Effects of Lysine N-ζ-Methylation in Ultrashort Tetrabasic Lipopeptides (UTBLPs) on the Potentiation of Rifampicin, Novobiocin, and Niclosamide in Gram-Negative Bacteria
Bibliographic record
Abstract
Outer membrane (OM) drug impermeability typically associated with a molecular weight above 600 Da and high hydrophobicity prevents accumulation of many antibiotics in Gram-negative bacteria (GNB). Previous studies have shown that ultrashort tetrabasic lipopeptides (UTBLPs) containing multiple lysine residues potentiate Gram-positive bacteria (GPB)-selective antibiotics in GNB by enhancing OM permeability. However, there is no available information on how N-substitution at the ζ-position of lysine in UTBLPs affects antibiotic potentiation in GNB. To study these effects, we prepared a series of branched and linear UTBLPs that differ in the degree of N-ζ-methylation and studied their potentiating effects with GPB-selective antibiotics including rifampicin, novobiocin, niclosamide, and chloramphenicol against wild-type and multidrug-resistant GNB isolates. Our results show that increasing N-ζ-methylation reduces or abolishes the potentiating effects of UTBLPs with rifampicin, novobiocin, and niclosamide against GNB. No trend was observed with chloramphenicol that is largely affected by efflux. We were unable to observe a correlation between the strength of the antibiotic potentiating effect to the increase in fluorescence in the 1-N-phenylnaphthylamine (NPN) OM permeability assay suggesting that other factors besides OM permeability of NPN play a role in antibiotic potentiation. In conclusion, our study has elucidated crucial structure–activity relationships for the optimization of polybasic antibiotic potentiators in GNB.
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".