Bone morphogenetic protein dominantly suppresses epidermal growth factor-induced proliferative expansion of adult forebrain neural precursors
Bibliographic record
Abstract
A single asymmetric division by an adult neural stem cell (NSC) ultimately generates dozens of differentiated progeny, a feat made possible by the proliferative expansion of transit-amplifying progenitor cells (TAPs). Although NSC activation and TAP expansion is determined by pro- and anti-proliferative signals found within the niche, remarkably little is known about how these cells integrate simultaneous conflicting signals. We investigated this question focusing on the subventricular zone (SVZ) niche of the adult murine forebrain. Using primary cultures of SVZ cells, we demonstrate that Epidermal Growth Factor (EGF) and Bone Morphogenetic Protein (BMP)-2 are particularly powerful pro- and anti-proliferative factors for SVZ-derived neural precursors. Dose-response experiments showed that when simultaneously exposed to both signals, BMP dominantly suppressed EGF-induced proliferation; moreover, this dominance extended to all parameters of neural precursor behavior tested, including inhibition of proliferation, modulation of cell cycle, promotion of differentiation, and increase of cell death. BMP's anti-proliferative effect did not involve inhibition of mTORC1 or ERK signaling, key mediators of EGF-induced proliferation, and had distinct stage-specific consequences, promoting TAP differentiation but NSC quiescence. In line with these in vitro data, in vivo experiments showed that exogenous BMP limits EGF-induced proliferation of TAPs while inhibition of BMP-SMAD signaling promotes activation of quiescent NSCs. These findings clarify the stage-specific effects of BMPs on SVZ neural precursors, and support a hierarchical model in which the anti-proliferative effects of BMP dominate over EGF proliferation signaling to constitutively drive TAP differentiation and NSC quiescence.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".