Compartment-specific, differential regulation of eukaryotic elongation factor 2 and its kinase within Aplysia sensory neurons
Bibliographic record
Abstract
Long-term facilitation (LTF) in Aplysia is a leading model for elucidating the biochemical mechanisms of synaptic plasticity underlying learning. LTF requires translational control downstream of target of rapamycin complex 1. Our lab has previously shown that treatment with the facilitating neurotransmitter, 5-hydroxytryptamine (5-HT), causes a target of rapamycin complex 1-mediated decrease in phosphorylation of eukaryotic elongation factor 2 (eEF2) within the neurites of sensory neurons involved in LTF. Here, we characterize the Aplysia orthologue of eEF2 kinase (eEF2K). We show that the Aplysia eEF2K orthologue contains an S6 kinase phosphorylation site and that a serine-to-alanine mutation at this site blocks the ability of 5-HT to decrease eEF2 phosphorylation in neurites. We also show that within the soma, 5-HT has the opposite effect, decreasing eEF2K phosphorylation at the S6 kinase site and, concomitantly, increasing eEF2 phosphorylation. Surprisingly, while eEF2K over-expression inhibits translation of a marker for internal ribosome entry site-dependent translation, it stimulates the translation of a marker for cap-dependent translation. This study demonstrates that eEF2 is differentially regulated in separate compartments and contributes to a growing body of evidence that inhibition of elongation can stimulate the translation of certain transcripts.
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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) | 0.000 | 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".