Nuclear Factor I Represses the Notch Effector HEY1 in Glioblastoma
Bibliographic record
Abstract
Glioblastomas (GBMs) are highly aggressive brain tumors with a dismal prognosis. Nuclear factor I (NFI) is a family of transcription factors that controls glial cell differentiation in the developing central nervous system. NFIs have previously been shown to regulate the expression of astrocyte markers such as glial fibrillary acidic protein (GFAP) in both normal brain and GBM cells. We used chromatin immunoprecipitation (ChIP)-on-chip to identify additional NFI targets in GBM cells. Analysis of our ChIP data revealed ~400 putative NFI target genes including an effector of the Notch signaling pathway, HEY1, implicated in the maintenance of neural stem cells. All four NFIs (NFIA, NFIB, NFIC, and NFIX) bind to NFI recognition sites located within 1 kb upstream of the HEY1 transcription site. We further showed that NFI negatively regulates HEY1 expression, with knockdown of all four NFIs in GBM cells resulting in increased HEY1 RNA levels. HEY1 knockdown in GBM cells decreased cell proliferation, increased cell migration, and decreased neurosphere formation. Finally, we found a general correlation between elevated levels of HEY1 and expression of the brain neural stem/progenitor cell marker B-FABP in GBM cell lines. Knockdown of HEY1 resulted in an increase in the RNA levels of the GFAP astrocyte differentiation marker. Overall, our data indicate that HEY1 is negatively regulated by NFI family members and is associated with increased proliferation, decreased migration, and increased stem cell properties in GBM cells.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".