Polysialic acid limits choline acetyltransferase activity induced by brain‐derived neurotrophic factor
Bibliographic record
Abstract
Choline acetyltransferase (ChAT), the enzyme synthesizing acetylcholine, is known to be activated by brain derived neurotrophic factor (BDNF). We found that the specific removal of the carbohydrate polysialic acid (PSA) significantly increased BDNF-induced ChAT-activity in embryonic septal neurons. Using a p75 neurotrophin receptor (p75(NTR)) function-blocking antibody and K252a, a-pan tropomyosin related kinase (Trk) inhibitor, we demonstrate that BDNF-induced ChAT activity requires the stimulation of p75(NTR) and TrkB. PSA removal drastically increased radioactive iodinated ([(125)I])BDNF's maximal binding capacity (Bmax), derived from concentrations of [(125)I]BDNF ranging from 1 pM to 3.2 nM. In the presence of unlabeled nerve growth factor to prevent the binding of [(125)I]BDNF to p75(NTR) sites, the impact of PSA removal on the binding capacity of [(125)I]BDNF was greatly reduced. In conclusion, PSA limits BDNF-induced ChAT activity and BDNF-receptor interactions. BDNF-induced ChAT activity is TrkB and p75(NTR) dependent, and upon PSA removal the additional binding of BDNF to its receptors, especially p75(NTR), likely contributes to the maximal ChAT activity observed. In vivo, the ontogenetic loss of PSA in the postnatal period may allow more interactions between BDNF and its receptors to increase ChAT activity and assure the proper development of the cholinergic septal 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) | 0.001 | 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".