Post-transcriptional Regulation of Acetylcholinesterase mRNAs in Nerve Growth Factor-treated PC12 Cells by the RNA-binding Protein HuD
Bibliographic record
Abstract
Expression of acetylcholinesterase (AChE) is greatly enhanced during neuronal differentiation, but the nature of the molecular mechanisms remains to be fully defined. In this study, we observed that nerve growth factor treatment of PC12 cells leads to a progressive increase in the expression of AChE transcripts, reaching approximately 3.5-fold by 72 h. Given that the AChE 3'-untranslated region (UTR) contains an AU-rich element, we focused on the potential role of the RNA-binding protein HuD in mediating the increase in AChE mRNA seen in differentiating neurons. Using PC12 cells engineered to stably express HuD or an antisense to HuD, our studies indicate that HuD can regulate the abundance of AChE transcripts in neuronal cells. Furthermore, transfection of a reporter construct containing the AChE 3'-UTR showed that this 3'-UTR can increase expression of the reporter gene product in cells expressing HuD but not in cells expressing the antisense. RNA gel shifts and Northwestern blots revealed an increase in the binding of several protein complexes in differentiated neurons. Immunoprecipitation experiments demonstrated that HuD can bind directly AChE transcripts. These results show the importance of post-transcriptional mechanisms in regulating AChE expression in differentiating neurons and implicate HuD as a key trans-acting factor in these events.
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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 | 0.001 |
| 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".