The PhzI/PhzR quorum-sensing system is required for pyrrolnitrin and phenazine production, and exhibits cross-regulation with RpoS in Pseudomonas chlororaphis PA23
Bibliographic record
Abstract
The aim of the current study was to determine how quorum sensing (QS) affects the production of secondary metabolites in Pseudomonas chlororaphis strain PA23. A phzR mutant (PA23phzR) and an N-acylhomoserine lactone (AHL)-deficient strain (PA23-6863) were generated that no longer inhibited the fungal pathogen Sclerotinia sclerotiorum in vitro. Both strains exhibited reduced pyrrolnitrin (PRN), phenazine (PHZ) and protease production. Moreover, phzA-lacZ and prnA-lacZ transcription was significantly reduced in PA23phzR and PA23-6863. As the majority of secondary metabolites are produced at the onset of stationary phase, we investigated whether cross-regulation occurs between QS and RpoS. Analysis of transcriptional fusions revealed that RpoS has a positive and negative effect on phzI and phzR, respectively. In a reciprocal manner, RpoS is positively regulated by QS. Characterization of a phzRrpoS double mutant showed reduced antifungal activity as well as PRN and PHZ production, similar to the QS-deficient strains. Furthermore, phzR but not rpoS was able to complement the phzRrpoS double mutant for the aforementioned traits, indicating that the Phz QS system is a central regulator of PA23-mediated antagonism. Finally, we discovered that QS and RpoS have opposing effects on PA23 biofilm formation. While both QS-deficient strains produced little biofilm, the rpoS mutant showed enhanced biofilm production compared with PA23. Collectively, our findings indicate that QS controls diverse aspects of PA23 physiology, including secondary metabolism, RpoS and biofilm formation. As such, QS is expected to play a crucial role in PA23 biocontrol and persistence in the environment.
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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 | 0.000 | 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 | 0.001 | 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".