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The following is a summary of “Multi-scalar data integration decoding risk genes for chronic kidney disease,” published in the October 2024 issue of Nephrology by Ding et al. 

Chronic Kidney Disease (CKD) affects over 10% of the global population, with advancements in high-throughput technologies revealing its complex physiology. 

Researchers conducted a retrospective study to explore genes and cell types associated with CKD by integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and single-cell RNA sequencing (scRNA-seq) data. 

They obtained GWAS summary data for end-stage renal failure (ESRF) and decreased estimated glomerular filtration rate (eGFR) in patients with CKD from the GWAS Catalog and the UK Biobank (UKB). They used 2 Gene Expression Omnibus (GEO) transcriptome datasets and 2 scRNA-seq datasets to analyze gene expression differences and key gene profiles in CKD patients compared to healthy individuals, focusing on differentially expressed genes (DEGs), gene-gene interactions, and pathway enrichment. 

The results showed that 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. Many risk genes are specific to renal failure, decreased eGFR, and (micro)proteinuria, yet share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys, with relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Immune responses, including T cell differentiation, were dysregulated, while the podocyte signature gene THSD7A was downregulated in CKD. Regulated CKD risk genes are mainly expressed in tubular and immune cells in the kidney. 

The study concluded that the integrated analysis identifies key genes, pathways, and cell types involved in kidney trait pathogenesis, laying the groundwork for future CKD research. 

Source: bmcnephrol.biomedcentral.com/articles/10.1186/s12882-024-03798-2