Epigenetic targets for melatonin: induction of histone H3 hyperacetylation and gene expression in C17.2 neural stem cells
Bibliographic record
Abstract
We have reported the induction of glial cell line-derived neurotrophic factor, a potent survival factor for dopaminergic neurons, in the C17.2 neural stem cell line following in vitro treatment with melatonin. Furthermore, we have detected the melatonin MT(1) receptor in these cells. Given these findings and recent evidence that melatonin may play a role in cellular differentiation, we examined whether this indoleamine induces morphological and transcriptional changes suggestive of a neuronal phenotype in C17.2 cells. Moreover, in order to extend preliminary evidence of a potential role for melatonin in epigenetic modulation, its effects on the mRNA expression of several histone deacetylase (HDAC) isoforms and on histone acetylation were examined. Physiological concentrations of melatonin (nanomolar range) increased neurite-like extensions and induced mRNA expression of the neural stem cell marker, nestin, the early neuronal marker beta-III-tubulin and the orphan nuclear receptor nurr1 in C17.2 cells. The indoleamine also significantly increased mRNA expression for various HDAC isoforms, including HDAC3, HDAC5, and HDAC7. Importantly, treatment with melatonin for 24 hr caused a significant increase in histone H3 acetylation, which is associated with chromatin remodeling and gene transcription. Since the melatonin MT(2) receptor was not detected in C17.2 cells, it is likely that the MT(1) receptor is involved in mediating these physiological effects of melatonin. These findings suggest novel roles for melatonin in stem cell differentiation and epigenetic modulation of gene transcription.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".