and nonmetabolic origin or prevent their development, progression, or both.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. In combination with standard clinical parameters, this panel improved prediction of the composite outcome of ESKD or a 40% decline in glomerular filtration rate. The panel was validated in an independent group (N = 68), who also had kidney transcriptomic profiles. One marker, plasma angiopoietin 2 (ANGPT2), was significantly associated with outcomes in cohorts from the Cardiovascular Health Study (N = 3,183) and the Chinese Cohort Study of Chronic Kidney Disease (N = 210). Glomerular transcriptional angiopoietin/Tie (ANG-TIE) pathway scores, derived from the expression of 154 ANG-TIE signaling mediators, correlated positively with plasma ANGPT2 levels and kidney outcomes. Higher receptor expression in glomeruli and higher ANG-TIE pathway scores in endothelial cells corroborated potential functional effects in the kidney from elevated plasma ANGPT2 levels. Our work suggests that ANGPT2 is a promising prognostic endothelial biomarker with likely functional impact on glomerular pathogenesis in DKD.

Research on kidney diseases is being transformed by the rapid expansion and innovations in omics technologies. The analysis, integration, and interpretation of big data, however, have been an impediment to the growing interest in applying these technologies to understand kidney function and failure. Targeting this urgent need, the University of Michigan O’Brien Kidney Translational Core Center (MKTC) and its Administrative Core established the Applied Systems Biology Core. The Core provides need-based support for the global kidney community centered on enabling incorporation of systems biology approaches by creating web-based, user-friendly analytic and visualization tools, like Nephroseq and Nephrocell, guiding with experimental design, and processing, analysis, and integration of large data sets. The enrichment core supports systems biology education and dissemination through workshops, seminars, and individualized training sessions. Meanwhile, the Pilot and Feasibility Program of the MKTC provides pilot funding to both early-career and established investigators new to the field, to integrate a systems biology approach into their research projects. The relevance and value of the portfolio of training and services offered by MKTC are reflected in the expanding community of young investigators, collaborators, and users accessing resources and engaging in systems biology-based kidney research, thereby motivating MKTC to persevere in its mission to serve the kidney research community by enabling access to state-of-the-art data sets, tools, technologies, expertise, and learning opportunities for transformative basic, translational, and clinical studies that will usher in solutions to improve the lives of people impacted by kidney disease.

BACKGROUND: Molecular characterization of nephropathies may facilitate pathophysiologic insight, development of targeted therapeutics, and transcriptome-based disease classification. Although membranous nephropathy (MN) is a common cause of adult-onset nephrotic syndrome, the molecular pathways of kidney damage in MN require further definition. METHODS: We applied a machine-learning framework to predict diagnosis on the basis of gene expression from the microdissected kidney tissue of participants in the Nephrotic Syndrome Study Network (NEPTUNE) cohort. We sought to identify differentially expressed genes between participants with MN versus those of other glomerulonephropathies across the NEPTUNE and European Renal cDNA Bank (ERCB) cohorts, to find MN-specific gene modules in a kidney-specific functional network, and to identify cell-type specificity of MN-specific genes using single-cell sequencing data from reference nephrectomy tissue. RESULTS: Glomerular gene expression alone accurately separated participants with MN from those with other nephrotic syndrome etiologies. The top predictive classifier genes from NEPTUNE participants were also differentially expressed in the ERCB participants with MN. We identified a signature of 158 genes that are significantly differentially expressed in MN across both cohorts, finding 120 of these in a validation cohort. This signature is enriched in targets of transcription factor NF-κB. Clustering these MN-specific genes in a kidney-specific functional network uncovered modules with functional enrichments, including in ion transport, cell projection morphogenesis, regulation of adhesion, and wounding response. Expression data from reference nephrectomy tissue indicated 43% of these genes are most highly expressed by podocytes. CONCLUSIONS: These results suggest that, relative to other glomerulonephropathies, MN has a distinctive molecular signature that includes upregulation of many podocyte-expressed genes, provides a molecular snapshot of MN, and facilitates insight into MN’s underlying pathophysiology.

Apolipoprotein L1 (APOL1)-associated focal segmental glomerulosclerosis (FSGS) is the dominant form of FSGS in Black individuals. There are no targeted therapies for this condition, in part because the molecular mechanisms underlying APOL1's pathogenic contribution to FSGS are incompletely understood. Studying the transcriptomic landscape of APOL1 FSGS in patient kidneys is an important way to discover genes and molecular behaviors that are unique or most relevant to the human disease. With the hypothesis that the pathology driven by the high-risk APOL1 genotype is reflected in alteration of gene expression across the glomerular transcriptome, we compared expression and co-expression profiles of 15,703 genes in 16 Black patients with FSGS at high-risk vs 14 Black patients with a low-risk APOL1 genotype. Expression data from APOL1-inducible HEK293 cells and normal human glomeruli were used to pursue genes and molecular pathways uncovered in these studies. We discovered increased expression of APOL1 and nine other significant differentially expressed genes in high-risk patients. This included stanniocalcin, which has a role in mitochondrial and calcium-related processes along with differential correlations between high- and low-risk APOL1 and metabolism pathway genes. There were similar correlations with extracellular matrix- and immune-related genes, but significant loss of co-expression of mitochondrial genes in high-risk FSGS, and an NF-κB-down regulating gene, NKIRAS1, as the most significant hub gene with strong differential correlations with NDUF family (mitochondrial respiratory genes) and immune-related (JAK-STAT) genes. Thus, differences in mitochondrial gene regulation appear to underlie many differences observed between high- and low-risk Black patients with FSGS.

OBJECTIVE Understanding mechanisms underlying rapid estimated glomerular filtration rate (eGFR) decline is important to predict and treat kidney disease in type 1 diabetes (T1D). RESEARCH DESIGN AND METHODS We performed a case-control study nested within four T1D cohorts to identify urinary proteins associated with rapid eGFR decline. Case and control subjects were categorized based on eGFR decline ≥3 and RESULTS The cohort study included 1,270 participants followed a median 8 years. In the discovery set, only cathepsin D peptide and protein were significant on full adjustment for clinical and laboratory variables. In the validation set, associations of cathepsin D with eGFR decline were replicated in minimally adjusted models but lost significance with adjustment for albuminuria. In a meta-analysis with combination of discovery and validation sets, the odds ratio for the association of cathepsin D with rapid eGFR decline was 1.29 per SD (95% CI 1.07–1.55). In complementary human cohorts, urine cathepsin D was associated with tubulointerstitial injury and tubulointerstitial cathepsin D expression was associated with increased cortical interstitial fractional volume. In mouse proximal tubular epithelial cell cultures, advanced glycation end product–BSA increased cathepsin D activity and inflammatory and tubular injury markers, which were further increased with cathepsin D siRNA. CONCLUSIONS Urine cathepsin D is associated with rapid eGFR decline in T1D and reflects kidney tubulointerstitial injury.

Maintenance of systemic homeostasis by kidney requires the coordinated response of diverse cell types. The use of single-cell RNA sequencing (scRNAseq) for patient tissue samples remains fraught with difficulties with cell isolation, purity, and experimental bias. The ability to characterize immune and parenchymal cells during transplant rejection will be invaluable in defining transplant pathology where tissue availability is restricted to needle biopsy fragments. Herein, we present feasibility data for multiplexing approach for droplet scRNAseq (Mux-Seq). Mux-Seq has the potential to minimize experimental batch bias and variation even with very small sample input. In this first proof-of-concept study for this approach, explant tissues from six normal and two transplant recipients after multiple early post-transplant rejection episodes leading to nephrectomy due to aggressive antibody mediated rejection, were pooled for Mux-Seq. A computational tool, Demuxlet was applied for demultiplexing the individual cells from the pooled experiment. Each sample was also applied individually in a single microfluidic run (singleplex) to correlate results with the pooled data from the same sample. Our applied protocol demonstrated that data from Mux-Seq correlated highly with singleplex (Pearson coefficient 0.982) sequencing results, with the ability to identify many known and novel kidney cell types including different infiltrating immune cells. Trajectory analysis of proximal tubule and endothelial cells demonstrated separation between healthy and injured kidney from transplant explant suggesting evolving stages of cell- specific differentiation in alloimmune injury. This study provides the technical groundwork for understanding the pathogenesis of alloimmune injury and host tissue response in transplant rejection and normal human kidney and provides a protocol for optimized processing precious and low input human kidney biopsy tissue for larger scale studies.

Introduction: Individuals with focal segmental glomerular sclerosis (FSGS) typically undergo kidney biopsy only once, which limits the ability to characterize kidney cell gene expression over time. Methods: We used single-cell RNA sequencing (scRNA-seq) to explore disease-related molecular signatures in urine cells from subjects with FSGS. We collected 17 urine samples from 12 FSGS subjects and captured these as 23 urine cell samples. The inflammatory signatures from renal epithelial and immune cells were evaluated in bulk gene expression data sets of FSGS and minimal change disease (MCD) (The Nephrotic Syndrome Study Network [NEPTUNE] study) and an immune single-cell data set from lupus nephritis (Accelerating Medicines Partnership). Results: We identified immune cells, predominantly monocytes, and renal epithelial cells in the urine. Further analysis revealed 2 monocyte subtypes consistent with M1 and M2 monocytes. Shed podocytes in the urine had high expression of marker genes for epithelial-to-mesenchymal transition (EMT). We selected the 16 most highly expressed genes from urine immune cells and 10 most highly expressed EMT genes from urine podocytes as immune signatures and EMT signatures, respectively. Using kidney biopsy transcriptomic data from NEPTUNE, we found that urine cell immune signature and EMT signature genes were more highly expressed in FSGS biopsies compared with MCD biopsies. Conclusion: The identification of monocyte subsets and podocyte expression signatures in the urine samples of subjects with FSGS suggests that urine cell profiling might serve as a diagnostic and prognostic tool in nephrotic syndrome. Furthermore, this approach may aid in the development of novel biomarkers and identifying personalized therapies targeting particular molecular pathways in immune cells and podocytes.

OBJECTIVE Diabetes mellitus (DM) is a major risk factor for severe coronavirus disease 2019 (COVID-19) for reasons that are unclear. RESEARCH DESIGN AND METHODS We leveraged the International Study of Inflammation in COVID-19 (ISIC), a multicenter observational study of 2,044 patients hospitalized with COVID-19, to characterize the impact of DM on in-hospital outcomes and assess the contribution of inflammation and hyperglycemia to the risk attributed to DM. We measured biomarkers of inflammation collected at hospital admission and collected glucose levels and insulin data throughout hospitalization. The primary outcome was the composite of in-hospital death, need for mechanical ventilation, and need for renal replacement therapy. RESULTS Among participants (mean age 60 years, 58.2% males), those with DM (n = 686, 33.5%) had a significantly higher cumulative incidence of the primary outcome (37.8% vs. 28.6%) and higher levels of inflammatory biomarkers than those without DM. Among biomarkers, DM was only associated with higher soluble urokinase plasminogen activator receptor (suPAR) levels in multivariable analysis. Adjusting for suPAR levels abrogated the association between DM and the primary outcome (adjusted odds ratio 1.23 [95% CI 0.78, 1.37]). In mediation analysis, we estimated the proportion of the effect of DM on the primary outcome mediated by suPAR at 84.2%. Hyperglycemia and higher insulin doses were independent predictors of the primary outcome, with effect sizes unaffected by adjusting for suPAR levels. CONCLUSIONS Our findings suggest that the association between DM and outcomes in COVID-19 is largely mediated by hyperinflammation as assessed by suPAR levels, while the impact of hyperglycemia is independent of inflammation.

Machine learning applied to digital pathology has been increasingly used to assess kidney function and diagnose the underlying cause of chronic kidney disease (CKD). We developed a novel computational framework, Clustering-based Spatial Analysis (CluSA), that leverages unsupervised learning to learn spatial relationships between local visual patterns in kidney tissue. This framework minimizes the need for time-consuming and impractical expert annotations. To incorporate spatial information over the clustered image patterns on the biopsy sample, we spatially encoded clustered patterns with colors and performed spatial analysis through graph neural network. A random forest classifier with various groups of features were used to predict CKD. For predicting high/low eGFR at the biopsy, we achieved a sensitivity of 0.97, specificity of 0.90, and accuracy of 0.95. AUC was 0.96. For predicting eGFR changes in one-year, we achieved a sensitivity of 0.83, specificity of 0.85, and accuracy of 0.84. AUC was 0.85. This study presents the first spatial analysis based on unsupervised machine learning algorithms. Without expert annotation, CluSA framework can not only accurately classify and predict the degree of kidney function at the biopsy and in one year, but also identify novel predictors of kidney function and renal prognosis.

Background Up to 70% of patients with ANCA-associated vasculitis (AAV) develop GN, with 26% progressing to ESKD. Diagnostic-grade and noninvasive tools to detect active renal inflammation are needed. Urinary soluble CD163 (usCD163) is a promising biomarker of active renal vasculitis, but a diagnostic-grade assay, assessment of its utility in prospective diagnosis of renal vasculitis flares, and evaluation of its utility in proteinuric states are needed. Methods We assessed a diagnostic-grade usCD163 assay in (1) a real-world cohort of 405 patients with AAV and 121 healthy and 488 non-AAV disease controls; (2) a prospective multicenter study of 84 patients with potential renal vasculitis flare; (3) a longitudinal multicenter cohort of 65 patients with podocytopathy; and (4) a cohort of 29 patients with AAV (with or without proteinuria) and ten controls. Results We established a diagnostic reference range, with a cutoff of 250 ng/mmol for active renal vasculitis (area under the curve [AUC], 0.978). Using this cutoff, usCD163 was elevated in renal vasculitis flare (AUC, 0.95) but remained low in flare mimics, such as nonvasculitic AKI. usCD163's specificity declined in patients with AAV who had nephrotic-range proteinuria and in those with primary podocytopathy, with 62% of patients with nephrotic syndrome displaying a "positive" usCD163. In patients with AAV and significant proteinuria, usCD163 normalization to total urine protein rather than creatinine provided the greatest clinical utility for diagnosing active renal vasculitis. Conclusions usCD163 is elevated in renal vasculitis flare and remains low in flare mimics. Nonspecific protein leakage in nephrotic syndrome elevates usCD163 in the absence of glomerular macrophage infiltration, resulting in false-positive results; this can be corrected with urine protein normalization.

Kidney fibrosis constitutes the shared final pathway of nearly all chronic nephropathies, but biomarkers for the non-invasive assessment of kidney fibrosis are currently not available. To address this, we characterize five candidate biomarkers of kidney fibrosis: Cadherin-11 (CDH11), Sparc-related modular calcium binding protein-2 (SMOC2), Pigment epithelium-derived factor (PEDF), Matrix-Gla protein, and Thrombospondin-2. Gene expression profiles in single-cell and single-nucleus RNA-sequencing (sc/snRNA-seq) datasets from rodent models of fibrosis and human chronic kidney disease (CKD) were explored, and Luminex-based assays for each biomarker were developed. Plasma and urine biomarker levels were measured using independent prospective cohorts of CKD: the Boston Kidney Biopsy Cohort, a cohort of individuals with biopsy-confirmed semiquantitative assessment of kidney fibrosis, and the Seattle Kidney Study, a cohort of patients with common forms of CKD. Ordinal logistic regression and Cox proportional hazards regression models were used to test associations of biomarkers with interstitial fibrosis and tubular atrophy and progression to end-stage kidney disease and death, respectively. Sc/snRNA-seq data confirmed cell-specific expression of biomarker genes in fibroblasts. After multivariable adjustment, higher levels of plasma CDH11, SMOC2, and PEDF and urinary CDH11 and PEDF were significantly associated with increasing severity of interstitial fibrosis and tubular atrophy in the Boston Kidney Biopsy Cohort. In both cohorts, higher levels of plasma and urinary SMOC2 and urinary CDH11 were independently associated with progression to end-stage kidney disease. Higher levels of urinary PEDF associated with end-stage kidney disease in the Seattle Kidney Study, with a similar signal in the Boston Kidney Biopsy Cohort, although the latter narrowly missed statistical significance. Thus, we identified CDH11, SMOC2, and PEDF as promising non-invasive biomarkers of kidney fibrosis.

BACKGROUND: Mechanisms underlying the pro gression of diabetic kidney disease to ESKD are not fully understood. METHODS: We performed global microRNA (miRNA) analysis on plasma from two cohorts consisting of 375 individuals with type 1 and type 2 diabetes with late diabetic kidney disease, and targeted proteomics analysis on plasma from four cohorts consisting of 746 individuals with late and early diabetic kidney disease. We examined structural lesions in kidney biopsy specimens from the 105 individuals with early diabetic kidney disease. Human umbilical vein endothelial cells were used to assess the effects of miRNA mimics or inhibitors on regulation of candidate proteins. RESULTS: In the late diabetic kidney disease cohorts, we identified 17 circulating miRNAs, represented by four exemplars (miR-1287-5p, miR-197-5p, miR-339-5p, and miR-328-3p), that were strongly associated with 10-year risk of ESKD. These miRNAs targeted proteins in the axon guidance pathway. Circulating levels of six of these proteins—most notably, EFNA4 and EPHA2—were strongly associated with 10-year risk of ESKD in all cohorts. Furthermore, circulating levels of these proteins correlated with severity of structural lesions in kidney biopsy specimens. In contrast, expression levels of genes encoding these proteins had no apparent effects on the lesions. In in vitro experiments, mimics of miR-1287-5p and miR-197-5p and inhibitors of miR-339-5p and miR-328-3p upregulated concentrations of EPHA2 in either cell lysate, supernatant, or both. CONCLUSIONS: This study reveals novel mechanisms involved in progression to ESKD and points to the importance of systemic factors in the development of diabetic kidney disease. Some circulating miRNAs and axon guidance pathway proteins represent potential targets for new therapies to prevent and treat this condition.

The lipid kinase VPS34 orchestrates autophagy, endocytosis, and metabolism and is implicated in cancer and metabolic disease. The proximal tubule in the kidney is a key metabolic organ that controls reabsorption of nutrients such as fatty acids, amino acids, sugars, and proteins. Here, by combining metabolomics, proteomics, and phosphoproteomics analyses with functional and superresolution imaging assays of mice with an inducible deficiency in proximal tubular cells, we revealed that VPS34 controlled the metabolome of the proximal tubule. In addition to inhibiting pinocytosis and autophagy, VPS34 depletion induced membrane exocytosis and reduced the abundance of the retromer complex necessary for proper membrane recycling and lipid retention, leading to a loss of fuel and biomass. Integration of omics data into a kidney cell metabolomic model demonstrated that VPS34 deficiency increased β-oxidation, reduced gluconeogenesis, and enhanced the use of glutamine for energy consumption. Furthermore, the omics datasets revealed that VPS34 depletion triggered an antiviral response that included a decrease in the abundance of apically localized virus receptors such as ACE2. VPS34 inhibition abrogated SARS-CoV-2 infection in human kidney organoids and cultured proximal tubule cells in a glutamine-dependent manner. Thus, our results demonstrate that VPS34 adjusts endocytosis, nutrient transport, autophagy, and antiviral responses in proximal tubule cells in the kidney.

Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. When combined with standard clinical parameters, this panel improved prediction of the composite outcome of ESKD or a 40% decline in glomerular filtration rate (GFR). The panel was validated in an independent group (N=68), which also had kidney transcriptomic profiles. One marker, plasma angiopoietin 2 (ANGPT2), was significantly associated with outcomes in cohorts from the American Cardiovascular Health Study (N=3183) and the Chinese Cohort Study of Chronic Kidney Disease (N=210). Glomerular transcriptional Angiopoietin/Tie (ANG-TIE) pathway scores, derived from the expression of 154 ANG-TIE signaling mediators, correlated positively with plasma ANGPT2 levels and kidney outcomes. Higher receptor expression in glomeruli and higher ANG-TIE pathway scores in endothelial cells corroborated potential functional effects in the kidney from elevated plasma ANGPT2 levels. Our work suggests that ANGPT2 is a promising prognostic endothelial biomarker with likely functional impact on glomerular pathogenesis in DKD.

Kidney organoids are a promising model to study kidney disease, but use is constrained by limited knowledge of their functional protein expression profile. We aimed to define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increased deposition of extracellular matrix but decreased expression of glomerular proteins. Single cell transcriptome integration revealed that most proteome changes localized to podocytes, tubular and stromal cells. TNFα-treatment of organoids effected 320 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 320 proteins was significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression was increased in both human tubular and organoid kidney cell populations, highlighting the potential for organoids to advance biomarker development. By integrating kidney organoid omic layers, incorporating a disease-relevant cytokine stressor and comparing to human data, we provide crucial evidence of functional relevance of the kidney organoid model to human kidney disease.

The molecular mechanisms of SGLT2 inhibitors (SGLT2i) remain incompletely understood. Single-cell RNA sequencing and morphometrics data were collected from research kidney biopsies donated by participants with youth onset type 2 diabetes (T2D), aged 12-21 years of age, and healthy controls (HC) to study the effects of SGLT2i on kidney transcriptomics. Participants with T2D were more obese, had higher glomerular filtration rate, mesangial and glomerular volumes than HC. There were no clinically significant differences between participants prescribed SGLT2i (T2Di(+), n=10) and other T2D (T2Di(-), n=6). Transcriptional profiles showed SGLT2 expression exclusively in the proximal tubular (PT) cluster. Transcriptional alterations in T2Di(+) compared to T2Di(-) were seen across most nephron segments, most prominently in the distal nephron. SGLT2i treatment was associated with suppression of genes in the glycolysis, gluconeogenesis, tricarboxylic acid cycle pathways in PT, but enhanced expression in thick ascending limb. The energy sensitive mTOR signaling pathway transcripts were suppressed towards HC level in all nephron segments in T2Di(+). These transcriptional changes were confirmed in a diabetes mouse model treated with SGLT2i. Therefore, the beneficial effects of SGLT2i treatment to the kidneys might be from mitigating diabetes-induced metabolic perturbations via suppression of mTORC1 signaling across nephron segments, including those not expressing SGLT2.

Aims/hypothesis Diabetic kidney disease (DKD) is the leading cause of kidney failure and has a substantial genetic component. Our aim was to identify novel genetic factors and genes contributing to DKD by performing meta-analysis of previous genome-wide association studies (GWAS) on DKD and by integrating the results with renal transcriptomics datasets. Methods We performed GWAS meta-analyses using ten phenotypic definitions of DKD, including nearly 27,000 individuals with diabetes. Meta-analysis results were integrated with estimated quantitative trait locus data from human glomerular (N=119) and tubular (N=121) samples to perform transcriptome-wide association study. We also performed gene aggregate tests to jointly test all available common genetic markers within a gene, and combined the results with various kidney omics datasets. Results The meta-analysis identified a novel intronic variant (rs72831309) in the TENM2 gene associated with a lower risk of the combined chronic kidney disease (eGFR2) and DKD (microalbuminuria or worse) phenotype (p=9.8×10−9; although not withstanding correction for multiple testing, p>9.3×10−9). Gene-level analysis identified ten genes associated with DKD (COL20A1, DCLK1, EIF4E, PTPRN–RESP18, GPR158, INIP–SNX30, LSM14A and MFF; p−6). Integration of GWAS with human glomerular and tubular expression data demonstrated higher tubular AKIRIN2 gene expression in individuals with vs without DKD (p=1.1×10−6). The lead SNPs within six loci significantly altered DNA methylation of a nearby CpG site in kidneys (p−11). Expression of lead genes in kidney tubules or glomeruli correlated with relevant pathological phenotypes (e.g. TENM2 expression correlated positively with eGFR [p=1.6×10−8] and negatively with tubulointerstitial fibrosis [p=2.0×10−9], tubular DCLK1 expression correlated positively with fibrosis [p=7.4×10−16], and SNX30 expression correlated positively with eGFR [p=5.8×10−14] and negatively with fibrosis [p−16]). Conclusions/interpretation Altogether, the results point to novel genes contributing to the pathogenesis of DKD. Data availability The GWAS meta-analysis results can be accessed via the type 1 and type 2 diabetes (T1D and T2D, respectively) and Common Metabolic Diseases (CMD) Knowledge Portals, and downloaded on their respective download pages (https://t1d.hugeamp.org/downloads.html; https://t2d.hugeamp.org/downloads.html; https://hugeamp.org/downloads.html). Graphical abstract

Kidney Precision Medicine Project (KPMP) is building a spatially specified human kidney tissue atlas in health and disease with single-cell resolution. Here, we describe the construction of an integrated reference map of cells, pathways, and genes using unaffected regions of nephrectomy tissues and undiseased human biopsies from 56 adult subjects. We use single-cell/nucleus transcriptomics, subsegmental laser microdissection transcriptomics and proteomics, near-single-cell proteomics, 3D and CODEX imaging, and spatial metabolomics to hierarchically identify genes, pathways, and cells. Integrated data from these different technologies coherently identify cell types/subtypes within different nephron segments and the interstitium. These profiles describe cell-level functional organization of the kidney following its physiological functions and link cell subtypes to genes, proteins, metabolites, and pathways. They further show that messenger RNA levels along the nephron are congruent with the subsegmental physiological activity. This reference atlas provides a framework for the classification of kidney disease when multiple molecular mechanisms underlie convergent clinical phenotypes.

Clinical trials indicate that sodium/glucose co-transporter 2 (SGLT2) inhibitors (SGLT2i) improve kidney function, yet, the molecular regulation of SGLT2 expression is incompletely understood. Here, we investigated the role of the intrarenal renin–angiotensin system (RAS) on SGLT2 expression. In adult non-diabetic participants in the Nephrotic Syndrome Study Network (NEPTUNE, n=163), multivariable linear regression analysis showed SGLT2 mRNA was significantly associated with angiotensinogen (AGT), renin, and angiotensin-converting enzyme (ACE) mRNA levels (P

Background: The G1 and G2 alleles of apolipoprotein L1 (APOL1) are common in the Black population and associated with increased risk of focal segmental glomerulosclerosis (FSGS). The molecular mechanisms linking APOL1 risk variants with FSGS are not clearly understood, and APOL1’s natural absence in laboratory animals makes studying its pathobiology challenging. Methods: In a cohort of 90 Black patients with either FSGS or minimal change disease (MCD) enrolled in the Nephrotic Syndrome Study Network (58% pediatric onset), we used kidney biopsy traits as an intermediate outcome to help illuminate tissue-based consequences of APOL1 risk variants and expression. We tested associations between APOL1 risk alleles or glomerular APOL1 mRNA expression and 83 light- or electron-microscopy traits measuring structural and cellular kidney changes. Results: Under both recessive and dominant models in the FSGS patient subgroup (61%), APOL1 risk variants were significantly correlated (defined as FDR

The application of deep learning for automated segmentation (delineation of boundaries) of histologic primitives (structures) from whole slide images can facilitate the establishment of novel protocols for kidney biopsy assessment. Here, we developed and validated deep learning networks for the segmentation of histologic structures on kidney biopsies and nephrectomies. For development, we examined 125 biopsies for Minimal Change Disease collected across 29 NEPTUNE enrolling centers along with 459 whole slide images stained with Hematoxylin & Eosin (125), Periodic Acid Schiff (125), Silver (102), and Trichrome (107) divided into training, validation and testing sets (ratio 6:1:3). Histologic structures were manually segmented (30048 total annotations) by five nephropathologists. Twenty deep learning models were trained with optimal digital magnification across the structures and stains. Periodic Acid Schiff-stained whole slide images yielded the best concordance between pathologists and deep learning segmentation across all structures (F-scores: 0.93 for glomerular tufts, 0.94 for glomerular tuft plus Bowman's capsule, 0.91 for proximal tubules, 0.93 for distal tubular segments, 0.81 for peritubular capillaries, and 0.85 for arteries and afferent arterioles). Optimal digital magnifications were 5X for glomerular tuft/tuft plus Bowman's capsule, 10X for proximal/distal tubule, arteries and afferent arterioles, and 40X for peritubular capillaries. Silver stained whole slide images yielded the worst deep learning performance. Thus, this largest study to date adapted deep learning for the segmentation of kidney histologic structures across multiple stains and pathology laboratories. All data used for training and testing and a detailed online tutorial will be publicly available.

COVID-19 morbidity and mortality are increased in patients with diabetes and kidney disease via unknown mechanisms. SARS-CoV-2 uses angiotensin-converting enzyme 2 (ACE2) for entry into host cells. Since ACE2 is a susceptibility factor for infection, we investigated how diabetic kidney disease and medications alter ACE2 receptor expression in kidneys. Single cell RNA profiling of healthy living donor and kidney biopsies from patients with diabetic kidney disease revealed ACE2 expression primarily in proximal tubular epithelial cells. This cell specific localization was confirmed by in situ hybridization. ACE2 expression levels were unaltered by exposures to renin angiotensin aldosterone system inhibitors in diabetic kidney disease. Bayesian integrative analysis of a large compendium of public-omics datasets identified molecular network modules induced in ACE2-expressing proximal tubular epithelial cells in diabetic kidney disease (searchable at hb.flatironinstitute.org/covid-kidney) that were linked to viral entry, immune activation, endomembrane reorganization, and RNA processing. The diabetic kidney disease ACE2-positive proximal tubular epithelial cell module overlapped with expression patterns seen in SARS-CoV-2 infected cells. Similar cellular programs were seen in ACE2-positive proximal tubular epithelial cells obtained from urine samples of 13hospitalized patients with COVID-19, suggesting a consistent ACE2-coregulated proximal tubular epithelial cell expression program that may interact with the SARS-CoV-2 infection processes. Thus SARS-CoV-2 receptor networks can seed further research into risk stratification and therapeutic strategies for COVID-19 related kidney damage., Graphical abstract

BACKGROUND AND AIMS Approximately 40% of people with type 2 diabetes (T2D) develop chronic kidney disease (CKD) and, despite current treatment, T2D is the most common cause of progression to kidney failure. This situation underscores the need for additional pharmacotherapeutic options. Analyses of cardiovascular outcomes trials suggest that glucagon-like peptide-1 receptor agonists (GLP-1RAs), such as semaglutide, lower albuminuria and attenuate estimated glomerular filtration rate (eGFR) decline in people with T2D. Previous analyses suggest that GLP-1RAs reduce hypoxia and inflammation and, thereby, have a mode of action on the kidneys that is distinct from other treatments, such as sodium-glucose cotransporter-2 (SGCT-2) inhibitors and renin–angiotensin–aldosterone system (RAAS) blockers. Furthermore, the primary benefit of semaglutide appears to be in people with an eGFR < 60 mL/min/1.73 m2, a group in which there is a significant residual risk of progression and a consequent unmet need for effective treatment. To gain further insights on the kidney-protective mechanism of action of semaglutide, the REMODEL trial is integrating investigative functional kidney magnetic resonance imaging (MRI) and kidney biopsies; recent developments in these techniques permit elucidation of the mechanisms underlying kidney protection with current therapies. Mechanistic findings of the REMODEL trial will complement those of the ongoing FLOW clinical trial, which is designed to evaluate clinical outcomes in people with T2D and CKD treated with once-weekly (OW) subcutaneous semaglutide. METHOD REMODEL (NCT04865770) is a 52-week, multicentre, international clinical trial (Figure 1). Primary endpoints are MRI-based and include change from baseline to week 52 in kidney oxygenation (measured with BOLD MRI R2*), global kidney perfusion (phase-contrast MRI) and kidney inflammation (T1 Mapping MRI). Secondary endpoints evaluated from kidney biopsies in a nested cohort (n ∼ 45) include change from baseline to week 52 in intrarenal mRNA expression, assessed by single-nucleus transcriptomics and glomerular basement membrane width, assessed by morphometry. Other secondary endpoints include the apparent diffusion coefficient (estimating renal fibrosis; evaluated with diffusion-weighted MRI), natriuresis, albumin excretion rate and creatinine clearance. MRI outcomes will also be evaluated at week 4 to identify potential early effects of semaglutide in the kidney. Examples of MRI and kidney biopsy single-cell gene expression profile data from participants with CKD are shown in Figure 2 (data not from REMODEL). Safety will be assessed throughout the trial. RESULTS REMODEL was initiated in April 2021 and is being conducted in Canada, France, Italy, Poland, South Africa, Spain and USA. CONCLUSION REMODEL will investigate the effect of the GLP-1RA semaglutide on inflammatory and hypoxia-related pathways in the kidney. The combination of MRI and tissue-level interrogation with biopsies will complement standard laboratory findings, enabling the identification of cells and pathways involved in kidney disease and protection. The trial will provide valuable mechanistic insights on the use of OW semaglutide in people with T2D and CKD and may stimulate the development of a precision medicine approach to the management of such individuals. In addition, REMODEL will complement the findings of the FLOW trial.

BACKGROUND AND AIMS Kidney organoids are a valuable and innovative model to understand genetic diseases, kidney development and transcriptomic dynamics. However, their proteome has not been analyzed so far. It is unclear how their proteome changes during differentiation, and if more complex disease processes such as inflammatory tissue responses could be modelled with this approach. METHOD Here, we used proteomics to compare organoids with existing model systems such as native glomeruli and cultured cells. We characterize the trajectory of organoid differentiation and delineate innate immune responses in organoids to expand its scope as a model system in nephrology. We also compared our proteomics with bulk and single cell transcriptomic data. RESULTS Genes involved in podocytopathies and cystic kidney diseases were abundantly expressed on protein level, distinguishing organoids from almost every available cell culture model. On their pathway to terminal differentiation, organoids developed increased deposition of extracellular matrix. Single cell transcriptomic analysis suggests that most changes locate to podocytes and early podocyte progenitors. This matrix deposition is different from commonly used animal models of glomerular disease. A novel signaling system discovered was the TNFα system, a system also available in podocytes. Incubation of organoids with high concentrations of TNFα led to an activation of NF-kB signaling, and secretion of cytokines and complement components, alongside with extracellular matrix components. CONCLUSION Interestingly, this signaling system directly links inflammatory signaling, production of cytokines and complement, and production of extracellular matrix. Thus, we provide a repository of human kidney organoid proteins that revealed the potential to model pathophysiological pathways beyond genetic diseases.

Adolescent- and adult-onset disease minimal change disease (MCD) may have a clinical course distinct from childhood-onset disease. We characterized the course of children and adults with MCD in the Cure Glomerulopathy Network (CureGN) and assessed predictors of rituximab response.Prospective, multicenter, observational study.CureGN participants with proven MCD on biopsy.Age at disease onset. Initiation of RAAS blockade and immunosuppression including rituximab during the study period.Relapse and remission, change in eGFR and kidney failure.Remission and relapse probabilities are estimated using Kaplan-Meier curves and gap time recurrent event models. Linear regression models were used for the outcome of change in estimated glomerular filtration rate (eGFR). Cox proportional hazards models were used to estimate the association between rituximab administration and remission.304 childhood (≤12 years old), 49 adolescent (13-17 years old), and 201 adult onset (≥18 years) participants were included with 2.7-3.2 years of follow-up after enrollment. Children had longer time to biopsy (238 days vs. 23 in adolescents, and 36 in adults, p0.001) and were more likely to have received therapy prior to biopsy. Children were more likely to be treated with immunosuppression but not RAAS blockade. The rate of relapse was higher in childhood-onset versus adult-onset participants (hazard ratio (HR) 1.69 (95% confidence interval (CI) 1.29-2.21)). The probability of remission was also higher in childhood-onset disease (HR 1.33 (95%CI 1.02-1.72)). eGFR loss in all groups was minimal. Children were more likely to remit after rituximab than adolescents and adults (adjusted HR 2.1, p=0.003). Across all groups, glucocorticoid-sensitivity was associated with a greater likelihood of achieving complete remission after rituximab (adjusted HR 2.62, p=0.002).CureGN was limited to biopsy-proven disease. Comparisons of childhood to non-childhood cases of MCD may be subject to selection bias, given that childhood cases who are biopsied may be limited to those patients who are least responsive to initial therapy.Among patients with MCD who underwent kidney biopsy, there were differences in the course (relapse and remission) of childhood-onset compared to adolescent and adult-onset disease, as well as rituximab response.

Objective: Understanding mechanisms underlying rapid estimated glomerular filtration rate (eGFR) decline is important to predict and treat kidney disease in type 1 diabetes (T1D). Research Design and Methods: We performed a case-control study nested within four T1D cohorts to identify urinary proteins associated with rapid eGFR decline. Cases and controls were defined by eGFR decline >3 and 2/year, respectively. We used targeted liquid chromatography-tandem mass spectrometry to measure 38 peptides from 20 proteins implicated in diabetic kidney disease. Significant proteins were investigated in complementary human cohorts and in mouse proximal tubular epithelial cell cultures. Results: The cohort study included 1270 participants followed a median 8 years. In the discovery set, only cathepsin D peptide and protein were significant on full adjustment for clinical and laboratory variables. In the validation set, associations of cathepsin D with eGFR decline were replicated in minimally-adjusted models but lost significance with adjustment for albuminuria. In a meta-analysis combining discovery and validation sets, the odds ratio for the association of cathepsin D with rapid eGFR decline was 1.29 per SD (95%CI 1.07-1.55). In complementary human cohorts, urine cathepsin D was associated with tubulointerstitial injury, and tubulointerstitial cathepsin D expression was associated with increased cortical interstitial fractional volume. In mouse proximal tubular epithelial cell cultures, advanced glycation end product-bovine serum albumin increased cathepsin D activity and inflammatory and tubular injury markers, which were further increased with cathepsin D siRNA. Conclusion: Urine cathepsin D is associated with rapid eGFR decline in T1D and reflects kidney tubulointerstitial injury.

The coronavirus disease 2019 (COVID-19) pandemic has infected >22.7 million and led to the deaths of 795,000 people worldwide. Patients with diabetes are highly susceptible to COVID-19–induced adverse outcomes and complications. The COVID-19 pandemic is superimposing on the preexisting diabetes pandemic to create large and significantly vulnerable populations of patients with COVID-19 and diabetes. This article provides an overview of the clinical evidence on the poorer clinical outcomes of COVID-19 infection in patients with diabetes versus patients without diabetes, including in specific patient populations, such as children, pregnant women, and racial and ethnic minorities. It also draws parallels between COVID-19 and diabetes pathology and suggests that preexisting complications or pathologies in patients with diabetes might aggravate infection course. Finally, this article outlines the prospects for long-term sequelae after COVID-19 for vulnerable populations of patients with diabetes.

Rationale & Objective Surrogate outcomes for end-stage kidney disease often assume linear changes, which may not reflect true estimated glomerular filtration rate (eGFR) trajectories. This study’s objective was to characterize nonlinear eGFR trajectories in nephrotic syndrome. Study Design Observational cohort study. Setting & Participants Nephrotic Syndrome Study Network (NEPTUNE) is a multicenter study of adult and pediatric patients with proteinuria enrolled at clinically indicated kidney biopsy or initial presentation of disease (pediatric only). Predictors Patient demographic, clinical, and pathology variables at study enrollment and follow-up time. Outcome eGFR was calculated using the Chronic Kidney Disease Epidemiology Collaboration (patients ≥ 18 years old) or modified Chronic Kidney Disease in Children Study–Schwartz (patients < 18 years) formulas. The probability of nonlinearity (PNL) was calculated for individual eGFR trajectories. Analytical Approach Associations between predictors and PNL were assessed using multivariable linear regression. Results 453 patients with ≥3 eGFR measurements and 1 or more year of follow-up were included (median follow-up, 3.6 years). Median PNL was 0.052; 56% and 16% had PNL < 10% and >50%, respectively. In both adults and pediatric patients, higher baseline eGFR was associated with higher PNL, whereas longer follow-up time was associated with lower PNL. Higher urine protein-creatinine ratio and steroid use were also associated with higher PNL in adults. Higher percentages of tubular atrophy and foot-process effacement were associated with lower and higher PNLs, respectively, in adults. Limitations Relatively short follow-up time, inability to assess acute kidney injury events, and variable eGFR measurement frequency across patients. Conclusions Although increasing follow-up time resulted in more linear trajectories, nonlinear eGFR trajectories were common in this cohort. Future studies in nephrotic syndrome should consider novel outcomes that do not rely on linearity assumptions., Graphical abstract

Significance Neuropeptide Y (NPY) is implicated in many pathological conditions including obesity, diabetes, and insulin resistance. However, a pathogenic role of NPY in kidney disease has not been described. We found that NPY is produced by the podocyte in the glomerulus, and this production decreases in renal disease, in contrast to an increase in circulating NPY levels. In the glomerulus, NPY signals via the NPY receptor 2 (NPY2R) and modulates PI3K, MAPK, and NFAT signaling, along with RNA processing and cell migration and, if prolonged, predicted nephrotoxicity. The pharmacological inhibition of NPY-NPY2R signaling also protected against albuminuria and kidney disease in a mouse model of glomerulosclerosis, suggesting that inhibiting this pathway may be therapeutically beneficial in the prevention of kidney disease., Albuminuria is an independent risk factor for the progression to end-stage kidney failure, cardiovascular morbidity, and premature death. As such, discovering signaling pathways that modulate albuminuria is desirable. Here, we studied the transcriptomes of podocytes, key cells in the prevention of albuminuria, under diabetic conditions. We found that Neuropeptide Y (NPY) was significantly down-regulated in insulin-resistant vs. insulin-sensitive mouse podocytes and in human glomeruli of patients with early and late-stage diabetic nephropathy, as well as other nondiabetic glomerular diseases. This contrasts with the increased plasma and urinary levels of NPY that are observed in such conditions. Studying NPY-knockout mice, we found that NPY deficiency in vivo surprisingly reduced the level of albuminuria and podocyte injury in models of both diabetic and nondiabetic kidney disease. In vitro, podocyte NPY signaling occurred via the NPY2 receptor (NPY2R), stimulating PI3K, MAPK, and NFAT activation. Additional unbiased proteomic analysis revealed that glomerular NPY-NPY2R signaling predicted nephrotoxicity, modulated RNA processing, and inhibited cell migration. Furthermore, pharmacologically inhibiting the NPY2R in vivo significantly reduced albuminuria in adriamycin-treated glomerulosclerotic mice. Our findings suggest a pathogenic role of excessive NPY-NPY2R signaling in the glomerulus and that inhibiting NPY-NPY2R signaling in albuminuric kidney disease has therapeutic potential.

Introduction Glomerular diseases are characterized by variable disease activity over many years. We aimed to analyze the relationship between clinical disease activity and duration of glomerular disease. Methods Disease activity in adults with chronic minimal change disease, focal segmental glomerulosclerosis, membranous nephropathy, and IgA nephropathy (IgAN; first diagnostic biopsy >5 years before enrollment; Of Longstanding Disease [OLD] cohort, n = 256) followed at Columbia University Medical Center (CUMC), was compared with disease activity of an internal and external cohort of patients with first diagnostic biopsy, Graphical abstract

The worldwide burden of kidney disease is rising, but public awareness remains limited, underscoring the need for more effective communication by stakeholders in the kidney health community. Despite this need for clarity, the nomenclature for describing kidney function and disease lacks uniformity. In June 2019, Kidney Disease: Improving Global Outcomes (KDIGO) convened a Consensus Conference with the goal of standardizing and refining the nomenclature used in the English language to describe kidney function and disease, and of developing a glossary that could be used in scientific publications. Guiding principles of the conference were that the revised nomenclature should be patient-centered, precise, and consistent with nomenclature used in the KDIGO guidelines. Conference attendees reached general consensus on the following recommendations: (i) to use "kidney" rather than "renal" or "nephro-" when referring to kidney disease and kidney function; (ii) to use "kidney failure" with appropriate descriptions of presence or absence of symptoms, signs, and treatment, rather than "end-stage kidney disease"; (iii) to use the KDIGO definition and classification of acute kidney diseases and disorders (AKD) and acute kidney injury (AKI), rather than alternative descriptions, to define and classify severity of AKD and AKI; (iv) to use the KDIGO definition and classification of chronic kidney disease (CKD) rather than alternative descriptions to define and classify severity of CKD; and (v) to use specific kidney measures, such as albuminuria or decreased glomerular filtration rate (GFR), rather than "abnormal" or "reduced" kidney function to describe alterations in kidney structure and function. A proposed 5-part glossary contains specific items for which there was general agreement. Conference attendees acknowledged limitations of the recommendations and glossary, but they considered standardization of scientific nomenclature to be essential for improving communication.

Chronic kidney diseases (CKDs) are currently classified according to their clinical features, associated comorbidities and pattern of injury on biopsy. Even within a given classification, considerable variation exists in disease presentation, progression and response to therapy, highlighting heterogeneity in the underlying biological mechanisms. As a result, patients and clinicians experience uncertainty when considering optimal treatment approaches and risk projection. Technological advances now enable large-scale datasets, including DNA and RNA sequence data, proteomics and metabolomics data, to be captured from individuals and groups of patients along the genotype–phenotype continuum of CKD. The ability to combine these high-dimensional datasets, in which the number of variables exceeds the number of clinical outcome observations, using computational approaches such as machine learning, provides an opportunity to re-classify patients into molecularly defined subgroups that better reflect underlying disease mechanisms. Patients with CKD are uniquely poised to benefit from these integrative, multi-omics approaches since the kidney biopsy, blood and urine samples used to generate these different types of molecular data are frequently obtained during routine clinical care. The ultimate goal of developing an integrated molecular classification is to improve diagnostic classification, risk stratification and assignment of molecular, disease-specific therapies to improve the care of patients with CKD. Classification of kidney diseases according to their molecular mechanisms has potential to improve patient outcomes through the identification of targeted therapeutic approaches. This Review provides an overview of the ways in which omics and other data types can be integrated to enhance our understanding of the mechanisms underlying kidney function and failure.

Die COVID-19-Pandemie kommt für europäische Länder einem Stresstest des Gesundheitssystems gleich. Der Beitrag widmet sich einem Vergleich Deutschlands und Dänemarks. Aufgezeigt werden Ausgangsbedingungen, gesellschaftliche Maßnahmen und die Be- oder Überlastungen des jeweiligen Gesundheitssystems. Dargelegt wird, warum die Pandemie kein Argument gegen eine notwendige Reform der deutschen Krankenhauslandschaft darstellt.

Seit 2007 erregt Dänemark mit einer umfangreichen Krankenhausreform weltweit Aufsehen. Das Hauptaugenmerk liegt auf den Maßnahmen zur Spezialisierung, Zentralisierung und Digitalisierung. Können Reformteile auch auf Deutschland übertragen werden? Welchen Einfluss haben Unterschiede, hinsichtlich Ausgestaltung, Versicherungspflicht, Steuerung oder Vernetzung der handelnden Personen und Institutionen auf die Morbidität oder die Wirtschaftlichkeit der Organisation?

Der dezentralisierten deutschen Krankenhauslandschaft mangelt es an einer gezielten Versorgungssteuerung und bundesweiten Rahmenplanung, was in eine Fehl-, Unter- und Überversorgung sowie Variation der Behandlungsqualität mündet. Vor diesem Hintergrund befasst sich der Artikel mit der Spezialisierungsplanung in Dänemark, die bei ähnlicher Ausgangslage implementiert wurde und zu einer Verbesserung der Behandlungsqualität beigetragen hat. Dabei wird insbesondere auf die Krebsversorgung fokussiert, da diese weiteren Reformierungen unterzogen wurde. Anhand des Beispiels Dänemark werden abschließend Handlungsempfehlungen für politische Entscheidungsträger*innen in Deutschland abgeleitet.