Swarming of<i>Pseudomonas aeruginosa</i>Is Controlled by a Broad Spectrum of Transcriptional Regulators, Including MetR
Bibliographic record
Abstract
Pseudomonas aeruginosa exhibits swarming motility on semisolid surfaces (0.5 to 0.7% agar). Swarming is a more than just a form of locomotion and represents a complex adaptation resulting in changes in virulence gene expression and antibiotic resistance. In this study, we used a comprehensive P. aeruginosa PA14 transposon mutant library to investigate how the complex swarming adaptation process is regulated. A total of 233 P. aeruginosa PA14 transposon mutants were verified to have alterations in swarming motility. The swarming-associated genes functioned not only in flagellar or type IV pilus biosynthesis but also in processes as diverse as transport, secretion, and metabolism. Thirty-three swarming-deficient and two hyperswarming mutants had transposon insertions in transcriptional regulator genes, including genes encoding two-component sensors and response regulators; 27 of these insertions were newly identified. Of the 25 regulatory mutants whose swarming motility was highly impaired (79 to 97%), only 1 (a PA1458 mutant) had a major defect in swimming, suggesting that this regulator might influence flagellar synthesis or function. Twitching motility, which requires type IV pili, was strongly affected in only two regulatory mutants (pilH and PA2571 mutants) and was moderately affected in three other mutants (algR, ntrB, and nosR mutants). Microarray analyses were performed to compare the gene expression profile of a swarming-deficient PA3587 mutant to that of the wild-type PA14 strain under swarming conditions. PA3587 showed 63% homology to metR, which encodes a regulator of methionine biosynthesis in Escherichia coli. The observed dysregulation in the metR mutant of nine different genes required for swarming motility provided a possible explanation for the swarming-deficient phenotype of this mutant.
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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) | 0.000 | 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 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".