Genetic Ablation of Ca <sub>V</sub> 3.2 Channels Enhances the Arterial Myogenic Response by Modulating the RyR-BK <sub>Ca</sub> Axis
Bibliographic record
Abstract
OBJECTIVE: In resistance arteries, there is an emerging view that smooth muscle CaV3.2 channels restrain arterial constriction through a feedback response involving the large-conductance Ca(2+)-activated K(+) channel (BKCa). Here, we used wild-type and CaV3.2 knockout (CaV3.2(-/-)) mice to definitively test whether CaV3.2 moderates myogenic tone in mesenteric arteries via the CaV3.2-ryanodine receptor-BKCa axis and whether this regulatory mechanism influences blood pressure regulation. APPROACH AND RESULTS: Using pressurized vessel myography, CaV3.2(-/-) mesenteric arteries displayed enhanced myogenic constriction to pressure but similar K(+)-induced vasoconstriction compared with wild-type C57BL/6 arteries. Electrophysiological and myography experiments subsequently confirmed the inability of micromolar Ni(2+), a CaV3.2 blocker, to either constrict arteries or suppress T-type currents in CaV3.2(-/-) smooth muscle cells. The frequency of BKCa-induced spontaneous transient outward K(+) currents dropped in wild-type but not in knockout arterial smooth muscle cells upon the pharmacological suppression of CaV3.2 channel. Line scan analysis performed on en face arteries loaded with Fluo-4 revealed the presence of Ca(2+) sparks in all arteries, with the subsequent application of Ni(2+) only affecting wild-type arteries. Although CaV3.2 channel moderated myogenic constriction of resistance arteries, the blood pressure measurements of CaV3.2(-/-) and wild-type animals were similar. CONCLUSIONS: Overall, our findings establish a negative feedback mechanism of the myogenic response in which CaV3.2 channel modulates downstream ryanodine receptor-BKCa to hyperpolarize and relax arteries.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".