Pseudomonas aeruginosa FpvB Is a High-Affinity Transporter for Xenosiderophores Ferrichrome and Ferrioxamine B
Bibliographic record
Abstract
Gram-negative bacteria express a variety of outer membrane transporters to import critical nutrients such as iron. Due to its insolubility, iron is taken up while bound to small-molecule chelators called siderophores. Pseudomonas aeruginosa takes up its own siderophores pyoverdine and pyochelin but can also steal siderophores produced by other bacteria and fungi, giving it a competitive advantage in iron-limited environments. Here, we used whole-cell reporter assays to show that FpvB, originally identified as a secondary transporter for pyoverdine, transports the chemically distinct fungal siderophore ferrichrome and the bacterial siderophore ferrioxamine B with high affinity. FpvB is also used by thiopeptide antibiotic thiostrepton for uptake. We predicted that all of these ligands bind to a common hydrophobic pocket in FpvB and used site-directed mutagenesis coupled with phenotypic assays to identify residues required for uptake. These analyses showed that siderophore and antibiotic uptake could be uncoupled. Our data show that FpvB is a promiscuous transporter of multiple chemically distinct ligands and fills in missing details of ferrichrome transport by P. aeruginosa. A clearer picture of the spectrum of outer membrane transporter substrate specificity is useful for the design of novel siderophore-antibiotic conjugates that can exploit nutrient uptake pathways to kill challenging Gram-negative pathogens.
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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) | 0.000 | 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 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".