RIG-I Mediates the Co-Induction of Tumor Necrosis Factor and Type I Interferon Elicited by Myxoma Virus in Primary Human Macrophages
Bibliographic record
Abstract
The sensing of pathogen infection and subsequent triggering of innate immunity are key to controlling zoonotic infections. Myxoma virus (MV) is a cytoplasmic DNA poxvirus that in nature infects only rabbits. Our previous studies have shown that MV infection of primary mouse cells is restricted by virus-induced type I interferon (IFN). However, little is known about the innate sensor(s) involved in activating signaling pathways leading to cellular defense responses in primary human immune cells. Here, we show that the complete restriction of MV infection in the primary human fibroblasts requires both tumor necrosis factor (TNF) and type I IFN. We also demonstrate that MV infection of primary human macrophages (pHMs) activates the cytoplasmic RNA sensor called retinoic acid inducible gene I (RIG-I), which coordinately induces the production of both TNF and type I IFN. Of note, RIG-I sensing of MV infection in pHMs initiates a sustained TNF induction through the sequential involvement of the downstream IFN-regulatory factors 3 and 7 (IRF3 and IRF7). Thus, RIG-I-mediated co-induction of TNF and type I IFN by virus-infected pHMs represents a novel innate defense mechanism to restrict viral infection in human cells. These results also reveal a new regulatory mechanism for TNF induction following viral infection.
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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) | 0.001 | 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".