The IL‐27/IL‐27R axis is altered in CD4<sup>+</sup> and CD8<sup>+</sup> T lymphocytes from multiple sclerosis patients
Bibliographic record
Abstract
Abstract Objectives Pro‐ and anti‐inflammatory properties have been attributed to interleukin‐27 (IL‐27). Nevertheless, the impact of this cytokine on chronic inflammatory diseases such as multiple sclerosis (MS) remains ill‐defined. We investigated the biology of IL‐27 and its specific receptor IL‐27Rα in MS patients. Methods Levels of IL‐27 and its natural antagonist (IL‐27‐Rα) were measured by ELISA in biological fluids. CD4+ and CD8+ T lymphocytes were isolated from untreated relapsing–remitting MS patients and healthy donors. Transcriptome‐wide analysis compared T‐cell subsets stimulated or not with IL‐27. Expression of the IL‐27Rα, key immune factors, STAT phosphorylation and cytokine production was assessed by flow cytometry. Results We observed elevated levels of IL‐27 in the serum and cerebrospinal fluid of MS patients compared with controls. Moreover, we show that specific IL‐27‐mediated effects on T lymphocytes are reduced in MS patients including the induction of PD‐L1. IL‐27‐triggered STAT3 signalling pathway is enhanced in CD4+ and CD8+ T lymphocytes from MS patients. Elevated IL‐27Rα levels in serum from MS patients are sufficient to impair the capacity of IL‐27 to act on immune cells. We demonstrate that shedding of IL‐27Rα by activated CD4+ T lymphocytes from MS patients contributes to the increased IL‐27Rα peripheral levels and consequently can dampen the IL‐27 responsiveness. Conclusion Our work identifies several mechanisms that are altered in the IL‐27/IL‐27R axis in MS patients, especially in T lymphocytes. Our results underline the importance of characterising the biology of cytokines in human patients prior to design new therapeutics.
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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".