Stochastic Expression of the Interferon-β Gene
Bibliographic record
Abstract
Virus infection of mammalian cells induces the production of high levels of type I interferons (IFNα and β), cytokines that orchestrate antiviral innate and adaptive immunity. Previous studies have shown that only a fraction of the infected cells produce IFN. However, the mechanisms responsible for this stochastic expression are poorly understood. Here we report an in depth analysis of IFN-expressing and non-expressing mouse cells infected with Sendai virus. Mouse embryonic fibroblasts in which an internal ribosome entry site/yellow fluorescent protein gene was inserted downstream from the endogenous IFNβ gene were used to distinguish between the two cell types, and they were isolated from each other using fluorescence-activated cell sorting methods. Analysis of the separated cells revealed that stochastic IFNβ expression is a consequence of cell-to-cell variability in the levels and/or activities of limiting components at every level of the virus induction process, ranging from viral replication and expression, to the sensing of viral RNA by host factors, to activation of the signaling pathway, to the levels of activated transcription factors. We propose that this highly complex stochastic IFNβ gene expression evolved to optimize both the level and distribution of type I IFNs in response to virus 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) | 0.000 | 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".