Perturbation of <i>Pseudomonas aeruginosa</i> peptidoglycan recycling by anti-folates and design of a dual-action inhibitor
Bibliographic record
Abstract
ABSTRACT Peptidoglycan (PG) is an important bacterial macromolecule that confers cell shape and structural integrity, and is a key antibiotic target. Its synthesis and turnover are carefully coordinated with other cellular processes and pathways. Despite established connections between the biosynthesis of PG and the outer membrane, or PG and DNA replication, links between PG and folate metabolism remain comparatively unexplored. Folate is an essential cofactor for bacterial growth and is required for the synthesis of many important metabolites. Here we show that inhibition of folate synthesis in the important Gram-negative pathogen Pseudomonas aeruginosa has downstream effects on PG metabolism and integrity that can manifest as the formation of a subpopulation of round cells that can undergo explosive lysis. Folate inhibitors potentiated β-lactams by perturbation of PG recycling, reducing expression of the AmpC β-lactamase. Supporting this mechanism, folate inhibitors also synergized with fosfomycin, an inhibitor of MurA, the first committed step in PG synthesis that can be bypassed by PG recycling. These insights led to the design of a dual-active inhibitor that overcomes NDM-1 metallo-β lactamase-mediated meropenem resistance and synergizes with the folate inhibitor, trimethoprim. We show that folate and PG metabolism are intimately connected, and targeting this connection can overcome antibiotic resistance in Gram-negative pathogens. IMPORTANCE To combat the alarming global increase in superbugs amid the simultaneous scarcity of new drugs, we can create synergistic combinations of currently available antibiotics or chimeric molecules with dual activities, to minimize resistance. Here we show that older anti-folate drugs synergize with specific cell wall biosynthesis inhibitors to kill the priority pathogen, Pseudomonas aeruginosa . Anti-folate drugs caused a dose-dependent loss of rod cell shape followed by explosive lysis, and synergized with β-lactams that target D,D-carboxypeptidases required to tailor the cell wall. Anti-folates impaired cell wall recycling and subsequent downstream expression of the chromosomally encoded β-lactamase, AmpC, which normally destroys β-lactam antibiotics. Building on the anti-folate-like scaffold of a metallo-β-lactamase inhibitor, we created a new molecule, MLLB-2201, that potentiates β-lactams and anti-folates and restores meropenem activity against metallo-β-lactamase-expressing Escherichia coli . These strategies are useful ways to tackle the ongoing rise in dangerous bacterial pathogens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".