Collateral sensitivity and the evolution of drug resistance in Escherichia coli
Bibliographic record
Abstract
Recently, collateral sensitivity networks have created excitement for designing new therapies that could reduce rates of evolution of antibiotic resistance in pathogens.To explore this idea, I tested the key assumption that collateral sensitivity should reduce the frequency of mutation to resistance.Thirty strains of Escherichia coli, each selected for resistance to a single antibiotic, were screened for minimum inhibitory concentration values against twelve drugs to find relationships of collateral sensitivity.Ceftazidime resistant strains showed susceptibility to chloramphenicol.It was therefore expected that growth in sub-MIC concentrations of chloramphenicol would reduce mutation rate to ceftazidime resistance, however, an increase in mutation rate was observed.The results showed that µ was significantly higher in populations evolved in sub-MIC concentrations of cef, compared to plain LB (p = 2.93x10 -5 ) For populations grown in LB µ was 0.011 per 10 9 cells, 95% CI [0.019, 0.005] whereas populations grown in LB and sub-MIC cef, µ was 0.21 per 10 9 cells, 95% CI [0.452,0.158].There are two likely hypotheses, the first is that the increase in mutation is due to antibiotic related SOS response, and the second is that multi-drug-resistance was selected for in both cases.This highlights the need for evolutionary considerations in designing new drug therapies, as apparent patterns of collateral sensitivity may not be a sufficient criterion.v 3.4 Rate of Mutation in Rich-Media vs. Low-Level Antibiotic Environments ..................
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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 | 0.000 |
| 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".