The Wsp system of <i>Pseudomonas aeruginosa</i> links surface sensing and cell envelope stress
Bibliographic record
Abstract
Surface sensing is a critical process that promotes the transition to a biofilm lifestyle. Several surface-sensing mechanisms have been described for a range of species, most involving surface appendages, such as flagella and pili. Pseudomonas aeruginosa uses the Wsp chemosensory-like signal transduction pathway to sense surfaces and promote biofilm formation. The methyl-accepting chemotaxis protein WspA recognizes an unknown surface-associated signal and initiates a phosphorylation cascade that activates the diguanylate cyclase WspR. We conducted a screen for Wsp-activating compounds and found that chemicals that impact the cell envelope induce Wsp signaling, increase intracellular c-di-GMP levels, and can promote surface attachment. To isolate the Wsp system from other P. aeruginosa surface-sensing systems, we heterologously expressed it in Escherichia coli and found it sufficient for sensing surfaces and the chemicals identified in our screen. Using well-characterized reporters for different E. coli cell envelope stress responses, we then determined that Wsp sensitivity overlapped with multiple E. coli cell envelope stress-response systems. Using mutational and CRISPRi analysis, we found that misfolded proteins in the periplasm appear to be a major stimulus of the Wsp system. Finally, we show that surface attachment appears to have an immediate, observable effect on cell envelope integrity. Collectively, our results provide experimental evidence that cell envelope stress represents an important feature of surface sensing in 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) | 0.001 | 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".