Differential Subcellular Targeting of Glutamate Receptor Subtypes during Homeostatic Synaptic Plasticity
Bibliographic record
Abstract
Homeostatic processes are believed to contribute to the stability of neuronal networks that are perpetually influenced by Hebbian forms of synaptic plasticity. Whereas the rules governing the targeting and trafficking of AMPA and NMDA subtypes of glutamate receptors during rapid Hebbian LTP have been extensively studied, those that are operant during homeostatic forms of synaptic strengthening are less well understood. Here, we used biochemical, biophysical, and pharmacological approaches to investigate glutamate receptor regulation during homeostatic synaptic plasticity. We show in rat organotypic hippocampal slices that prolonged network silencing induced a robust surface upregulation of GluA2-lacking AMPARs, not only at synapses, but also at extrasynaptic dendritic and somatic regions of CA1 pyramidal neurons. We also detected a shift in NMDAR subunit composition that, in contrast to the cell-wide surface delivery of GluA2-lacking AMPARs, occurred exclusively at synapses. The subunit composition and subcellular distribution of AMPARs and NMDARs are therefore distinctly regulated during homeostatic synaptic plasticity. Thus, because subunit composition dictates key channel properties, such as agonist affinity, gating kinetics, and calcium permeability, the homeostatic synaptic process transcends the simple modulation of synaptic strength by also regulating the signaling and integrative properties of central synapses.
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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 | 0.001 |
| 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 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".