Direct Regulation of Hyperpolarization-Activated Cyclic-Nucleotide Gated (HCN1) Channels by Cannabinoids
Bibliographic record
Abstract
Cannabinoids are a broad class of molecules that act primarily on neurons, affecting pain sensation, appetite, mood, learning, and memory. In addition to interacting with specific cannabinoid receptors (CBRs), cannabinoids can directly modulate the function of various ion channels. Here, we examine whether cannabidiol (CBD) and Δ9-tetrahydrocannabinol (THC), the most prevalent phytocannabinoids in Cannabis sativa, can regulate the function of hyperpolarization-activated cyclic-nucleotide-gated (HCN1) channels independently of CBRs. HCN1 channels were expressed in Xenopus oocytes since they do not express CBRs, and the effects of cannabinoid treatment on HCN1 currents were examined by a two-electrode voltage clamp. We observe opposing effects of CBD and THC on HCN1 current, with CBD acting to stimulate HCN1 function, while THC inhibited current. These effects persist in HCN1 channels lacking the cyclic-nucleotide binding domain (HCN1ΔCNBD). However, changes to membrane fluidity, examined by treating cells with TX-100, inhibited HCN1 current had more pronounced effects on the voltage-dependence and kinetics of activation than THC, suggesting this is not the primary mechanism of HCN1 regulation by cannabinoids. Our findings may contribute to the overall understanding of how cannabinoids may act as promising therapeutic molecules for the treatment of several neurological disorders in which HCN function is disturbed.
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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".