DNA Methyltransferase Inhibitors Induce Cerebral Dopamine Neurotrophic Factor Expression in C6 Glioma Cells
Bibliographic record
Abstract
Cerebral dopamine neurotrophic factor (CDNF) and mesencephalic astrocyte-derived neurotrophic factor (MANF) are involved in neuroprotection and mitigating endoplasmic reticulum (ER) stress in the brain and peripheral organs. In earlier work, an increase in histone acetylation, following treatment with an epigenetic modulator, valproic acid, was associated with induction of CDNF and MANF in cultured cells and rat brain. These findings prompted an investigation of the effects of DNA methyltransferase (DNMT) inhibitors, which can alter epigenetic function, on the expression of CDNF and MANF. Rat C6 glioma cells were treated with a micromolar range of DNMT inhibitors: 5-aza-2’-deoxycytidine (DAC or decitabine), 5-azacytidine (AZA) or zebularine (ZEB) for 24 h. Subsequently, qPCR analysis was used to examine the mRNA expression of DNMT1, ten-eleven translocation methylcytosine dioxygenase 2 (TET-2), CDNF and MANF. A significant dose-dependent decrease in DNMT1 mRNA levels, together with a significant increase in TET-2 expression, was observed following treatment with AZA or DAC. Importantly, DAC, AZA and ZEB caused a significant dose-dependent increase in CDNF mRNA levels. In contrast, MANF mRNA expression decreased following treatment with AZA, with no significant effects observed with DAC or ZEB. Western analysis revealed no significant changes in CDNF protein levels following treatment with DAC for 24 h. The significant increase in CDNF expression, following treatment with DNMT1 inhibitors, suggests that DNA methylation is involved in the regulation of this neurotrophic factor. Clarification of the epigenetic or other mechanisms underlying the regulation of CDNF may provide novel therapeutic approaches in neurodegenerative and ER stress-related disorders.
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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 | 0.001 |
| 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 | 0.001 |
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".