Social cheating in a <i>Pseudomonas aeruginosa</i> quorum-sensing variant
Bibliographic record
Abstract
The opportunistic bacterial pathogen Pseudomonas aeruginosa has a layered acyl-homoserine lactone (AHL) quorum-sensing (QS) system, which controls production of a variety of extracellular metabolites and enzymes. The LasRI system activates genes including those coding for the extracellular protease elastase and for the second AHL QS system, RhlRI. Growth of P. aeruginosa on casein requires elastase production and LasR-mutant social cheats emerge in populations growing on casein. P. aeruginosa colonizes the lungs of individuals with the genetic disease cystic fibrosis (CF), and LasR mutants can be isolated from the colonized lungs; however, unlike laboratory-generated LasR mutants, many of these CF isolates have functioning RhlR-RhlI systems. We show that one such mutant can use the RhlR-RhlI system to activate expression of elastase and grow on casein. We carried out social-evolution experiments by growing this isolate on caseinate and, as with wild-type P. aeruginosa , elastase-negative mutants emerge as cheats, but these are not RhlR mutants; rather, they are mutants that do not produce the non-AHL Pseudomonas quinolone signal (PQS). Furthermore, we generated a RhlRI mutant and showed it had a fitness defect when growing together with the parent. Apparently, RhlR QS and PQS collude to support growth on caseinate in the absence of a functional LasR. Our findings provide a plausible explanation as to why P. aeruginosa LasR mutants, but not RhlR mutants, are common in CF lungs.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.000 | 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 teacher head, 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".