Targeting the alternative sigma factor RpoN to combat virulence in Pseudomonas aeruginosa
Bibliographic record
Abstract
Abstract Pseudomonas aeruginosa is a Gram-negative, opportunistic pathogen that infects immunocompromised and cystic fibrosis patients. Treatment is difficult due to antibiotic resistance, and new antimicrobials are needed to treat infections. The alternative sigma factor 54 (σ54, RpoN), regulates many virulence-associated genes. Thus, we evaluated inhibition of virulence in P. aeruginosa by a designed peptide (RpoN molecular roadblock, RpoN*) which binds specifically to RpoN consensus promoters. We expected that RpoN* binding to its consensus promoter sites would repress gene expression and thus virulence by blocking RpoN and/or other transcription factors. RpoN* reduced transcription of approximately 700 genes as determined by microarray analysis, including genes related to virulence. RpoN* expression significantly reduced motility, protease secretion, pyocyanin and pyoverdine production, rhamnolipid production, and biofilm formation. Given the effectiveness of RpoN* in vitro, we explored its effects in a Caenorhabditis elegans–P. aeruginosa infection model. Expression of RpoN* protected C. elegans in a paralytic killing assay, whereas worms succumbed to paralysis and death in its absence. In a slow killing assay, which mimics establishment and proliferation of an infection, C. elegans survival was prolonged when RpoN* was expressed. Thus, blocking RpoN consensus promoter sites is an effective strategy for abrogation of P. aeruginosa virulence.
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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 | 0.001 |
| 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".