Production of <scp>IL</scp>‐27 in multiple sclerosis lesions by astrocytes and myeloid cells: Modulation of local immune responses
Bibliographic record
Abstract
The mechanisms whereby human glial cells modulate local immune responses are not fully understood. Interleukin-27 (IL-27), a pleiotropic cytokine, has been shown to dampen the severity of experimental autoimmune encephalomyelitis, but it is still unresolved whether IL-27 plays a role in the human disease multiple sclerosis (MS). IL-27 contribution to local modulation of immune responses in the brain of MS patients was investigated. The expression of IL-27 subunits (EBI3 and p28) and its cognate receptor IL-27R (the gp130 and TCCR chains) was elevated within post-mortem MS brain lesions compared with normal control brains. Moreover, astrocytes (GFAP(+) cells) as well as microglia and macrophages (Iba1(+) cells) were important sources of IL-27. Brain-infiltrating CD4 and CD8 T lymphocytes expressed the IL-27R specific chain (TCCR) implying that these cells could respond to local IL-27 sources. In primary cultures of human astrocytes inflammatory cytokines increased IL-27 production, whereas myeloid cell inflammatory M1 polarization and inflammatory cytokines enhanced IL-27 expression in microglia and macrophages. Astrocytes in postmortem tissues and in vitro expressed IL-27R. Moreover, IL-27 triggered the phosphorylation of the transcription regulator STAT1, but not STAT3 in human astrocytes; indeed IL-27 up-regulated MHC class I expression on astrocytes in a STAT1-dependent manner. These findings demonstrated that IL-27 and its receptor were elevated in MS lesions and that local IL-27 can modulate immune properties of astrocytes and infiltrating immune cells. Thus, therapeutic strategies targeting IL-27 may influence not only peripheral but also local inflammatory responses within the brain of MS patients.
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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) | 0.001 | 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 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".