Ibuprofen involves with the reduced expression of pelD and pelF in pathogenic Pseudomonas aeruginosa strains

NRGCNMDA: Microbe-Drug Association Prediction Based on Residual Graph Convolutional Networks and Conditional Random Fields

The process of discovering new drugs related to microbes through traditional biological methods is lengthy and costly. In response to these issues, a new computational model (NRGCNMDA) is proposed to predict microbe-drug associations. First, Node2vec is used to extract potential associations between microorganisms and drugs, and a heterogeneous network of microbes and drugs is constructed. Then, a Graph Convolutional Network incorporating a fusion residual network mechanism (REGCN) is utilized to learn meaningful high-order similarity features. In addition, conditional random fields (CRF) are applied to ensure that microbes and drugs have similar feature embeddings. Finally, unobserved microbe-drug associations are scored based on combined embeddings. The experimental findings demonstrate that the NRGCNMDA approach outperforms several existing deep learning methods, and its AUC and AUPR values are 95.16% and 93.02%, respectively. The case study demonstrates that NRGCNMDA accurately predicts drugs associated with Enterococcus faecalis and Listeria monocytogenes, as well as microbes associated with ibuprofen and tetracycline.

Ibuprofen as an adjuvant to conventional antimicrobials and essential oil compounds against skin pathogens

AIMS

Antimicrobial resistance continues to be a growing concern, resulting in increased use of drug combinations. Antibiotic adjuvants are an emerging strategy that may potentiate an antibiotics efficacy. Ibuprofen's polypharmacological properties have been investigated for their antimicrobial and host-modulating potential. This study aimed to investigate the potential of a novel multidrug combination involving ibuprofen, essential oil compounds (EOCs), and conventional antimicrobials against skin pathogens.

METHODS AND RESULTS

The minimum inhibitory concentrations of ibuprofen, conventional antimicrobials, and EOCs were determined and then combined and tested against 14 (reference and clinical) skin pathogens. The cytotoxicity was analysed using the MTT assay, whilst the anti-inflammatory effects were evaluated using lipopolysaccharide activated RAW264.7 murine macrophages. Four pairwise (Ibuprofen and antibiotic) (ΣFIC 0.33-0.50) and three triple (Ibuprofen and antibiotic with EOC) (ΣFIC 0.44-0.47) synergistic antimicrobial interactions were identified. These combinations demonstrated cell viability of 77.59%-100%. No combination significantly reduced nitric oxide production.

CONCLUSION

The results from this study provide insight into the potential of a multidrug combination involving ibuprofen with conventional antimicrobials and EOCs against common skin pathogens.

Diclofenac-loaded PLGA nanoparticles downregulate LasI/R quorum sensing genes in pathogenic P. aeruginosa isolates

Poly(lactic-co-glycolic acid) (PLGA) is a biocompatible polymer that can gradually and consistently release drugs in a controlled manner. In this study, diclofenac sodium-loaded PLGA nanoparticles (DS-PLGA NPs) were produced by solvent evaporation technique and characterized using SEM, DLS, and zeta potential analyses. The antibacterial and antivirulence potential of DS-PLGA NPs against P. aeruginosa strains were examined using broth microdilution, crystal violet staining, hemolysis, and twitching quantification assays. Furthermore, the expression of the quorum sensing (QS) genes, lasI and lasR in P. aeruginosa strains after treatment with 1/2 MIC of DS-PLGA NPs was assessed using real-time PCR. SEM imaging of the synthesized NPs exhibited that the NPs have a spherical structure with a size range of 60-150 nm. The zeta potential of the NPs was - 15.2 mV, while the size of the particles in the aquatic environment was in a range of 111.5-153.8 nm. The MIC of prepared NPs against various strains of P. aeruginosa ranged from 4.5 to 9 mg/mL. Moreover, exposure of bacteria to sub-MIC of DS-PLGA NPs significantly down-regulated the expression of the lasI and lasR genes to 0.51- and 0.75-fold, respectively. Further, prepared NPs efficiently reduced the biofilm formation of P. aeruginosa strains by 9-27%, compared with the controls. Besides, DS-PLGA NPs showed considerable attenuation in bacterial hemolytic activity by 32-88% and twitching motility by 0-32.3%, compared with untreated cells. Overall, the present work exhibited the anti-QS activity of DS-PLGA NPs, which could be a safe and useful approach for treating P. aeruginosa infections.

In vitro and in silico assessment of anti-quorum sensing activity of Naproxen against Pseudomonas aeruginosa

Serious infections caused by Pseudomonas aeruginosa are usually related to quorum sensing (QS)-dependent virulence factors. Hence, QS inhibition is a promising approach to overcoming P. aeruginosa infections. This study aimed to investigate the effect of naproxen on biofilm formation and QS-related virulence traits of P. aeruginosa. Furthermore, the anti-QS potential of naproxen was evaluated using real-time PCR and molecular docking analysis. Our findings supported the anti-QS activity of naproxen, as evidenced by down-regulation of the lasI and rhlI genes expression as well as the attenuation of bacterial protease, hemolysin, pyocyanin, biofilm, and motility. Additionally, the high binding affinity of naproxen with QS regulatory proteins was determined in the molecular docking simulation. Altogether, these findings suggest that naproxen has a promising potential in inhibiting QS-associated traits of P. aeruginosa.