Conversion of short-term potentiation to long-term potentiation in mouse CA1 by coactivation of β-adrenergic and muscarinic receptors
Bibliographic record
Abstract
Encoding new information requires dynamic changes in synaptic strength. The brain can boost synaptic plasticity through the secretion of neuromodulatory substances, including acetylcholine and noradrenaline. Considerable effort has focused on elucidating how neuromodulatory substances alter synaptic properties. However, determination of the potential synergistic interactions between different neuromodulatory systems remains incomplete. Previous results indicate that coactivation of β-adrenergic and cholinergic receptors facilitated the conversion of STP to LTP through an extracellular signal-regulated kinase (ERK)-dependent mechanism. ERK signaling has been linked to synaptically localized translation regulation. Thus, we hypothesized that costimulation of noradrenergic and cholinergic receptors could initiate the transformation of STP to LTP through up-regulation of protein synthesis. Our results indicate that a protocol which yields STP (5 Hz, 5 sec) when paired with coapplication of the β-adrenergic agonist, isoproterenol (ISO), and the cholinergic agonist, carbachol (CCh), induces translation-dependent LTP in mouse CA1. This form of LTP requires both β1-adrenergic and M1 muscarinic receptor activation, as blocking either receptor subtype prevented LTP induction. Blocking ERK, mTOR, or translation reduced the expression of LTP induced with ISO + CCh. Taken together, our data demonstrate that coactivation of β-adrenergic and muscarinic receptors facilitates the conversion of STP to LTP through a mechanism requiring translation initiation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".