Peptides derived from the knuckle epitope of BMP-9 induce the cholinergic differentiation and inactivate GSk3beta in human SH-SY5Y neuroblastoma cells
Bibliographic record
Abstract
The incidence of brain degenerative disorders like Alzheimer's disease (AD) will increase as the world population ages. While there is presently no known cure for AD and current treatments having only a transient effect, an increasing number of publications indicate that growth factors (GF) may be used to treat AD. GFs like the bone morphogenetic proteins (BMPs), especially BMP-9, affect many aspects of AD. However, BMP-9 is a big protein that cannot readily cross the blood-brain barrier. We have therefore studied the effects of two small peptides derived from BMP-9 (pBMP-9 and SpBMP-9). We investigated their capacity to differentiate SH-SY5Y human neuroblastoma cells into neurons with or without retinoic acid (RA). Both peptides induced Smad 1/5 phosphorylation and their nuclear translocation. They increased the number and length of neurites and the expression of neuronal markers MAP-2, NeuN and NSE better than did BMP-9. They also promoted differentiation to the cholinergic phenotype more actively than BMP-9, SpBMP-9 being the most effective as shown by increases in intracellular acetylcholine, ChAT and VAchT. Finally, both peptides activated the PI3K/Akt pathway and inhibited GSK3beta, a current AD therapeutic target. BMP-9-derived peptides, especially SpBMP-9, with or without RA, are promising molecules that warrant further investigation.
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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".