Activation of β‐adrenergic receptors facilitates heterosynaptic translation‐dependent long‐term potentiation
Bibliographic record
Abstract
Noradrenaline critically modulates the ability of synapses to undergo long-term plasticity on time scales extending well beyond fast synaptic transmission. Noradrenergic signalling through β-adrenergic receptors (β-ARs) enhances memory consolidation and can boost the longevity of long-term potentiation (LTP). Previous research has shown that stimulation of one synaptic pathway with a protocol that induces persistent, translation-dependent LTP can enable the induction of LTP by subthreshold stimulation at a second, independent synaptic pathway. This heterosynaptic facilitation depends on the regulation and synthesis of proteins. Recordings taken from area CA1 in mouse hippocampal slices showed that induction of β-AR-dependent LTP at one synaptic pathway (S1) can facilitate LTP at a second, independent pathway (S2) when low-frequency, subthreshold stimulation is applied after a 30 min delay. β-AR-dependent heterosynaptic facilitation requires protein synthesis as inhibition of mammalian target of rapamycin (mTOR), extracellular signal-regulated kinase (ERK), or translation, prevented homo- and heterosynaptic LTP. Shifting application of a translational repressor, emetine, to coincide with S2 stimulation did not block LTP. Heterosynaptic LTP was prevented in the presence of the cell-permeable cAMP-dependent protein kinase inhibitor, PKI. Conversely, the time window for inter-pathway transfer of heterosynaptic LTP was extended through inhibition of GluR2 endocytosis. Our data show that activation of β-ARs boosts the heterosynaptic expression of translation-dependent LTP. These results suggest that engagement of the noradrenergic system may extend the associative capacity of hippocampal synapses through facilitation of intersynaptic crosstalk.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".