Thiocillin and Micrococcin Exploit the Ferrioxamine Receptor of <i>Pseudomonas aeruginosa</i> for Uptake
Bibliographic record
Abstract
ABSTRACT Thiopeptides are a class of Gram-positive antibiotics that inhibit protein synthesis. They have been underutilized as therapeutics due to solubility issues, poor bioavailability, and lack of activity against Gram-negative pathogens. We discovered recently that a member of this family, thiostrepton, has activity against Pseudomonas aeruginosa and Acinetobacter baumannii under iron-limiting conditions. Thiostrepton uses pyoverdine siderophore receptors to cross the outer membrane, and combining thiostrepton with an iron chelator yielded remarkable synergy, significantly reducing the minimal inhibitory concentration. These results led to the hypothesis that other thiopeptides could also inhibit growth by using siderophore receptors to gain access to the cell. Here, we screened six thiopeptides for synergy with the iron chelator deferasirox against P. aeruginosa and a mutant lacking the pyoverdine receptors FpvA and FpvB. Our findings suggest that thiopeptides such as thiocillin cross the outer membrane using FoxA, the ferrioxamine siderophore receptor. Other structurally related thiopeptides did not inhibit growth of P. aeruginosa , but had greater potency against methicillin-resistant Staphylococcus aureus than thiostrepton and related thiopeptides. These results suggest that thiopeptide structures have evolved with considerations for target affinity and entry into cells.
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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".