<scp> <i>Pseudomonas aeruginosa</i> </scp> isolates defective in function of the <scp>LasR</scp> quorum sensing regulator are frequent in diverse environmental niches
Bibliographic record
Abstract
The saprophyte Pseudomonas aeruginosa is a versatile opportunistic pathogen causing infections in immunocompromised individuals. To facilitate its adaptation to a large variety of niches, this bacterium exploits population density-dependent gene regulation systems called quorum sensing (QS). In P. aeruginosa, three distinct but interrelated QS systems (las, rhl and pqs) regulate the production of many survival and virulence functions. In prototypical strains, the las system, through its transcriptional regulator LasR, is important for the full activation of the rhl and pqs systems. Still, LasR-deficient isolates have been reported, mostly sampled from the lungs of people with cystic fibrosis, where they are considered selected by the chronic infection environment. In this study, we show that a defect in LasR activity appears to be an actually widespread mechanism of adaptation in this bacterium. Indeed, we found abundant LasR-defective isolates sampled from hydrocarbon-contaminated soils, hospital sink drains and meat/fish market environments, using an approach based on phenotypic profiling, supported by gene sequencing. Interestingly, several LasR-defective isolates maintain an active rhl system or are deficient in pqs system signalling. The high prevalence of a LasR-defective phenotype among environmental P. aeruginosa isolates questions the role of QS in niche adaptation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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".