Miconazole and phenothiazine hinder the quorum sensing regulated virulence in Pseudomonas aeruginosa
Bibliographic record
Abstract
Antibiotic resistance is a major health problem worldwide. Pseudomonas aeruginosa is a Gram-negative pathogen with an arsenal of virulence factors and elevated antimicrobial resistance. It is a leading cause of nosocomial infections with high morbidity and mortality. The significant time and effort required to develop new antibiotics can be circumvented using alternative therapeutic strategies, including anti-virulence targets. This study aimed to investigate the anti-virulence activity of the FDA-approved drugs miconazole and phenothiazine against P. aeruginosa. The phenotypic effect of sub-inhibitory concentrations of miconazole and phenothiazine on biofilm, pyocyanin, protease, rhamnolipid and hemolysin activities in PAO1 strain was examined. qRT-PCR was used to assess the effect of drugs on quorum-sensing genes that regulate virulence. Further, the anti-virulence potential of miconazole and phenothiazine was evaluated in silico and in vivo. Miconazole showed significant inhibition of Pseudomonas virulence by reducing biofilm-formation approximately 45-48%, hemolytic-activity by 59%, pyocyanin-production by 47-49%, rhamnolipid-activity by approximately 42-47% and protease activity by 36-40%. While, phenothiazine showed lower anti-virulence activity, it inhibited biofilm (31-35%), pyocyanin (37-39%), protease (32-40%), rhamnolipid (35-40%) and hemolytic activity (47-56%). Similarly, there was significantly reduced expression of RhlR, PqsR, LasI and LasR following treatment with miconazole, but less so with phenothiazine. In-silico analysis revealed that miconazole had higher binding affinity than phenothiazine to LasR, RhlR, and PqsR QS-proteins. Furthermore, there was 100% survival in mice injected with PAO1 treated with miconazole. In conclusion, miconazole and phenothiazine are promising anti-virulence agents for P. aeruginosa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".