Regulating type 1 IFN effects in CD8 T cells during viral infections: changing STAT4 and STAT1 expression for function
Bibliographic record
Abstract
Type 1 IFNs can conditionally activate all of the signal transducers and activators of transcription molecules (STATs), including STAT4. The best-characterized signaling pathways use STAT1, however, and type 1 IFN inhibition of cell proliferation is STAT1 dependent. We report that type 1 IFNs can basally stimulate STAT1- and STAT4-dependent effects in CD8 T cells, but that CD8 T cells responding to infections of mice with lymphocytic choriomenigitis virus have elevated STAT4 and lower STAT1 expression with significant consequences for modifying the effects of type 1 IFN exposure. The phenotype was associated with preferential type 1 IFN activation of STAT4 compared with STAT1. Stimulation through the TCR induced elevated STAT4 expression, and STAT4 was required for peak expansion of antigen-specific CD8 T cells, low STAT1 levels, and resistance to type 1 IFN-mediated inhibition of proliferation. Thus, a mechanism is discovered for regulating the consequences of type 1 IFN exposure in CD8 T cells, with STAT4 acting as a key molecule in driving optimal antigen-specific responses and overcoming STAT1-dependent inhibition of proliferation.
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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 | 0.000 |
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".