Growth factor–dependent phosphorylation of Gα <sub>i</sub> shapes canonical signaling by G protein–coupled receptors
Bibliographic record
Abstract
A long-standing question in the field of signal transduction is how distinct signaling pathways interact with each other to control cell behavior. Growth factor receptors and G protein–coupled receptors (GPCRs) are the two major signaling hubs in eukaryotes. Given that the mechanisms by which they signal independently have been extensively characterized, we investigated how they may cross-talk with each other. Using linear ion trap mass spectrometry and cell-based biophysical, biochemical, and phenotypic assays, we found at least three distinct ways in which epidermal growth factor affected canonical G protein signaling by the G i -coupled GPCR CXCR4 through the phosphorylation of Gα i . Phosphomimicking mutations in two residues in the α E helix of Gα i (tyrosine-154/tyrosine-155) suppressed agonist-induced Gα i activation while promoting constitutive Gβγ signaling. Phosphomimicking mutations in the P loop (serine-44, serine-47, and threonine-48) suppressed G i activation entirely, thus completely segregating growth factor and GPCR pathways. As expected, most of the phosphorylation events appeared to affect intrinsic properties of Gα i proteins, including conformational stability, nucleotide binding, and the ability to associate with and to release Gβγ. However, one phosphomimicking mutation, targeting the carboxyl-terminal residue tyrosine-320, promoted mislocalization of Gα i from the plasma membrane, a previously uncharacterized mechanism of suppressing GPCR signaling through G protein subcellular compartmentalization. Together, these findings elucidate not only how growth factor and chemokine signals cross-talk through the phosphorylation-dependent modulation of Gα i but also how such cross-talk may generate signal diversity.
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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) | 0.001 | 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".