Abstract WMP78: Brain Ischemia Induces Diversified Neuroantigen-Specific T Cell Responses That Exacerbate Brain Injury
Bibliographic record
Abstract
Background: Autoimmune responses can occur when antigens from the central nervous system (CNS) are presented to lymphocytes in the periphery or CNS. However, whether autoimmune responses emerge after brain ischemia remains controversial. Similarly, it is debated whether genesis of autoimmune responses can impact outcome of ischemic stroke. We hypothesized that that brain ischemia can diversify T cell responses against CNS antigens, and that expanded CNS antigen-specific T cells can promote ischemic brain injury. Methods: We quantified the presence and status of T cells from brain slices of ischemic patients. Using a mouse strain that harbor a transgenic T cell receptor (TCR) to a CNS antigen, the myelin oligodendrocyte glycoprotein (MOG 35-55 ) epitope (2D2), we determined the anatomic location and involvement of antigen presentation cells in the development of T cell reactivity after brain ischemia and how T cell reactivity impacts stroke outcome. Transient middle cerebral artery occlusion (MCAO) and photothrombotic stroke models were used in this study. Results: By coupling transfer of labeled myelin oligodendrocyte glycoprotein (MOG) 35-55 -specific (2D2) T cells with tetramer tracking, we show an expansion in reactivity of 2D2 T cells to MOG 97-108 and MOG 103-125 in transient MCAO and photothrombotic stroke models. This reactivity and T cell activation occur in the brain and not in the peripheral lymphoid organs after ischemia. Also, microglia act as antigen-presenting cells (APCs) that effectively present MOG antigen, and depletion of microglia ablates expansion of 2D2 reactive T cells. Notably, the adoptive transfer of neuroantigen-experienced 2D2 T cells exacerbates Th1/Th17 responses and brain injury. Finally, T cell activation and MOG-specific T cells can be seen in the brain of ischemic stroke patients. Conclusion: Our findings suggest that brain ischemia activates and diversifies T cell responses locally, which exacerbates ischemic brain injury.
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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) | 0.004 | 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".