Intermicrobial interaction: Aspergillus fumigatus siderophores protect against competition by Pseudomonas aeruginosa
Bibliographic record
Abstract
Pseudomonas aeruginosa and Aspergillus fumigatus are pathogens frequently co-inhabiting immunocompromised patient airways, particularly in people with cystic fibrosis. Both microbes depend on the availability of iron, and compete for iron in their microenvironment. We showed previously that the P. aeruginosa siderophore pyoverdine is the main instrument in battling A. fumigatus biofilms, by iron chelation and denial of iron to the fungus. Here we show that A. fumigatus siderophores defend against anti-fungal P. aeruginosa effects. P. aeruginosa supernatants produced in the presence of wildtype A. fumigatus planktonic supernatants (Afsup) showed less activity against A. fumigatus biofilms than P. aeruginosa supernatants without Afsup, despite higher production of pyoverdine by P. aeruginosa. Supernatants of A. fumigatus cultures lacking the sidA gene (AfΔsidA), unable to produce hydroxamate siderophores, were less capable of protecting A. fumigatus biofilms from P. aeruginosa supernatants and pyoverdine. AfΔsidA biofilm was more sensitive towards inhibitory effects of pyoverdine, the iron chelator deferiprone (DFP), or amphothericin B than wildtype A. fumigatus biofilm. Supplementation of sidA-deficient A. fumigatus biofilm with A. fumigatus siderophores restored resistance to pyoverdine. The A. fumigatus siderophore production inhibitor celastrol sensitized wildtype A. fumigatus biofilms towards the anti-fungal activity of DFP. In conclusion, A. fumigatus hydroxamate siderophores play a pivotal role in A. fumigatus competition for iron against 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 | 0.001 |
| Research integrity | 0.001 | 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".