Thiocillin and micrococcin exploit the ferrioxamine receptor of <i>Pseudomonas aeruginosa</i> for uptake
Bibliographic record
Abstract
BACKGROUND: Thiopeptides are a class of antibiotics that are active against Gram-positive bacteria and inhibit translation. They were considered inactive against Gram-negative bacteria due to their inability to cross the outer membrane. However, we discovered previously that a member of this class, thiostrepton (TS), has activity against Pseudomonas aeruginosa and Acinetobacter baumannii under iron-limiting conditions. TS hijacks the pyoverdine siderophore receptors of P. aeruginosa to cross the outer membrane and synergizes with iron chelators. OBJECTIVES: To test other thiopeptides for antimicrobial activity against P. aeruginosa and determine their mechanism of uptake, action and spectrum of activity. METHODS: Eight thiopeptides were screened in chequerboard assays against a mutant of P. aeruginosa PA14 lacking both pyoverdine receptors. Thiopeptides that retain activity against a pyoverdine receptor-null mutant may use alternative siderophore receptors for entry. Susceptibility testing against siderophore receptor mutants was used to determine thiopeptide mechanism of uptake. RESULTS: The thiopeptides thiocillin (TC) and micrococcin (MC) use the ferrioxamine siderophore receptor (FoxA) for uptake and inhibit the growth of P. aeruginosa at low micromolar concentrations. The activity of TC required the TonB-ExbBD system used to energize siderophore uptake. TC acted through its canonical mechanism of action of translation inhibition. CONCLUSIONS: Multiple thiopeptides have antimicrobial activity against P. aeruginosa, countering the historical assumption that they cannot cross the outer membrane. These results demonstrate the potential for thiopeptides to act as antipseudomonal antibiotics.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".