<i>Pseudomonas aeruginosa</i> FpvB is a high-affinity transporter for xenosiderophores ferrichrome and ferrioxamine B
Bibliographic record
Abstract
ABSTRACT Iron is essential for many biological functions in bacteria but its poor solubility is a limiting factor for growth. Bacteria produce siderophores, soluble natural products that bind iron with high affinity, to overcome this challenge. Siderophore-iron complexes return to the cell through specific outer-membrane transporters. The opportunistic pathogen Pseudomonas aeruginosa makes multiple transporters that recognize its own siderophores, pyoverdine and pyochelin, and xenosiderophores produced by other bacteria, which gives it a competitive advantage. Some antibiotics exploit these transporters to bypass the membrane to reach their intercellular targets – including the thiopeptide antibiotic, thiostrepton (TS), which uses the pyoverdine transporters FpvA and FpvB to cross the outer membrane. Here, we assessed TS susceptibility in the presence of various siderophores and discovered that ferrichrome and ferrioxamine B antagonized TS uptake via FpvB. Unexpectedly, we found that FpvB transports ferrichrome and ferrioxamine B with higher affinity than pyoverdine. Site-directed mutagenesis of FpvB coupled with competitive growth inhibition and affinity label quenching studies suggested that the siderophores and antibiotic share a common binding site in an aromatic pocket formed by the plug and barrel domains, but have differences in their binding mechanism and molecular determinants for uptake. This work describes an alternative uptake pathway for ferrichrome and ferrioxamine B in P. aeruginosa and emphasizes the promiscuity of siderophore transporters, with implications for Gram-negative antibiotic development via the Trojan horse approach.
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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) | 0.001 | 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".