Histone modifications around individual BDNF gene promoters in prefrontal cortex are associated with extinction of conditioned fear
Bibliographic record
Abstract
Extinction of conditioned fear is an important model both of inhibitory learning and of behavior therapy for human anxiety disorders. Like other forms of learning, extinction learning is long-lasting and depends on regulated gene expression. Epigenetic mechanisms make an important contribution to persistent changes in gene expression; therefore, in these studies, we have investigated whether epigenetic regulation of gene expression contributes to fear extinction. Since brain-derived neurotrophic factor (BDNF) is crucial for synaptic plasticity and for the maintenance of long-term memory, we examined histone modifications around two BDNF gene promoters after extinction of cued fear, as potential targets of learning-induced epigenetic regulation of gene expression. Valproic acid (VPA), used for some time as an anticonvulsant and a mood stabilizer, modulates the expression of BDNF, and is a histone deacetylase (HDAC) inhibitor. Here, we report that extinction of conditioned fear is accompanied by a significant increase in histone H4 acetylation around the BDNF P4 gene promoter and increases in BDNF exon I and IV mRNA expression in prefrontal cortex, that VPA enhances long-term memory for extinction because of its HDAC inhibitor effects, and that VPA potentiates the effect of weak extinction training on histone H4 acetylation around both the BDNF P1 and P4 gene promoters and on BDNF exon IV mRNA expression. These results suggest a relationship between histone H4 modification, epigenetic regulation of BDNF gene expression, and long-term memory for extinction of conditioned fear. In addition, they suggest that HDAC inhibitors may become a useful pharmacological adjunct to psychotherapy for human anxiety disorders.
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".