Characterization of the Direct Interaction between Hybrid Sensor Kinases PA1611 and RetS That Controls Biofilm Formation and the Type III Secretion System in <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
One of the leading causes of morbidity and mortality in cystic fibrosis (CF) patients is pulmonary infection with Pseudomonas aeruginosa, and the pathophysiology of pulmonary infection in CF is affected by the lifestyle of this micro-organism. RetS-GacS/A-RsmA is a key regulatory pathway in P. aeruginosa that determines the bacterium's lifestyle choice. Previously, we identified PA1611, a hybrid sensor kinase, as a new player in this pathway that interacts with RetS and influences biofilm formation and type III secretion system. In this study, we explored the structural and mechanistic basis of the interaction between PA1611 and RetS. We identified the amino acid residues critical for PA1611-RetS interactions by molecular modeling. These residues were then targeted for site-directed mutagenesis. Amino acid substitutions were carried out at seven key positions in PA1611 and at six corresponding key positions in RetS. The influence of such substitutions in PA1611 on the interaction was analyzed by bacterial two-hybrid assays. We carried out functional analysis of these mutants in P. aeruginosa for their effect on specific phenotypes. Two residues, F269 and E276, located within the histidine kinase A and histidine kinase-like ATPase domains of PA1611 were found to play crucial roles in the PA1611-RetS interaction and had profound effects on phenotypes. Corresponding mutations in RetS demonstrated similar results. We further confirmed that these mutations in PA1611 function through the GacS/GacA-RsmY/Z signaling pathway. Collectively, our findings provide a noncognate sensor kinase direct interaction model for a signaling pathway, key for lifestyle selection in P. aeruginosa, and targeting such interaction may serve as a novel way of controlling infections with P. aeruginosa.
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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 | 0.000 |
| 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".