DNA polymerase theta protects leukemia cells from metabolic-induced DNA damage
Bibliographic record
Abstract
Leukemia cells accumulate DNA damage, but altered DNA repair mechanisms protect them from apoptosis. We showed here that formaldehyde generated by serine/1-carbon cycle metabolism contributed to the accumulation of toxic DNA-protein crosslinks (DPCs) in leukemia cells, especially in driver clones harboring oncogenic tyrosine kinases (OTKs: FLT3(internal tandem duplication [ITD]), JAK2(V617F), BCR-ABL1). To counteract this effect, OTKs enhanced the expression of DNA polymerase theta (POLθ) via ERK1/2 serine/threonine kinase-dependent inhibition of c-CBL E3 ligase-mediated ubiquitination of POLθ and its proteasomal degradation. Overexpression of POLθ in OTK-positive cells resulted in the efficient repair of DPC-containing DNA double-strand breaks by POLθ-mediated end-joining. The transforming activities of OTKs and other leukemia-inducing oncogenes, especially of those causing the inhibition of BRCA1/2-mediated homologous recombination with and without concomitant inhibition of DNA-PK-dependent nonhomologous end-joining, was abrogated in Polq-/- murine bone marrow cells. Genetic and pharmacological targeting of POLθ polymerase and helicase activities revealed that both activities are promising targets in leukemia cells. Moreover, OTK inhibitors or DPC-inducing drug etoposide enhanced the antileukemia effect of POLθ inhibitor in vitro and in vivo. In conclusion, we demonstrated that POLθ plays an essential role in protecting leukemia cells from metabolically induced toxic DNA lesions triggered by formaldehyde, and it can be targeted to achieve a therapeutic effect.
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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".