Cholesterol-dependent Separation of the β2-Adrenergic Receptor from Its Partners Determines Signaling Efficacy
Bibliographic record
Abstract
Determining the role of lipid raft nanodomains in G protein-coupled receptor signaling remains fraught by the lack of assays directly monitoring rafts in native membranes. We thus combined extensive biochemical and pharmacological approaches to a nanoscale strategy based on bioluminescence resonance energy transfer (BRET) to assess the spatial and functional influence of cholesterol-rich liquid-ordered lipid nanodomains on beta2 adrenergic receptor (beta2AR) signaling. The data revealed that whereas beta2AR did not partition within liquid-ordered lipid phase, a pool of G protein and adenylyl cyclase (AC) were sequestered in these domains. Destabilization of the liquid-ordered phase by cholesterol depletion led to a lateral redistribution of Galphas and AC that favored interactions between the receptor and its signaling partners as assessed by BRET. This resulted in an increased basal and agonist-promoted beta2AR-stimulated cAMP production that was partially dampened as a result of constitutive protein kinase A-dependent phosphorylation and desensitization of the receptor. This restraining influence of nanodomains on beta2AR signaling was further substantiated by showing that liquid-ordered lipid phase stabilization using caveolin overexpression or increasing membrane cholesterol amount led to an inhibition of beta2AR-associated signaling. Given the emerging concept that clustering of receptors and effectors into signaling platforms contributes to the efficacy and selectivity of signal transduction, our results support a model whereby cholesterol-promoted liquid-ordered lipid phase-embedding Gs and AC allows their lateral separation from the receptor, thus restraining the basal activity and controlling responsiveness of beta2AR signaling machinery within larger signaling platforms.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".