Tripartite Loops Reverse Antibiotic Resistance
Bibliographic record
Abstract
Antibiotic resistance threatens to undo many of the advancements of modern medicine. A slow antibiotic development pipeline makes it impossible to outpace bacterial evolution, making alternative strategies essential to combat resistance. In this study, we introduce cyclic antibiotic regimens composed of 3 drugs or "tripartite loops" to contain resistance within a closed drug cycle. Through 424 discrete adaptive laboratory evolution experiments we show that as bacteria sequentially evolve resistance to the drugs in a loop, they continually trade their past resistance for fitness gains, reverting back to sensitivity. Through fitness and genomic analyses, we find that tripartite loops guide bacterial strains toward evolutionary paths that mitigate fitness costs and reverse resistance to component drugs in the loops and drive levels of resensitization not achievable through previously suggested pairwise regimens. We then apply this strategy to reproducibly resensitize or eradicate 4 drug-resistant clinical isolates over the course of 216 evolutionary experiments. Resensitization occurrs even when bacteria adapted through plasmid-bound mutations instead of chromosomal changes. Combined, these findings outline a sequential antibiotic regimen with high resensitization frequencies, which may improve the clinical longevity of existing antibiotics even in the face of antibiotic resistance.
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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 | 0.001 |
| 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".