Regulation of Neuronal mRNA Translation by CaM-Kinase I Phosphorylation of eIF4GII
Bibliographic record
Abstract
Ca²⁺/calmodulin-dependent kinases (CaMKs) are essential for neuronal development and plasticity, processes requiring de novo protein synthesis. Roles for CaMKs in modulating gene transcription are well established, but their involvement in mRNA translation is evolving. Here we report that activity-dependent translational initiation in cultured rat hippocampal neurons is enhanced by CaMKI-mediated phosphorylation of Ser1156 in eukaryotic initiation factor eIF4GII (4GII). Treatment with bicuculline or gabazine to enhance neuronal activity promotes recruitment of wild-type 4GII, but not the 4GII S1156A mutant or 4GI, to the heterotrimeric eIF4F (4F) complex that assembles at the 5' cap structure (m⁷GTP) of mRNA to initiate ribosomal scanning. Recruitment of 4GII to 4F is suppressed by pharmacological inhibition (STO-609) of CaM kinase kinase, the upstream activator of CaMKI. Post hoc in vitro CaMKI phosphorylation assays confirm that activity promotes phosphorylation of S1156 in transfected 4GII in neurons. Changes in cap-dependent and cap-independent translation were assessed using a bicistronic luciferase reporter transfected into neurons. Activity upregulates cap-dependent translation, and RNAi knockdown of CaMKIβ and γ isoforms, but not α or δ, led to its attenuation as did blockade of NMDA receptors. Furthermore, RNAi knockdown of 4GII attenuates cap-dependent translation and reduces density of dendritic filopodia and spine formation without effect on dendritic arborization. Together, our results provide a mechanistic link between Ca²⁺ influx due to neuronal activity and regulation of cap-dependent RNA translation via CaMKI activation and selective recruitment of phosphorylated 4GII to the 4F complex, which may function to regulate activity-dependent changes in spine density.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".