Restoring Methicillin-Resistant <i>Staphylococcus aureus</i> Susceptibility to β-Lactam Antibiotics
Bibliographic record
Abstract
Despite the need for new antibiotics to treat drug-resistant bacteria, current clinical combinations are largely restricted to β-lactam antibiotics paired with β-lactamase inhibitors. We have adapted a Staphylococcus aureus antisense knockdown strategy to genetically identify the cell division Z ring components-FtsA, FtsZ, and FtsW-as β-lactam susceptibility determinants of methicillin-resistant S. aureus (MRSA). We demonstrate that the FtsZ-specific inhibitor PC190723 acts synergistically with β-lactam antibiotics in vitro and in vivo and that this combination is efficacious in a murine model of MRSA infection. Fluorescence microscopy localization studies reveal that synergy between these agents is likely to be elicited by the concomitant delocalization of their cognate drug targets (FtsZ and PBP2) in MRSA treated with PC190723. A 2.0 Å crystal structure of S. aureus FtsZ in complex with PC190723 identifies the compound binding site, which corresponds to the predominant location of mutations conferring resistance to PC190723 (PC190723(R)). Although structural studies suggested that these drug resistance mutations may be difficult to combat through chemical modification of PC190723, combining PC190723 with the β-lactam antibiotic imipenem markedly reduced the spontaneous frequency of PC190723(R) mutants. Multiple MRSA PC190723(R) FtsZ mutants also displayed attenuated virulence and restored susceptibility to β-lactam antibiotics in vitro and in a mouse model of imipenem efficacy. Collectively, these data support a target-based approach to rationally develop synergistic combination agents that mitigate drug resistance and effectively treat MRSA infections.
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) | 0.000 | 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".