Continuous immunosurveillance of oligodendrocyte antigens leads to the generation of effector T-cell pools that might contribute to CNS autoimmune disease (P3123)
Bibliographic record
Abstract
Abstract Immunosurveillance of antigens from the CNS is tightly regulated in order to avoid autoimmune disease, such as multiple sclerosis. To test how CNS antigens are recognized by the peripheral immune system, we created transgenic mice having constitutive or inducible expression of EGFP-fused model neoantigens in myelinating glial cells. We examined the level of systemic priming to CNS neoantigens and compared it to priming from non-immune privileged gut tissue. We report here that the level of the EGFP-triple peptide in both the constitutive and inducible models is sufficiently high in the CNS to induce neoantigen-specific T cell priming in the periphery; however, there is no recruitment of these cells to the CNS. Immunosurveillance of CNS antigens in the peripheral immune tissues is enhanced during neuroinflammation, during which time activated neoantigen-specific T cells accumulate in the CNS. These data suggest that CNS antigens are surveilled in the periphery, leading to the generation of effector T cell pools. Additional inflammatory signals from the CNS induce the migration of effector T cells, which form microclusters with dendritic cells and autophagic oligodendrocytes that leads to the amplification of CNS inflammation. Increased immunosurveillance and inflammation might contribute to the chronic phase of CNS autoimmune disease. These new models can help us dissect the mechanisms of disease initiation and maintenance that is of clinical importance.
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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 | 0.001 |
| 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".