IFN-α Suppresses Myeloid Cytokine Production, Impairing IL-12 Production and the Ability to Support T-Cell Proliferation
Bibliographic record
Abstract
BACKGROUND: Interferon-α (IFN-α) can suppress production of T-cell polarizing cytokines or induce inhibitory antigen-presenting cells that suppress T-cell activation. Previous studies showed that IFN-α therapy fails to boost virus-specific T-cell immunity in patients with chronic hepatitis B virus infection. Our aim was to determine whether IFN-α exposure alters human antigen-presenting cell function in vivo. METHODS: We investigated the immunomodulatory effects using peripheral blood mononuclear cells from healthy donors exposed to IFN-α and chronic hepatitis B (CHB) patients starting IFN-α therapy. RESULTS: IFN-α increased HLA-DR, CD80, CD86, and PD-L1 expression on healthy donor monocytes. In contrast to the activated phenotype, IFN-α inhibited Toll-like receptor-induced cytokine production and monocyte-induced T-cell proliferation. In CHB patients, peg-IFN treatment induced an interferon-stimulated gene signature in monocytes and increased HLA-DR, CD80, CD86, and PD-L1 expression. As early as 3 days after CHB patients started treatment, IFN-α inhibited monocyte cytokine production and T-cell stimulation ex vivo. IFN-α-mediated inhibition of IL-12 production, rather than inhibitory receptor expression, was responsible for inhibition of T-cell proliferation. Addition of IL-12 restored T-cell proliferation to baseline levels. CONCLUSIONS: Understanding how professional antigen-presenting cells respond to immunomodulation is important for both new innate and adaptive-targeted immunotherapies. CLINICAL TRIALS REGISTRATION: NCT00962871.
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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) | 0.001 | 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".