High-throughput screening of natural compounds for prophage induction in controlling pathogenic bacteria in food
Bibliographic record
Abstract
Introduction: The clean label trend emphasizes the need for natural approaches to combat pathogenic bacteria in food. This study explores the potential of inducing prophages within bacterial genomes as a novel strategy to control pathogenic and spoilage bacterial growth. Methods: A luminescence-based high-throughput assay was developed to identify natural compounds capable of inducing prophages. Bioactive compounds from four chemical libraries were screened at a final concentration of 10 µM. The assay measured luminescence production in Escherichia coli BR513, a genetically modified strain producing β-galactosidase upon prophage λ induction. Luminescence values were normalized to cell concentration (OD600) and the interquartile mean of each 384-well plate. A cut-off for normalized luminescence values, set at 2.25 standard deviations above the mean, defined positive prophage induction. Results: Four naturally-derived compounds (osthol, roccellic acid, galanginee, and sclareol) exhibited positive prophage induction, along with previously identified inducers, rosemary, and gallic acid. Dose-response experiments were conducted to determine optimal concentrations for prophage induction. However, the results could not distinguish between prophage-induced cell death and other mechanisms, making it challenging to identify ideal concentrations. Discussion: The high-throughput luminescent prophage induction assay serves as a valuable tool for the initial screening of natural bioactive compounds that have the potential to enhance food safety and quality by inducing prophages. Further research is required to understand the mechanism of bacterial cell death and to establish optimal concentrations for prophage induction in a food preservation context.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".