Large Conductance Ca2+-Activated K+ Channel (BKCa) α-Subunit Splice Variants in Resistance Arteries from Rat Cerebral and Skeletal Muscle Vasculature
Bibliographic record
Abstract
Previous studies report functional differences in large conductance Ca2+ activated-K+ channels (BKCa) of smooth muscle cells (VSMC) from rat cerebral and cremaster muscle resistance arteries. The present studies aimed to determine if this complexity in BKCa activity may, in part, be due to splice variants in the pore-forming α-subunit. BKCa variants in the intracellular C terminus of the α-subunit, and their relative expression to total α-subunit, were examined by qPCR. Sequencing of RT-PCR products showed two α-subunit variants, ZERO and STREX, to be identical in cremaster and cerebral arteries. Levels of STREX mRNA expression were, however, significantly higher in cremaster VSMCs (28.9±4.2% of total α-BKCa) compared with cerebral vessels (16.5±0.9%). Further, a low level of BKCa SS4 α-subunit variant was seen in cerebral arteries, while undetectable in cremaster arteries. Protein biotinylation assays, in expression systems and arterial preparations, were used to determine whether differences in splice variant mRNA expression affect surface membrane/cytosolic location of the channel. In AD-293 and CHO-K1 cells, rat STREX was more likely to be located at the plasma membrane compared to ZERO, although the great majority of channel protein was in the membrane in both cases. Co-expression of β1-BKCa subunit with STREX or ZERO did not influence the dominant membrane expression of α-BKCa subunits, whereas in the absence of α-BKCa, a significant proportion of β1-subunit remained cytosolic. Biotinylation assays of cremaster and cerebral arteries showed that differences in STREX/ZERO expression do not alter membrane/cytosolic distribution of the channel under basal conditions. These data, however, revealed that the amount of α-BKCa in cerebral arteries is approximately 20X higher than in cremaster vessels. Thus, the data support the major functional differences in BKCa activity in cremaster, as compared to cerebral VSMCs, being related to total α-BKCa expression, regardless of differences in splice variant expression.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".