Induction of Neurotrophic and Differentiation Factors in Neural Stem Cells by Valproic Acid
Bibliographic record
Abstract
Valproic acid (VPA), a short-chain fatty acid, is used clinically as an anticonvulsant and mood stabilizer. Valproic acid also inhibits histone deacetylase activity, which is associated with histone hyperacetylation and changes in gene expression. In this study, we examined the effects of VPA on the expression of selected neurotrophic and differentiation factors in C17.2 neural stem cells. Reverse transcription-polymerase chain reaction analysis revealed a significant increase in conserved dopamine neurotrophic factor (CDNF) and glial cell line-derived neurotrophic factor mRNA expression, after treatment with clinically relevant concentrations of VPA (0.5 or 1.0 mM) for 24 hr. Increases in the protein expression of CDNF and mesencephalic astrocyte-derived neurotrophic factor were also observed, after similar treatment with VPA. In addition, significant increases in the mRNA levels of the early dopaminergic neuron marker, nuclear receptor-related protein 1(Nurr1), and the transcriptional regulator, early growth response protein 1 (Egr1), were observed after treatment with VPA for 24 hr. C17.2 neural stem cells exhibited high basal mRNA expression of brain-derived neurotrophic factor and SRY (sex determining region Y)-box 2 (Sox2), which was not altered by VPA treatment. Western analysis revealed hyperacetylation of histone H3 proteins in C17.2 cells after VPA treatment for 24 hr or 48 hr, suggesting involvement of an epigenetic mechanism in the observed gene induction by this drug. These findings support a role for VPA in modulating neurotrophic and differentiation factor expression, in keeping with its reported neuroprotective and neurodevelopmental effects.
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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".