Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine.

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Blood immunophenotyping identifies distinct kidney histopathology and outcomes in patients with lupus nephritis.

Lupus nephritis (LN) is a frequent manifestation of systemic lupus erythematosus, and fewer than half of patients achieve complete renal response with standard immunosuppressants. Identifying non-invasive, blood-based pathologic immune alterations associated with renal injury could aid therapeutic decisions. Here, we used mass cytometry immunophenotyping of peripheral blood mononuclear cells in 145 patients with biopsy-proven LN and 40 healthy controls to evaluate the heterogeneity of immune activation in patients with LN and to identify correlates of renal parameters and treatment response. Unbiased analysis identified 3 immunologically distinct groups of patients with LN that were associated with different patterns of histopathology, renal cell infiltrates, urine proteomic profiles, and treatment response at one year. Patients with enriched circulating granzyme B+ T cells at baseline showed more severe disease and increased numbers of activated CD8 T cells in the kidney, yet they had the highest likelihood of treatment response. A second group characterized primarily by a high type I interferon signature had a lower likelihood of response to therapy, while a third group appeared immunologically inactive by immunophenotyping at enrollment but with chronic renal injuries. Main immune profiles could be distilled down to 5 simple cytometric parameters that recapitulate several of the associations, highlighting the potential for blood immune profiling to translate to clinically useful non-invasive metrics to assess immune-mediated disease in LN.

Rationale and design of the Nephrotic Syndrome Study Network (NEPTUNE) Match in glomerular diseases: designing the right trial for the right patient, today.

Glomerular diseases are classified using a descriptive taxonomy that is not reflective of the heterogeneous underlying molecular drivers. This limits not only diagnostic and therapeutic patient management, but also impacts clinical trials evaluating targeted interventions. The Nephrotic Syndrome Study Network (NEPTUNE) is poised to address these challenges. The study has enrolled >850 pediatric and adult patients with proteinuric glomerular diseases who have contributed to deep clinical, histologic, genetic, and molecular profiles linked to long-term outcomes. The NEPTUNE Knowledge Network, comprising combined, multiscalar data sets, captures each participant's molecular disease processes at the time of kidney biopsy. In this editorial, we describe the design and implementation of NEPTUNE Match, which bridges a basic science discovery pipeline with targeted clinical trials. Noninvasive biomarkers have been developed for real-time pathway analyses. A Molecular Nephrology Board reviews the pathway maps together with clinical, laboratory, and histopathologic data assembled for each patient to compile a Match report that estimates the fit between the specific molecular disease pathway(s) identified in an individual patient and proposed clinical trials. The NEPTUNE Match report is communicated using established protocols to the patient and the attending nephrologist for use in their selection of available clinical trials. NEPTUNE Match represents the first application of precision medicine in nephrology with the aim of developing targeted therapies and providing the right medication for each patient with primary glomerular disease.

WNT-dependent interaction between inflammatory fibroblasts and FOLR2+ macrophages promotes fibrosis in chronic kidney disease.

Chronic kidney disease (CKD) is a public health problem driven by myofibroblast accumulation, leading to interstitial fibrosis. Heterogeneity is a recently recognized characteristic in kidney fibroblasts in CKD, but the role of different populations is still unclear. Here, we characterize a proinflammatory fibroblast population (named CXCL-iFibro), which corresponds to an early state of myofibroblast differentiation in CKD. We demonstrate that CXCL-iFibro co-localize with macrophages in the kidney and participate in their attraction, accumulation, and switch into FOLR2+ macrophages from early CKD stages on. In vitro, macrophages promote the switch of CXCL-iFibro into ECM-secreting myofibroblasts through a WNT/ß-catenin-dependent pathway, thereby suggesting a reciprocal crosstalk between these populations of fibroblasts and macrophages. Finally, the detection of CXCL-iFibro at early stages of CKD is predictive of poor patient prognosis, which shows that the CXCL-iFibro population is an early player in CKD progression and demonstrates the clinical relevance of our findings.

Pediatric contributions and lessons learned from the NEPTUNE cohort study.

Primary glomerular diseases are rare entities. This has hampered efforts to better understand the underlying pathobiology and to develop novel safe and effective therapies. NEPTUNE is a rare disease network that is focused on patients of all ages with minimal change disease, focal segmental glomerulosclerosis, and membranous nephropathy. It is a longitudinal cohort study that collects detailed demographic, clinical, histopathologic, genomic, transcriptomic, and metabolomic data. The goal is to develop a molecular classification for these disorders that supersedes the traditional pathological features-based schema. Pediatric patients are important contributors to this ongoing project. In this review, we provide a snapshot of the children and adolescents enrolled in NEPTUNE and summarize some key observations that have been made based on the data accumulated during the study. In addition, we describe the development of NEPTUNE Match, a program that aims to leverage the multi-scalar information gathered for each individual patient to provide guidance about potential clinical trial participation based on the molecular characterization and non-invasive biomarker profile. This represents the first organized effort to apply principles of precision medicine to the treatment of patients with primary glomerular disease. NEPTUNE has proven to be an invaluable asset in the study of glomerular diseases in patients of all ages including children and adolescents.

Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project.

Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.

Sodium glucose co-transporter 2 inhibition increases epidermal growth factor expression and improves outcomes in patients with type 2 diabetes.

Underlying molecular mechanisms of the kidney protective effects of sodium glucose co-transporter 2 (SGLT2) inhibitors are not fully elucidated. Therefore, we studied the association between urinary epidermal growth factor (uEGF), a mitogenic factor involved in kidney repair, and kidney outcomes in patients with type 2 diabetes (T2D). The underlying molecular mechanisms of the SGLT2 inhibitor canagliflozin on EGF using single-cell RNA sequencing from kidney tissue were examined. Urinary EGF-to-creatinine ratio (uEGF/Cr) was measured in 3521 CANagliflozin cardioVascular Assessment Study (CANVAS) participants at baseline and week 52. Associations of uEGF/Cr with kidney outcome were assessed using multivariable-adjusted Cox regression models. Single-cell RNA sequencing was performed using protocol kidney biopsy tissue from ten young patients with T2D on SGLT2i, six patients with T2D on standard care only, and six healthy controls (HCs). In CANVAS, each doubling in baseline uEGF/Cr was associated with a 12% (95% confidence interval 1-22) decreased risk of kidney outcome. uEGF/Cr decreased after 52 weeks with placebo and remained stable with canagliflozin (between-group difference +7.3% (2.0-12.8). In young persons with T2D, EGF mRNA was primarily expressed in the thick ascending loop of Henle. Expression in biopsies from T2D without SGLT2i was significantly lower compared to HCs, whereas treatment with SGLT2i increased EGF levels closer to the healthy state. In young persons with T2D without SGLT2i, endothelin-1 emerged as a key regulator of the EGF co-expression network. SGLT2i treatment was associated with a shift towards normal EGF expression. Thus, decreased uEGF represents increased risk of kidney disease progression in patients with T2D. Canagliflozin increased kidney tissue expression of EGF and was associated with a downstream signaling cascade linked to tubular repair and reversal of tubular injury.

Clustering-based spatial analysis (CluSA) framework through graph neural network for chronic kidney disease prediction using histopathology images.

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. 107,471 histopathology images obtained from 172 biopsy cores were used in the clustering and in the deep learning model. 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 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.

An integrated organoid omics map extends modeling potential of kidney disease.

Kidney organoids are a promising model to study kidney disease, but their use is constrained by limited knowledge of their functional protein expression profile. Here, we define the organoid proteome and transcriptome trajectories over culture duration and upon exposure to TNFα, a cytokine stressor. Older organoids increase deposition of extracellular matrix but decrease expression of glomerular proteins. Single cell transcriptome integration reveals that most proteome changes localize to podocytes, tubular and stromal cells. TNFα treatment of organoids results in 322 differentially expressed proteins, including cytokines and complement components. Transcript expression of these 322 proteins is significantly higher in individuals with poorer clinical outcomes in proteinuric kidney disease. Key TNFα-associated protein (C3 and VCAM1) expression is 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 with human data, we provide crucial evidence for the functional relevance of the kidney organoid model to human kidney disease.

Rapid Progression of Focal Segmental Glomerulosclerosis in Patients with High-Risk APOL1 Genotypes.

BACKGROUND: FSGS is a heterogeneous diagnosis with a guarded prognosis. Polymorphisms in the apolipoprotein L1 ( APOL1 ) gene are associated with developing FSGS and faster progression to kidney failure in affected patients. Better understanding the natural history of patients with FSGS and APOL1 risk alleles is essential to improve patient care and support the design and interpretation of interventional studies. The objective of this study was to evaluate the quantitative association between APOL1 and kidney disease progression and the interaction with other clinical and laboratory factors. METHODS: CureGN cohort study participants with biopsy diagnosis of FSGS, regardless of self-identified race, were included. The exposure of interest was two APOL1 risk alleles (high risk) versus zero to one risk alleles (low risk). The primary outcome was eGFR slope categorized as rapid progressor (eGFR slope ≤-5 ml/min per year), intermediate progressor (slope between 0 and -5), or nonprogressor (slope ≥0). Multivariable ordinal logistic and linear regressions were used for adjusted analyses. Missing data were addressed using multiple imputation. RESULTS: Of 650 participants, 476 (73%) had genetic testing, among whom 87 (18%) were high risk. High-risk participants were more likely to have lower median eGFR (62 [interquartile range, 36-81] versus low-risk participants 76 ml/min per 1.73 m 2 [interquartile range, 44-106]; P <0.01). In adjusted analysis, the odds of more rapid progression of eGFR was 2.75 times higher (95% confidence interval, 1.67 to 4.53; P <0.001) in the high-risk versus low-risk groups. CONCLUSIONS: In patients with FSGS, high-risk APOL1 genotype is the predominant factor associated with more rapid loss of kidney function.

VPS34-dependent control of apical membrane function of proximal tubule cells and nutrient recovery by the kidney.

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 ß-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.

Molecular programs associated with glomerular hyperfiltration in early diabetic kidney disease.

Hyperfiltration is a state of high glomerular filtration rate (GFR) observed in early diabetes that damages glomeruli, resulting in an iterative process of increasing filtration load on fewer and fewer remaining functional glomeruli. To delineate underlying cellular mechanisms of damage associated with hyperfiltration, transcriptional profiles of kidney biopsies from Pima Indians with type 2 diabetes with or without early-stage diabetic kidney disease were grouped into two hyperfiltration categories based on annual iothalamate GFR measurements. Twenty-six participants with a peak GFR measurement within two years of biopsy were categorized as the hyperfiltration group, and 26 in whom biopsy preceded peak GFR by over two years were considered pre-hyperfiltration. The hyperfiltration group had higher hemoglobin A1c, higher urine albumin-to-creatinine ratio, increased glomerular basement membrane width and lower podocyte density compared to the pre-hyperfiltration group. A glomerular 1240-gene transcriptional signature identified in the hyperfiltration group was enriched for endothelial stress response signaling genes, including endothelin-1, tec-kinase and transforming growth factor-ß1 pathways, with the majority of the transcripts mapped to endothelial and inflammatory cell clusters in kidney single cell transcriptional data. Thus, our analysis reveals molecular pathomechanisms associated with hyperfiltration in early diabetic kidney disease involving putative ligand-receptor pairs with downstream intracellular targets linked to cellular crosstalk between endothelial and mesangial cells.

Unsupervised machine learning for identifying important visual features through bag-of-words using histopathology data from chronic kidney disease.

Pathologists use visual classification to assess patient kidney biopsy samples when diagnosing the underlying cause of kidney disease. However, the assessment is qualitative, or semi-quantitative at best, and reproducibility is challenging. To discover previously unknown features which predict patient outcomes and overcome substantial interobserver variability, we developed an unsupervised bag-of-words model. Our study applied to the C-PROBE cohort of patients with chronic kidney disease (CKD). 107,471 histopathology images were obtained from 161 biopsy cores and identified important morphological features in biopsy tissue that are highly predictive of the presence of CKD both at the time of biopsy and in one year. To evaluate the performance of our model, we estimated the AUC and its 95% confidence interval. We show that this method is reliable and reproducible and can achieve 0.93 AUC at predicting glomerular filtration rate at the time of biopsy as well as predicting a loss of function at one year. Additionally, with this method, we ranked the identified morphological features according to their importance as diagnostic markers for chronic kidney disease. In this study, we have demonstrated the feasibility of using an unsupervised machine learning method without human input in order to predict the level of kidney function in CKD. The results from our study indicate that the visual dictionary, or visual image pattern, obtained from unsupervised machine learning can predict outcomes using machine-derived values that correspond to both known and unknown clinically relevant features.

Urine Single-Cell RNA Sequencing in Focal Segmental Glomerulosclerosis Reveals Inflammatory Signatures.

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.

Multiplexed droplet single-cell sequencing (Mux-Seq) of normal and transplant kidney.

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.

Serum Level of Polyubiquitinated PTEN and Loss of Kidney Function in American Indians With Type 2 Diabetes.

RATIONALE & OBJECTIVE: Fibrosis is a major driver of chronic kidney disease, and epithelial-mesenchymal transition (EMT) may contribute to its development. A polyubiquitinated form of phosphatase and tensin homolog (PTENK27polyUb) promotes EMT in vitro. Thus, it is a potentially useful biomarker of progressive kidney fibrosis and may predict loss of kidney function. STUDY DESIGN: Observational cohort study. SETTING & PARTICIPANTS: Southwest United States, American Indians (154 women, 80 men) with or at high risk for diabetic kidney disease (DKD). PREDICTORS: Serum level of PTENK27polyUb. OUTCOME: ≥40% loss of glomerular filtration rate (GFR) or onset of kidney failure. Kidney structural measures in a subset of study participants who underwent research kidney biopsies (n = 77). ANALYTICAL APPROACH: Cox proportional hazards models adjusted for age, sex, diabetes duration, hemoglobin A1c (HbA1c), blood pressure, use of renin angiotensin system (RAS) blockers, measured GFR, and albuminuria. Spearman correlations for associations with structural measures. RESULTS: At baseline, the participants' mean age was 42.8 ± 10.5 (SD) years, diabetes duration 11.5 ± 7.1 years, mean arterial pressure 90.5 ± 9.5 mm Hg, HbA1c 9.3 ± 2.4%, GFR 152 ± 45 mL/min, and median urinary albumin-creatinine ratio 38 (interquartile range, 14-215) mg/g. RAS blockers were being used by 64 participants (27.4%). A higher PTENK27polyUb value was associated with a greater risk of ≥40% loss of GFR during a median follow-up period of 6.3 years (HR for quartile 4 [Q4] vs Q1, 3.95 [95% CI, 2.23-6.98], P < 0.001). Serum PTENK27polyUb was associated with an increased risk of kidney failure over a median follow-up period of 15.8 years (HR for Q4 vs Q1, 5.66 [95% CI, 1.99-16.13], P = 0.001). Baseline serum PTENK27polyUb in the biopsy subset correlated with structural measures including glomerular basement membrane width (ρ = 0.370, P < 0.001) and mesangial fractional volume (ρ = 0.392, P < 0.001). LIMITATIONS: Small study in single population. CONCLUSIONS: Higher serum PTENK27polyUb is associated with increased risk for GFR decline and kidney failure in American Indians with type 2 diabetes.

Quantification of Glomerular Structural Lesions: Associations With Clinical Outcomes and Transcriptomic Profiles in Nephrotic Syndrome.

RATIONALE & OBJECTIVE: The current classification system for focal segmental glomerulosclerosis (FSGS) and minimal change disease (MCD) does not fully capture the complex structural changes in kidney biopsies nor the clinical and molecular heterogeneity of these diseases. STUDY DESIGN: Prospective observational cohort study. SETTING & PARTICIPANTS: 221 MCD and FSGS patients enrolled in the Nephrotic Syndrome Study Network (NEPTUNE). EXPOSURE: The NEPTUNE Digital Pathology Scoring System (NDPSS) was applied to generate scores for 37 glomerular descriptors. OUTCOME: Time from biopsy to complete proteinuria remission, time from biopsy to kidney disease progression (40% estimated glomerular filtration rate [eGFR] decline or kidney failure), and eGFR over time. ANALYTICAL APPROACH: Cluster analysis was used to group patients with similar morphologic characteristics. Glomerular descriptors and patient clusters were assessed for associations with outcomes using adjusted Cox models and linear mixed models. Messenger RNA from glomerular tissue was used to assess differentially expressed genes between clusters and identify genes associated with individual descriptors driving cluster membership. RESULTS: Three clusters were identified: X (n = 56), Y (n = 68), and Z (n = 97). Clusters Y and Z had higher probabilities of proteinuria remission (HRs of 1.95 [95% CI, 0.99-3.85] and 3.29 [95% CI, 1.52-7.13], respectively), lower hazards of disease progression (HRs of 0.22 [95% CI, 0.08-0.57] and 0.11 [95% CI, 0.03-0.45], respectively), and lower loss of eGFR over time compared with X. Cluster X had 1,920 genes that were differentially expressed compared with Y+Z; these reflected activation of pathways of immune response and inflammation. Six descriptors driving the clusters individually correlated with clinical outcomes and gene expression. LIMITATIONS: Low prevalence of some descriptors and biopsy at a single time point. CONCLUSIONS: The NDPSS allows for categorization of FSGS/MCD patients into clinically and biologically relevant subgroups, and uncovers histologic parameters associated with clinical outcomes and molecular signatures not included in current classification systems.

Kidney Injury Molecule-1 and Periostin Urinary Excretion and Tissue Expression Levels and Association with Glomerular Disease Outcomes.

INTRODUCTIONS: Kidney injury molecule-1 (KIM-1) and periostin (POSTN) are proximal and distal tubule injury biomarkers. We tested whether baseline urine KIM-1/creatinine (uKIM-1/cr) and/or uPOSTN/cr correlated with disease severity or improved a remission prediction model. METHODS: Baseline uKIM1/cr and uPOSTN/cr were measured on spot urine samples from immunosuppression-free patients enrolled in Nephrotic Syndrome Study Network until December 15, 2014. Urine protein/creatinine (UPCR) and albumin/creatinine (UACR) were measured at baseline, 4 months, and until last follow-up. Glomerular and tubulointerstitial (TI) expression arrays were analyzed from a baseline research renal biopsy core collected during a clinically indicated biopsy.Renal diagnoses were centrally confirmed, sections scanned, and measured morphometrically. Correlations between baseline uKIM-1/cr and uPOSTN/cr and UPCR, UACR, histopathologic features, glomerular and TI KIM-1 and POSTN expression levels, and renal outcomes were assessed. RESULTS: Baseline uKIM-1/cr correlated with UPCR and UACR, and were associated with complete remission after adjustment for proteinuria, histopathologic diagnosis, and treatment. Baseline uKIM-1/cr also correlated with degree of foot process effacement and acute tubular injury. Glomerular and TI KIM-1 expression levels correlated with UPCR and UACR. Higher TI KIM-1 expression levels correlated with interstitial fibrosis, tubular atrophy, and global glomerulosclerosis, while glomerular KIM-1 expression correlated with time to remission. Findings for POSTN were of lesser statistical strength. DISCUSSION/CONCLUSION: Lower baseline uKIM-1/cr values were associated with more rapid time to complete remission after adjusting for proteinuria, histopathologic diagnosis, and treatment. Increased TI KIM-1 expression levels in proteinuric states were associated with chronic morphological injury; lower glomerular expression levels were associated with a greater potential for proteinuria reversibility.

Comprehensive Search for Novel Circulating miRNAs and Axon Guidance Pathway Proteins Associated with Risk of ESKD in Diabetes.

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.

Pro-cachectic factors link experimental and human chronic kidney disease to skeletal muscle wasting programs.

Skeletal muscle wasting is commonly associated with chronic kidney disease (CKD), resulting in increased morbidity and mortality. However, the link between kidney and muscle function remains poorly understood. Here, we took a complementary interorgan approach to investigate skeletal muscle wasting in CKD. We identified increased production and elevated blood levels of soluble pro-cachectic factors, including activin A, directly linking experimental and human CKD to skeletal muscle wasting programs. Single-cell sequencing data identified the expression of activin A in specific kidney cell populations of fibroblasts and cells of the juxtaglomerular apparatus. We propose that persistent and increased kidney production of pro-cachectic factors, combined with a lack of kidney clearance, facilitates a vicious kidney/muscle signaling cycle, leading to exacerbated blood accumulation and, thereby, skeletal muscle wasting. Systemic pharmacological blockade of activin A using soluble activin receptor type IIB ligand trap as well as muscle-specific adeno-associated virus-mediated downregulation of its receptor ACVR2A/B prevented muscle wasting in different mouse models of experimental CKD, suggesting that activin A is a key factor in CKD-induced cachexia. In summary, we uncovered a crosstalk between kidney and muscle and propose modulation of activin signaling as a potential therapeutic strategy for skeletal muscle wasting in CKD.