<i>Pseudomonas aeruginosa</i> GacA, a factor in multihost virulence, is also essential for biofilm formation
Bibliographic record
Abstract
We have investigated a potential role for GacA, the response regulator of the GacA/GacS two-component regulatory system, in Pseudomonas aeruginosa biofilm formation. When gacA was disrupted in strain PA14, a 10-fold reduction in biofilm formation capacity resulted relative to wild-type PA14. However, no significant difference was observed in the planktonic growth rate of PA14 gacA(-). Providing gacA in trans on the multicopy vector pUCP-gacA abrogated the biofilm formation defect. Scanning electron microscopy of biofilms formed by PA14 gacA(-) revealed diffuse clusters of cells that failed to aggregate into microcolonies, implying a deficit in biofilm development or surface translocation. Motility assays revealed no decrease in PA14 gacA(-) twitching or swimming abilities, indicating that the defect in biofilm formation is independent of flagellar-mediated attachment and solid surface translocation by pili. Autoinducer and alginate bioassays were performed similarly, and no difference in production levels was observed, indicating that this is not merely an upstream effect on either quorum sensing or alginate production. Antibiotic susceptibility profiling demonstrated that PA14 gacA(-) biofilms have moderately decreased resistance to a range of antibiotics relative to PA14 wild type. This study establishes GacA as a new and independent regulatory element in P. aeruginosa biofilm formation.
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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".