Glycogen Synthase Kinase-3β Facilitates IFN-γ-Induced STAT1 Activation by Regulating Src Homology-2 Domain-Containing Phosphatase 2
Bibliographic record
Abstract
Glycogen synthase kinase-3beta (GSK-3beta)-modulated IFN-gamma-induced inflammation has been reported; however, the mechanism that activates GSK-3beta and the effects of activation remain unclear. Inhibiting GSK-3beta decreased IFN-gamma-induced inflammation. IFN-gamma treatment rapidly activated GSK-3beta via neutral sphingomyelinase- and okadaic acid-sensitive phosphatase-regulated dephosphorylation at Ser(9), and proline-rich tyrosine kinase 2 (Pyk2)-regulated phosphorylation at Tyr(216). Pyk2 was activated through phosphatidylcholine-specific phospholipase C (PC-PLC)-, protein kinase C (PKC)-, and Src-regulated pathways. The activation of PC-PLC, Pyk2, and GSK-3beta was potentially regulated by IFN-gamma receptor 2-associated Jak2, but it was independent of IFN-gamma receptor 1. Furthermore, Jak2/PC-PLC/PKC/cytosolic phospholipase A(2) positively regulated neutral sphingomyelinase. Inhibiting GSK-3beta activated Src homology-2 domain-containing phosphatase 2 (SHP2), thereby preventing STAT1 activation in the late stage of IFN-gamma stimulation. All these results showed that activated GSK-3beta synergistically affected IFN-gamma-induced STAT1 activation by inhibiting SHP2.
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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".