Functional β-adrenergic receptor signalling on nuclear membranes in adult rat and mouse ventricular cardiomyocytes
Bibliographic record
Abstract
OBJECTIVE: We sought to determine if different beta-adrenergic receptor (betaAR) subtypes, and their associated signalling machinery, are functionally localized to nuclear membranes. METHODS: Employing enriched nuclear preparations, we assayed the specific presence of betaAR by measuring 125I-cyanopindolol (CYP) binding, Western blotting, confocal microscopy and functional assays. RESULTS: Western blots of rat heart nuclear fractions and confocal immunofluorescent analysis of adult rat and mouse ventricular cardiomyocytes displayed the presence of beta 1AR and beta 3AR but, surprisingly, not the beta 2AR on nuclear membranes. Nuclear localization of downstream signalling partners Gs, Gi and adenylyl cyclases II and V/VI was also demonstrated. The functional relevance of nuclear betaAR was shown by receptor-mediated stimulation of adenylyl cyclase activity by isoproterenol but not the beta 3AR-selective agonist CL 316243 in enriched nuclear preparations. We also examined the effect of subtype-selective ligands on the initiation of RNA synthesis in isolated nuclei. Both isoproterenol and another beta 3AR-selective agonist, BRL 37344, increased RNA synthesis which was inhibited by pertussis toxin (PTX). Neither a beta 1AR-selective agonist, xamoterol, nor a beta 2AR-selective agonist, procaterol, was able to stimulate transcription. However, both CGP 20712A and ICI 118,551 blocked isoproterenol-mediated effects to varying extents. PTX treatment also revealed that nuclear betaAR may be coupled to other signalling pathways in addition to Gi, as stimulation under these conditions reduced initiation of transcription below basal levels. CONCLUSION: These results highlight differential subcellular localization for betaAR subtypes and indicate that betaAR may have specific roles in regulating nuclear function in cardiomyocytes.
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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 | 0.000 |
| 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".