Forging new antibiotic combinations under iron-limiting conditions
Bibliographic record
Abstract
ABSTRACT Pseudomonas aeruginosa is a multidrug-resistant nosocomial pathogen. We showed previously that thiostrepton (TS), a gram-positive thiopeptide antibiotic, was imported via pyoverdine receptors and synergized with iron chelator deferasirox (DSX) to inhibit the growth of P. aeruginosa and Acinetobacter baumannii clinical isolates. A small number of P. aeruginosa and A. baumannii isolates were resistant to the combination, prompting us to search for other compounds that could synergize with TS against those strains. From literature surveys we selected 14 compounds reported to have iron-chelating activity, plus one iron analogue, and tested them for synergy with TS. Doxycycline (DOXY), ciclopirox olamine (CO), tropolone (TRO), clioquinol (CLI), and gallium nitrate (GN) synergized with TS. Individual compounds were bacteriostatic but the combinations were bactericidal. Our spectrophotometric data and chrome azurol S agar assay confirmed that the chelators potentate TS activity through iron sequestration rather than through their innate antimicrobial activities. A triple combination of TS + DSX + DOXY had the most potent activity against P. aeruginosa and A. baumannii isolates. One P. aeruginosa clinical isolate was resistant to the triple combination, but susceptible to a triple combination containing higher concentrations of CLI, CO, or DOXY. All A. baumannii isolates were susceptible to the triple combinations. Our data reveal a diverse set of compounds with dual activity as antibacterial agents and TS adjuvants, allowing combinations to be tailored for resistant clinical isolates.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".