Commiphora myrrha resin extract inhibits the biofilms and quorum sensing controlled virulence factors of Gram-negative foodborne bacterial pathogens
Bibliographic record
Abstract
Antimicrobial resistance (AMR) is a global health threat. Multi-drug-resistant pathogens now cause significant mortality worldwide. Widespread antibiotic misuse has fueled resistance, prompting interest in antivirulence approaches over traditional bactericidal drugs. Targeting biofilms and quorum sensing (QS) is a promising antimicrobial strategy, recognized as an effective inhibitor. In the current study, Commiphora myrrha was assessed on the virulent traits and biofilms of Gram-negative bacteria ( Pseudomonas aeruginosa PAO1, Serratia marcescens MTCC 97, Chromobacterium violaceum ATCC 12472, and Proteus mirabilis MTCC 425). Methanolic extract of C. myrrha resin was prepared, and MIC was determined using the microdilution method. At sub-MICs, violacein production, QS-regulated virulence factors and biofilm development were estimated using spectroscopic assays. Phytochemicals were investigated using GC/MS analysis. Molecular docking was conducted between the QS-associated proteins (LasR, RhlR, and CviR) and the most abundant phytocompound of C. myrrha . MIC of CMRE against test strains was in the range of 0.5, and 2 mg/ml. CMRE reduced the violacein production in C. violaceum 12472 by 82.7%. In P. aeruginosa PAO1, production of virulence factors was reduced by >70%. The cell surface hydrophobicity was decreased to 18.9% compared to the control cells of P. aeruginosa PAO1 (76.4%). CMRE at ½ × MIC resulted in reduced biofilm formation in the range of 69.1–76.9%. A similar dose-dependent effect was observed on the exopolysaccharides production of the tested Gram-negative bacteria. Curzerene was identified as the most abundant (18.56%) phytoconstituent. Molecular docking revealed that curzerene interacted at the active sites of the tested proteins. Finally, molecular simulations validated the stability of curzerene with these proteins under an aqueous environment. The findings of this study may prove to be useful in the development of new anti-virulence bacterial drugs against Gram-negative bacteria.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".