Defining the Molecular Basis of K <sub>IR</sub> Channel Mechanosensitivity in Vascular Smooth Muscle
Bibliographic record
Abstract
In cerebral arteries, the inwardly rectifying potassium channel (K IR ) contributes to the setting of membrane potential and development of myogenic tone. K IR channels have been recently identified as a target of mechanical regulation in vascular smooth muscle, by which their activity is suppressed by pressure. However, the mechanism underlying this process remains unknown. The actin cytoskeleton and caveolae are often part of signaling domains implicated in established models of mechanosensitivity. Consequently, this study investigated whether these distinct structural components enable K IR pressure sensing in rat cerebral arterial smooth muscle. Whole‐cell patch clamp electrophysiology was used to monitor K IR activity and initial experiments confirmed that this current was suppressed by mechanical stimulation. Pre‐treatment of cells with actin‐disrupting agents, Latrunculin A and Cytochalasin D, prevented the suppression of K IR , indicative of the cytoskeleton’s participation. As K IR channels reside in caveolae, we next assessed the involvement of caveolin proteins in mechanoregulation. As expected, inhibition of caveolin‐1 signaling with blocking peptides diminished the ability of K IR channels to respond to pressure. The current data highlights that the actin cytoskeleton and caveolae form key parts of a signal transduction structure that confers mechanosensitivity to K IR . Ongoing work seeks to identify other K IR ‐protein interactions in cerebral arterial smooth muscle using pull‐down and proximity ligation assay techniques. The functional impact of signaling complex disruption will also be assessed with vessel myography. Support or Funding Information Research supported by the Canadian Institutes of Health Research.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".