Sequential antibiotic exposure restores antibiotic susceptibility
Bibliographic record
Abstract
BACKGROUND: The prevalence of antibiotic resistance continues to rise, rendering many valuable antimicrobial drugs ineffective. Pairwise cyclic antibiotic therapy, where treatment is rapidly switched between two antibiotics, has been demonstrated in vitro to limit the evolution of antibiotic resistance. However, what happens when resistance inevitably evolves to one of the drugs? METHODS: In this study, we perform over 450 evolution experiments to test the resilience of four proposed cyclic therapies. We use soft agar gradient evolution and 'flat plates' to identify resistance trade-offs that are resilient to compensatory mitigation. Resensitizations were detected by antimicrobial susceptibility assays, and their mechanistic underpinnings were elucidated via genomic and phenotypic analyses. RESULTS: Resistance evolves readily and collateral sensitivity (CS) (where resistance to drug A leads to hypersensitivity to drug B) does not hinder the evolution of multidrug resistance and does not predict or promote resensitization. However, if resistance to drug B increases susceptibility to A, a phenomenon we term backward CS, resistance to A can be reduced or even reversed. For example, we show that Escherichia coli cells frequently become hypersensitive to β-lactams upon aminoglycoside resistance acquisition, due to conflicting modifications to the proton motive force and efflux pumps. We also find for the first time that polymyxin B resistance can be entirely reversed by exposure to tigecycline, through the acquisition of compensatory mutations that reduce the fitness penalty of tigecycline resistance. CONCLUSIONS: The longevity of drug cycling protocols can be significantly improved by leveraging backwards CS to resensitize cells as antibiotic resistance evolves.
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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 | 0.001 |
| 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) | 0.002 | 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".