#1924 The overlooked players: immunomodulatory endothelial cells in diabetic kidney disease

Abstract Background and Aims Diabetic kidney disease (DKD) has become the leading cause of end-stage kidney disease (ESKD) worldwide. DKD is a microvascular disease characterized by renal endothelial cell (EC) dysfunction. Studies by the Carmeliet lab and others using scRNA-seq highlighted EC heterogeneity in different pathophysiological contexts, including kidney disease. Further, these studies identified similar patterns of gene expression in EC subsets as in immune cells (ICs) raising the question if these immunomodulatory ECs (coined “IMECs”) participate in immune responses, such as during vascular inflammation and associated EC dysfunction. However, while inflammation, oxidative stress and fibrosis are considered to contribute to EC dysfunction in DKD, a comprehensive unbiased understanding of signatures in renal ECs at single cell resolution is lacking. We are characterizing the (patho)-biological role of ECs in dkd by using single nuclei/cell multi-omics. We identified several unexplored genes (lacking functional annotation and publications) with a predicted IMEC function using in-house generated AI-based tools. Their functional role in the context of DKD will be further investigated. Method Droplet-based snRNA-seq (10X Genomics Chromium) was performed on cryopreserved kidney biopsy cores from 6 patients with severe DKD (6 patients (3 females; 3 males), median age 65 yrs; median eGFR: 15 ml/min/1.73 m2; median albuminuria: 1 g/g creatinine) and 6 sex/age-matched healthy controls. Healthy control samples were pre-perfusion biopsies from patients undergoing living kidney donation. EC subclusters were annotated based on the expression of canonical EC marker genes. Differential gene expression analysis for patient samples vs. controls included all ECs and differentially expressed genes (DEGs) were filtered for log2FC > 0.25 or < −0.25 and adjusted p-value of <0.05. Gene set enrichment analysis (GSEA) on the identified DEGs was performed with Enrich, using GO terms. Leveraging generated snRNA-seq data and a curated set of training genes with documented immunomodulatory functions, we used in-house generated AI-based gene discovery and prioritization tools to select the most promising IMEC unexplored candidate drivers of EC dysfunction in DKD. In vitro, the RNA expression levels in HUVECs were analyzed using qRT-PCR after 72 hours of culture under three conditions: control (5.5 mM glucose), hyperglycemic (33 mM glucose), or inflammatory (10 ng/ml TNF-α). Results Cell type annotation showed the presence of ca 6,000 ECs in the snRNA-seq dataset (Fig. 1A). Initial differential gene expression analysis of the EC population in the patient vs control samples identified several potential immunostimulatory genes. Using the in-house AI-based prioritization tools, we identified three potential unexplored IMEC genes, coded KIMEC1, KIMEC2, and KIMEC3 (the identity of the genes cannot be disclosed due to IP reasons). These genes were among the top upregulated, with log2FC values of 0.57, 0.34, and 0.522 (adjusted p-value of <0.05), respectively. Of note, all 3 genes are predicted to be involved in oxidative stress. In addition, they tend to be also upregulated in ECs in vivo in various inflammatory conditions (Fig. 1B.; shown for KIMEC3) and in vitro in hyperglycemic and/or pro-inflammatory culture conditions (shown for KIMEC3 in Fig. 1C–D). Conclusion We identified previously unexplored IMEC genes expressed in renal EC in DKD. Their potential role in mediating vascular inflammation and endothelial cell dysfunction in DKD will be further investigated.