Myelin antigen-specific effector CD8 <sup>+</sup> T cells induce chronic CNS autoimmunity in a CD4 <sup>+</sup> T cell-dependent manner
Bibliographic record
Abstract
Abstract Both CD4 + and CD8 + T cells play critical roles in the immunopathogenesis of multiple sclerosis (MS). 1C6 T cell receptor transgenic (TcR-Tg) mice on the nonobese diabetic (NOD) background have a MOG [35-55]- specific, MHC class II-restricted, TcR that selects for both CD4 + and CD8 + T cells. Adoptive transfer of 1C6 CD4 + T helper 17 (Th17) cells can induce experimental autoimmune encephalomyelitis (EAE) with a progressive disease course. In the current study, we assessed the function and pathogenicity of 1C6 CD8 + T cells. We found that they proliferated and produced inflammatory cytokines in response to MOG [35-55] peptide under both T cytotoxic 1 (Tc1) and Tc17 differentiation conditions, albeit with reduced expansion relative to their Th1 or Th17 counterparts. Both 1C6 Tc1 and Tc17 cells were able to induce EAE upon adoptive transfer to NOD. Scid mice. Intriguingly, we noted in vivo expansion of CD4 + T cells in the spleen and CNS of NOD. Scid recipients as well as in lymphocyte-sufficient animals, despite 1C6 Tc cells being purified on CD8 expression prior to transfer. Furthermore, 1C6 Tc17 cells expressed ThPOK, a master differentiation factor for CD4 + T cells. Finally, anti-CD4 + T cell blockade abrogated CD8 + T cell infiltration of the CNS and disease induction in Tc17 recipient mice. Our data provide insight into the interplay of CD4 + and CD8 + T cells in CNS autoimmunity.
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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) | 0.001 | 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 | 0.001 |
| 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".