Norepinephrine triggers metaplasticity of LTP by increasing translation of specific mRNAs
Bibliographic record
Abstract
Norepinephrine (NE) is a key modulator of synaptic plasticity in the hippocampus, a brain structure crucially involved in memory formation. NE boosts synaptic plasticity mostly through initiation of signaling cascades downstream from beta (β)-adrenergic receptors (β-ARs). Previous studies demonstrated that a β-adrenergic receptor agonist, isoproterenol, can modify the threshold for long-term potentiation (LTP), a putative cellular mechanism for learning and memory, in a process known as "metaplasticity." Metaplasticity is the ability of synaptic plasticity to be modified by prior experience. We asked whether NE itself could engage metaplastic mechanisms in area CA1 of mouse hippocampal slices. Using extracellular field potential recording and stimulation, we show that application of NE (10 µM), which did not alter basal synaptic strength, enhances the future maintenance of LTP elicited by subthreshold, high-frequency stimulation (HFS: 1 × 100 Hz, 1 sec). HFS applied 30 min after NE washout induced long-lasting (>4 h) LTP, which was significantly extended in duration relative to HFS alone. This NE-induced metaplasticity required β1-AR activation, as coapplication of the β1-receptor antagonist CGP-20712A (1 µM) attenuated maintenance of LTP. We also found that NE-mediated metaplasticity was translation- and transcription-dependent. Polysomal profiles of CA1 revealed increased translation rates for specific mRNAs during NE-induced metaplasticity. Thus, activation of β-ARs by NE primes synapses for future long-lasting plasticity on time scales extending beyond fast synaptic transmission; this may facilitate neural information processing and the subsequent formation of lasting 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 | 0.001 |
| 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".