Klf15 Is Critical for the Development and Differentiation of Drosophila Nephrocytes

Insect nephrocytes are highly endocytic scavenger cells that represent the only invertebrate model for the study of human kidney podocytes. Despite their importance, nephrocyte development is largely uncharacterised. This work tested whether the insect ortholog of mammalian Kidney Krüppel-Like Factor (Klf15), a transcription factor required for mammalian podocyte differentiation, was required for insect nephrocyte development. It was found that expression of Drosophila Klf15 (dKlf15, previously known as Bteb2) was restricted to the only two nephrocyte populations in Drosophila, the garland cells and pericardial nephrocytes. Loss of dKlf15 function led to attrition of both nephrocyte populations and sensitised larvae to the xenotoxin silver nitrate. Although pericardial nephrocytes in dKlf15 loss of function mutants were specified during embryogenesis, they failed to express the slit diaphragm gene sticks and stones and did not form slit diaphragms. Conditional silencing of dKlf15 in adults led to reduced surface expression of the endocytic receptor Amnionless and loss of in vivo scavenger function. Over-expression of dKlf15 increased nephrocyte numbers and rescued age-dependent decline in nephrocyte function. The data place dKlf15 upstream of sns and Amnionless in a nephrocyte-restricted differentiation pathway and suggest dKlf15 expression is both necessary and sufficient to sustain nephrocyte differentiation. These findings explain the physiological relevance of dKlf15 in Drosophila and imply that the role of KLF15 in human podocytes is evolutionarily conserve

An update on the use of animal models in diabetic nephropathy research

In the current review, we discuss limitations and recent advances in animal models of diabetic nephropathy (DN). As in human disease, genetic factors may determine disease severity with the murine FVB and DBA/2J strains being more susceptible to DN than C57BL/6J mice. On the black and tan, brachyuric (BTBR) background, leptin deficient (ob/ob) mice develop many of the pathological features of human DN. Hypertension synergises with hyperglycemia to promote nephropathy in rodents. Moderately hypertensive endothelial nitric oxide synthase (eNOS(−/−)) deficient diabetic mice develop hyaline arteriosclerosis and nodular glomerulosclerosis and induction of renin-dependent hypertension in diabetic Cyp1a1mRen2 rats mimics moderately severe human DN. In addition, diabetic eNOS(−/−) mice and Cyp1a1mRen2 rats recapitulate many of the molecular pathways activated in the human diabetic kidney. However, no model exhibits all the features of human DN; therefore, researchers should consider biochemical, pathological, and transcriptomic data in selecting the most appropriate model to study their molecules and pathways of interest

In Vivo RNA interference models of inducible and reversible sirt1 knockdown in kidney cells

The silent mating type information regulation 2 homolog 1 gene (Sirt1) encodes an NAD-dependent deacetylase that modifies the activity of well-known transcriptional regulators affected in kidney diseases. Sirt1 is expressed in the kidney podocyte, but its function in the podocyte is not clear. Genetically engineered mice with inducible and reversible Sirt1 knockdown in widespread, podocyte-specific, or tubular-specific patterns were generated. We found that mice with 80% knockdown of renal Sirt1 expression have normal glomerular function under the basal condition. When challenged with doxorubicin (Adriamycin), these mice develop marked albuminuria, glomerulosclerosis, mitochondrial injury, and impaired autophagy of damaged mitochondria. Reversal of Sirt1 knockdown during the early phase of Adriamycin-induced nephropathy prevented the progression of glomerular injury and reduced the accumulation of dysmorphic mitochondria in podocytes but did not reverse the progression of albuminuria and glomerulosclerosis. Sirt1 knockdown mice with diabetes mellitus, which is known to cause mitochondrial dysfunction in the kidney, developed more albuminuria and mitochondrial dysfunction compared with diabetic mice without Sirt1 knockdown. In conclusion, these results demonstrate that our RNA interference-mediated Sirt1 knockdown models are valid and versatile tools for characterizing the function of Sirt1 in the kidney; Sirt1 plays a role in homeostatic maintenance of podocytes under the condition of mitochondrial stress/injury

Clinical characterization and prediction of clinical severity of SARS-CoV-2 infection among US adults using data from the US National COVID Cohort Collaborative

Importance The National COVID Cohort Collaborative (N3C) is a centralized, harmonized, high-granularity electronic health record repository that is the largest, most representative COVID-19 cohort to date. This multicenter data set can support robust evidence-based development of predictive and diagnostic tools and inform clinical care and policy. Objectives To evaluate COVID-19 severity and risk factors over time and assess the use of machine learning to predict clinical severity. Design, Setting, and Participants In a retrospective cohort study of 1 926 526 US adults with SARS-CoV-2 infection (polymerase chain reaction >99% or antigen <1%) and adult patients without SARS-CoV-2 infection who served as controls from 34 medical centers nationwide between January 1, 2020, and December 7, 2020, patients were stratified using a World Health Organization COVID-19 severity scale and demographic characteristics. Differences between groups over time were evaluated using multivariable logistic regression. Random forest and XGBoost models were used to predict severe clinical course (death, discharge to hospice, invasive ventilatory support, or extracorporeal membrane oxygenation). Main Outcomes and Measures Patient demographic characteristics and COVID-19 severity using the World Health Organization COVID-19 severity scale and differences between groups over time using multivariable logistic regression. Results The cohort included 174 568 adults who tested positive for SARS-CoV-2 (mean [SD] age, 44.4 [18.6] years; 53.2% female) and 1 133 848 adult controls who tested negative for SARS-CoV-2 (mean [SD] age, 49.5 [19.2] years; 57.1% female). Of the 174 568 adults with SARS-CoV-2, 32 472 (18.6%) were hospitalized, and 6565 (20.2%) of those had a severe clinical course (invasive ventilatory support, extracorporeal membrane oxygenation, death, or discharge to hospice). Of the hospitalized patients, mortality was 11.6% overall and decreased from 16.4% in March to April 2020 to 8.6% in September to October 2020 (P = .002 for monthly trend). Using 64 inputs available on the first hospital day, this study predicted a severe clinical course using random forest and XGBoost models (area under the receiver operating curve = 0.87 for both) that were stable over time. The factor most strongly associated with clinical severity was pH; this result was consistent across machine learning methods. In a separate multivariable logistic regression model built for inference, age (odds ratio [OR], 1.03 per year; 95% CI, 1.03-1.04), male sex (OR, 1.60; 95% CI, 1.51-1.69), liver disease (OR, 1.20; 95% CI, 1.08-1.34), dementia (OR, 1.26; 95% CI, 1.13-1.41), African American (OR, 1.12; 95% CI, 1.05-1.20) and Asian (OR, 1.33; 95% CI, 1.12-1.57) race, and obesity (OR, 1.36; 95% CI, 1.27-1.46) were independently associated with higher clinical severity. Conclusions and Relevance This cohort study found that COVID-19 mortality decreased over time during 2020 and that patient demographic characteristics and comorbidities were associated with higher clinical severity. The machine learning models accurately predicted ultimate clinical severity using commonly collected clinical data from the first 24 hours of a hospital admission