POS-360 MULTISCALAR DATA INTEGRATION LINKS GLOMERULAR ANGIOPOIETIN SIGNALING PATHWAY ACTIVATION WITH PROGRESSION OF DIABETIC KIDNEY DISEASE

Abstract

Diabetes is the primary cause of chronic kidney disease (CKD) worldwide. Current clinical parameters lack specificity or sensitivity in predicting diabetic kidney disease (DKD) progression. Our study aims to identify prognostic biomarkers and understand the molecular mechanisms underlying the association of biomarkers and DKD progression through plasma proteomic and kidney transcriptomic data integrative analysis. Two subgroups of patients from the C-PORBE (Clinical Phenotyping Resource and Biobank Core) were used as discovery and validation cohorts (Table 1 & Figure 1). Baseline plasma samples were measured using SOMAscan Assay 1.3k. Cox proportional model was applied to identify proteins associated with progression, defined as composite event of developing ESRD or loss > 40% of baseline eGFR. Lasso cox was used to establish marker panel. C statistic and likelihood ratio (LR) test were used to evaluate additive value. Pathway Interaction, Reactome and Netpath databases were used for pathway query. Pathway score was calculated using the z score method. Single-cell RNAseq data analysis was processed according to the KPMP single cell protocol.View Large Image Figure ViewerDownload Hi-res image Download (PPT) To identify circulating biomarkers associated with DKD progression, we applied cox proportional model on over 1300 plasma proteins and identified 84 of them are significantly associated with a composite endpoint of ESRD or 40% baseline eGFR reduction. A lasso cox-based machine learning procedure identified three biomarkers (EGFR, ANGPT2 and CLEC4M) that significantly improved the prediction of progression on top of the clinical model, including age, sex, race, eGFR and uACR (LR test p=0.003). This significant improvement was validated in the validation cohort (Table 2). ANGPT2 was significantly associated with outcome in both cohorts. To understand the mechanisms underlying the association between circulating ANGPT2 and DKD progression, we investigated the kidney-specific Angiopoietin-Tek signaling pathway by compiling a 21 gene-set through public database queries and transcriptomic data-driven approach. An Angiopoietin-Tek pathway activation score was calculated using this 21-gene-set for C-PROBE patients with overlapping transcriptomic, proteomic and longitudinal outcome data available. A significant positive association was observed between circulating ANGPT2 and Angiopoietin-Tek score in the glomerular compartment (r=0.39, p=0.03), but not in tubulointerstitium. The ANGPT2-Tek pathway score is significantly higher in progressors compared to non-progressors in C-PROBE (p=0.04). Consistent with a compartment-specific regulation of the pathway, TEK receptor showed maximal expression in glomeruli compared to tubulointerstitium with expression in glomerular endothelial cells. In two publicly available transcriptomic datasets derived from patients with DKD confirmed the association of ANGPT2-Tek signaling pathway activation with advanced DKD. We identified and validated a panel of three biomarkers (ANGPT2, EGFR, and CLEC4M) that exhibited significantly improved predictive performance for DKD progression when combined with the clinical model. Using comprehensive bioinformatic analysis, a kidney glomerular signaling cascade underlying the association of circulating ANGPT2 and DKD progression was identified providing a rational to target the ANGPT2-TEK signaling pathway in patients with DKD.