The LuxS family of bacterial autoinducers: biosynthesis of a novel quorum‐sensing signal molecule
Bibliographic record
Abstract
Many bacteria control gene expression in response to cell population density, and this phenomenon is called quorum sensing. In Gram-negative bacteria, quorum sensing typically involves the production, release and detection of acylated homoserine lactone signalling molecules called autoinducers. Vibrio harveyi, a Gram-negative bioluminescent marine bacterium, regulates light production in response to two distinct autoinducers (AI-1 and AI-2). AI-1 is a homoserine lactone. The structure of AI-2 is not known. We have suggested previously that V. harveyi uses AI-1 for intraspecies communication and AI-2 for interspecies communication. Consistent with this idea, we have shown that many species of Gram-negative and Gram-positive bacteria produce AI-2 and, in every case, production of AI-2 is dependent on the function encoded by the luxS gene. We show here that LuxS is the AI-2 synthase and that AI-2 is produced from S-adenosylmethionine in three enzymatic steps. The substrate for LuxS is S-ribosylhomocysteine, which is cleaved to form two products, one of which is homocysteine, and the other is AI-2. In this report, we also provide evidence that the biosynthetic pathway and biochemical intermediates in AI-2 biosynthesis are identical in Escherichia coli, Salmonella typhimurium, V. harveyi, Vibrio cholerae and Enterococcus faecalis. This result suggests that, unlike quorum sensing via the family of related homoserine lactone autoinducers, AI-2 is a unique, 'universal' signal that could be used by a variety of bacteria for communication among and between species.
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) | 0.001 | 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".