Uncovering the K <sub>IR</sub> Mechanosignaling Complex in Vascular Smooth Muscle
Bibliographic record
Abstract
Inwardly rectifying potassium (K IR ) channels contribute to the setting of membrane potential in vascular smooth muscle (VSM). Recent work has demonstrated their regulation by pressure, although the underlying mechanism remains unclear. This study sought to identify key signaling components that enable this mechanosensitive response in rat cerebral arterial myocytes. Initial patch clamp electrophysiology confirmed that pressure, initiated by a hyposmotic challenge, suppresses K IR currents in isolated cells. Actin disruption with Cytochalasin D and Latrunculin A abolished this response, implying participation of the cytoskeleton. Interactions between ion channels and actin filaments are often facilitated through structural protein intermediates. In this context, we examined syntrophin and caveolin‐1 scaffolding proteins as they have each been previously found to interact with K IR channels. Peptide blockade of caveolin‐1 prevented K IR suppression after hyposmotic challenge in subsequent patch clamp experiments. Immunohistochemistry highlighted expression of both syntrophin and caveolin‐1 in VSM, while proximity ligation assay revealed their co‐localization with K IR 2.2 subunits. These findings provide evidence that K IR mechanosensitivity involves interactions with the cytoskeleton which are likely mediated by scaffolding proteins.
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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".