Influence of Quorum Sensing and Iron on Twitching Motility and Biofilm Formation in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
Reducing iron (Fe) levels in a defined minimal medium reduced the growth yields of planktonic and biofilm Pseudomonas aeruginosa, though biofilm biomass was affected to the greatest extent and at FeCl3 concentrations where planktonic cell growth was not compromised. Highlighting this apparently greater need for Fe, biofilm growth yields were markedly reduced in a mutant unable to produce pyoverdine (and, so, deficient in pyoverdine-mediated Fe acquisition) at concentrations of FeCl3 that did not adversely affect biofilm yields of a pyoverdine-producing wild-type strain. Concomitant with the reduced biofilm yields at low Fe concentrations, P. aeruginosa showed enhanced twitching motility in Fe-deficient versus Fe-replete minimal media. A mutant deficient in low-Fe-stimulated twitching motility but normal as regards twitching motility on Fe-rich medium was isolated and shown to be disrupted in rhlI, whose product is responsible for synthesis of the N-butanoyl homoserine lactone (C4-HSL) quorum-sensing signal. In contrast to wild-type cells, which formed thin, flat, undeveloped biofilms in Fe-limited medium, the rhlI mutant formed substantially developed though not fully mature biofilms under Fe limitation. C4-HSL production increased markedly in Fe-limited versus Fe-rich P. aeruginosa cultures, and cell-free low-Fe culture supernatants restored the twitching motility of the rhlI mutant on Fe-limited minimal medium and stimulated the twitching motility of rhlI and wild-type P. aeruginosa on Fe-rich minimal medium. Still, addition of exogenous C4-HSL did not stimulate the twitching motility of either strain on Fe-replete medium, indicating that some Fe-regulated and RhlI/C4-HSL-dependent extracellular product(s) was responsible for the enhanced twitching motility (and reduced biofilm formation) seen in response to Fe limitation.
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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".