Identification of a Small Molecule Compound Active against Antibiotic-Tolerant Staphylococcus aureus by Boosting ATP Synthesis
Bibliographic record
Abstract
Antibiotic tolerance poses a threat to current antimicrobial armamentarium. Bacteria at a tolerant state survive in the presence of antibiotic treatment and account for persistence, relapse and recalcitrance of infections. Antibiotic treatment failure may occur due to antibiotic tolerance. Persistent infections are difficult to treat and are often associated with poor prognosis, imposing an enormous burden on the healthcare system. Effective strategies targeting antibiotic-tolerant bacteria are therefore highly warranted. In this study, small molecule compound SA-558 was identified to be effective against Staphylococcus aureus that are tolerant to being killed by conventional antibiotics. SA-558 mediated electroneutral transport across the membrane and led to increased ATP and ROS generation, resulting in a reduction of the population of antibiotic-tolerant bacteria. In a murine chronic infection model, of which vancomycin treatment failed, we demonstrated that SA-558 alone and in combination with vancomycin caused significant reduction of MRSA abundance. Our results indicate that SA-558 monotherapy or combinatorial therapy with vancomycin is an option for managing persistent S. aureus bacteremia infection and corroborate that bacterial metabolism is an important target for counteracting antibiotic tolerance.
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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".