Inhibition of ERK activation enhances the repair of double-stranded breaks via non-homologous end joining by increasing DNA-PKcs activation
Bibliographic record
Abstract
Non-homologous end joining (NHEJ) is one of the major pathways that repairs double-stranded DNA breaks (DSBs). Activation of DNA-PK is required for NHEJ. However, the mechanism leading to DNA-PKcs activation remains incompletely understood. We provide evidence here that the MEK-ERK pathway plays a role in DNA-PKcs-mediated NHEJ. In comparison to the vehicle control (DMSO), etoposide (ETOP)-induced DSBs in MCF7 cells were more rapidly repaired in the presence of U0126, a specific MEK inhibitor, based on the reduction of γH2AX and tail moments. Additionally, U0126 increased reactivation of luciferase activity, which resulted from the repair of restriction enzyme-cleaved DSBs. Furthermore, while inhibition of ERK activation using the dominant-negative MEK1K97M accelerated the repair of DSBs, enforcing ERK activation with the constitutively active MEK1Q56P reduced DSB repair. In line with MEK activating ERK1 and ERK2 kinases, knockdown of either ERK1 or ERK2 increased DSB repair. Consistent with the activation of DNA-PKcs being required for NHEJ, we demonstrated that inhibition of ERK activation using U0126, MEK1K97M, and knockdown of ERK1 or ERK2 enhanced ETOP-induced activation of DNA-PKcs. Conversely, enforcing ERK activation by MEK1Q56P reduced ETOP-initiated DNA-PKcs activation. Taken together, we demonstrate that ERK reduces NHEJ-mediated repair of DSBs via attenuation of DNA-PKcs activation.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".