A novel non-canonical Notch signaling regulates expression of synaptic vesicle proteins in excitatory neurons
Bibliographic record
Abstract
Notch signaling plays crucial roles for cellular differentiation during development through γ-secretase-dependent intramembrane proteolysis followed by transcription of target genes. Although recent studies implicate that Notch regulates synaptic plasticity or cognitive performance, the molecular mechanism how Notch works in mature neurons remains uncertain. Here we demonstrate that a novel Notch signaling is involved in expression of synaptic proteins in postmitotic neurons. Levels of several synaptic vesicle proteins including synaptophysin 1 and VGLUT1 were increased when neurons were cocultured with Notch ligands-expressing NIH3T3 cells. Neuron-specific deletion of Notch genes decreased these proteins, suggesting that Notch signaling maintains the expression of synaptic vesicle proteins in a cell-autonomous manner. Unexpectedly, cGMP-dependent protein kinase (PKG) inhibitor, but not γ-secretase inhibitor, abolished the elevation of synaptic vesicle proteins, suggesting that generation of Notch intracellular domain is dispensable for this function. These data uncover a ligand-dependent, but γ-secretase-independent, non-canonical Notch signaling involved in presynaptic protein expression in postmitotic 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 | 0.000 |
| 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".