Norepinephrine stabilizes translation‐dependent, homosynaptic long‐term potentiation through mechanisms requiring the cAMP sensor Epac, mTOR and MAPK
Bibliographic record
Abstract
Neuromodulators regulate higher-order cognitive processes including learning and memory through modulation of synaptic transmission and plasticity. Norepinephrine is a neuromodulator that is secreted throughout the brain in response to novelty or increased arousal, which alters neural circuits by increasing the modifiability of CNS synapses. Norepinephrine activates metabotropic receptors, initiating complex intracellular signalling cascades that can promote enduring changes in synaptic strength including long-term potentiation (LTP). In particular, activation of beta-adrenergic receptors (β-ARs) by norepinephrine enhances LTP through downstream engagement of signalling cascades which upregulate protein synthesis at synapses. Here, we sought to determine the select signalling pathways recruited by norepinephrine to promote homosynaptic LTP at hippocampal synapses in mice. Application of norepinephrine initiated a long-lasting form of homosynaptic LTP that requires protein synthesis. Norepinephrine-mediated enhancement of LTP was reduced by inhibition of mammalian target of rapamycin and exchange protein directly activated by cAMP (Epac) but not cAMP-dependent protein kinase A, suggesting that the endogenous β-AR ligand norepinephrine may preferentially recruit Epac signalling to promote enduring changes in synaptic strength. These findings advance our understanding of the mechanisms through which norepinephrine regulates synaptic plasticity associated with formation of new memories.
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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".