Studies of Pseudomonas aeruginosa Mutants Indicate Pyoverdine as the Central Factor in Inhibition of Aspergillus fumigatus Biofilm
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa and Aspergillus fumigatus are common opportunistic bacterial and fungal pathogens, respectively. They often coexist in airways of immunocompromised patients and individuals with cystic fibrosis, where they form biofilms and cause acute and chronic illnesses. Hence, the interactions between them have long been of interest and it is known that P. aeruginosa can inhibit A. fumigatus in vitro . We have approached the definition of the inhibitory P. aeruginosa molecules by studying 24 P. aeruginosa mutants with various virulence genes deleted for the ability to inhibit A. fumigatus biofilms. The ability of P. aeruginosa cells or their extracellular products produced during planktonic or biofilm growth to affect A. fumigatus biofilm metabolism or planktonic A. fumigatus growth was studied in agar and liquid assays using conidia or hyphae. Four mutants, the pvdD pchE , pvdD , lasR rhlR , and lasR mutants, were shown to be defective in various assays. This suggested the P. aeruginosa siderophore pyoverdine as the key inhibitory molecule, although additional quorum sensing-regulated factors likely contribute to the deficiency of the latter two mutants. Studies of pure pyoverdine substantiated these conclusions and included the restoration of inhibition by the pyoverdine deletion mutants. A correlation between the concentration of pyoverdine produced and antifungal activity was also observed in clinical P. aeruginosa isolates derived from lungs of cystic fibrosis patients. The key inhibitory mechanism of pyoverdine was chelation of iron and denial of iron to A. fumigatus . IMPORTANCE Interactions between human pathogens found in the same body locale are of vast interest. These interactions could result in exacerbation or amelioration of diseases. The bacterium Pseudomonas aeruginosa affects the growth of the fungus Aspergillus fumigatus . Both pathogens form biofilms that are resistant to therapeutic drugs and host immunity. P. aeruginosa and A. fumigatus biofilms are found in vivo , e.g., in the lungs of cystic fibrosis patients. Studying 24 P. aeruginosa mutants, we identified pyoverdine as the major anti- A. fumigatus compound produced by P. aeruginosa . Pyoverdine captures iron from the environment, thus depriving A. fumigatus of a nutrient essential for its growth and metabolism. We show how microbes of different kingdoms compete for essential resources. Iron deprivation could be a therapeutic approach to the control of pathogen growth.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".