CBX7 inhibitors affect H3K9 methyltransferase-regulated gene repression in leukemic cells
Bibliographic record
Abstract
The epigenome of leukemic cells is dysregulated, and genes required for cell cycle arrest and differentiation may become repressed, which contributes to the accumulation of undifferentiated malignant blood cells. Here, we show that the Polycomb group protein CBX7 can interact with H3K9 methyltransferases EHMT1/2 and SETDB1. We aimed to assess whether combined interfering with these H3K9 methyltransferases and CBX7 could derepress target genes and thereby induce growth arrest of leukemic cells. We found that pharmacologic inhibition of CBX7 abolishes the interaction of CBX7 with EHMT1/2 and SETDB1 and subsequently reduces H3K9 methylation levels which reactivates target gene expression. Reversely, upon pharmacologic inhibition of H3K9 methyltransferases, CBX7 can take over gene repression. Finally, we found that combined inhibition of CBX7 and EHMT1/2 or SETDB1 had additive effects on reducing cell growth and inducing differentiation. However, we did not detect changes in epigenetic modifications, nor target gene derepression, after combination treatment. In contrast, CBX7 inhibitors alone did affect both Polycomb-associated H2Aub-mediated gene repression as well as H3K9 methyltransferase activity. Therefore, we suggest that CBX7 is a promising therapeutic target in leukemia, as its inhibition can reactivate Polycomb and H3K9 methyltransferase target gene expression.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".