Assessing the Molecular Targets and Mode of Action of Furanone C-30 on Pseudomonas aeruginosa Quorum Sensing.
Victor MarkusKarina GolbergKerem TeraliNazmi OzerEsti Kramarsky-WinterRobert S MarksAriel KushmaroPublished in: Molecules (Basel, Switzerland) (2021)
Quorum sensing (QS), a sophisticated system of bacterial communication that depends on population density, is employed by many pathogenic bacteria to regulate virulence. In view of the current reality of antibiotic resistance, it is expected that interfering with QS can address bacterial pathogenicity without stimulating the incidence of resistance. Thus, harnessing QS inhibitors has been considered a promising approach to overriding bacterial infections and combating antibiotic resistance that has become a major threat to public healthcare around the globe. Pseudomonas aeruginosa is one of the most frequent multidrug-resistant bacteria that utilize QS to control virulence. Many natural compounds, including furanones, have demonstrated strong inhibitory effects on several pathogens via blocking or attenuating QS. While the natural furanones show no activity against P. aeruginosa, furanone C-30, a brominated derivative of natural furanone compounds, has been reported to be a potent inhibitor of the QS system of the notorious opportunistic pathogen. In the present study, we assess the molecular targets and mode of action of furanone C-30 on P. aeruginosa QS system. Our results suggest that furanone C-30 binds to LasR at the ligand-binding site but fails to establish interactions with the residues crucial for the protein's productive conformational changes and folding, thus rendering the protein dysfunctional. We also show that furanone C-30 inhibits RhlR, independent of LasR, suggesting a complex mechanism for the agent beyond what is known to date.
Keyphrases
- pseudomonas aeruginosa
- biofilm formation
- healthcare
- multidrug resistant
- cystic fibrosis
- acinetobacter baumannii
- escherichia coli
- single molecule
- staphylococcus aureus
- antimicrobial resistance
- molecular dynamics simulations
- gram negative
- drug resistant
- mental health
- risk factors
- machine learning
- protein protein
- molecular dynamics
- emergency department
- klebsiella pneumoniae
- big data
- social media
- health information
- deep learning