Functional reconstitution of an eicosanoid-modulated Cl<sup>−</sup>channel from bovine tracheal smooth muscle
Bibliographic record
Abstract
We describe the biochemical properties of an eicosanoid-modulated Cl- channel and assess the mechanisms by which the epoxyeicosatrienoic acids (EETs) alter both its unitary conductance and its open probability (P(o)). After a purification protocol involving wheat-germ agglutinin affinity and anion-exchange chromatography, the proteins were sequentially inserted into liposomes, which were then fused into PLBs. Functional and biochemical characterization tests confirm that the Cl- channel is a 55-kDa glycosylated monomer with voltage- and Ca(2+) concentration-independent activity. 5,6- and 8,9-EET decreased the conductance of the native channel (control conductance: 70 +/- 5 pS in asymmetrical 50 mM trans/250 mM cis CsCl) in a concentration-dependent manner, with respective 50% inhibitory concentration values of 0.31 and 0.42 microM. These regioisomers similarly decreased the conductance of the purified channel (control conductance value: 75 +/- 5 pS in asymmetrical 50 mM trans/250 mM cis CsCl), which had been stripped of its native proteic and lipidic environment. On the other hand, 5,6- and 8,9-EETs decreased the P(o) of the native channel with respective 50% inhibitory concentration values of 0.27 and 0.30 microM but failed to alter the P(o) of the purified protein. Thus we suggest that the effects of these EETs on channel conductance likely result from direct interactions of EET- anions with the channel pore, whereas the alteration of P(o) requires a lipid environment of specific composition that is lost on solubilization and purification of the protein.
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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 | 0.001 |
| 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".