P.159 Negative feedback between Ezh2 and Cyclin D1 governs granule neuron precursor fate
Bibliographic record
Abstract
Background: While developing a differentiation therapy for Sonic Hedgehog Medulloblastoma (MB), we discovered a potential paradoxical feedback cycle between Ezh2, a protein that temporarily keeps differentiation genes silenced via trimethylating H3K27, and Cyclin D1, a protein that regulates cell cycle entry. Methods: We quantified H3K27me3 in P7 purified cerebral GNPs using chromatin immunoprecipitation sequencing and correlated it with gene expression via RNA sequencing (RNAseq). To assess transcriptional effects of Ezh2 loss, we purified P7 GNPs from Math1-Cre, Ezh2-flox knockout mice. MB cells were cultured in suspension spheres and imaged using the ImageXpress Micro XLS system, with nuclei segmented based on DAPI staining. Results: Cyclin D1 ranked among the top 7.37% of expressed genes but was heavily marked by the repressive histone mark H3K27me3 (top 5.5%) in GNPs. Ezh2 overexpression increased G0-arrested MB cells 2.7-fold, while, in GNPs, RNAseq showed significant Cyclin D1 upregulation in Ezh2 knockout mice (Log2FC: 1.301). Cyclin D1 regulates the pRb/E2F1 complex, and we observe that Ezh2 expression depends on pRb/E2F1 complex abundance, forming a feedback loop. Notably, combining the Hedgehog inhibitor Vismodegib with an Ezh2 inhibitor rescued MB cells from Vismodegib-induced death. Conclusions: Our study introduces a model that promotes GNP differentiation, leading tumor cells to differentiate into neurons.
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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) | 0.011 | 0.003 |
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".