The Combination of IFN β and TNF Induces an Antiviral and Immunoregulatory Program via Non-Canonical Pathways Involving STAT2 and IRF9
Bibliographic record
Abstract
Interferon (IFN) β and Tumor Necrosis Factor (TNF) are key players in immunity against viruses. Compelling evidence has shown that the antiviral and inflammatory transcriptional response induced by IFNβ is reprogrammed by crosstalk with TNF. IFNβ mainly induces interferon-stimulated genes by the Janus kinase (JAK)/signal transducer and activator of transcription (STAT) pathway involving the canonical ISGF3 transcriptional complex, composed of STAT1, STAT2, and IRF9. The signaling pathways engaged downstream of the combination of IFNβ and TNF remain elusive, but previous observations suggested the existence of a response independent of STAT1. Here, using genome-wide transcriptional analysis by RNASeq, we observed a broad antiviral and immunoregulatory response initiated in the absence of STAT1 upon IFNβ and TNF costimulation. Additional stratification of this transcriptional response revealed that STAT2 and IRF9 mediate the expression of a wide spectrum of genes. While a subset of genes was regulated by the concerted action of STAT2 and IRF9, other gene sets were independently regulated by STAT2 or IRF9. Collectively, our data supports a model in which STAT2 and IRF9 act through non-canonical parallel pathways to regulate distinct pool of antiviral and immunoregulatory genes in conditions with elevated levels of both IFNβ and TNF.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".