Vanillin inhibits PqsR-mediated virulence in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Reduced efficacy of antibiotics in bacterial diseases is a global concern in clinical settings. Development of anti-virulence compounds which disarm bacterial virulence is an attractive therapeutic agent for complementary antibiotics usage. One potential target for anti-virulence compounds is quorum sensing (QS), the intercellular communication system in most pathogens, such as Pseudomonas aeruginosa. QS inhibitors (QSIs) can inhibit QS effectively, attenuate QS-mediated virulence, and improve host clearance of infections. While studies focused on developing homoserine-based las QSI, few targeted the quinolone-based pqs QS, which implicated host cytotoxicity and biofilm formation. It is imperative to develop novel anti-pqs-QS therapeutics for combinatorial antibiotic treatment of microbial diseases. We employed a gfp-based transcriptional pqs biosensor to screen a natural compounds library and identify vanillin (4-hydroxy-3-methoxybenzaldehyde), the primary phenolic aldehyde of vanilla bean. The vanillin inhibited pqs expression and its associated phenotypes, namely pyocyanin production and twitching motility in P. aeruginosa. Molecular docking results revealed that vanillin binds to the active site of PqsR, the PQS-binding response regulator. Combinatorial treatment of vanillin with antimicrobial peptide (colistin) inhibited biofilm growth in vitro and improved treatment in the in vivo C. elegans acute infection model. We demonstrated that vanillin could dampen pqs QS and associated virulence, thus providing novel therapeutic strategies against P. aeruginosa infections.
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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".