IFN-γ Activates the C3G/Rap1 Signaling Pathway
Bibliographic record
Abstract
IFN-gamma transduces signals by activating the IFN-gamma receptor-associated Jak-1 and Jak-2 kinases and by inducing tyrosine phosphorylation and activation of the Stat-1 transcriptional activator. We report that IFN-gamma activates a distinct signaling cascade involving the c-cbl protooncogene product, CrkL adapter, and small G protein Rap1. During treatment of NB-4 human cells with IFN-gamma, c-cbl protooncogene product is rapidly phosphorylated on tyrosine and provides a docking site for the src homology 2 domain of CrkL, which also undergoes IFN-gamma-dependent tyrosine phosphorylation. CrkL then regulates activation of the guanine exchange factor C3G, with which it interacts constitutively via its N terminus src homology 3 domain. This results in the IFN-gamma-dependent activation of Rap1, a protein known to exhibit tumor suppressor activity and mediate growth inhibitory responses. In a similar manner, Rap1 is also activated in response to treatment of cells with type I IFNs (IFN-alpha, IFN-beta), which also engage CrkL in their signaling pathways. On the other hand, IFN-gamma does not induce formation of nuclear CrkL-Stat5 DNA-binding complexes, which are induced by IFN-alpha and IFN-beta, indicating that pathways downstream of CrkL are differentially regulated by different IFN subtypes. Taken altogether, our data demonstrate that, in addition to activating the Stat pathway, IFN-gamma activates a distinct signaling cascade that may play an important role in the generation of its growth inhibitory effects on target cells.
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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) | 0.001 | 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) | 0.002 | 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".