Protein N-glycans in Healthy and Sclerotic Glomeruli in Diabetic Kidney Disease.

BACKGROUND: Diabetes is expected to directly impact renal glycosylation, yet to date, there has not been a comprehensive evaluation of alterations in N-glycan composition in the glomeruli of patients with diabetic kidney disease (DKD). METHODS: We used untargeted mass spectrometry imaging to identify N-glycan structures in healthy and sclerotic glomeruli in FFPE sections from needle biopsies of five patients with DKD and three healthy kidney samples. Regional proteomics was performed on glomeruli from additional biopsies from the same patients to compare the abundances of enzymes involved in glycosylation. Secondary analysis of single nuclei transcriptomics (snRNAseq) data was used to inform on transcript levels of glycosylation machinery in different cell types and states. RESULTS: We detected 120 N-glycans, and among them identified twelve of these protein post-translated modifications that were significantly increased in glomeruli. All glomeruli-specific N-glycans contained an N-acetyllactosamine (LacNAc) epitope. Five N-glycan structures were highly discriminant between sclerotic and healthy glomeruli. Sclerotic glomeruli had an additional set of glycans lacking fucose linked to their core, and they did not show tetra-antennary structures that are common in healthy glomeruli. Orthogonal omics analyses revealed lower protein abundance and lower gene expression involved in synthesizing fucosylated and branched N-glycans in sclerotic podocytes. In snRNAseq and regional proteomics analyses, we observed that genes and/or proteins involved in sialylation and LacNAc synthesis were also downregulated in DKD glomeruli, but this alteration remained undetectable by our spatial N-glycomics assay. CONCLUSIONS: Integrative spatial glycomics, proteomics, and transcriptomics revealed protein N-glycosylation characteristic of sclerotic glomeruli in DKD.

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.

Genetic Analysis of Obesity-Induced Diabetic Nephropathy in BTBR Mice.

Diabetic nephropathy (DN) is the leading cause of end-stage renal disease in the U.S. and has a significant impact on human suffering. Leptin-deficient BTBR (BTBRob/ob) mice develop hallmark features of obesity-induced DN, whereas leptin-deficient C57BL/6J (B6ob/ob) mice do not. To identify genetic loci that underlie this strain difference, we constructed an F2 intercross between BTBRob/ob and B6ob/ob mice. We isolated kidneys from 460 F2 mice and histologically scored them for percent mesangial matrix and glomerular volume (∼50 glomeruli per mouse), yielding ∼45,000 distinct measures in total. The same histological measurements were made in kidneys from B6 and BTBR mice, either lean or obese (Lepob/ob), at 4 and 10 weeks of age, allowing us to assess the contribution of strain, age, and obesity to glomerular pathology. All F2 mice were genotyped for ∼5,000 single nucleotide polymorphisms (SNPs), ∼2,000 of which were polymorphic between B6 and BTBR, enabling us to identify a quantitative trait locus (QTL) on chromosome 7, with a peak at ∼30 Mbp, for percent mesangial matrix, glomerular volume, and mesangial volume. The podocyte-specific gene nephrin (Nphs1) is physically located at the QTL and contains high-impact SNPs in BTBR, including several missense variants within the extracellular immunoglobulin-like domains.

Biomolecular condensates in kidney physiology and disease.

The regulation and preservation of distinct intracellular and extracellular solute microenvironments is crucial for the maintenance of cellular homeostasis. In mammals, the kidneys control bodily salt and water homeostasis. Specifically, the urine-concentrating mechanism within the renal medulla causes fluctuations in extracellular osmolarity, which enables cells of the kidney to either conserve or eliminate water and electrolytes, depending on the balance between intake and loss. However, relatively little is known about the subcellular and molecular changes caused by such osmotic stresses. Advances have shown that many cells, including those of the kidney, rapidly (within seconds) and reversibly (within minutes) assemble membraneless, nano-to-microscale subcellular assemblies termed biomolecular condensates via the biophysical process of hyperosmotic phase separation (HOPS). Mechanistically, osmotic cell compression mediates changes in intracellular hydration, concentration and molecular crowding, rendering HOPS one of many related phase-separation phenomena. Osmotic stress causes numerous homo-multimeric proteins to condense, thereby affecting gene expression and cell survival. HOPS rapidly regulates specific cellular biochemical processes before appropriate protective or corrective action by broader stress response mechanisms can be initiated. Here, we broadly survey emerging evidence for, and the impact of, biomolecular condensates in nephrology, where initial concentration buffering by HOPS and its subsequent cellular escalation mechanisms are expected to have important implications for kidney physiology and disease.

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.

Inhibiting NLRP3 signaling in aging podocytes improves their life- and health-span.

The decrease in the podocyte's lifespan and health-span that typify healthy kidney aging cause a decrease in their normal structure, physiology and function. The ability to halt and even reverse these changes becomes clinically relevant when disease is superimposed on an aged kidney. RNA-sequencing of podocytes from middle-aged mice showed an inflammatory phenotype with increases in the NLRP3 inflammasome, signaling for IL2/Stat5, IL6 and TNF, interferon gamma response, allograft rejection and complement, consistent with inflammaging. Furthermore, injury-induced NLRP3 signaling in podocytes was further augmented in aged mice compared to young ones. The NLRP3 inflammasome (NLRP3, Caspase-1, IL1ß IL-18) was also increased in podocytes of middle-aged humans. Higher transcript expression for NLRP3 in human glomeruli was accompanied by reduced podocyte density and increased global glomerulosclerosis and glomerular volume. Pharmacological inhibition of NLRP3 with MCC950, or gene deletion, reduced podocyte senescence and the genes typifying aging in middle-aged mice, which was accompanied by an improved podocyte lifespan and health-span. Moreover, modeling the injury-dependent increase in NLRP3 signaling in human kidney organoids confirmed the anti-senescence effect of MC9950. Finally, NLRP3 also impacted liver aging. Together, these results suggest a critical role for the NLRP3 inflammasome in podocyte and liver aging.

Identification and validation of urinary CXCL9 as a biomarker for diagnosis of acute interstitial nephritis.

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.

Tobacco exposure in adults and children with proteinuric glomerulopathies: a NEPTUNE cohort study.

BACKGROUND: Tobacco exposure has been recognized as a risk factor for cardiovascular disease (CVD) and progression of kidney disease. Patients with proteinuric glomerulopathies are at increased risk for cardiovascular morbidity and mortality. Multiple studies have linked tobacco exposure to CVD and chronic kidney disease, but the relationships between smoking and proteinuric glomerulopathies in adults and children have not been previously explored. METHODS: Data from the Nephrotic Syndrome Study Network (NEPTUNE), a multi-center prospective observational study of participants with proteinuric glomerulopathies, was analyzed. 371 adults and 192 children enrolled in NEPTUNE were included in the analysis. Self-reported tobacco exposure was classified as non-smoker, active smoker, former smoker, or exclusive passive smoker. Baseline serum cotinine levels were measured in a sub-cohort of 178 participants. RESULTS: The prevalence of active smokers, former smokers and exclusive passive smoking among adults at baseline was 14.6%, 29.1% and 4.9%, respectively. Passive smoke exposure was 16.7% among children. Active smoking (reference non-smoking) was significantly associated with greater total cholesterol among adults (ß 17.91 95% CI 0.06, 35.76, p = 0.049) while passive smoking (reference non-smoking) was significantly associated with greater proteinuria over time among children (ß 1.23 95% CI 0.13, 2.33, p = 0.03). Higher cotinine levels were associated with higher baseline eGFR (r = 0.17, p = 0.03). CONCLUSION: Tobacco exposure is associated with greater risk for CVD and worse kidney disease outcomes in adults and children with proteinuric glomerulopathies. Preventive strategies to reduce tobacco exposure may help protect against future cardiovascular and kidney morbidity and mortality in patients with proteinuric glomerulopathies.

SGLT2 inhibitors mitigate kidney tubular metabolic and mTORC1 perturbations in youth-onset type 2 diabetes.

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.

Multi-Scalar Data Integration Links Glomerular Angiopoietin-Tie Signaling Pathway Activation With Progression of Diabetic Kidney Disease.

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.

Phospholipase Cε insufficiency causes ascending aortic aneurysm and dissection.

Phospholipase Cε (PLCε) is a phospholipase C isoform with a wide range of physiological functions. It has been implicated in aortic valve disorders, but its role in frequently associated aortic disease remains unclear. To determine the role of PLCε in thoracic aortic aneurysm and dissection (TAAD) we used PLCε-deficient mice, which develop aortic valve insufficiency and exhibit aortic dilation of the ascending thoracic aorta and arch without histopathological evidence of injury. Fourteen days of infusion of Plce1+/+ and Plce1-/- mice with angiotensin II (ANG II), which induces aortic dilation and dissection, led to sudden death secondary to ascending aortic dissection in 43% of Plce1-/- versus 5% of Plce1+/+ mice (P < 0.05). Medial degeneration and TAAD were detected in 80% of Plce1-/- compared with 10% of Plce1+/+ mice (P < 0.05) after 4 days of ANG II. Treatment with ANG II markedly increased PLCε expression within the ascending aortic adventitia. Total RNA sequencing demonstrated marked upregulation of inflammatory and fibrotic pathways mediated by interleukin-1ß, interleukin-6, and tumor necrosis factor-α. In silico analysis of whole exome sequences of 258 patients with type A dissection identified 5 patients with nonsynonymous PLCE1 variants. Our data suggest that PLCε deficiency plays a role in the development of TAAD and aortic insufficiency.NEW & NOTEWORTHY We describe a novel phenotype by which PLCε deficiency predisposes to aortic valve insufficiency and ascending aortic aneurysm, dissection, and sudden death in the setting of ANG II-mediated hypertension. We demonstrate PLCE1 variants in patients with type A aortic dissection and aortic insufficiency, suggesting that PLCE1 may also play a role in human aortic disease. This finding is of very high significance because it has not been previously demonstrated that PLCε directly mediates aortic dissection.

Upregulated PD-1 signaling antagonizes glomerular health in aged kidneys and disease.

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.

The Michigan O'Brien Kidney Research Center: transforming translational kidney research through systems biology.

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.

Micro-dissection and integration of long and short reads to create a robust catalog of kidney compartment-specific isoforms.

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.

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.

Quantitative morphometrics reveals glomerular changes in patients with infrequent segmentally sclerosed glomeruli.

AIMS: Detection of one segmentally sclerosed glomerulus (SSG) identifies patients with focal segmental glomerulosclerosis (FSGS) but rare SSGs may be missed in kidney biopsies. It is unknown whether alterations of unaffected glomeruli in patients with infrequent SSG can be detected by quantitative morphometrics. METHODS: We determined SSG frequency and obtained quantitative morphometrics in glomeruli without a pathologic phenotype in large kidney sections of non-involved kidney tissue from 137 patients undergoing total nephrectomy. We used multivariate modelling to identify morphometrics independently associated with increasing frequency of SSG and Receiver Operator Curve (ROC) analysis to determine the ability of quantitative morphometrics to identify patients with FSGS. We used the geometric distribution to estimate the sensitivity and specificity of a needle biopsy to identify patients with FSGS. RESULTS: In seventy-one patients (51.8%), at least one SSG was observed, and of those, 39 (54.9%) had an SSG lesion in less than 2% of all glomeruli (mean of 249 glomeruli per specimen). Increasing percent of SSG was independently associated with decreasing podocyte density and increasing mesangial index in multivariate modelling. For infrequent SSG lesions (<1% of glomeruli), kidney biopsy could miss FSGS diagnosis more than 74% of the time, and podocyte density had an area under the curve (AUC) of 0.77, and mesangial index, an AUC of 0.79 to identify patients with FSGS. CONCLUSIONS: More than half of patients had FSGS, although 30% had infrequent SSG. Quantitative morphometrics in glomeruli without pathology, such as podocyte density and mesangial index, identified patients with infrequent SSG and may serve as clinical markers to identify patients with FSGS.

Continuous Renal Replacement Therapy among Patients with COVID-19 and Acute Kidney Injury.

BACKGROUND AND OBJECTIVES: Acute kidney injury (AKI) is a common complication among patients with COVID-19 and acute respiratory distress syndrome. Reports suggest that COVID-19 confers a pro-thrombotic state, which presents challenges in maintaining hemofilter patency and delivering continuous renal replacement therapy (CRRT). We present our initial experience with CRRT in critically ill patients with COVID-19, emphasizing circuit patency and the association between fluid balance during CRRT and respiratory parameters. DESIGN, SETTING, PARTICIPANTS, AND MEASUREMENTS: Retrospective chart review of 32 consecutive patients with COVID-19 and AKI managed with continuous venovenous hemodiafiltration with regional citrate anticoagulation (CVVHDF-RCA) according to the University of Michigan protocol. Primary outcome was mean CRRT circuit life per patient during the first 7 days of CRRT. We used simple linear regression to assess the relationship between patient characteristics and filter life. We also explored the relationship between fluid balance on CRRT and respiratory parameters using repeated measures modeling. RESULTS: Patients' mean age was 54.8 years and majority were Black (75%). Comorbidities included hypertension (90.6%), obesity (70.9%) diabetes (56.2%), and chronic kidney disease (40.6%). Median CRRT circuit life was 53.5 [interquartile range 39.1-77.6] hours. There was no association between circuit life and inflammatory or pro-thrombotic laboratory values (ferritin p = 0.92, C-reactive protein p = 0.29, D-dimer p = 0.24), or with systemic anticoagulation (p = 0.37). Net daily fluid removal during the first 7 days of CRRT was not associated with daily (closest recorded values to 20:00) PaO2/FIO2 ratio (p = 0.21) or positive end-expiratory pressure requirements (p = 0.47). CONCLUSIONS: We achieved adequate CRRT circuit life in COVID-19 patients using an established CVVHDF-RCA protocol. During the first 7 days of CRRT therapy, cumulative fluid balance was not associated with improvements in respiratory parameters, even after accounting for baseline fluid balance.

Critical timing of ACEi initiation prevents compensatory glomerular hypertrophy in the remaining single kidney.

Increasing evidence suggests that single in kidney states (e.g., kidney transplantation and living donation) progressive glomerulosclerosis limits kidney lifespan. Modeling shows that post-nephrectomy compensatory glomerular volume (GV) increase drives podocyte depletion and hypertrophic stress resulting in proteinuria and glomerulosclerosis, implying that GV increase could serve as a therapeutic target to prevent progression. In this report we examine how Angiotensin Converting Enzyme inhibition (ACEi), started before uninephrectomy can reduce compensatory GV increase in wild-type Fischer344 rats. An unbiased computer-assisted method was used for morphometric analysis. Urine Insulin-like growth factor-1 (IGF-1), the major diver of body and kidney growth, was used as a readout. In long-term (40-week) studies of uni-nephrectomized versus sham-nephrectomized rats a 2.2-fold increase in GV was associated with reduced podocyte density, increased proteinuria and glomerulosclerosis. Compensatory GV increase was largely prevented by ACEi started a week before but not after uni-nephrectomy with no measurable impact on long-term eGFR. Similarly, in short-term (14-day) studies, ACEi started a week before uni-nephrectomy reduced both GV increase and urine IGF-1 excretion. Thus, timing of ACEi in relation to uni-nephrectomy had significant impact on post-nephrectomy "compensatory" glomerular growth and outcomes that could potentially be used to improve kidney transplantation and live kidney donation outcomes.