Lytic Transglycosylase Deficiency Increases Susceptibility to β‐lactam Antibiotics But Reduces Susceptibility to Vancomycin in <i>Escherichia coli</i>
Bibliographic record
Abstract
In Staphylococcus aureus, a gram-positive pathogen, vancomycin-resistant strains become susceptible to β-lactam antibiotics, referred to as the "seesaw effect." However, in gram-negative bacteria, the phenomenon is less clear. Here, we analyzed the gene-knockout effects of eight lytic transglycosylases (slt, mltA, mltB, mltC, mltD, mltE, mltF, mltG) on antibiotic sensitivity in Escherichia coli. Knockout of both slt and mltG increased sensitivity to β-lactam antibiotics and reduced sensitivity to vancomycin. The β-lactam antibiotic sensitivity and vancomycin resistance of the slt-knockout mutant were abolished by the introduction of the wild-type slt gene but remained unchanged by the introduction of the mutant slt gene encoding an amino acid substitution variant of the transglycosylase catalytic centre. The double-knockout strain for slt and mltB was more sensitive to ampicillin and more resistant to vancomycin than each single-knockout strain. The double-knockout strain for slt and mltG was more sensitive to ampicillin and more resistant to vancomycin than each single-knockout strain. These results suggest that loss of lytic transglycosylase activity causes β-lactam antibiotic sensitivity and vancomycin resistance in E. coli.
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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".