Inhibition of WTA Synthesis Blocks the Cooperative Action of PBPs and Sensitizes MRSA to β-Lactams
Bibliographic record
Abstract
Rising drug resistance is limiting treatment options for infections by methicillin-resistant Staphylococcus aureus (MRSA). Herein we provide new evidence that wall teichoic acid (WTA) biogenesis is a remarkable antibacterial target with the capacity to destabilize the cooperative action of penicillin-binding proteins (PBPs) that underlie β-lactam resistance in MRSA. Deletion of gene tarO, encoding the first step of WTA synthesis, resulted in the restoration of sensitivity of MRSA to a unique profile of β-lactam antibiotics with a known selectivity for penicillin binding protein 2 (PBP2). Of these, cefuroxime was used as a probe to screen for previously approved drugs with a cryptic capacity to potentiate its activity against MRSA. Ticlopidine, the antiplatelet drug Ticlid, strongly potentiated cefuroxime, and this synergy was abolished in strains lacking tarO. The combination was also effective in a Galleria mellonella model of infection. Using both genetic and biochemical strategies, we determined the molecular target of ticlopidine as the N-acetylglucosamine-1-phosphate transferase encoded in gene tarO and provide evidence that WTA biogenesis represents an Achilles heel supporting the cooperative function of PBP2 and PBP4 in creating highly cross-linked muropeptides in the peptidoglycan of S. aureus. This approach represents a new paradigm to tackle MRSA infection.
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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".