Interaction Between CaMKII and GluN2B Controls ERK-Dependent Plasticity
Bibliographic record
Abstract
Understanding how brief synaptic events can lead to sustained changes in synaptic structure and strength is a necessary step in solving the rules governing learning and memory. Activation of ERK1/2 (extracellular signal regulated protein kinase 1/2) plays a key role in the control of functional and structural synaptic plasticity. One of the triggering events that activates ERK1/2 cascade is an NMDA receptor (NMDAR)-dependent rise in free intracellular Ca(2+) concentration. However the mechanism by which a short-lasting rise in Ca(2+) concentration is transduced into long-lasting ERK1/2-dependent plasticity remains unknown. Here we demonstrate that although synaptic activation in mouse cultured cortical neurons induces intracellular Ca(2+) elevation via both GluN2A and GluN2B-containing NMDARs, only GluN2B-containing NMDAR activation leads to a long-lasting ERK1/2 phosphorylation. We show that αCaMKII, but not βCaMKII, is critically involved in this GluN2B-dependent activation of ERK1/2 signaling, through a direct interaction between GluN2B and αCaMKII. We then show that interfering with GluN2B/αCaMKII interaction prevents synaptic activity from inducing ERK-dependent increases in synaptic AMPA receptors and spine volume. Thus, in a developing circuit model, the brief activity of synaptic GluN2B-containing receptors and the interaction between GluN2B and αCaMKII have a role in long-term plasticity via the control of ERK1/2 signaling. Our findings suggest that the roles that these major molecular elements have in learning and memory may operate through a common pathway.
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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".