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Record W1607460945 · doi:10.1113/jphysiol.2012.241562

Mechanisms underlying regional differences in the Ca<sup>2+</sup>sensitivity of BK<sub>Ca</sub>current in arteriolar smooth muscle

2013· article· en· W1607460945 on OpenAlexafffund
Yan Yang, Yoshiro Sohma, Zahra Nourian, Srikanth R. Ella, Min Li, Aaron Stupica, Ronald J. Korthuis, Michael J. Davis, Andrew P. Braun, Michael A. Hill

Bibliographic record

VenueThe Journal of Physiology · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicIon channel regulation and function
Canadian institutionsUniversity of Calgary
FundersNational Heart, Lung, and Blood InstituteCanadian Institutes of Health Research
KeywordsCremaster muscleChemistryProtein subunitBiophysicsGatingVascular smooth musclePatch clampBK channelMembrane potentialAnatomyInternal medicineMicrocirculationBiochemistrySmooth muscleBiologyMedicine

Abstract

fetched live from OpenAlex

Key points The plasma membrane large‐conductance Ca2+‐activated, K+channel (BKCa) is a major ion channel contributing to the regulation of membrane potential. Activation of large‐conductance Ca2+‐activated K+channel by both depolarization and increased intracellular Ca2+results in hyperpolarization that acts to limit agonist and mechanically induced vasoconstriction in small arteries. Using patch‐clamp techniques we demonstrate that regional differences exist in how BKCais regulated, particularly with respect to its Ca2+sensitivity. Using single‐channel recordings and siRNA to manipulate protein subunit expression, it is argued that the β1‐subunit plays a more dominant role in cerebral blood vessels as compared with small arteries from skeletal muscle. Subtle differences in the regulation of membrane potential in different vascular beds allow local blood flow and pressure to be closely adapted to the tissue's metabolic needs. Abstract β1‐Subunits enhance the gating properties of large‐conductance Ca2+‐activated K+channels (BKCa) formed by α‐subunits. In arterial vascular smooth muscle cells (VSMCs), β1‐subunits are vital in coupling SR‐generated Ca2+sparks to BKCaactivation, affecting contractility and blood pressure. Studies in cremaster and cerebral VSMCs show heterogeneity of BKCaactivity due to apparent differences in the functional β1‐subunit:α‐subunit ratio. To define these differences, studies were conducted at the single‐channel level while siRNA was used to manipulate specific subunit expression. β1 modulation of the α‐subunit Ca2+sensitivity was studied using patch‐clamp techniques. BKCachannel normalized open probability (NPo)versusmembrane potential (Vm) curves were more left‐shifted in cerebralversuscremaster VSMCs as cytoplasmic Ca2+was raised from 0.5 to 100 μm. CalculatedV1/2values of channel activation decreased from 72.0 ± 6.1 at 0.5 μmCa2+ito −89 ± 9 mV at 100 μmCa2+iin cerebral compared with 101 ± 10 to −63 ± 7 mV in cremaster VSMCs. Cremaster BKCachannels thus demonstrated an ∼2.5‐fold weaker apparent Ca2+sensitivity such that at a value ofVmof −30 mV, a mean value of [Ca2+]iof 39 μmwas required to open half of the channels in cremasterversus16 μm[Ca2+]iin cerebral VSMCs. Further, shortened mean open and longer mean closed times were evident in BKCachannel events from cremaster VSMCs at either −30 or 30 mV at any given [Ca2+]. β1‐Subunit‐directed siRNA decreased both the apparent Ca2+sensitivity of BKCain cerebral VSMCs and the appearance of spontaneous transient outward currents. The data are consistent with a higher ratio of β1‐subunit:α‐subunit of BKCachannels in cerebral compared with cremaster VSMCs. Functionally, this leads both to higher Ca2+sensitivity andNPofor BKCachannels in the cerebral vasculature relative to that of skeletal muscle.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.031
GPT teacher head0.251
Teacher spread0.220 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations41
Published2013
Admission routes2
Has abstractyes

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