The IRF-3 Transcription Factor Mediates Sendai Virus-Induced Apoptosis
Bibliographic record
Abstract
Virus infection of target cells can result in different biological outcomes: lytic infection, cellular transformation, or cell death by apoptosis. Cells respond to virus infection by the activation of specific transcription factors involved in cytokine gene regulation and cell growth control. The ubiquitously expressed interferon regulatory factor 3 (IRF-3) transcription factor is directly activated following virus infection through posttranslational modification. Phosphorylation of specific C-terminal serine residues results in IRF-3 dimerization, nuclear translocation, and activation of DNA-binding and transactivation potential. Once activated, IRF-3 transcriptionally up regulates alpha/beta interferon genes, the chemokine RANTES, and potentially other genes that inhibit viral infection. We previously generated constitutively active [IRF-3(5D)] and dominant negative (IRF-3 DeltaN) forms of IRF-3 that control target gene expression. In an effort to characterize the growth regulatory properties of IRF-3, we observed that IRF-3 is a mediator of paramyxovirus-induced apoptosis. Expression of the constitutively active form of IRF-3 is toxic, preventing the establishment of stably transfected cells. By using a tetracycline-inducible system, we show that induction of IRF-3(5D) alone is sufficient to induce apoptosis in human embryonic kidney 293 and human Jurkat T cells as measured by DNA laddering, terminal deoxynucleotidyltransferase-mediated dUTP-biotin nick end labeling assay, and analysis of DNA content by flow cytometry. Wild-type IRF-3 expression augments paramyxovirus-induced apoptosis, while expression of IRF-3 DeltaN blocks virus-induced apoptosis. In addition, we demonstrate an important role of caspases 8, 9, and 3 in IRF-3-induced apoptosis. These results suggest that IRF-3, in addition to potently activating cytokine genes, regulates apoptotic signalling following 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".