Detection of N-acyl homoserine lactones using a traI-luxCDABE -based biosensor as a high-throughput screening tool
Bibliographic record
Abstract
BACKGROUND: Bacteria use N-acyl homoserine lactone (AHL) molecules to regulate the expression of genes in a density-dependent manner. Several biosensors have been developed and engineered to detect the presence of all types of AHLs. RESULTS: In this study, we describe the usefulness of a traI-luxCDABE-based biosensor to quickly detect AHLs from previously characterized mutants of Burkholderia cenocepacia and Pseudomonas aeruginosa in both liquid and soft-agar co-culture assays in a high-throughput manner. The technique uses a co-culture system where the strain producing the AHLs is grown simultaneously with the reporter strain. Use of this assay in liquid co-culture allows the measurement of AHL activity in real time over growth. We tested this assay with Burkholderia cenocepacia and Pseudomonas aeruginosa but it should be applicable to a broad range of gram negative species that produce AHLs. CONCLUSION: The co-culture assays described enable the detection of AHL production in both P. aeruginosa and B. cenocepacia and should be applicable to AHL analysis in other bacterial species. The high-throughput adaptation of the liquid co-culture assay could facilitate the screening of large libraries for the identification of mutants or compounds that block the synthesis or activity of AHLs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".