Murine Mast Cells That Are Deficient in IFNAR-Signaling Respond to Viral Infection by Producing a Large Amount of Inflammatory Cytokines, a Low Level of Reactive Oxygen Species, and a High Rate of Cell Death
Bibliographic record
Abstract
Mat cells (MCs) are located in the skin and mucous membranes at points where the body meets the environment. When activated, MCs release inflammatory cytokines, which help the immune system to fight viruses. MCs produce, and have receptors for interferons (IFNs), which belong to a family of cytokines recognized for their antiviral properties. Previously, we reported that MCs produced proinflammatory cytokines in response to a recombinant vesicular stomatitis virus (rVSVΔm51) and that IFNAR signaling was required to down-modulate these responses. Here, we have demonstrated that UV-irradiated rVSVΔm51 did not cause any inflammatory cytokines in either in vitro cultured mouse IFNAR-intact (IFNAR+/+), or in IFNAR-knockout (IFNAR-/-) MCs. However, the non-irradiated virus was able to replicate more effectively in IFNAR-/- MCs and produced a higher level of inflammatory cytokines compared with the IFNAR+/+ MCs. Interestingly, MCs lacking IFNAR expression displayed reduced levels of reactive oxygen species (ROS) compared with IFNAR+/+ MCs. Additionally, upon the viral infection, these IFNAR-/- MCs were found to coexist with many dying cells within the cell population. Based on our findings, IFNAR-intact MCs exhibit a lower rate of rVSVΔm51 infectivity and lower levels of cytokines while demonstrating higher levels of ROS. This suggests that MCs with intact IFNAR signaling may survive viral infections by producing cell-protective ROS mechanisms and are less likely to die than IFNAR-/- 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".