Exopolysaccharide-Repressing Small Molecules with Antibiofilm and Antivirulence Activity against Pseudomonas aeruginosa
Bibliographic record
Abstract
ABSTRACT Biofilm formation is a universal virulence strategy in which bacteria grow in dense microbial communities enmeshed within a polymeric extracellular matrix that protects them from antibiotic exposure and the immune system. Pseudomonas aeruginosa is an archetypal biofilm-forming organism that utilizes a biofilm growth strategy to cause chronic lung infections in cystic fibrosis (CF) patients. The extracellular matrix of P. aeruginosa biofilms is comprised mainly of exopolysaccharides (EPS) and DNA. Both mucoid and nonmucoid isolates of P. aeruginosa produce the Pel and Psl EPS, each of which have important roles in antibiotic resistance, biofilm formation, and immune evasion. Given the central importance of the EPS for biofilms, they are attractive targets for novel anti-infective compounds. In this study, we used a high-throughput gene expression screen to identify compounds that repress expression of the pel genes. The pel repressors demonstrated antibiofilm activity against microplate and flow chamber biofilms formed by wild-type and hyperbiofilm-forming strains. To determine the potential role of EPS in virulence, pel / psl mutants were shown to have reduced virulence in feeding behavior and slow killing virulence assays in Caenorhabditis elegans . The antibiofilm molecules also reduced P. aeruginosa PAO1 virulence in the nematode slow killing model. Importantly, the combination of antibiotics and antibiofilm compounds increased killing of P. aeruginosa biofilms. These small molecules represent a novel anti-infective strategy for the possible treatment of chronic P. aeruginosa infections.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".