RAS/ERK Signaling Controls Proneural Genetic Programs in Cortical Development and Gliomagenesis
Bibliographic record
Abstract
Neural cell fate specification is well understood in the embryonic cerebral cortex, where the proneural genes Neurog2 and Ascl1 are key cell fate determinants. What is less well understood is how cellular diversity is generated in brain tumors. Gliomas and glioneuronal tumors, which are often localized in the cerebrum, are both characterized by a neoplastic glial component, but glioneuronal tumors also have an intermixed neuronal component. A core abnormality in both tumor groups is overactive RAS/ERK signaling, a pro-proliferative signal whose contributions to cell differentiation in oncogenesis are largely unexplored. We found that RAS/ERK activation levels differ in two distinct human tumors associated with constitutively active BRAF. Pilocytic astrocytomas, which contain abnormal glial cells, have higher ERK activation levels than gangliogliomas, which contain abnormal neuronal and glial cells. Using in vivo gain of function and loss of function in the mouse embryonic neocortex, we found that RAS/ERK signals control a proneural genetic switch, inhibiting Neurog2 expression while inducing Ascl1, a competing lineage determinant. Furthermore, we found that RAS/ERK levels control Ascl1's fate specification properties in murine cortical progenitors--at higher RAS/ERK levels, Ascl1(+) progenitors are biased toward proliferative glial programs, initiating astrocytomas, while at moderate RAS/ERK levels, Ascl1 promotes GABAergic neuronal and less glial differentiation, generating glioneuronal tumors. Mechanistically, Ascl1 is phosphorylated by ERK, and ERK phosphoacceptor sites are necessary for Ascl1's GABAergic neuronal and gliogenic potential. RAS/ERK signaling thus acts as a rheostat to influence neural cell fate selection in both normal cortical development and gliomagenesis, controlling Neurog2-Ascl1 expression and Ascl1 function.
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".