Mechanistic insights and in vivo efficacy of thiosemicarbazones against methicillin-resistant Staphylococcus aureus
Bibliographic record
Abstract
Staphylococcus aureus poses a significant threat in both community and hospital settings due to its infective and pathogenic nature combined with its ability to resist the action of chemotherapeutic agents. Methicillin-resistant S. aureus (MRSA) represents a critical challenge. Metal-chelating thiosemicarbazones (TSCs) have shown promise in combating MRSA and while previous studies hinted at the antimicrobial potential of TSCs, their mechanisms of action against MRSA are still under investigation. We screened a chemical library for anti-staphylococcal compounds and identified a potent molecule named R91 that contained the NNSN structural motif found within TSCs. We identified that R91 and several structural analogs exhibited antimicrobial activity against numerous S. aureus isolates as well as other Gram-positive bacteria. RNAseq analysis revealed that R91 induces copper and oxidative stress responses. Checkerboard assays demonstrated synergy of R91 with copper, nickel, and zinc. Mutation of the SrrAB two-component regulatory system sensitizes S. aureus to R91 killing, further linking the oxidative stress response to R91 resistance. Moreover, R91 was found to induce hydrogen peroxide production, which contributed to its antimicrobial activity. Remarkably, no mutants with elevated R91 resistance were identified, despite extensive attempts. We further demonstrate that R91 can be used to effectively treat an intracellular reservoir of S. aureus in cell culture and can reduce bacterial burdens in a murine skin infection model. Combined, these data position R91 as a potent TSC effective against MRSA and other Gram-positive bacteria, with implications for future therapeutic development.
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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".