Two‐way crosstalk between BER and c‐NHEJ repair pathway is mediated by Pol‐β and Ku70
Bibliographic record
Abstract
Multiple DNA repair pathways may be involved in the removal of the same DNA lesion caused by endogenous or exogenous agents. Although distinct DNA repair machinery fulfill overlapping roles in the repair of DNA lesions, the mechanisms coordinating different pathways have not been investigated in detail. Here, we show that Ku70, a core protein of nonhomologous end-joining (NHEJ) repair pathway, can directly interact with DNA polymerase-β (Pol-β), a central player in the DNA base excision repair (BER), and this physical complex not only promotes the polymerase activity of Pol-β and BER efficiency but also enhances the classic NHEJ repair. Moreover, we find that DNA damages caused by methyl methanesulfonate (MMS) or etoposide promote the formation of Ku70-Pol-β complexes at the repair foci. Furthermore, suppression of endogenous Ku70 expression by small interfering RNA reduces BER efficiency and leads to higher sensitivity to MMS and accumulation of the DNA strand breaks. Similarly, Pol-β knockdown impairs total-NHEJ capacity but only has a slight influence on alternative NHEJ. These results suggest that Pol-β and Ku70 coordinate 2-way crosstalk between the BER and NHEJ pathways.-Xia, W., Ci, S., Li, M., Wang, M., Dianov, G. L., Ma, Z., Li, L., Hua, K., Alagamuthu, K. K., Qing, L., Luo, L., Edick, A. M., Liu, L., Hu, Z., He, L., Pan, F., Guo, Z. Two-way crosstalk between BER and c-NHEJ repair pathway is mediated by Pol-β and Ku70.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 | 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".