Human CD4 and CD8 T cells distinctly upregulate Suppressors of Cytokine Signaling 1 and 3 following Interferon-β treatment. (117.24)
Bibliographic record
Abstract
Abstract Suppressor of cytokine signaling (SOCS) proteins have been identified as a negative feedback loop to cytokine signaling. Emerging evidence supports the potential role of SOCS in controlling immune disorders, but data regarding their expression in human T cells is still sparse. Interferon-β (IFN-β) is a broadly used and effective treatment for multiple sclerosis (MS), although mechanisms of actions are still incompletely resolved. We postulate that IFN-β mediates beneficial effects through the induction of SOCS in peripheral T cells. We first evaluated the expression of SOCS-1 and SOCS-3 at the mRNA (qRT-PCR) and protein levels (FACS, Western blot, immunocytochemistry) by human CD4 and CD8 T cells following IFN-β treatment. A rapid and significant increase of SOCS-1 and SOCS-3 was observed upon cytokine addition, especially in the CD8 T cell compartment. To mimic patients under IFN-β treatment, both T cell subsets were chronically exposed to physiological doses of IFN-β measured in treated patients. Only SOCS-1 but not SOCS-3 levels were elevated after each IFN-β re-exposure. These effects were more pronounced in CD8 T cells. Our preliminary data indicate different expression of SOCS in untreated and IFN-β-treated MS patients. Our results suggest that SOCS-1 contributes to the IFN-β-mediated beneficial effects in MS and that SOCS proteins are distinctly expressed in human T cell subsets. We are currently testing the functional impact of SOCS-1 on human T cell responses.
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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) | 0.005 | 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".