Type-I interferon signaling through ISGF3 complex is required for sustained Rip3 activation and necroptosis in macrophages
Bibliographic record
Abstract
Myeloid cells play a critical role in perpetuating inflammation during various chronic diseases. Recently the death of macrophages through programmed necrosis (necroptosis) has emerged as an important mechanism in inflammation and pathology. We evaluated the mechanisms that lead to the induction of necrotic cell death in macrophages. Our results indicate that type I IFN (IFN-I) signaling is a predominant mechanism of necroptosis, because macrophages deficient in IFN-α receptor type I (IFNAR1) are highly resistant to necroptosis after stimulation with LPS, polyinosinic-polycytidylic acid, TNF-α, or IFN-β in the presence of caspase inhibitors. IFN-I-induced necroptosis occurred through both mechanisms dependent on and independent of Toll/IL-1 receptor domain-containing adaptor inducing IFN-β (TRIF) and led to persistent phosphorylation of receptor-interacting protein 3 (Rip3) kinase, which resulted in potent necroptosis. Although various IFN-regulatory factors (IRFs) facilitated the induction of necroptosis in response to IFN-β, IRF-9-STAT1- or -STAT2-deficient macrophages were highly resistant to necroptosis. Our results indicate that IFN-β-induced necroptosis of macrophages proceeds through tonic IFN-stimulated gene factor 3 (ISGF3) signaling, which leads to persistent expression of STAT1, STAT2, and IRF9. Induction of IFNAR1/Rip3-dependent necroptosis also resulted in potent inflammatory pathology in vivo. These results reveal how IFN-I mediates acute inflammation through macrophage necroptosis.
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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) | 0.002 | 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".