Swarming of<i>Pseudomonas aeruginosa</i>Is a Complex Adaptation Leading to Increased Production of Virulence Factors and Antibiotic Resistance
Bibliographic record
Abstract
In addition to exhibiting swimming and twitching motility, Pseudomonas aeruginosa is able to swarm on semisolid (viscous) surfaces. Recent studies have indicated that swarming is a more complex type of motility influenced by a large number of different genes. To investigate the adaptation process involved in swarming motility, gene expression profiles were analyzed by performing microarrays on bacteria from the leading edge of a swarm zone compared to bacteria growing in identical medium under swimming conditions. Major shifts in gene expression patterns were observed under swarming conditions, including, among others, the overexpression of a large number of virulence-related genes such as those encoding the type III secretion system and its effectors, those encoding extracellular proteases, and those associated with iron transport. In addition, swarming cells exhibited adaptive antibiotic resistance against polymyxin B, gentamicin, and ciprofloxacin compared to what was seen for their planktonic (swimming) counterparts. By analyzing a large subset of up-regulated genes, we were able to show that two virulence genes, lasB and pvdQ, were required for swarming motility. These results clearly favored the conclusion that swarming of P. aeruginosa is a complex adaptation process in response to a viscous environment resulting in a substantial change in virulence gene expression and antibiotic resistance.
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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".