Fitness Costs of Antibiotic Resistance Impede the Evolution of Resistance to Other Antibiotics
Bibliographic record
Abstract
Antibiotic resistance is a major threat to global health, claiming the lives of millions every year. With a nearly dry antibiotic development pipeline, novel strategies are urgently needed to combat resistant pathogens. One emerging strategy is the use of sequential antibiotic therapy, postulated to reduce the rate at which antibiotic resistance evolves. Here, we use the soft agar gradient evolution (SAGE) system to carry out high-throughput in vitro bacterial evolution against antibiotic pressure. We find that evolution of resistance to the antibiotic chloramphenicol (CHL) severely affects bacterial fitness, slowing the rate at which resistance to the antibiotics nitrofurantoin and streptomycin emerges. In vitro acquisition of compensatory mutations in the CHL-resistant cells markedly improves fitness and nitrofurantoin adaptation rates but fails to restore rates to wild-type levels against streptomycin. Genome sequencing reveals distinct evolutionary paths to resistance in fitness-impaired populations, suggesting resistance trade-offs in favor of mitigation of fitness costs. We show that the speed of bacterial fronts in SAGE plates is a reliable indicator of adaptation rates and evolutionary trajectories to resistance. Identification of antibiotics whose mutational resistance mechanisms confer stable impairments may help clinicians prescribe sequential antibiotic therapies that are less prone to resistance evolution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".