Noradrenaline goes nuclear: epigenetic modifications during long‐lasting synaptic potentiation triggered by activation of β‐adrenergic receptors
Bibliographic record
Abstract
KEY POINTS: Transcription is recruited by noradrenaline in the hippocampus. Epigenetic mechanisms are recruited by hippocampal noradrenergic receptor activation. Epigenetic regulation by noradrenaline offers a novel mechanism for long-term potentiation ABSTRACT: Noradrenaline (NA) is a neuromodulator that can effect long-lasting changes in synaptic strength such as long-term potentiation (LTP), a putative cellular mechanism for memory formation in the mammalian brain. Persistent LTP requires alterations in gene expression that may involve epigenetic mechanisms such as DNA methylation, histone acetylation and histone phosphorylation. It is known that β-adrenergic receptors and NA can boost LTP maintenance by regulating translation. However, it is unclear whether NA can additionally engage epigenetic mechanisms to regulate transcription and boost LTP endurance. To address this issue, we probed NA-treated mouse hippocampal slices with pharmacological inhibitors targeting epigenetic regulatory pathways and discovered that NA activates β-adrenergic receptors to boost LTP maintenance in area CA1 through DNA methylation and post-translational histone modifications. Specifically, NA paired with 100 Hz stimulation enhanced histone H3 acetylation and phosphorylation, both of which were required for NA-induced boosting of LTP maintenance. Together, our findings identify NA as a neuromodulatory transmitter capable of triggering epigenetic, transcriptional control of genes required for establishing persistent LTP in the mouse hippocampus. These modifications may contribute to the stabilization of memory.
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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) | 0.002 | 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".