Identification of key targets driving biofilm formation and virtual screening of potential inhibitors in Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa, a member of the "ESKAPE" pathogens, possesses a robust ability to form biofilms-a key factor that contributes to its antibiotic resistance and poses significant challenges for clinical management. Identifying potential therapeutic targets through bioinformatic analysis of genomic data is therefore critical for developing more effective treatment strategies.

METHODS: In this study, five gene expression datasets from the Gene Expression Omnibus were integrated to investigate transcriptional differences between planktonic and biofilm-associated P. aeruginosa populations. R was used to identify differentially expressed genes (DEGs), followed by weighted gene co-expression network analysis (WGCNA), to determine significant co-expression modules. Overlapping genes between the DEGs and WGCNA-derived modules were subsequently analyzed to screen for robust hub genes using three machine-learning algorithms: Random Forest, Support Vector Machine-Recursive Feature Elimination, and Least Absolute Shrinkage and Selection Operator. Their diagnostic performance and discriminative ability were evaluated using receiver operating characteristic analysis and boxplot visualization. Furthermore, a systematic literature review was conducted to examine the relationship between P. aeruginosa biofilm formation and host immune status, enabling a preliminary assessment of associations between the identified biomarkers and immune cell infiltration. Finally, potential inhibitory compounds were screened from an FDA-approved drug library, validated through molecular docking and molecular dynamics simulations, and subjected to RNA-sequencing (RNA-seq) analysis to assess their effects on biofilm-forming P. aeruginosa.

RESULTS: By intersecting the key module genes with the DEGs, a total of 25 overlapping genes were identified, among which wspA emerged as a candidate hub gene functionally associated with P. aeruginosa biofilm formation. Virtual screening revealed that DL-menthol exhibits a strong and stable binding affinity toward the wspA protein. RNA-seq further confirmed its significant effects on P. aeruginosa during active biofilm formation, with wspA representing a major responsive target. Collectively, these findings provide new insights into therapeutic strategies for biofilm-associated infections, highlight the potential clinical utility of wspA inhibitors, and offer a foundation for developing more effective intervention strategies.