Nerve Growth Factor Induces Expression and Activity of CTP:Phosphocholine Cytidylyltransferase β2 in PC12 Cells and Cultures Primary Neurons
Bibliographic record
Abstract
Nerve growth factor (NGF) is essential for growth of sympathetic neurons and promotes neuronal differentiation of PC12 cells and Neurite outgrowth increases the demand for phosphatidylcholine (PC). We hypothesized that NGF increases PC synthesis in response to this demand. We, therefore, investigated the influence of NGF on CTP:phosphocholine cytidylyltransferase (CT). Cellular CT activity, CT protein, and PC levels were increased in PC12 cells during NGF‐induced differentiation suggesting that increased CT was responsible for the increase in PC. To distinguish which CT isoform is implicated, we investigated whether or not NGF increased the protein and mRNA levels of CT isoforms. Results indicate that NGF treatment increases both the amount of CTβ 2 protein and mRNA level following NGF treatment of PC12 cells. Conversely, protein amount and mRNA expression of CTα are unchanged. We have reproduced the experiment using primary cultures of sympathetic neurons. Our results indicate that NGF also increases the expression of CTβ 2 in primary neurons, confirming the direct influence of NGF on CTβ2 mRNA and protein and protein. We conclude that promotion of neurite outgrowth by NGF involves the induction of CTβ 2 expression which increases the amount CTβ 2 protein and CT activity, thereby increasing the amount of PC required to support neurite elongation.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".