Type I Interferon α/β Receptor-Mediated Signaling Negatively Regulates Antiviral Cytokine Responses in Murine Bone-Marrow-Derived Mast Cells and Protects the Cells from Virus-Induced Cell Death
Bibliographic record
Abstract
Mast cells (MCs) are critical for initiating inflammatory responses to pathogens including viruses. Type I interferons (IFNs) that exert their antiviral functions by interacting with the type I IFN receptor (IFNAR) play a central role in host cellular responses to viruses. Given that virus-induced excessive toxic inflammatory responses are associated with aberrant IFNAR signaling and considering MCs are an early source of inflammatory cytokines during viral infections, we sought to determine whether IFNAR signaling plays a role in antiviral cytokine responses of MCs. IFNAR-intact, IFNAR-blocked, and IFNAR-knockout (IFNAR−/−) bone-marrow-derived MCs (BMMCs) were treated in vitro with a recombinant vesicular stomatitis virus (rVSVΔm51) to assess cytokine production by these cells. All groups of MCs produced the cytokines interleukin-6 and tumor necrosis factor-α in response to rVSVΔm51. However, production of the cytokines was lowest in IFNAR-intact cells as compared with IFNAR−/− or IFNAR-blocked cells at 20 h post-stimulation. Surprisingly, rVSVΔm51 was capable of infecting BMMCs, but functional IFNAR signaling was able to protect these cells from virus-induced death. This study showed that BMMCs produced pro-inflammatory cytokines in response to rVSVΔm51 and that IFNAR signaling was required to down-modulate these responses and protect the cells from dying from 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 | 0.001 |
| 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".