Cell-type-specific translational control of spatial working memory by the cap-binding protein 4EHP
Bibliographic record
Abstract
Abstract The consolidation of learned information into long-lasting memories requires the strengthening of synaptic connections through de novo protein synthesis. Translation initiation factors play a cardinal role in gating the production of new proteins thereby regulating memory formation. Both positive and negative regulators of translation play a critical role in learning and memory consolidation. The eukaryotic initiation factor 4E (eIF4E) homologous protein (4EHP, encoded by the gene Eif4e2 ) is a pivotal negative regulator of translation but its role in learning and memory is unknown. To address this gap in knowledge, we generated excitatory (glutamatergic: CaMKIIα-positive) and inhibitory (GABAergic: GAD65-positive) conditional knockout mice for 4EHP, which were analyzed in various behavioral memory tasks. Knockout of 4EHP in Camk2a -expressing neurons (4EHP-cKO exc ) did not impact long-term memory in either contextual fear conditioning or Morris water maze tasks. Similarly, long-term contextual fear memory was not altered in Gad2 -directed 4EHP knockout mice (4EHP-cKO inh ). However, when subjected to a short-term T-maze working memory task, both mouse models exhibited impaired cognition. We therefore tested the hypothesis that de novo protein synthesis plays a direct role in working memory. We discovered that phosphorylation of ribosomal protein S6, a measure of mTORC1 activity, is dramatically reduced in the CA1 hippocampus of 4EHP-cKO exc mice. Consistently, genetic reduction of mTORC1 activity in either excitatory or inhibitory neurons was sufficient to impair working memory. Taken together, these findings indicate that translational control by 4EHP and mTORC1 in both excitatory and inhibitory neurons are necessary for working memory.
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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".