Contribution of Kv1.2 Voltage-gated Potassium Channel to D2 Autoreceptor Regulation of Axonal Dopamine Overflow
Bibliographic record
Abstract
Impairments in axonal dopamine release are associated with neurological disorders such as schizophrenia and attention deficit hyperactivity disorder and pathophysiological conditions promoting drug abuse and obesity. The D2 dopamine autoreceptor (D2-AR) exerts tight regulatory control of axonal dopamine (DA) release through a mechanism suggested to involve K(+) channels. To evaluate the contribution of Kv1 voltage-gated potassium channels of the Shaker gene family to the regulation of axonal DA release by the D2-AR, the present study employed expression analyses, real time measurements of striatal DA overflow, K(+) current measurements and immunoprecipitation assays. Kv1.1, -1.2, -1.3, and -1.6 mRNA and protein were detected in midbrain DA neurons purified by fluorescence-activated cell sorting and in primary DA neuron cultures. In addition, Kv1.1, -1.2, and -1.6 were localized to DA axonal processes in the dorsal striatum. By means of fast scan cyclic voltammetry in striatal slice preparations, we found that the inhibition of stimulation-evoked DA overflow by a D2 agonist was attenuated by Kv1.1, -1.2, and -1.6 toxin blockers. A particular role for the Kv1.2 subunit in the process whereby axonal D2-AR inhibits DA overflow was established with the use of a selective Kv1.2 blocker and Kv1.2 knock-out mice. Moreover, we demonstrate the ability of D2-AR activation to increase Kv1.2 currents in co-transfected cells and its reliance on Gβγ subunit signaling along with the physical coupling of D2-AR and Kv1.2-containing channels in striatal tissue. These findings underline the contribution of Kv1.2 in the regulation of nigrostriatal DA release by the D2-AR and thereby offer a novel mechanism by which DA release is regulated.
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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) | 0.001 | 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".