NMDA receptor–dependent dephosphorylation of serine 387 in Argonaute 2 increases its degradation and affects dendritic spine density and maturation
Bibliographic record
Abstract
Argonaute (AGO) proteins are essential components of the microRNA (miRNA) pathway. AGO proteins are loaded with miRNAs to target mRNAs and thereby regulate mRNA stability and protein translation. As such, AGO proteins are important actors in controlling local protein synthesis, for instance, at dendritic spines and synapses. Although miRNA-mediated regulation of dendritic mRNAs has become a focus of intense interest over the past years, the mechanisms regulating neuronal AGO proteins remain largely unknown. Here, using rat hippocampal neurons, we report that dendritic Ago2 is down-regulated by the proteasome upon NMDA receptor activation. We found that Ser-387 in Ago2 is dephosphorylated upon NMDA treatment and that this dephosphorylation precedes Ago2 degradation. Expressing Ser-387 phosphorylation-deficient or phosphomimetic Ago2 in neurons, we observed that this phosphorylation site is involved in modulating dendritic spine morphology and postsynaptic density protein 95 (PSD-95) expression in spines. Collectively, our results point toward a signaling pathway linking NMDA receptor-dependent Ago2 dephosphorylation and turnover to postsynaptic structural changes. They support a model in which NMDA receptor-mediated dephosphorylation of Ago2 and Ago2 turnover contributes to the de-repression of mRNAs involved in spine growth and maturation.
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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) | 0.002 | 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".