Sensitization of gram‐negative bacteria by targeting the membrane potential
Bibliographic record
Abstract
Toward generating new tools for fighting multidrug-resistant (MDR) bacteria, we assessed the ability of a membrane-active peptide to sensitize gram-negative bacteria to various antibiotics. The mechanism for affecting inner and/or outer membrane functions was assessed by complementary biophysical methods (SPR, DSC, ITC). The implication of efflux pumps was examined using Acr-AB mutants, as tested with representative antibiotics, host defense peptides, and synthetic mimics. The ability to affect disease course systemically was compared for a single therapy and combination therapy, using the mouse thigh-infection model. The data show that potent antibiotic action can be provoked in vitro and in vivo, by a treatment combining two antibacterial compounds whose individual inefficiency against gram-negative bacteria stems from their efflux. Thus, at subminimal inhibitory concentrations, the lipopeptide-like sequence, N(α)(ω7)dodecenoyl-lysyl-[lysyl-aminododecanoyl-lysyl]-amide (designated C12(ω7)K-β12), has, nonetheless, rapidly achieved a transient membrane depolarization, which deprived bacteria of the proton-motive force required for active efflux. Consequently, bacteria became significantly sensitive to intracellular targeting antibiotics. Collectively, these findings suggest a potentially useful approach for expanding the antibiotics sensitivity spectrum of MDR gram-negative bacteria to include efflux substrates.
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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 | 0.001 |
| 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".