GluA1 Phosphorylation Contributes to Postsynaptic Amplification of Neuropathic Pain in the Insular Cortex
Bibliographic record
Abstract
Long-term potentiation of glutamatergic transmission has been observed after physiological learning or pathological injuries in different brain regions, including the spinal cord, hippocampus, amygdala, and cortices. The insular cortex is a key cortical region that plays important roles in aversive learning and neuropathic pain. However, little is known about whether excitatory transmission in the insular cortex undergoes plastic changes after peripheral nerve injury. Here, we found that peripheral nerve ligation triggered the enhancement of AMPA receptor (AMPAR)-mediated excitatory synaptic transmission in the insular cortex. The synaptic GluA1 subunit of AMPAR, but not the GluA2/3 subunit, was increased after nerve ligation. Genetic knock-in mice lacking phosphorylation of the Ser845 site, but not that of the Ser831 site, blocked the enhancement of the synaptic GluA1 subunit, indicating that GluA1 phosphorylation at the Ser845 site by protein kinase A (PKA) was critical for this upregulation after nerve injury. Furthermore, A-kinase anchoring protein 79/150 (AKAP79/150) and PKA were translocated to the synapses after nerve injury. Genetic deletion of adenylyl cyclase subtype 1 (AC1) prevented the translocation of AKAP79/150 and PKA, as well as the upregulation of synaptic GluA1-containing AMPARs. Pharmacological inhibition of calcium-permeable AMPAR function in the insular cortex reduced behavioral sensitization caused by nerve injury. Our results suggest that the expression of AMPARs is enhanced in the insular cortex after nerve injury by a pathway involving AC1, AKAP79/150, and PKA, and such enhancement may at least in part contribute to behavioral sensitization together with other cortical regions, such as the anterior cingulate and the prefrontal cortices.
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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) | 0.001 | 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 | 0.001 |
| 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".