The <i>Pseudomonas putida</i> CsrA/RsmA homologues negatively affect c‐di‐GMP pools and biofilm formation through the GGDEF/EAL response regulator CfcR
Bibliographic record
Abstract
Expression of cfcR, encoding the only GGDEF/EAL response regulator in Pseudomonas putida, is transcriptionally regulated by RpoS, ANR and FleQ, and the functionality of CfcR as a diguanylate cyclase requires the multisensor CHASE3/GAF hybrid histidine kinase named CfcA. Here an additional level of cfcR control, operating post-transcriptionally via the RNA-binding proteins RsmA, RsmE and RsmI, is unraveled. Specific binding of the three proteins to an Rsm-binding motif (5'CANGGANG3') encompassing the translational start codon of cfcR was confirmed. Although RsmA exhibited the highest binding affinity to the cfcR transcript, single deletions of rsmA, rsmE or rsmI caused minor derepression in CfcR translation compared to a ΔrsmIEA triple mutant. RsmA also showed a negative impact on c-di-GMP levels in a double mutant ΔrsmIE through the control of cfcR, which is responsible for most of the free c-di-GMP during stationary phase in static conditions. In addition, a CfcR-dependent c-di-GMP boost was observed during this stage in ΔrsmIEA confirming the negative effect of Rsm proteins on CfcR translation and explaining the increased biofilm formation in this mutant compared to the wild type. Overall, these results suggest that CfcR is a key player in biofilm formation regulation by the Rsm proteins in P. putida.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".