Exploring the Genetic Interactions of Quinolone Resistance Mutations in Escherichia coli
Bibliographic record
Abstract
Globally, antimicrobial resistance (AMR) is a rapidly growing public health concern which demands the need for new treatment strategies.One promising approach is the use of drug adjuvants, which function to restore the efficacy of pre-existing antimicrobials.Despite their importance, there are no approved adjuvants for quinolones.This study used a genetic approach to identify potential drug targets for novel adjuvant therapies for treating quinolone resistance caused by mutations in the gyrA gene.I screened 10 Escherichia coli genes in clinically relevant gyrA mutant backgrounds to identify genes whose knockout impacts resistance and fitness.The knockout of tus and priA genes largely reduced resistance in the gyrA S83L background but had no effect in other gyrA mutants, indicating genetic background dependency.Deleting tolC and xseA genes effectively reversed ciprofloxacin resistance across all gyrA mutants, suggesting their potential as drug targets.
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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".