Sivasankaran, Sathesh K.

Cardiovascular-kidney-metabolic (CKM) syndrome is a complex disease that affects up to 90% of US adults. This work characterized the renal transcriptomic landscape of a novel model of CKM in aging swine. The cardiovascular, renal, and metabolic parameters in pigs are very similar to those in humans, and our work may guide future studies aimed at modulating specific age-related renal abnormalities in CKM to slow disease progression and reduce mortality.

Abstract Background and Aims Almost 36 million US adults (1 in 7, 14%) suffer from chronic kidney disease (CKD), and precision strategies to offset the progression to end-stage kidney disease are in dire need. We took advantage of a novel preclinical translational model of CKD in swine and single nucleus RNA sequencing (snRNA-seq) to identify potential therapeutic targets via characterization of the renal cell-specific transcriptomic landscape. Method Normal and CKD pigs were euthanized after 14 weeks (n = 3/group), kidneys were harvested, nuclei isolated, libraries prepared, and snRNA-seq performed. UMAP plots were generated with Seurat (v4.4.0) using default parameters. The expression profile of marker genes across single nuclei clusters was visualized to define renal cell types and ranked based on the number of differentially expressed genes (DEGs). The top cell type was subjected to subsequent analysis and DEG sorted based on their involvement in angiogenesis, inflammation, and fibrosis, which are prominent injury pathways in the pathophysiology of CKD. To mechanistically study the role of top overlapping candidate DEGs and support their potential as therapeutic targets, in vitro studies in primary renal vascular endothelial cells (RECs) were performed. Results A total of 39,935 nuclei were analyzed. Thirty clusters were identified and filtered by canonical gene markers, revealing 18 unique renal cell types (Fig. 1A). Endothelial cells exhibited the highest number of DEGs (Fig. 1B). Volcano plots (Fig. 1C) and Venn diagram (Fig. 1D) analysis of endothelial cells identified unique DEGs among three main renal injury pathways in CKD (angiogenesis, inflammation, fibrosis), with 2 overlapping DEGs (VWF, LAMA3) upregulated and participating in inflammation and fibrosis. Targeted silencing of VWF and LAMA3 (randomly selected) in RECs using siRNA ameliorated C5b9 and TGF-ẞ immunoreactivity, suggesting attenuated inflammatory and fibrogenic activity, respectively (Fig. 1E). Conclusion Our work characterized the single-nuclear renal cell transcriptomic landscape of a translational model of CKD and identified endothelial cells as the cluster with the largest number of DEGs. Our observations support the candidacy of VWF and LAMA3 as potential targets to decrease renal endothelial cell inflammation and fibrosis, which could pave the way for new therapeutic strategies in patients with CKD.

Key Points We characterized the renal transcriptional profile at the single-nuclear level in a preclinical swine model of CKD. Our study reveals that renal endothelial cells represent the cluster with the highest number of differentially expressed genes related to inflammatory and fibrotic activity. These results offer new insights into CKD-induced transcriptional changes in renal endothelial cells at the single-nuclear level. Background The prevalence of CKD is on the rise, and precision strategies to offset the progression to ESKD are needed. We used a well-established preclinical translational model of CKD in swine and single-nucleus RNA sequencing (snRNA-seq) to identify potential therapeutic targets through characterization of the renal cell-specific transcriptomic landscape. Methods Normal and CKD pigs were studied in vivo and ex vivo after 14 weeks ( n =6/group). In randomly selected pigs ( n =3/group), kidneys were harvested, nuclei isolated, libraries prepared, and snRNA-seq performed. Protein expression of candidate differentially expressed genes (DEGs, log2FC >0.25, adjusted P value < 0.05) was determined by immunohistochemistry, and their expression in primary normal and CKD renal vascular endothelial cells was modulated (small interfering RNA) in vitro . Results A total of 52,213 nuclei were analyzed. Thirty clusters were identified and filtered by canonical gene markers, revealing 16 unique renal cell types. Endothelial cells were the top cell type exhibiting the highest number of DEGs, which were subsequently filtered by angiogenesis, inflammation, and fibrosis (major injurious pathways altered in CKD kidneys). Venn diagram analysis identified five unique overlapping DEGs in endothelial cells: vWF , LAMA3 , and KDR upregulated and PTGIS and ICAM1 downregulated in CKD versus normal kidneys. Venn diagram analysis indicates that vWF , LAMA3 , and ICAM1 participate in inflammatory and fibrotic signaling. Renal protein expression of these DEGs matched snRNA-seq findings. Furthermore, in vitro silencing of vWF and LAMA3 ameliorated endothelial cell inflammatory and fibrotic signaling. Conclusions Our work characterized the single-nuclear renal cell transcriptomic landscape of a translational model of CKD and singled out genes implicated in major renal injury pathways. These genes could serve as potential targets to pave the way for new therapeutic strategies in patients with CKD.