Immunometabolic drivers of renal fibrosis and potential therapeutic targets in diabetic kidney disease

Abstract

Aim: Diabetic kidney disease (DKD) is a serious complication of diabetes, whose precise pathogenesis remains incompletely understood. Identifying therapeutic targets of DKD remains of great importance.

Methods: Four independent DKD microarray datasets were analyzed to identify differentially expressed genes. The expression quantitative trait locus (eQTL) data and DKD data from Genome-Wide Association Studies (GWAS) were utilized for Mendelian randomization (MR) analysis to pinpoint genes associated with DKD. The intersection of genes derived from two approaches was identified as key genes for DKD. Key genes were then subjected to enrichment analyses, immune infiltration assessment. Colocalization analysis, and quantitative polymerase chain reaction (qPCR) validation were used to identify core genes. A human DKD single-cell RNA sequencing dataset was analyzed to validate the cell-type-specific expression patterns of the core genes.

Results: We identified 275 up- and 184 downregulated genes. Combined with MR, seven key genes were determined. They were involved in lipid metabolism, protein secretion, signal transduction, immune response, and fibrosis. Cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) analysis revealed the unique distribution of immune cells in DKD and the regulation of immune cells by key genes. Colocalization analysis indicated a strong association between cystatin A (CSTA), lipoprotein lipase (LPL), lysozyme (LYZ), transforming growth factor beta induced (TGFBI), interferon induced protein with tetratricopeptide repeats 1 (IFIT1), and DKD. The qPCR confirmed that CSTA, LYZ, and TGFBI were differentially expressed, serving as core genes. LYZ was the most crucial. Single-cell analysis further unmasked the specific upregulation of CSTA, LYZ, and TGFBI in macrophages, where simulated knockout suggested a regulatory role in restraining antigen presentation.

Conclusion: This study demonstrated the potential and underlying mechanisms of gene-targeted therapy for DKD, providing a foundation for future investigations.

Keywords

Diabetic kidney disease / microarray data / Mendelian randomization / experimental verification / colocalization analysis / single-cell RNA sequencing / cellular heterogeneity

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