Multi-Scalar Data Integration Decoding Risk Genes for Chronic Kidney Disease

Abstract

Abstract Background: Chronic Kidney Disease (CKD) impacts over 10% of the global population and recently advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. Through the integration of Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and single-cell RNA sequencing (scRNA-seq) summary statistics, our study aimed to explore the genes and cell types relevant to CKD traits. Methods: The GWAS Catalog and the UK Biobank (UKB) database provided GWAS summary data for the end stage of renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria. Gene Expression Omnibus (GEO) transcriptome datasets were utilized to establish glomerular and tubular gene expression between CKD patients and healthy individuals. The expression of key genes at the single-cell level were obtained from the ScRNA-seq dataset available on Zenodo. The differentially expressed genes (DEGs), crosstalk co-expression networks, and enrichment analysis were further conducted for these CKD risk genes. Results: A total of 779 distinct SNPs were identified from GWAS across different traits of CKDs, which were involved in 681 genes. Majority of these risk genes are identical referring in certain CKD trait, but share the common pathways, including extracellular matrix (ECM), circadian entrainment, and energy metabolism. The ECM modelling was also enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls and the expression of relevant collagen genes, COL8A1, COL6A3, and COL1A2, are prevalent in fibroblasts/myofibroblasts. Meanwhile, physiological functions of kidney, including circadian entrainment, were downregulated in CKD kidneys. LUC7L3 was downregulated in CKD and enriched in podocytes. We also highlighted the regulated risk genes of CKD mainly expressed in tubular cells and immune cells in the kidney. Conclusions: Our integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.