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 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".