The impact of IL-27 on human T cells is altered in multiple sclerosis patients
Bibliographic record
Abstract
Abstract IL-27 exhibits pro- and anti-inflammatory properties. This cytokine dampens the severity of experimental autoimmune encephalomyelitis (EAE), a model of multiple sclerosis (MS). Whether IL-27 plays a role in pathobiology of the human disease is still unresolved. We recently showed that both IL-27 and IL-27Receptor (IL-27R composed of IL-27Rα and gp130 chains) are elevated in MS brains compared to controls. We reported expression of IL-27R by brain-infiltrating CD4 and CD8 T cells in MS tissues. To determine whether IL-27 contributes to altered immune responses in MS, we investigated the impact of this cytokine on peripheral blood T cells obtained from untreated MS patients and age/sex-matched healthy donors. Surface IL-27R is present on lower proportions of effector memory (CCR7−) CD4 and CD8 T cells compared to naïve (CCR7+CD45RA+) and central memory (CCR7+CD45RO+) counterparts in all donors. However, reduced proportions of all CD4 T cell subsets but enhanced percentages of CD8 T cell subsets express IL-27R in MS patients compared to controls. IL-27 triggers rapid phosphorylation of STAT1 and STAT3 in all T cell subsets. The percentage and intensity of pSTAT1 detection in all T cell subsets were similar in both donor groups. In contrast, increased proportions of all CD4 and CD8 T cell subsets express pSTAT3 in response to IL-27 compared to cells from controls. Finally, elevated amounts of both IL-27 and its recently identified natural antagonist (soluble) IL-27Rα are present in serum from MS patients compared to controls. Thus, our results reveal that IL-27 has an altered impact on T cells from MS patients which could contribute to aberrant immune responses associated with this inflammatory/autoimmune disease of the central nervous system.
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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) | 0.003 | 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".