Noradrenergic stabilization of heterosynaptic LTP requires activation of Epac in the hippocampus
Bibliographic record
Abstract
Beta-adrenergic receptor (β-AR) activation by norepinephrine (NE) enhances memory and stabilizes long-term potentiation (LTP), a form of synaptic plasticity believed to underlie some forms of hippocampal memory. LTP can occur at multiple synaptic pathways as a result of strong stimulation to one pathway preceding milder stimulation of an adjacent, independent pathway. Synaptic tagging allows LTP to be transferred, or captured, at heterosynaptic pathways. Previous research has shown that β-AR activation promotes heterosynaptic LTP by engaging various signaling cascades. In particular, cyclic adenosine monophosphate (cAMP) activates cAMP-dependent protein kinase A (PKA) and guanine nucleotide exchange protein activated by cAMP (Epac), to enhance LTP. Epac activation can occlude subsequent induction of stable homosynaptic LTP after β-AR activation, but it is unclear whether Epac activation is required for heterosynaptic LTP following pairing of the natural transmitter, NE, with one 100 Hz train of stimulation ("NE-LTP"). Using electrophysiologic recordings of CA1 field excitatory postsynaptic potentials during stimulation of two independent synaptic pathways in murine hippocampal slices, we show that distinct inhibitors of Epac blocked stabilization of homo- and heterosynaptic NE-LTP. PKA inhibition also attenuated heterosynaptic transfer of NE-LTP, but only when a PKA inhibitor was applied during tetanization of a second, heterosynaptic pathway that was not treated with NE. Our data suggest that NE, paired with 100 Hz, activates Epac to stabilize homo- and heterosynaptic LTP. Epac may regulate the production of plasticity-related proteins and subsequent synaptic capture of NE-LTP at a heterosynaptic pathway. Epac activation under these conditions may enable behavioral experiences that engage noradrenergic inputs to hippocampal circuits to be transformed into stable long-term 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".