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It is important to find better treatments for diabetic nephropathy (DN), a debilitating renal complication. Targeting early features of DN, including renal extracellular matrix accumulation (ECM) and glomerular hypertrophy, can prevent disease progression. Here we show that a megacluster of nearly 40 microRNAs and their host long non-coding RNA transcript (lnc-MGC) are coordinately increased in the glomeruli of mouse models of DN, and mesangial cells treated with transforming growth factor-β1 (TGF- β1) or high glucose. Lnc-MGC is regulated by an endoplasmic reticulum (ER) stress-related transcription factor, CHOP. Cluster microRNAs and lnc-MGC are decreased in diabetic Chop −/− mice that showed protection from DN. Target genes of megacluster microRNAs have functions related to protein synthesis and ER stress. A chemically modified oligonucleotide targeting lnc-MGC inhibits cluster microRNAs, glomerular ECM and hypertrophy in diabetic mice. Relevance to human DN is also demonstrated. These results demonstrate the translational implications of targeting lnc-MGC for controlling DN progression. Nephropathy is a common and hard-to-treat consequence of diabetes. Here Kato et al . show that a megacluster of microRNAs regulates early development of diabetic nephropathy in mice, and that inhibition of the cluster's host long non-coding RNA transcript attenuates disease symptoms, suggesting a new therapy for diabetic nephropathy.

The molecular mechanisms of sodium-glucose cotransporter-2 (SGLT2) inhibitors (SGLT2i) remain incompletely understood. Single-cell RNA sequencing and morphometric data were collected from research kidney biopsies donated by young persons with type 2 diabetes (T2D), aged 12 to 21 years, and healthy controls (HCs). Participants with T2D were obese and had higher estimated glomerular filtration rates and mesangial and glomerular volumes than HCs. Ten T2D participants had been prescribed SGLT2i (T2Di[+]) and 6 not (T2Di[-]). Transcriptional profiles showed SGLT2 expression exclusively in the proximal tubular (PT) cluster with highest expression in T2Di(-) patients. However, transcriptional alterations with SGLT2i treatment were seen across nephron segments, particularly in the distal nephron. SGLT2i treatment was associated with suppression of transcripts in the glycolysis, gluconeogenesis, and tricarboxylic acid cycle pathways in PT, but had the opposite effect in thick ascending limb. Transcripts in the energy-sensitive mTORC1-signaling pathway returned toward HC levels in all tubular segments in T2Di(+), consistent with a diabetes mouse model treated with SGLT2i. Decreased levels of phosphorylated S6 protein in proximal and distal tubules in T2Di(+) patients confirmed changes in mTORC1 pathway activity. We propose that SGLT2i treatment benefits the kidneys by mitigating diabetes-induced metabolic perturbations via suppression of mTORC1 signaling in kidney tubules.

With an aging population, kidney health becomes an important medical and socioeconomic factor. Kidney aging mechanisms are not well understood. We previously showed that podocytes isolated from aged mice exhibit increased expression of programmed cell death protein 1 (PD-1) surface receptor and its 2 ligands (PD-L1 and PD-L2). PDCD1 transcript increased with age in microdissected human glomeruli, which correlated with lower estimated glomerular filtration rate and higher segmental glomerulosclerosis and vascular arterial intima-to-lumen ratio. In vitro studies in podocytes demonstrated a critical role for PD-1 signaling in cell survival and in the induction of a senescence-associated secretory phenotype. To prove PD-1 signaling was critical to podocyte aging, aged mice were injected with anti-PD-1 antibody. Treatment significantly improved the aging phenotype in both kidney and liver. In the glomerulus, it increased the life span of podocytes, but not that of parietal epithelial, mesangial, or endothelial cells. Transcriptomic and immunohistochemistry studies demonstrated that anti-PD-1 antibody treatment improved the health span of podocytes. Administering the same anti-PD-1 antibody to young mice with experimental focal segmental glomerulosclerosis (FSGS) lowered proteinuria and improved podocyte number. These results suggest a critical contribution of increased PD-1 signaling toward both kidney and liver aging and in FSGS.

BackgroundAcute tubulointerstitial nephritis (AIN) is one of the few causes of acute kidney injury with diagnosis-specific treatment options. However, due to the need to obtain a kidney biopsy for histological confirmation, AIN diagnosis can be delayed, missed, or incorrectly assumed. Here, we identify and validate urinary CXCL9, an IFN-γ-induced chemokine involved in lymphocyte chemotaxis, as a diagnostic biomarker for AIN.MethodsIn a prospectively enrolled cohort with pathologist-adjudicated histological diagnoses, termed the discovery cohort, we tested the association of 180 immune proteins measured by an aptamer-based assay with AIN and validated the top protein, CXCL9, using sandwich immunoassay. We externally validated these findings in 2 cohorts with biopsy-confirmed diagnoses, termed the validation cohorts, and examined mRNA expression differences in kidney tissue from patients with AIN and individuals in the control group.ResultsIn aptamer-based assay, urinary CXCL9 was 7.6-fold higher in patients with AIN than in individuals in the control group (P = 1.23 × 10-5). Urinary CXCL9 measured by sandwich immunoassay was associated with AIN in the discovery cohort (n = 204; 15% AIN) independently of currently available clinical tests for AIN (adjusted odds ratio for highest versus lowest quartile: 6.0 [1.8-20]). Similar findings were noted in external validation cohorts, where CXCL9 had an AUC of 0.94 (0.86-1.00) for AIN diagnosis. CXCL9 mRNA expression was 3.9-fold higher in kidney tissue from patients with AIN (n = 19) compared with individuals in the control group (n = 52; P = 5.8 × 10-6).ConclusionWe identified CXCL9 as a diagnostic biomarker for AIN using aptamer-based urine proteomics, confirmed this association using sandwich immunoassays in discovery and external validation cohorts, and observed higher expression of this protein in kidney biopsies from patients with AIN.FundingThis study was supported by National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) awards K23DK117065 (DGM), K08DK113281 (KM), R01DK128087 (DGM), R01DK126815 (DGM and LGC), R01DK126477 (KNC), UH3DK114866 (CRP, DGM, and FPW), R01DK130839 (MES), and P30DK079310 (the Yale O'Brien Center). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Podocytes are key to the glomerular filtration barrier by forming a slit diaphragm between interdigitating foot processes; however, the molecular details and functional importance of protein folding and degradation in the ER remain unknown. Here, we show that the SEL1L-HRD1 protein complex of ER-associated degradation (ERAD) is required for slit diaphragm formation and glomerular filtration function. SEL1L-HRD1 ERAD is highly expressed in podocytes of both mouse and human kidneys. Mice with podocyte-specific Sel1L deficiency develop podocytopathy and severe congenital nephrotic syndrome with an impaired slit diaphragm shortly after weaning and die prematurely, with a median lifespan of approximately 3 months. We show mechanistically that nephrin, a type 1 membrane protein causally linked to congenital nephrotic syndrome, is an endogenous ERAD substrate. ERAD deficiency attenuated the maturation of nascent nephrin, leading to its retention in the ER. We also show that various autosomal-recessive nephrin disease mutants were highly unstable and broken down by SEL1L-HRD1 ERAD, which attenuated the pathogenicity of the mutants toward the WT allele. This study uncovers a critical role of SEL1L-HRD1 ERAD in glomerular filtration barrier function and provides insights into the pathogenesis associated with autosomal-recessive disease mutants.

Studying isoform expression at the microscopic level has always been a challenging task. A classical example is kidney, where glomerular and tubulo-interstitial compartments carry out drastically different physiological functions and thus presumably their isoform expression also differs. We aim at developing an experimental and computational pipeline for identifying isoforms at microscopic structure-level. We microdissected glomerular and tubulo-interstitial compartments from healthy human kidney tissues from two cohorts. The two compartments were separately sequenced with the PacBio RS II platform. These transcripts were then validated using transcripts of the same samples by the traditional Illumina RNA-Seq protocol, distinct Illumina RNA-Seq short reads from European Renal cDNA Bank (ERCB) samples, and annotated GENCODE transcript list, thus identifying novel transcripts. We identified 14,739 and 14,259 annotated transcripts, and 17,268 and 13,118 potentially novel transcripts in the glomerular and tubulo-interstitial compartments, respectively. Of note, relying solely on either short or long reads would have resulted in many erroneous identifications. We identified distinct pathways involved in glomerular and tubulo-interstitial compartments at the isoform level, creating an important experimental and computational resource for the kidney research community.

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.

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.

Horizontal information transfer between cells via microparticles is a newly identified communication system. MicroRNAs regulate gene expression and are detected in microparticles. Cantaluppi et al. suggest that microparticles derived from circulating angiogenic cells—’endothelial progenitor cells’ (EPCs)—harbor endothelial-protective miRNAs such as miR-126 and that delivery of EPC-derived microparticles during acute kidney ischemia–reperfusion in rats ameliorates kidney dysfunction and damage. We highlight the importance, potential future impact, and limitations of this study.