A Pathogenic Role for CD8+ T Cells in a Spontaneous Model of Demyelinating Disease
Bibliographic record
Abstract
Transgenic (Tg) mice that overexpress the costimulatory ligand B7.2/CD86 on microglia spontaneously develop a T cell-mediated demyelinating disease. Characterization of the inflammatory infiltrates in the nervous tissue revealed a predominance of CD8+ T cells, suggesting a prominent role of this T cell subset in the pathology. In this study, we show that the same neurological disease occurred in Tg mice deficient in the generation of CD4+ T cells, with an earlier time of onset. Analysis of the CD8+ T cell repertoire at early stage of disease revealed the presence of selected clonal expansions in the CNS but not in peripheral lymphoid organs. We further show that Tg animals deficient in IFN-gamma receptor expression were completely resistant to disease development. Microglia activation that is an early event in disease development is IFN-gamma dependent and thus appears as a key element in disease pathogenesis. Collectively, our data indicate that the spontaneous demyelinating disease in this animal model occurs as a consequence of an inflammatory response initiated through the activation of CNS-specific CD8+ T cells by Tg expression of B7.2 within the target organ. Thus, autoreactive CD8+ T cells can contribute directly to the pathogenesis of neuroinflammatory diseases such as multiple sclerosis.
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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 | 0.001 | 0.001 |
| 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".