Mnk is a negative regulator of cap‐dependent translation in <i>Aplysia</i> neurons
Bibliographic record
Abstract
To investigate the mechanisms underlying regulation of eukaryotic initiation factor 4E (eIF4E) phosphorylation in Aplysia neurons, we have cloned the Aplysia homolog of the vertebrate eIF4E kinases, Mnk1 and -2. Aplysia Mnk shares many conserved regions with vertebrate Mnk, including putative eukaryotic initiation factor 4G binding regions, activation loop phosphorylation sites, and a carboxy-terminal anchoring site for MAP kinases. As expected, purified Aplysia Mnk phosphorylated Aplysia eIF4E at a conserved carboxy-terminal serine and over-expression of Aplysia Mnk in sensory neurons led to increased phosphorylation of endogenous eIF4E. Over-expression of Aplysia Mnk led to strong decreases in cap-dependent translation, while generally sparing internal ribosomal entry site (IRES)-dependent translation. However, decreases in cap-dependent translation seen after expression of Aplysia Mnk could only be partly explained by increases in eIF4E phosphorylation. In Aplysia sensory neurons, phosphorylation of eIF4E is reduced during intermediate memory formation. However, we found that this physiological regulation of eIF4E phosphorylation was independent of changes in Aplysia Mnk phosphorylation. We propose that changes in eIF4E phosphorylation in Aplysia neurons are a consequence of changes in cap-dependent translation that are independent of regulation of Aplysia Mnk.
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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".