The SMC-like RecN protein is at the crossroads of several genotoxic stress responses in Escherichia coli
Bibliographic record
Abstract
Introduction: DNA damage repair (DDR) is an essential process for living organisms and contributes to genome maintenance and evolution. DDR involves different pathways including Homologous recombination (HR), Nucleotide Excision Repair (NER) and Base excision repair (BER) for example. The activity of each pathway is revealed with particular drug inducing lesions, but the repair of most DNA lesions depends on concomitant or subsequent action of the multiple pathways. Methods: . We combined genomic methods (TIS and Hi-SC2) and imaging assays with genetic dissections. Results: We demonstrate that only a small set of DDR proteins are common to the repair of the lesions induced by these two drugs. Among them, RecN, an SMC-like protein, plays an important role by controlling sister chromatids dynamics and genome morphology at different steps of the repair processes. We further demonstrate that RecN influence on sister chromatids dynamics is not equivalent during the processing of the lesions induced by the two drugs. We observed that RecN activity and stability requires a pre-processing of the MMC-induced lesions by the NER but not for BLM-induced lesions. Discussion: Those results show that RecN plays a major role in rescuing toxic intermediates generated by the BER pathway in addition to its well-known importance to the repair of double strand breaks by HR.
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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 | 0.000 |
| 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".