Multi-scalar data integration links glomerular angiopoietin-tie signaling pathway activation with progression of diabetic kidney disease

Abstract

AbstractDiabetes is the leading cause of chronic kidney disease. Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of diabetic kidney disease (DKD). In the Clinical Phenotyping and Resource Biobank study, an unbiased, machine learning approach identified a three-marker panel from plasma proteomics which, when added to standard clinical parameters, improved the prediction of outcome of end-stage kidney disease (ESKD) or 40% decline in baseline glomerular filtration rate (GFR) in a discovery DKD group (N=58) and was validated in an independent group (N=68) who also had kidney transcriptomic profiles available. Of the three markers, plasma angiopoietin 2 (ANGPT2) remained significantly associated with composite outcome in 210 Chinese Cohort Study of Chronic Kidney Disease participants with DKD. The glomerular transcriptional Angiopoietin/Tie (ANG-TIE) activation scores, derived from the expression of 154 literature-curated ANG-TIE signaling mediators, positively correlated with plasma ANGPT2 levels and outcome, explained by substantially higher TEK receptor expression in glomeruli and higher ANG-TIE activation scores in endothelial cells in DKD by single cell RNA sequencing. Our work suggests that activation of glomerular ANG-TIE signaling in the kidneys underlies the association of plasma ANGPT2 with disease progression, thereby providing potential targets to prevent DKD progression.