Caveolin‐1 knockout alters β‐adrenoceptor (β‐AR) function in mouse small intestine
Bibliographic record
Abstract
β‐ARs transduce signals intracellularly via G‐proteins. β 2 ‐ARs localize in caveolae and co‐immunoprecipitate with caveolin‐3 in cardiac myocytes. Cholesterol depletion disrupts caveolae and alters both β 2 ‐AR function and localization, suggesting that caveolins regulate these receptors. β‐AR activation in mouse small intestine relaxes smooth muscle, mainly via β3, but also via β1 and β 2 ‐ARs. Here we examined the effects of caveolin‐1 (cav1) knockout on β‐AR function in mouse small intestine in vitro . In tissue segments contracted with carbachol, isoprenaline (iPr) induced relaxation. The iPr dose‐effect (D‐E) curve in the cav1 knockout (cav1 ‐/‐ ) mice was shifted to the right compared to wild type controls. In control mice, SR 59230A (β3‐blocker) and timolol (β 1 /β 2 ‐blocker) showed similar rightward shifts in the iPr D‐E curve that were greater compared to CGP20712A (β 1 ‐blocker). In intestine of cav1 ‐ / ‐ , SR 59230A and CGP 20712A were without effect; only timolol significantly shifted the D‐E curve. BRL 37344 (β 3 ‐agonist) was more effective in controls than in cav1 ‐/‐ intestine, but salbutamol (β 2 ‐agonist) relaxed both similarly. Dibutyryl cAMP relaxed intestine in control mice‐ this was prevented by H‐89 (PKA inhibitor). In cav1 ‐/‐ , H‐89 had no effect on relaxation. These results suggest that cav1 knockout reduces β 3 (and perhaps β 1 ) receptor function in the small intestine. β 2 ‐ARs may partially compensate for this loss. Reduced PKA responses may explain these results. Functional and immunohistochemical experiments are underway to elucidate the interaction between the different β‐receptor subtypes and cav1. Supported by CIHR.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".