Integrative informatics analysis identifies that ginsenoside Re improves renal fibrosis through regulation of autophagy.

We previously demonstrated that ginsenoside Re (G-Re) has protective effects on acute kidney injury. However, the underlying mechanism is still unclear. In this study, we conducted a meta-analysis and pathway enrichment analysis of all published transcriptome data to identify differentially expressed genes (DEGs) and pathways of G-Re treatment. We then performed in vitro studies to measure the identified autophagy and fibrosis markers in HK2 cells. In vivo studies were conducted using ureteric obstruction (UUO) and aristolochic acid nephropathy (AAN) models to evaluate the effects of G-Re on autophagy and kidney fibrosis. Our informatics analysis identified autophagy-related pathways enriched for G-Re treatment. Treatment with G-Re in HK2 cells reduced autophagy and mRNA levels of profibrosis markers with TGF-ß stimulation. In addition, induction of autophagy with PP242 neutralized the anti-fibrotic effects of G-Re. In murine models with UUO and AAN, treatment with G-Re significantly improved renal function and reduced the upregulation of autophagy and profibrotic markers. A combination of informatics analysis and biological experiments confirmed that ginsenoside Re could improve renal fibrosis and kidney function through the regulation of autophagy. These findings provide important insights into the mechanisms of G-Re's protective effects in kidney injuries.

Integrated multiomics implicates dysregulation of ECM and cell adhesion pathways as drivers of severe COVID-associated kidney injury.

COVID-19 has been a significant public health concern for the last four years; however, little is known about the mechanisms that lead to severe COVID-associated kidney injury. In this multicenter study, we combined quantitative deep urinary proteomics and machine learning to predict severe acute outcomes in hospitalized COVID-19 patients. Using a 10-fold cross-validated random forest algorithm, we identified a set of urinary proteins that demonstrated predictive power for both discovery and validation set with 87% and 79% accuracy, respectively. These predictive urinary biomarkers were recapitulated in non-COVID acute kidney injury revealing overlapping injury mechanisms. We further combined orthogonal multiomics datasets to understand the mechanisms that drive severe COVID-associated kidney injury. Functional overlap and network analysis of urinary proteomics, plasma proteomics and urine sediment single-cell RNA sequencing showed that extracellular matrix and autophagy-associated pathways were uniquely impacted in severe COVID-19. Differentially abundant proteins associated with these pathways exhibited high expression in cells in the juxtamedullary nephron, endothelial cells, and podocytes, indicating that these kidney cell types could be potential targets. Further, single-cell transcriptomic analysis of kidney organoids infected with SARS-CoV-2 revealed dysregulation of extracellular matrix organization in multiple nephron segments, recapitulating the clinically observed fibrotic response across multiomics datasets. Ligand-receptor interaction analysis of the podocyte and tubule organoid clusters showed significant reduction and loss of interaction between integrins and basement membrane receptors in the infected kidney organoids. Collectively, these data suggest that extracellular matrix degradation and adhesion-associated mechanisms could be a main driver of COVID-associated kidney injury and severe outcomes.