Interferon-β Suppresses Murine Th1 Cell Function in the Absence of Antigen-Presenting Cells
Bibliographic record
Abstract
Interferon (IFN)-β is a front-line therapy for the treatment of the relapsing-remitting form of multiple sclerosis. However, its immunosuppressive mechanism of function remains incompletely understood. While it has been proposed that IFN-β suppresses the function of inflammatory myelin antigen-reactive T cells by promoting the release of immunomodulatory cytokines such as IL-27 from antigen-presenting cells (APCs), its direct effects on inflammatory CD4+ Th1 cells are less clear. Here, we establish that IFN-β inhibits mouse IFN-γ+ Th1 cell function in the absence of APCs. CD4+ T cells express the type I interferon receptor, and IFN-β can suppress Th1 cell proliferation under APC-free stimulation conditions. IFN-β-treated myelin antigen-specific Th1 cells are impaired in their ability to induce severe experimental autoimmune encephalomyelitis (EAE) upon transfer to lymphocyte-deficient Rag1-/- mice. Polarized Th1 cells downregulate IFN-γ and IL-2, and upregulate the negative regulatory receptor Tim-3, when treated with IFN-β in the absence of APCs. Further, IFN-β treatment of Th1 cells upregulates phosphorylation of Stat1, and downregulates phosphorylation of Stat4. Our data indicate that IFN-γ-producing Th1 cells are directly responsive to IFN-β and point to a novel mechanism of IFN-β-mediated T cell suppression that is independent of APC-derived signals.
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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 | 0.000 |
| 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".