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TIMP4 as a Potential Complementary Biomarker and Therapeutic Target in Membranous Nephropathy: A Multi-Omics
Qingsong Chen1, Gang Wang1, Ruo Zhao1
1Department of Nephrology, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People's Republic of China.
Background:
Despite genome-wide association studies having revealed risk loci for membranous nephropathy (MN), the functional mechanisms linking genetic variants to disease pathogenesis remain poorly understood.
Methods:
We implemented a multi-stage analytical framework, integrating proteome-wide association studies (PWAS) and transcriptome-wide association studies (TWAS) to systematically prioritize MN-associated biomarkers. Genetic causality was rigorously established through summary data-based Mendelian randomization (SMR) and Bayesian colocalization analysis. Clinical validation was performed in a MN cohort. Additionally, single-cell transcriptomic profiling resolved cell-type-specific expression patterns of candidate targets. The therapeutic potential was further evaluated through drug target interaction and molecular docking.
Results:
PWAS identified three proteins associated with MN (PLA2R1, LY75, TIMP4), while TWAS of whole blood and kidney cortex tissues implicated significant associations for LY75, POLR2I, and NFKB1 in MN. TIMP4 was uniquely prioritized as a high-confidence candidate through concordant evidence from PWAS significance (P = 2.67 × 10-2), causal association by SMR (P SMR = 3.61 × 10-4, P HEIDI = 3.49 × 10-1), and genetic colocalization (PP.H4 = 8.01 × 10-1). Clinically, serum TIMP4 levels were significantly elevated in MN patients versus controls (2267.1 vs 1581.4 pg/mL, P < 0.001). Single-cell analysis revealed TIMP4 expression was predominant in endothelial cells, mesangial cells, fibroblasts and proximal tubule cells. Molecular docking showed the binding between TIMP4 and potential therapeutic drugs.
Conclusion:
This study suggests TIMP4 may serve as a novel complementary biomarker and potential therapeutic target in MN. Systematic integration of multi-omics and clinical data provides promising insights for future drug development and mechanistic exploration.
