Calcitonin gene-related peptide potentiated the excitatory transmission and network propagation in the anterior cingulate cortex of adult mice
Bibliographic record
Abstract
The neuropeptide of calcitonin gene-related peptide (CGRP) plays critical roles in chronic pain, especially in migraine. Immunohistochemistry and in situ hybridization studies have shown that CGRP and its receptors are expressed in cortical areas including pain perception-related prefrontal anterior cingulate cortex. However, less information is available for the functional roles of CGRP in cortical regions such as the anterior cingulate cortex (ACC). Recent studies have consistently demonstrated that long-term potentiation is a key cellular mechanism for chronic pain in the ACC. In the present study, we used 64-electrode array field recording system to investigate the effect of CGRP on excitatory transmission in the ACC. We found that CGRP induced potentiation of synaptic transmission in a dose-dependently manner (1, 10, 50, and 100 nM). CGRP also recruited inactive circuit in the ACC. An application of the calcitonin receptor-like receptor antagonist CGRP 8-37 blocked CGRP-induced chemical long-term potentiation and the recruitment of inactive channels. CGRP-induced long-term potentiation was also blocked by N-methyl-D-aspartate (NMDA) receptor antagonist AP-5. Consistently, the application of CGRP increased NMDA receptor-mediated excitatory postsynaptic currents. Finally, we found that CGRP-induced long-term potentiation required the activation of calcium-stimulated adenylyl cyclase subtype 1 (AC1) and protein kinase A. Genetic deletion of AC1 using AC1 −/− mice, an AC1 inhibitor NB001 or a protein kinase A inhibitor KT5720, all reduced or blocked CGRP-induced potentiation. Our results provide direct evidence that CGRP may contribute to synaptic potentiation in important physiological and pathological conditions in the ACC, an AC1 inhibitor NB001 may be beneficial for the treatment of chronic headache.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".