Related Experiment Videos
From Genetic Variants to Immune Dysregulation: Multi-Omics Mendelian Randomization Decodes Molecular Networks in
Meng Chen1, Bowen Tan1, Jia He1
1Department of Dermatovenereology, Affiliated Hospital of North Sichuan Medical College, Sichuan, China.
Background:
Rosacea is a chronic inflammatory skin disease with incompletely understood pathogenesis.
Objective:
This study aimed to identify key genes and regulatory mechanisms using multi-omics Mendelian randomization (MR) and machine learning, and to validate the findings in animal experiments.
Methods:
Bidirectional MR analysis using eQTL, pQTL, and mQTL datasets (GWAS ID: GCST90476178) identified rosacea-associated genes. LASSO regression and random forest were used to screen key genes. Immune infiltration, GSEA/GSVA, non-coding RNA network analysis, and molecular docking were used to explore gene functions and drug targets. Real-time quantitative polymerase chain reaction (RT-qPCR) and Western blot validated findings in a rosacea mouse model.
Results:
Five key genes (GSTM3, NEO1, UBE2M, CD300A, and EPHB4) were identified. These genes correlated with plasma cells and Tregs and were enriched in Wnt, transforming growth factor (TGF)-β, and interleukin (IL)-17 pathways. Molecular docking predicted the binding of CD300A to bisphenol A and GSTM3 to niacinamide. RT-qPCR confirmed gene expression changes. Western blot supported IL-17 pathway activation.
Conclusion:
This study reveals five key genes and immune-related mechanisms in rosacea, offering novel targets for diagnosis and therapy.
Related Concept Videos
Pharmacogenetic Phenotypes: Alterations in Pharmacokinetics, Drug Targets and Biologic Milieu
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
Pharmacogenomics: Identification of New Drug Targets
Principles of Pharmacogenetics: Types of Genetic Variants