Ligand-biased regulation of PtdIns(3,4,5)P3-dependent signal transduction in GPCR control of pituitary hormone release
Bibliographic record
Abstract
Biased signaling describes the selective activation of signal transduction cascades by structurally related ligands downstream of shared G protein-coupled receptors (GPCRs). Although class I phosphoinositide 3-kinases (PI3Ks) are important components of GPCR-controlled transduction networks, little is known regarding the potential for biased regulation of class I PI3K-dependent signaling. The full complement of class I PI3K catalytic subunits (p110α, p110β, p110δ, and p110γ) first appears in bony fishes and, despite being associated with distinct cellular functions, all class I PI3Ks produce the lipid second-messenger phosphatidylinositol 3,4,5-trisphosphate [PtdIns(3,4,5)P3]. We have previously shown that 2 endogenous gonadotropin-releasing hormones (GnRH2 and GnRH3), which both signal through shared Gαq/11-coupled receptors, selectively activate different subsets of class I PI3K isoforms in their control of hormone release from goldfish (Carassius auratus) pituitary cells. Here, we tested the hypothesis that the biased activation of class I PI3K isoforms results in the selective recruitment of PtdIns(3,4,5)P3-sensitive effectors downstream of GnRH-stabilized GPCRs using pharmacological mapping. Our results reveal that distinct PtdIns(3,4,5)P3-sensitive effectors are involved in the differential control of GnRH2- and GnRH3-stimulated, as well as basal, hormone release and implicate the participation of noncanonical PtdIns(3,4,5)P3-sensitive transduction elements. Furthermore, observations using a selective inhibitor of the shared Gβγ-effector interaction surface indicate a role for Gβγ-dependent signaling in the integrated control of pituitary hormone exocytosis. These findings add to our understanding of functional selectivity in GPCR signal transduction networks, in general, and reveal the complexity of biased signaling downstream of class I PI3K catalytic activity.
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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".