KDM2B, an H3K36-specific demethylase, regulates apoptotic response of GBM cells to TRAIL
Bibliographic record
Abstract
Tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) can selectively kill tumor cells. TRAIL resistance in cancers is associated with aberrant expression of the key components of the apoptotic program. However, how these components are regulated at the epigenetic level is not understood. In this study, we investigated novel epigenetic mechanisms regulating TRAIL response in glioblastoma multiforme (GBM) cells by a short-hairpin RNA loss-of-function screen. We interrogated 48 genes in DNA and histone modification pathways and identified KDM2B, an H3K36-specific demethylase, as a novel regulator of TRAIL response. Accordingly, silencing of KDM2B significantly enhanced TRAIL sensitivity, the activation of caspase-8, -3 and -7 and PARP cleavage. KDM2B knockdown also accelerated the apoptosis, as revealed by live-cell imaging experiments. To decipher the downstream molecular pathways regulated by KDM2B, levels of apoptosis-related genes were examined by RNA-sequencing upon KDM2B loss, which revealed derepression of proapoptotic genes Harakiri (HRK), caspase-7 and death receptor 4 (DR4) and repression of antiapoptotic genes. The apoptosis phenotype was partly dependent on HRK upregulation, as HRK knockdown significantly abrogated the sensitization. KDM2B-silenced tumors exhibited slower growth in vivo. Taken together, our findings suggest a novel mechanism, where the key apoptosis components are under epigenetic control of KDM2B in GBM cells.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".