Backward collateral sensitivity can restore antibiotic susceptibility
Bibliographic record
Abstract
Abstract The prevalence of antibiotic resistance continues to rise, rendering many valuable drugs ineffective. Antibiotic cycling regimens that incorporate collateral sensitivity (CS), the phenomenon where resistance to one antibiotic leads to hypersensitivity to another, are hypothesized to slow the evolution of antibiotic resistance. However, the repeatability of CS interactions and their ability to drive bacterial extinction and resensitizations remain unclear. In this study, we thoroughly investigate four drug pairs proposed for cycling regimens with experimental evolution. We find that reported pairwise CS interactions are not always robust, and even when they are, forward CS (where resistance to drug A leads to hypersensitivity to drug B) does not reliably reduce resistance or promote bacterial extinction. Instead, we find that if evolution of resistance to drug B in naive cells is associated with CS to drug A, a phenomenon we term backward CS, drug A-resistant cells can be rendered more sensitive to A again when resistance to B develops. We describe the mechanism of resistance disruption via backward CS in an aminoglycoside-β-lactam pair, where perturbation of the electron transport chain to inhibit aminoglycoside entry impairs β-lactam efflux. Overall, we highlight the importance of applying antibiotics in the correct order in cycling regimens and identify robust CS interactions that may be used to design treatment regimens less likely to lead to resistance evolution. TOC Graphic
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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) | 0.005 | 0.001 |
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".