Broad-Scale Phosphoprotein Profiling of Beta Adrenergic Receptor (β-AR) Signaling Reveals Novel Phosphorylation and Dephosphorylation Events
Bibliographic record
Abstract
β-adrenergic receptors (β-ARs) are model G-protein coupled receptors that mediate signal transduction in the sympathetic nervous system. Despite the widespread clinical use of agents that target β-ARs, the signaling pathways that operate downstream of β-AR stimulation have not yet been completely elucidated. Here, we utilized a lysate microarray approach to obtain a broad-scale perspective of phosphoprotein signaling downstream of β-AR. We monitored the time course of phosphorylation states of 54 proteins after β-AR activation mouse embryonic fibroblast (MEF) cells. In response to stimulation with the non-selective β-AR agonist isoproterenol, we observed previously described phosphorylation events such as ERK1/2(T202/Y204) and CREB(S133), but also novel phosphorylation events such as Cdc2(Y15) and Pyk2(Y402). All of these events were mediated through cAMP and PKA as they were reproduced by stimulation with the adenylyl cyclase activator forskolin and were blocked by treatment with H89, a PKA inhibitor. In addition, we also observed a number of novel isoproterenol-induced protein dephosphorylation events in target substrates of the PI3K/AKT pathway: GSK3β(S9), 4E-BP1(S65), and p70s6k(T389). These dephosphorylations were dependent on cAMP, but were independent of PKA and correlated with reduced PI3K/AKT activity. Isoproterenol stimulation also led to a cAMP-dependent dephosphorylation of PP1α(T320), a modification known to correlate with enhanced activity of this phosphatase. Dephosphorylation of PP1α coincided with the secondary decline in phosphorylation of some PKA-phosphorylated substrates, suggesting that PP1α may act in a feedback loop to return these phosphorylations to baseline. In summary, lysate microarrays are a powerful tool to profile phosphoprotein signaling and have provided a broad-scale perspective of how β-AR signaling can regulate key pathways involved in cell growth and metabolism.
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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".