Bioautography and GC-MS based identification of piperine and trichostachine as the active quorum quenching compounds in black pepper
Bibliographic record
Abstract
In the search of new and safe antibacterial compounds, the quorum sensing system (QS) modulation by natural products has been studied. As a result, many plant-derived compounds have been identified as potent quorum sensing inhibitors. Piper nigrum L. (black pepper) ethanolic extract inhibits the QS in some Gram-negative bacteria but the active components have not been previously identified. Thus, the objective of this work was to identify the P. nigrum peppercorns main components that block the QS, applying bioassay and chromatographic techniques. Piperine and trichostachine were identified as the main components responsible for the quorum quenching (QQ) activity of P. nigrum peppercorns extract. Piperine at 30 mg/L, decreased the violacein production by Chromobacterium violaceum CV026 by 35%, without affecting bacterial growth. Piperine concentration of 40 mg/L decreases violacein production by C. violaceum CV026 by 70% and growth in only 4.34%. Trichostachine at 50 mg/L decreases violacein production by C. violaceum CV026 by 12%, without affecting bacterial growth. P. nigrum e xtract concentration of 0.5 g/L decreased violacein production in 40 % and no effects on growth were observed. Neither P. nigrum extract, piperine, nor trichostachine did affect QS of Pseudomonas aeruginosa PAO1. Data here described exhibit the potential of piperamides as modulators of QS, not previously reported.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".