Bibliographic record
Abstract
Antibiotic resistance is a major threat to public health, undermining our ability to treat infectious disease.Often, isolates bearing resistance mutations suffer a cost of resistance, that is, lower fitness than their susceptible counterparts.Nonetheless, fitness can be ameliorated by secondary site mutations, known as compensatory mutations.These mutations restore fitness to normal levels without eliminating resistance.Despite the potential importance of compensation for public health strategies to combat antibiotic resistance, relatively little is known about the molecular mechanisms of compensation.Here, we investigated mechanisms of compensation for quinolone resistance mutations in Escherichia coli.We found substantial costs of resistance for two genotypes, derived from MG1655 (K-12): a gyrA D87G mutant, and a marR R94C mutant.Subsequent selection in the absence of antibiotics led to an improvement in fitness, with at least partial retention of quinolone resistance.Wholegenome sequencing was used to identify potential compensatory mutations.Secondsite mutations that arose in a gyrA D87G mutant encode for proteins involved in cell adhesion, while mutations on the marR R94C background occurred in genes with roles in outer membrane function.This work will provide insight into the mechanisms of compensation of the costs of quinolone resistance in Escherichia 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".