<i>R</i>-2-hydroxyglutarate-mediated inhibition of KDM4A compromises telomere integrity
Bibliographic record
Abstract
Mutation, deletion, or silencing of genes encoding cellular metabolism factors occurs frequently in human malignancies. Neomorphic mutations in isocitrate dehydrogenases 1 and 2 (IDH1/2) promoting the production of R-2-hydroxyglutarate (R-2HG) instead of α-ketoglutarate (αKG) are recurrent in human brain cancers and constitute an early event in low-grade gliomagenesis. Due to its structural similarity with αKG, R-2HG acts as an inhibitor of αKG-dependent enzymes. These include the JUMONJI family of lysine demethylases, among which KDM4A is particularly sensitive to R-2HG-mediated inhibition. However, the precise molecular mechanism through which inhibition of αKG-dependent enzymes by R-2HG promotes gliomagenesis remains poorly understood. Here, we show that treatment with R-2HG induces cellular senescence in a p53-dependent manner. Furthermore, expression of mutated IDH1R132H or exposure to R-2HG, which leads to KDM4A inhibition, causes telomeric dysfunction. We demonstrate that KDM4A localizes to telomeric repeats and regulates abundance of H3K9(me3) at telomeres. We show that R-2HG caused reduced replication fork progression, and that depletion of SMARCAL1, a helicase involved in replication fork reversal, rescues telomeric defects caused by R-2HG or KDM4A depletion. These results establish a model whereby IDH1/2 mutations cause R-2HG-mediated inhibition of KDM4A, leading to telomeric DNA replication defects, telomere dysfunction, and associated genomic instability.
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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 | 0.001 |
| 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".