Novel intermicrobial molecular interaction: Pseudomonas aeruginosa Quinolone Signal (PQS) modulates Aspergillus fumigatus response to iron
Bibliographic record
Abstract
Pseudomonas aeruginosa (Pa) and Aspergillus fumigatus (Af), the commonest bacterium and fungus in compromised host airways, compete for iron (Fe). The Pseudomonas quinolone signal (PQS), a Pa quorum sensing molecule, also chelates Fe, and delivers Fe to the Pa cell membrane using Pa siderophores. In models of Af biofilm formation or preformed biofilms, PQS inhibited Af in a low Fe environment. AfΔsidA (mutant unable to produce siderophores) biofilm was more sensitive to PQS inhibition than wild-type (WT), as was planktonic AfΔsidA growth. PQS decreased WT Af growth on agar. All these inhibitory actions were reversed by Fe. The Pa siderophore pyoverdin, or Af siderophore inhibitor celastrol, act cooperatively with PQS in Af inhibition. These findings all indicate PQS inhibition is owing to Fe chelation. Remarkably, in high Fe environments, PQS enhanced Af biofilm at 1/100 to 1/2000 Fe concentration required for Fe alone to enhance. Planktonic Af growth, and on agar, Af conidiation, were also enhanced by PQS+Fe compared to Fe alone. In contrast, neither AfΔsidA biofilm, nor planktonic AfΔsidA, were enhanced by PQS-Fe compared to Fe. When Af siderophore ferricrocin (FC),+PQS, were added to AfΔsidA, Af was then boosted more than by FC alone. Moreover, FC+PQS+Fe boosted AfΔsidA more than Fe, FC, FC+Fe, PQS+FC or PQS+Fe. Thus PQS-Fe maximal stimulation requires Af siderophores. PQS inhibits Af via chelation under low Fe conditions. In a high Fe environment, PQS paradoxically stimulates Af efficiently, and this involves Af siderophores. PQS production by Pa could stimulate Af in cystic fibrosis airways, where Fe homeostasis is altered and Fe levels increase, supporting fungal growth.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".