Altered Pseudomonas Strategies to Inhibit Surface Aspergillus Colonies
Bibliographic record
Abstract
Pseudomonas aeruginosa and Aspergillus fumigatus infections frequently co-localize in lungs of immunocompromised patients and individuals with cystic fibrosis (CF). The antifungal activity of P. aeruginosa has been described for its filtrates. Pyoverdine and pyocyanin are the principal antifungal P. aeruginosa molecules active against A. fumigatus biofilm metabolism present in iron-limited or iron-replete planktonic P. aeruginosa culture filtrates, respectively. Using various P. aeruginosa laboratory wild-type strains (PA14, PAO1, PAK), we found antifungal activity against Aspergillus colonies on agar. Comparing 36 PA14 and 7 PAO1 mutants, we found that mutants lacking both major siderophores, pyoverdine and pyochelin, display higher antifungal activity on agar than their wild types, while quorum sensing mutants lost antifungal activity. Addition of ferric iron, but not calcium or magnesium, reduced the antifungal effects of P. aeruginosa on agar, whereas iron-poor agar enhanced antifungal effects. Antifungal activity on agar was mediated by PQS and HHQ, via MvfR. Among the MvfR downstream factors, rhamnolipids and elastase were produced in larger quantities by pyoverdine–pyochelin double mutants and showed antifungal activity on agar. In summary, antifungal factors produced by P. aeruginosa on agar differ from those produced by bacteria grown in liquid cultures, are dependent on quorum sensing, and are downregulated by the availability of ferric iron. Rhamnolipids and elastase seem to be major mediators of Pseudomonas’ antifungal activity on a solid surface.
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) | 0.001 | 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) | 0.002 | 0.001 |
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".