Candidate Drugs Screening for Behcet's Disease Based on Bioinformatics Analysis and Mouse Experiments

Area of Science:

• Immunology
• Genetics
• Pharmacology

Background:

• Behcet's disease (BD) is a chronic, multisystem immune disorder with unclear pathogenesis.
• Novel therapeutic strategies are crucial due to the lack of effective treatments.

Purpose of the Study:

• To identify potential therapeutic drugs for Behcet's disease using bioinformatics approaches.
• To validate identified drug candidates in a relevant animal model.

Main Methods:

• Screening of Behcet's disease-related genes and proteins from public databases.
• Functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG).
• Protein-protein interaction network construction and hub gene identification.
• Gene-drug interaction analysis and experimental validation in a mouse model of uveitis.

Main Results:

• 249 differentially expressed genes/proteins identified, enriched in cytokine regulation and inflammatory pathways.
• 10 hub genes identified, with the top 3 highly relevant to Behcet's disease.
• Seven potential drugs identified targeting the top hub genes; four showed promise for BD treatment.
• Experimental validation in EAU mice indicated that rabeprazole and celastrol reduced ocular inflammation.

Conclusions:

• Bioinformatic analysis successfully identified potential therapeutic targets and drugs for Behcet's disease.
• The study provides new insights into BD pathogenesis and potential drug candidates for clinical investigation.
• Experimental validation supports the therapeutic potential of identified drugs in managing Behcet's disease complications.