Bidirectional Regulation of eEF2 Phosphorylation Controls Synaptic Plasticity by Decoding Neuronal Activity Patterns
Bibliographic record
Abstract
At the sensory-motor neuron synapse of Aplysia, either spaced or continuous (massed) exposure to serotonin (5-HT) induces a form of intermediate-term facilitation (ITF) that requires new protein synthesis but not gene transcription. However, spaced and massed ITF use distinct molecular mechanisms to maintain increased synaptic strength. Synapses activated by spaced applications of 5-HT generate an ITF that depends on persistent protein kinase A (PKA) activity, whereas an ITF produced by massed 5-HT depends on persistent protein kinase C (PKC) activity. In this study, we demonstrate that eukaryotic elongation factor 2 (eEF2), which catalyzes the GTP-dependent translocation of the ribosome during protein synthesis, acts as a biochemical sensor that is tuned to the pattern of neuronal stimulation. Specifically, we find that massed training leads to a PKC-dependent increase in phosphorylation of eEF2, whereas spaced training results in a PKA-dependent decrease in phosphorylation of eEF2. Importantly, by using either pharmacological or dominant-negative strategies to inhibit eEF2 kinase (eEF2K), we were able to block massed 5-HT-dependent increases in eEF2 phosphorylation and subsequent PKC-dependent ITF. In contrast, pharmacological inhibition of eEF2K during the longer period of time required for spaced training was sufficient to reduce eEF2 phosphorylation and induce ITF. Finally, we find that the massed 5-HT-dependent increase in synaptic strength requires translation elongation, but not translation initiation, whereas the spaced 5-HT-dependent increase in synaptic strength is partially dependent on translation initiation. Thus, bidirectional regulation of eEF2 is critical for decoding distinct activity patterns at synapses by activating distinct modes of translation regulation.
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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".