The impact of IL-27 on human T cells is altered in multiple sclerosis patients
Bibliographic record
Abstract
Abstract Interleukin-27 (IL-27) exhibits pro- and anti-inflammatory properties. Although this cytokine dampens the severity of experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis (MS), whether IL 27 contributes to the pathobiology of MS remains unclear. We detected the IL-27 receptor (IL-27R) on brain-infiltrating T cells in MS tissues suggesting that they are susceptible to this cytokine. Notably, beneficial effects of MS immunomodulatory therapies are associated with IL-27 induction. We compared blood samples from untreated MS patients to healthy donors (HC) for i) serum levels of IL-27 and expression of IL-27R by T cells and ii) effects of IL-27 on phenotype and functions of T cells. Reduced proportions of CD4 T cells but greater percentages of CD8 T cells expressed IL-27R in MS patients compared to HC. IL-27 triggered phosphorylation of STAT1 (pSTAT1) and STAT3 (pSTAT3) in all T cell subsets; similar levels of pSTAT1 were induced in T cells from MS and HC. In contrast, increased proportions of CD4 and CD8 T cells exhibited pSTAT3 in response to IL-27 compared to cells from HC. Moreover, addition of IL-27 to activated T cells significantly decreased the percentages of GM-CSF or IL-17 producing CD4 and CD8 T cells from MS patients but had a minimal impact on HC’s cells. Finally, sera from MS patients contained elevated amounts of soluble IL-27 and IL-27Rα (a natural IL-27 antagonist) compared to HC. Our results reveal that IL-27 has an enhanced impact on T cells from MS patients, which could contribute to dampen key immune responses (e.g. GM-CSF, IL-17) associated with this inflammatory/autoimmune disease. However, elevated in vivo levels of its natural antagonist could hinder IL-27-mediated anti-inflammatory properties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".