Syntaxin-1A inhibition of P-1075, cromakalim, and diazoxide actions on mouse cardiac ATP-sensitive potassium channel
Bibliographic record
Abstract
AIMS: Syntaxin (Syn)-1A binds sulfonylurea receptor (SUR) nucleotide binding folds of cardiac myocyte (SUR2A) and islet beta-cells (SUR1) to inhibit ATP-sensitive potassium (K(ATP)) channels. We further reported that Syn-1A reduced the potency and efficacy of beta-cell-specific K(ATP) channel openers (KCOs). Here, we examined whether Syn-1A would influence non-specific (diazoxide) and SUR2-specific KCOs [N-cyano-N'-(1,1-dimethylpropyl)-N''-3-pyridylguanidine (P-1075) and cromakalim] on cardiac myocyte K(ATP) channels activation. METHODS AND RESULTS: Confocal microscopy and Western blotting verified the presence of both Syn-1A and -1B expressions on rodent cardiac ventricular myocytes. Inside-out patch-clamp electrophysiology was utilized to examine the effects of these syntaxins on K(ATP) macroscopic currents activated by various KCOs from a stable cell line expressing the potassium inward rectifier 6.2 (Kir6.2)/SUR2A and from C57BL/6 male mouse ventricular myocytes. Syn-1A inhibited the current amplitude activated by P-1075, cromakalim and diazoxide via its H3 but not Habc domain. Syn-1B exhibited similar inhibitory effects on P-1075 activation of K(ATP) currents. In examining for direct effects of Syn-1A on the KCO binding to cardiac SUR2 receptors, we found that Syn-1A did not directly affect [(3)H]-P-1075 binding to rat cardiac membrane SUR2A at maximum binding capacity, but was able to mildly reduce the affinity of cold P-1075 and cromakalim to displace [(3)H]-P-1075 binding. CONCLUSION: In conclusion, Syn-1A (and Syn-1B) could inhibit K(ATP) currents activated by SUR2A-acting KCOs. Potential fluctuations in the levels of these syntaxins in the myocardium may affect the therapeutic effectiveness of cardiac KCOs.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".