Regulatory effects of mTORC2 complexes in type I IFN signaling and in the generation of IFN responses
Bibliographic record
Abstract
IFNs transduce signals by binding to cell surface receptors and activating cellular pathways and regulatory networks that control transcription of IFN-stimulated genes (ISGs) and mRNA translation, leading to generation of protein products that mediate biological responses. Previous studies have shown that type I IFN receptor-engaged pathways downstream of AKT and mammalian target of rapamycin complex (mTORC) 1 play important roles in mRNA translation of ISGs and the generation of IFN responses, but the roles of mTORC2 complexes in IFN signaling are unknown. We provide evidence that mTORC2 complexes control IFN-induced phosphorylation of AKT on serine 473 and their function is ultimately required for IFN-dependent gene transcription via interferon-stimulated response elements. We also demonstrate that such complexes exhibit regulatory effects on other IFN-dependent mammalian target of rapamycin-mediated signaling events, likely via engagement of the AKT/mTORC1 axis, including IFN-induced phosphorylation of S6 kinase and its effector rpS6, as well as phosphorylation of the translational repressor 4E-binding protein 1. We also show that induction of ISG protein expression and the generation of antiviral responses are defective in Rictor and mLST8-KO cells. Together, our data provide evidence for unique functions of mTORC2 complexes in the induction of type I IFN responses and suggest a critical role for mTORC2-mediated signals in IFN signaling.
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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 | 0.001 | 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".