MRE11 promotes AKT phosphorylation in direct response to DNA double-strand breaks
Bibliographic record
Abstract
AKT is hyper-activated in many human cancers and promotes proliferation and cancer cell survival in response to DNA damaging agents. Ionizing radiation (IR) produces DNA double strand breaks (DSB) and activates AKT, however a direct mechanism linking intra-nuclear DSB and AKT signaling is lacking. Here we demonstrate that AKT is phosphorylated following IR in benign and malignant cells and, using colony-forming assays and in vitro rejoining assays, show that AKT promotes non-homologous end joining-mediated DSB repair and cell survival following IR. Further studies revealed that pAKT-S473, but not pAKT-T308 or total AKT, accumulates in the vicinity of IR-induced DSB and co-localizes with γH2AX and ATM-pSer1981. Based on whole-cell IR, nuclear UV microbeam, and endonuclease-induced DSB studies, we observed that pAKT-S473 is up-regulated by a DSB-induced signaling cascade, and this is dependent on the DSB sensor protein, MRE11. MRE11-dependent pAKT-S473 did not require the MRE11 endonuclease domain. The histone ubiquitin ligase RNF168 is also required for DSB-induced pAKT-S473, and DSB-induced pAKT-S473 is independent of DNA-PKcs, PI3K, and ATR. These data demonstrate that DSB activate a signaling cascade that directly promotes a PI3K-independent pathway of AKT phosphorylation that is dependent on MRE11-ATM-RNF168 signaling. Thus, these data directly link the presence of DNA breaks to AKT-mediated cell survival and support AKT as a target for cancer therapy.
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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) | 0.002 | 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".