The Spectrum of Replication Errors in the Absence of Error Correction Assayed Across the Whole Genome of <i>Escherichia coli</i>
Bibliographic record
Abstract
Abstract Proofreading during DNA replication and post-replication mismatch repair are two major defenses against mutations. Foster et al. and Niccum et al. used mutation accumulation and whole genome sequencing to assemble a database of thousands... When the DNA polymerase that replicates the Escherichia coli chromosome, DNA polymerase III, makes an error, there are two primary defenses against mutation: proofreading by the ϵ subunit of the holoenzyme and mismatch repair. In proofreading-deficient strains, mismatch repair is partially saturated and the cell’s response to DNA damage, the SOS response, may be partially induced. To investigate the nature of replication errors, we used mutation accumulation experiments and whole-genome sequencing to determine mutation rates and mutational spectra across the entire chromosome of strains deficient in proofreading, mismatch repair, and the SOS response. We report that a proofreading-deficient strain has a mutation rate 4000-fold greater than wild-type strains. While the SOS response may be induced in these cells, it does not contribute to the mutational load. Inactivating mismatch repair in a proofreading-deficient strain increases the mutation rate another 1.5-fold. DNA polymerase has a bias for converting G:C to A:T base pairs, but proofreading reduces the impact of these mutations, helping to maintain the genomic G:C content. These findings give an unprecedented view of how polymerase and error-correction pathways work together to maintain E. coli’s low mutation rate of 1 per 1000 generations.
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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 | 0.001 | 0.001 |
| 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".