IL-17A secretion by CD8+ T cells supports Th17-mediated autoimmune encephalomyelitis
Bibliographic record
Abstract
IL-17-producing CD8+ T (Tc17) cells are detectible in multiple sclerosis (MS) lesions; however, their contribution to the disease is unknown. To identify functions of Tc17 cells, we induced EAE, a murine model of MS, in mice lacking IFN regulatory factor 4 (IRF4). IRF4-deficient mice failed to generate Tc17 and Th17 cells and were resistant to EAE. After adoptive transfer of WT CD8+ T cells and subsequent immunization for EAE induction in these mice, the CD8+ T cells developed a Tc17 phenotype in the periphery but could not infiltrate the CNS. Similarly, transfer of small numbers of WT CD4+ T cells alone did not evoke EAE, but when transferred together with CD8+ T cells, IL-17-producing CD4+ (Th17) T cells accumulated in the CNS and mice developed severe disease. Th17 accumulation and development of EAE required IL-17A production by CD8+ T cells, suggesting that Tc17 cells are required to promote CD4+ T cell-mediated induction of EAE. Accordingly, patients with early-stage MS harbored a greater number of Tc17 cells in the cerebrospinal fluid than in peripheral blood. Our results reveal that Tc17 cells contribute to the initiation of CNS autoimmunity in mice and humans by supporting Th17 cell pathogenicity.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".