Elevated ASCL1 activity creates de novo regulatory elements associated with neuronal differentiation
Bibliographic record
Abstract
BACKGROUND: The pro-neural transcription factor ASCL1 is a master regulator of neurogenesis and a key factor necessary for the reprogramming of permissive cell types to neurons. Endogenously, ASCL1 expression is often associated with neuroblast stem-ness. Moreover, ASCL1-mediated reprogramming of fibroblasts to differentiated neurons is commonly achieved using artificially high levels of ASCL1 protein, where ASCL1 acts as an "on-target" pioneer factor. However, the genome-wide effects of enhancing ASCL1 activity in a permissive neurogenic environment has not been thoroughly investigated. Here, we overexpressed ASCL1 in the neuronally-permissive context of neuroblastoma (NB) cells where modest endogenous ASCL1 supports the neuroblast programme. RESULTS: Increasing ASCL1 in neuroblastoma cells both enhances binding at existing ASCL1 sites and also leads to creation of numerous additional, lower affinity binding sites. These extensive genome-wide changes in ASCL1 binding result in significant reprogramming of the NB transcriptome, redirecting it from a proliferative neuroblastic state towards one favouring neuronal differentiation. Mechanistically, ASCL1-mediated cell cycle exit and differentiation can be increased further by preventing its multi-site phosphorylation, which is associated with additional changes in genome-wide binding and gene activation profiles. CONCLUSIONS: Our findings show that enhancing ASCL1 activity in a neurogenic environment both increases binding at endogenous ASCL1 sites and also results in additional binding to new low affinity sites that favours neuronal differentiation over the proliferating neuroblast programme supported by the endogenous protein. These findings have important implications for controlling processes of neurogenesis in cancer and cellular reprogramming.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".