Reduced KCNQ4-Encoded Voltage-Dependent Potassium Channel Activity Underlies Impaired β-Adrenoceptor–Mediated Relaxation of Renal Arteries in Hypertension
Bibliographic record
Abstract
KCNQ4-encoded voltage-dependent potassium (Kv7.4) channels are important regulators of vascular tone that are severely compromised in models of hypertension. However, there is no information as to the role of these channels in responses to endogenous vasodilators. We used a molecular knockdown strategy, as well as pharmacological tools, to examine the hypothesis that Kv7.4 channels contribute to β-adrenoceptor-mediated vasodilation in the renal vasculature and underlie the vascular deficit in spontaneously hypertensive rats. Quantitative PCR and immunohistochemistry confirmed gene and protein expression of KCNQ1, KCNQ3, KCNQ4, KCNQ5, and Kv7.1, Kv7.4, and Kv7.5 in rat renal artery. Isoproterenol produced concentration-dependent relaxation of precontracted renal arteries and increased Kv7 channel currents in isolated smooth muscle cells. Application of the Kv7 blocker linopirdine attenuated isoproterenol-induced relaxation and current. Isoproterenol-induced relaxations were also reduced in arteries incubated with small interference RNAs targeted to KCNQ4 that produced a ≈60% decrease in Kv7.4 protein level. Relaxation to isoproterenol and the Kv7 activator S-1 were abolished in arteries from spontaneously hypertensive rats, which was associated with ≈60% decrease in Kv7.4 abundance. This study provides the first evidence that Kv7 channels contribute to β-adrenoceptor-mediated vasodilation in the renal vasculature and that abrogation of Kv7.4 channels is strongly implicated in the impaired β-adrenoceptor pathway in spontaneously hypertensive rats. These findings may provide a novel pathogenic link between arterial dysfunction and hypertension.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".