ADAR2-mediated Q/R editing of GluK2 regulates kainate receptor upscaling in response to suppression of synaptic activity
Bibliographic record
Abstract
Kainate receptors (KARs) regulate neuronal excitability and network function. Most KARs contain the subunit GluK2 (also known as GRIK2), and the properties of these receptors are determined in part by ADAR2 (also known as ADARB1)-mediated mRNA editing of GluK2, which changes a genomically encoded glutamine residue into an arginine residue (Q/R editing). Suppression of synaptic activity reduces ADAR2-dependent Q/R editing of GluK2 with a consequential increase in GluK2-containing KAR surface expression. However, the mechanism underlying this reduction in GluK2 editing has not been addressed. Here, we show that induction of KAR upscaling, a phenomenon in which surface expression of receptors is increased in response to a chronic decrease in synaptic activity, results in proteasomal degradation of ADAR2, which reduces GluK2 Q/R editing. Because KARs incorporating unedited GluK2(Q) assemble and exit the ER more efficiently, this leads to an upscaling of KAR surface expression. Consistent with this, we demonstrate that partial ADAR2 knockdown phenocopies and occludes KAR upscaling. Moreover, we show that although the AMPA receptor (AMPAR) subunit GluA2 (also known as GRIA2) also undergoes ADAR2-dependent Q/R editing, this process does not mediate AMPAR upscaling. These data demonstrate that activity-dependent regulation of ADAR2 proteostasis and GluK2 Q/R editing are key determinants of KAR, but not AMPAR, trafficking and upscaling.This article has an associated First Person interview with the first author of the paper.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".