Mol Med. 2026 Jul 01.
Kidney fibrosis, characterized by excessive extracellular matrix deposition and progressive renal tissue remodeling, frequently culminates in End-stage kidney disease (ESKD). Emerging evidence links dysregulated lipid metabolism to renal fibrogenesis; however, the precise molecular mechanisms connecting these processes remain elusive. Apolipoprotein E (APOE), a central coordinator of lipid transport and metabolic homeostasis, is implicated in various metabolic and inflammatory diseases, yet its specific contribution to renal fibrotic signaling is poorly defined. In this study, we utilized an integrative framework comprising machine learning-assisted transcriptomic analysis, clinical validation, and CRISPR/Cas9 gene editing to elucidate the role of APOE in kidney fibrosis. Machine learning analysis of public transcriptomic datasets pinpointed APOE as a top-ranked gene highly correlated with renal fibrotic signatures and metabolic pathways. Consistently, APOE expression was significantly altered in clinical blood samples from Maintenance hemodialysis patients compared to healthy controls. To mechanistically validate these findings, we generated an in vitro APOE-knockout model using CRISPR/Cas9, followed by Angiotensin II stimulation to induce renal fibrogenesis. APOE ablation profoundly impacted the expression of canonical pro-fibrotic markers (COL1A1, FN1, and α-SMA) alongside crucial lipid metabolism genes (FASN and ACC), as confirmed by quantitative real-time PCR and Western blot analyses. Pathway enrichment further corroborated APOE's role as a critical regulatory node bridging lipid homeostasis and fibrotic cascades in the kidney. Overall, our findings establish APOE as a pivotal regulator of renal fibrotic remodeling, highlighting a direct mechanistic interplay between lipid metabolism and kidney fibrosis. Consequently, therapeutic modulation of APOE-dependent pathways may offer a promising strategy for treating chronic kidney disease and associated fibrotic pathologies.
Keywords: APOE; CRISPR/Cas9; Fibrosis; Lipid metabolism; Machine learning