Antimutator and Mutational Spectrum Effects Can Combine to Reduce Evolutionary Potential in <i>Escherichia coli</i> Δ<i>nudJ</i>
Bibliographic record
Abstract
The rate of spontaneous mutation is a key factor in determining the capacity of a population to adapt to a novel environment, for example, a bacterial population exposed to antibiotics. Genetic and environmental factors controlling the mutation rate commonly also cause shifts in the relative rates of different mutational classes, i.e. the mutational spectrum. When the mutational spectrum is altered, the relatively enriched and depleted mutations may differ in their fitness effects. Here, we explore how a reduced mutation rate and altered mutational spectrum can contribute to adaptation in Escherichia coli. We measure mutation rates across a set of Nudix hydrolase deletants, finding multiple strains with an antimutator phenotype. We focus on the antimutator ΔnudJ, which can cause a 6-fold mutation rate reduction relative to the wildtype, with an altered mutational spectrum biased towards A > C transversions. Its reduced mutation rate, most pronounced at low population densities, appears to occur via NudJ's role in nucleotide and/or prenyl metabolism, with a reduced internal ATP pool. Its effects may be reversed by mutations to genes, including waaZ, affecting the outer membrane. Not only does nudJ deletion reduce the probability of antibiotic resistance arising at all but through enhancing an existing hotspot for low fitness A > C rifampicin resistance mutations reduces the expected fitness of strains when resistance does arise. Thus, our findings with ΔnudJ suggest future anti-evolution drug strategies could suppress spontaneous resistance evolution not only through minimizing resistance mutations but also by specifically limiting access to the fittest mutations.
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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 | 0.000 |
| 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".