Peptide 1018 inhibits swarming and influences Anr-regulated gene expression downstream of the stringent stress response in Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa is a ubiquitous opportunistic pathogen that causes considerable human morbidity and mortality, particularly in nosocomial infections and individuals with cystic fibrosis. P. aeruginosa can adapt to surface growth by undergoing swarming motility, a rapid multicellular movement that occurs on viscous soft surfaces with amino acids as a nitrogen source. Here we tested the small synthetic host defense peptide, innate defense regulator 1018, and found that it inhibited swarming motility at concentrations as low as 0.75 μg/ml, well below the MIC for strain PA14 planktonic cells (64 μg/ml). A screen of the PA14 transposon insertion mutant library revealed 29 mutants that were more tolerant to peptide 1018 during swarming, five of which demonstrated significantly greater swarming than the WT in the presence of peptide. Transcriptional analysis (RNA-Seq) of cells that were inoculated on swarming plates containing 1.0 μg/ml peptide revealed differential expression of 1,190 genes compared to cells swarming on plates without peptide. Furthermore, 1018 treatment distinctly altered the gene expression profile of cells when compared to that untreated cells in the centre of the swarm colonies. Peptide-treated cells exhibited changes in the expression of genes implicated in the stringent stress response including those regulated by anr, which is involved in anaerobic adaptation, indicative of a mechanism by which 1018 might inhibit swarming motility. Overall, this study illustrates potential mechanisms by which peptide 1018 inhibits swarming surface motility, an important bacterial adaptation associated with antibiotic resistance, virulence, and dissemination of 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".