Attenuation of quorum sensing regulated virulence functions and biofilm of pathogenic bacteria by medicinal plant <i>Artemisia annua</i> and its phytoconstituent 1, 8‐cineole
Bibliographic record
Abstract
The emergence of multidrug resistance (MDR) in bacterial pathogens is a serious public health concern. A significant therapeutic target for MDR infections is the quorum sensing-regulated bacterial pathogenicity. Determining the anti-quorum sensing abilities of certain medicinal plants against bacterial pathogens as well as the in-silico interactions of particular bioactive phytocompounds with QS and biofilm-associated proteins were the objectives of the present study. In this study, 6 medicinal plants were selected based on their ethnopharmacological usage, screened for Anti-QS activity and Artemisia annua leaf extract (AALE) demonstrated pigment inhibitory activity against Chromobacterium violaceum CV12472. Further, the methanol active fraction significantly inhibited the virulence factors (pyocyanin, pyoverdine, rhamnolipid and swarming motility) of Pseudomonas aeruginosa PAO1 and Serratia marcescens MTCC 97 at respective sub-MICs. The inhibition of biofilm was determined using a microtiter plate test and scanning electron microscopy. Biofilm formation was impaired by 70%, 72% and 74% in P. aeruginosa, C. violaceum and S. marcescens, respectively at 0.5xMIC of the extract. The phytochemical content of the extract was studied using GC-MS and 1, 8-cineole was identified as major bioactive compound. Furthermore, 1, 8-cineole was docked with quorum sensing (QS) proteins (LasI, LasR, CviR, and rhlR) and biofilm proteins (PilY1 and PilT). In silico docking and dynamics simulations studies suggested interactions with QS-receptors CviR', LasI, LasR, and biofilm proteins PilY1, PilT for anti-QS activity. Further, 1, 8-cineole demonstrated 66% and 51% reduction in violacein production and biofilm formation, respectively to validate the findings of computational analysis. Findings of the present investigation suggests that 1, 8-cineole plays a crucial role in the QS and biofilm inhibitory activity demonstrated by Artemisia annua extract. RESEARCH HIGHLIGHTS: Artemisia annua leaf extract (AALE) methanol fraction demonstrated broad-spectrum QS and biofilm inhibition Scanning electron microscopy (SEM) confirmed biofilm inhibition Molecular docking and simulation studies suggested positive interactions of 1,8-cineol with QS-receptors and biofilm proteins.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".