A Staphylococcus aureus clpX Mutant Used as a Unique Screening Tool to Identify Cell Wall Synthesis Inhibitors that Reverse β-Lactam Resistance in MRSA
Bibliographic record
Abstract
Staphylococcus aureus is a leading cause of bacterial infections world-wide. Staphylococcal infections are preferentially treated with β-lactam antibiotics, however, methicillin-resistant S. aureus (MRSA) strains have acquired resistance to this superior class of antibiotics. We have developed a growth-based, high-throughput screening approach that directly identifies cell wall synthesis inhibitors capable of reversing β-lactam resistance in MRSA. The screen is based on the finding that S. aureus mutants lacking the ClpX chaperone grow very poorly at 30°C unless specific steps in teichoic acid synthesis or penicillin binding protein (PBP) activity are inhibited. This property allowed us to exploit the S. aureus clpX mutant as a unique screening tool to rapidly identify biologically active compounds that target cell wall synthesis. We tested a library of ∼50,000 small chemical compounds and searched for compounds that inhibited growth of the wild type while stimulating growth of the clpX mutant. Fifty-eight compounds met these screening criteria, and preliminary tests of 10 compounds identified seven compounds that reverse β-lactam resistance of MRSA as expected for inhibitors of teichoic acid synthesis. The hit compounds are therefore promising candidates for further development as novel combination agents to restore β-lactam efficacy against MRSA.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".