Long-Chain Acyl-Homoserine Lactone Quorum-Sensing Regulation of <i>Rhodobacter capsulatus</i> Gene Transfer Agent Production
Bibliographic record
Abstract
Many proteobacteria use acyl-homoserine lactones as quorum-sensing signals. Traditionally, biological detection systems have been used to identify bacteria that produce acyl-homoserine lactones, although the specificities of these detection systems can limit discovery. We used a sensitive approach that did not require a bioassay to detect production of long-acyl-chain homoserine lactone production by Rhodobacter capsulatus and Paracoccus denitrificans. These long-chain acyl-homoserine lactones are not readily detected by standard bioassays. The most abundant acyl-homoserine lactone was N-hexadecanoyl-homoserine lactone. The long-chain acyl-homoserine lactones were concentrated in cells but were also found in the culture fluid. An R. capsulatus gene responsible for long-chain acyl-homoserine lactone synthesis was identified. A mutation in this gene, which we named gtaI, resulted in decreased production of the R. capsulatus gene transfer agent, and gene transfer agent production was restored by exogenous addition of N-hexadecanoyl-homoserine lactone. Thus, long-chain acyl-homoserine lactones serve as quorum-sensing signals to enhance genetic exchange in R. capsulatus.
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 | 0.000 | 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) | 0.000 | 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".