Environmental Regulation of <i>Pseudomonas aeruginosa</i> PAO1 Las and Rhl Quorum-Sensing Systems
Bibliographic record
Abstract
The lasI-lasR and the rhlI-rhlR quorum-sensing systems in Pseudomonas aeruginosa regulate the expression of numerous cellular and secreted virulence factor genes and play important roles in the development of biofilms. The las and rhl systems themselves are known to be directly or indirectly regulated by a number of transcriptional regulators, and consequently, their expression is sensitive to environmental conditions. In this report, the activities of these two quorum-sensing systems have been examined systematically under 46 growth conditions, and the regulation by environmental conditions has been investigated. The relative timing and strength of expression of these two systems varied significantly under different conditions, which contrasts with the notion of a preset hierarchy with these two systems in P. aeruginosa. Depending on the growth conditions, the correlation between each synthase and its cognate transcriptional regulator also varied, suggesting that the transcription of these genes independently allows for further fine tuning of each system. Finally, we observe that the activities of both the lasI-lasR and the rhlI-rhlR quorum-sensing systems were dramatically enhanced in the presence of extracts of sputum samples from cystic fibrosis patients.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".